Aim
The current systematic review aimed to present the pooled estimated prevalence and risk factors of PPD.
Background
Postpartum depression seriously affects the physical and mental health of the ...mother and child. However, high‐quality meta‐analysis is limited, which restricts the screening and intervention of postpartum depression.
Design
A systematic review and meta‐analysis.
Methods
Cochrane Library, PubMed, Embase and Web of Science were searched for cohort and case‐control studies investigating the prevalence and risk factors of postpartum depression from inception to December 31st, 2020. Meta‐analyses were performed to identify postpartum depression prevalence and risk factors using a random‐effects model.
Results
Of the 33 citations evaluated, 27 reported the prevalence of postpartum depression in 33 separate study populations containing 133,313.
subjects. Pooled prevalence in all studies was 14.0% (95%CI, 12.0%–15.0%). The prevalence varied according to country (from 5.0% to 26.32%) and developing countries, especially China, have a high prevalence of postpartum depression. The following risk factors were associated with postpartum depression: gestational diabetes mellitus(OR = 2.71, 95%CI 1.78–4.14, I2 = 0.0%), depression during pregnancy(OR = 2.40, 95%CI 1.96–2.93, I2 = 96.7%), pregnant women give birth to boys(OR = 1.62; 95%CI 1.28–2.05; I2 = 0.0%), history of depression during pregnancy(OR = 4.82, 95%CI 1.32–17.54, I2 = 74.9%), history of depression(OR = 3.09, 95%CI 1.62–5.93, I2 = 86.5%) and epidural anaesthesia during delivery(OR = .81, 95%CI .13–4.87, I2 = 90.1%).
Conclusions
The prevalence of postpartum depression seems to be high, especially in developing countries. Gestational diabetes mellitus, depression during pregnancy, pregnant women give birth to boys, history of depression during pregnancy, history of depression, epidural anaesthesia during delivery were identified as risk factors for postpartum depression. Understanding the risk factors of PPD can provide the healthcare personnel with the theoretical basis for the patients’ management and treatment.
Implications for practice: This systematic review and meta‐analysis identified six significant risk factors for PPD, which provides nurses with a theoretical basis for managing and treating women with PPD to effectively improve the screening rate, intervention rate and referral rate of women with PPD.
Sleep disorder significantly affects the life quality of a large number of people but is still an underrecognized disease. Dietary nutrition is believed to play a significant impact on sleeping ...wellness. Many nutritional supplements have been used trying to benefit sleep wellness. However, the relationship between nutritional components and sleep is complicated. Nutritional factors vary dramatically with different diet patterns and depend significantly on the digestive and metabiotic functions of each individual. Moreover, nutrition can profoundly affect the hormones and inflammation status which directly or indirectly contribute to insomnia. In this review, we summarized the role of major nutritional factors, carbohydrates, lipids, amino acids, and vitamins on sleep and sleep disorders and discussed the potential mechanisms.
Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants ...controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification.
A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8'-hydroxylase, is verified to play a negative role in plant drought tolerance.
This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.
In this paper, we detect communities from trajectories. Existing algorithms for trajectory clustering usually rely on simplex representation and a single proximity-related metric. Unfortunately, ...additional information markers (e.g., social interactions or semantics in the spatial layout) are ignored, leading to the inability to fully discover the communities in trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) are especially useful in capturing latent relationships among community members. To overcome this limitation, we propose TODMIS, a general framework for Trajectory-based community Detection by diffusion modeling on Multiple Information Sources. TODMIS combines additional information with raw trajectory data and construct the diffusion process on multiple similarity metrics. It also learns the consistent graph Laplacians by constructing the multi-modal diffusion process and optimizing the heat kernel coupling on each pair of similarity matrices from multiple information sources. Then, dense sub-graph detection is used to discover the set of distinct communities (including community size) on the coupled multi-graph representation. At last, based on the community information, we propose a novel model for online recommendation. We evaluate TODMIS and our online recommendation methods using different real-life datasets. Experimental results demonstrate the effectiveness and efficiency of our methods.
•Boosted regression tree model is robust in charactering nonlinear non-point source pollution generation and transport processes.•The nonlinear responses of critical source areas to influencing ...factors was evaluated quantitatively.•The thresholds for influencing factors can provide supportive information for watershed management.•Land use and fertilizer application have higher importance in determining the occurrence of critical source areas.•Machine learning techniques have great potential for predicting critical source areas under climate change.
Critical Source Areas (CSAs) are areas that contribute disproportionate high levels of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of the complex interaction between the factors related to the sources and transport processes of NPS pollution. A systematic understanding of how these influencing factors affect CSAs is essential for successful watershed management. In this study, we applied a statistical data mining technique boosted regression tree model to quantify the contribution of eight influencing factors (soil type, slope, elevation, RUSLE LS factor, RUSLE K factor, runoff, fertilizer application rate and land use) on two types of CSAs (TN-CSAs and TP-CSAs), as well as the marginal effects and potential thresholds of influencing factors on the occurrence of CSAs. Results show that land use (37.35%, 25.03%), fertilizer application (36.93%, 57.83%) and soil type (17.59%, 13.70%) have higher importance in determining the occurrence of TN-CSAs and TP-CSAs; and the incidence of TN-CSAs is positively correlated with most factors before the threshold for each influencing factor, after which the marginal effect largely leveled off or dropped slightly; TP-CSAs have essentially the same characteristics as TN-CSAs, but TP-CSAs are more likely to occur in areas with an annual runoff of around 244.92 mm. In addition, this study discussed the application of machine learning techniques in predicting CSAs under climate change without physical-based models, as well as a preliminary watershed management planning for NPS pollution control in the study watershed. These results provided important information for nutrient management regulations.
All-inorganic perovskite solar cells (pero-SCs) are attracting considerable attention due to their promising thermal stability, but their inferior power-conversion efficiency (PCE) and moisture ...instability are hindering their application. Here, we used a gradient thermal annealing (GTA) method to control the growth of α-CsPbI2Br crystals and then utilized a green anti-solvent (ATS) isopropanol to further optimize the morphology of α-CsPbI2Br film. Through this GTA-ATS synergetic effect, the growth of α-CsPbI2Br crystals could be precisely controlled, leading to a high-quality perovskite film with one-micron average grain size, low root-mean-square of 25.9 nm, and reduced defect density. Pero-SCs based on this CsPbI2Br film achieved a champion scan PCE of 16.07% (stabilized efficiency of 15.75%), which is the highest efficiency reported in all-inorganic pero-SCs. Moreover, the CsPbI2Br pero-SC demonstrates excellent robustness against moisture and oxygen, and maintains 90% of initial PCE after aging 120 hr under 100 mW/cm2 UV irradiation.
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•Precisely control the crystal growth of CsPbI2Br film•A record efficiency of 16.07% was achieved with GTA-ATS-IPA treatment•Low defect density device was obtained with excellent moisture and UV stability
All-inorganic perovskite solar cells (pero-SCs) develop rapidly due to their excellent thermal stability. However, their lower efficiency and humidity instability limit their application. CsPbI2Br materials with a suitable bandgap offer a good trade-off between stability and light harvesting. For the first time, we precisely control CsPbI2Br crystal growth by a synergistic effect of gradient thermal annealing (GTA) and anti-solvent (ATS). We demonstrated a high-quality CsPbI2Br film and realized a record efficiency of 16.07% (stabilized efficiency of 15.75%). These CsPbI2Br films-based pero-SCs also showed excellent robustness against moisture, oxygen, and UV light. Therefore, we believe that our results would provide significant progress in the field of all-inorganic pero-SCs, in particular for promoting their efficiency and stability toward commercialization. Thus, this work would be interesting to a wide readership across the opto-electronics community.
We propose a GTA + ATS processing method for precisely controlling CsPbI2Br crystal growth by a synergistic effect of gradient thermal annealing (GTA) and anti-solvent (ATS), thus resulting in a uniform and high-quality film. More importantly, we achieve a record efficiency of 16.07% (stabilized efficiency of 15.75%) by this strategy, which is the highest efficiency in all-inorganic perovskite solar cells.
All‐inorganic perovskites have emerged as promising photovoltaic materials due to their superior thermal stability compared to their organic–inorganic hybrid counterparts. However, the inferior film ...quality and doped hole transport layer (HTL) have a strong tendency to degrade the perovskite under high temperatures or harsh operating conditions. To solve these problems, a one‐source strategy using the same polymer donor material (PDM) to simultaneously dope CsPbI2Br perovskite films via antisolvent engineering and fabricating the HTL is proposed. The doping assists perovskite film growth and forms a top–down gradient distribution, generating CsPbI2Br with enlarged grain size and reduced defect density. The PDM as the HTL suppresses the energy barrier and forms favorable electrical contacts for hole extraction, and assemble into a fingerprint‐like morphology that improves the conductivity, facilitating the creation of a dopant‐free HTL. Based on this one‐source strategy using PBDB‐T as PDM, the CsPbI2Br perovskite solar cell with a dopant‐free HTL achieves a power conversion efficiency (PCE) of 16.40%, which is one of the highest PCEs reported among all‐inorganic CsPbI2Br pero‐SCs with a dopant‐free HTL. Importantly, the devices exhibit the highest thermal stability at 85 °C and operational stability under continuous illumination even with Ag as the top electrode and present good universality.
A one‐source strategy using the same polymer donor material to simultaneously dope CsPbI2Br perovskite films by antisolvent engineering and fabricating the hole transport layer is proposed. The perovskite solar cell (pero‐SC) based on this one‐source strategy exhibits a remarkable power conversion efficiency of 16.40% and possesses excellent thermal stability and operational stability at the same time.
Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated ...as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on the Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space. We propose multimodal Similarity Gaussian Process latent variable model (m-SimGP), which learns the mapping functions between the intra-modal similarities and latent representation. We further propose multimodal distance-preserved similarity GPLVM (m-DSimGP) to preserve the intra-modal global similarity structure, and multimodal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the latent space. We propose m-DRSimGP, which combines the distance preservation in m-DSimGP and semantic preservation in m-RSimGP to learn the latent representation. The overall objective functions of the four models are solved by simple and scalable gradient decent techniques. They can be applied to various tasks to discover the nonlinear correlations and to obtain the comparable low-dimensional representation for heterogeneous modalities. On five widely used real-world data sets, our approaches outperform existing models on cross-modal content retrieval and multimodal classification.
Fucoxanthin (Fx), an allenic carotenoid from brown seaweeds or diatoms, has been demonstrated to prevent obesity. Gut dysbiosis and inflammation are two counted important incidence reasons of obesity ...and related diseases. In this paper, a mouse model induced by high-fat diet (HFD) was used to reveal the role of Fx in modulating intestinal homeostasis and treating obesity. In addition, 16S rRNA sequencing results inferred that Fx alleviated HFD-induced gut microbiota dysbiosis by significantly inhibiting the growth of obesity-/inflammation-related Lachnospiraceae and Erysipelotrichaceae while promoting the growth of
/
,
, and some butyrate-producing bacteria. The correlation analysis showed that some gut microbiota taxa were strongly correlated with obesity phenotypes and the inflammation level. In conclusion, dietary Fx has the potential to alleviate the development of obesity and related symptoms through mediating the composition of gut microbiota as demonstrated in mice. This study provides scientific evidence for the potential effects of Fx on obesity treatment.
In multi-label learning, each example is represented by a single instance and associated with multiple class labels. Existing multi-label learning algorithms mainly exploit label correlations ...globally, by assuming that the label correlations are shared by all the examples. Moreover, these multi-label learning algorithms exploit the positive label correlations among different class labels. In practical applications, however, different examples may share different label correlations, and the labels are not only positive correlated, but also mutually exclusive with each other. In this paper, we propose a simple and effective Bayesian model for multi-label classification by exploiting Local positive and negative Pairwise Label Correlations, named LPLC. In the training stage, the positive and negative label correlations of each ground truth label for all the training examples are discovered. In the test stage, the k nearest neighbors and their corresponding positive and negative pairwise label correlations for each test example are first identified, then we make prediction through maximizing the posterior probability, which is estimated on the label distribution, the local positive and negative pairwise label correlations embodied in the k nearest neighbors. A comparative study with the state-of-the-art approaches manifests a competitive performance of our proposed method.
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