•A novel nonlinear hybrid model is proposed for wind speed forecasting.•Propose hysteretic character based modified ELM to improve forecasting performance.•DE is used to select the number of hidden ...layers and neurons count in LSTM network.•Propose a LSTM-based nonlinear combined method to learn the nonlinear relationship.•The effectiveness of proposed model is validated on two case studies data.
Accurate and stable wind speed forecasting is essential for the planning, scheduling and control of wind energy generation and conversion in wind power industry. In this paper, a novel nonlinear hybrid model aiming at improving prediction performance of wind speed called LSTMDE-HELM is presented by using Long Short Term Memory neural network (LSTM), Hysteretic Extreme Learning Machine (HELM), Differential Evolution algorithm (DE), and nonlinear combined mechanism. First, to enhance the performance of Extreme Learning Machine (ELM), a biological neural system property called hysteresis is embedded into neuron activation function of ELM. Second, as there isn’t a clear knowledge to set the number of hidden layers in LSTM and neurons count in each hidden layer, DE is introduced to optimize these numbers by minimizing a weighted objective function for keeping balance between learning performance and model complexity. Finally, the forecasting results of each predictor in LSTMDE-HELM are aggregated by a novel nonlinear combined mechanism composed of LSTM network and also the DE is used to optimize this LSTM. The proposed nonlinear hybrid model is employed on the data gathered from a wind farm in Inner Mongolia, China. Two forecasting horizons i.e., ten-minute ahead (utmost short term) and one-hour ahead (short term) are adopted for experiments. Empirical results fully demonstrate the superiority of the proposed hybrid model compared with other models in terms of four performance indices and statistical tests.
•The interaction effect of Hotel Star Class (HSC) and Review Rating (RR) is confirmed to affect review helpfulness.•The concept of review helpfulness is posed to include three variables. All had a ...varying and significant effect on review helpfulness.•Model tree (M5P) outperformed linear regression and support vector regression.•Certain predictors (such as badges earned) are back-traced to the time when a review was written in order to improve prediction accuracy of existing studies.•Prediction accuracy improves after review visibility, interaction effect and improved data collection are taken into account.
The tourism industry has been strongly influenced by electronic word-of-mouth (eWOM) in recent years. Currently, there are only limited studies available that look into hotel review helpfulness. This present study addresses three hidden assumptions prevalent in online review studies: (1) all reviews are visible equally to online users, (2) review rating (RR) and hotel star class (HSC) affect review helpfulness individually with no interaction, and (3) characteristics of reviews and reviewer status stay constant.
Four categories of input variables were considered in the present study: review content, sentiment, author, and visibility. Our findings confirmed the interaction effect between HSC and RR. The data set was sub-divided into eight subsets as a result. Three review visibility indicators (including days since a review was posted, days since a review has remained on the home page, and number of reviews with the same rating at the time a review was written) had a varying and strong effect on review helpfulness. The model performance was greatly improved after taking account of review visibility features, the interaction effect of HSC and RR, and a more accurate measurement of variables. Model tree (M5P) outperformed linear regression and support vector regression as it better modeled the interaction effect.
Iron homeostasis disturbance has been implicated in Alzheimer's disease (AD), and excess iron exacerbates oxidative damage and cognitive defects. Ferroptosis is a nonapoptotic form of cell death ...dependent upon intracellular iron. However, the involvement of ferroptosis in the pathogenesis of AD remains elusive. Here, we report that ferroportin1 (Fpn), the only identified mammalian nonheme iron exporter, was downregulated in the brains of APPswe/PS1dE9 mice as an Alzheimer's mouse model and Alzheimer's patients. Genetic deletion of Fpn in principal neurons of the neocortex and hippocampus by breeding Fpn
mice with NEX-Cre mice led to AD-like hippocampal atrophy and memory deficits. Interestingly, the canonical morphological and molecular characteristics of ferroptosis were observed in both Fpn
and AD mice. Gene set enrichment analysis (GSEA) of ferroptosis-related RNA-seq data showed that the differentially expressed genes were highly enriched in gene sets associated with AD. Furthermore, administration of specific inhibitors of ferroptosis effectively reduced the neuronal death and memory impairments induced by Aβ aggregation in vitro and in vivo. In addition, restoring Fpn ameliorated ferroptosis and memory impairment in APPswe/PS1dE9 mice. Our study demonstrates the critical role of Fpn and ferroptosis in the progression of AD, thus provides promising therapeutic approaches for this disease.
Big data has been widely used in more and more fields due to its characteristics of many types, large amount of data and fast transmission speed. However, the application of big data in land ...engineering is just emerging. Induction and analysis of related documents and reports, list of big data in the reclamation of cultivated land management, project planning and schedule control decisions, and the land engineering the conditions needed for the development of the big data are discussed in this paper, in order to promote big data technology application in the land engineering, eventually to promote land engineering data value, promote the land management to further improve the quality.
Arbuscular mycorrhizal (AM) symbiosis is known to stimulate plant drought tolerance. However, the molecular basis for the direct involvement of AM fungi (AMF) in plant water relations has not been ...established.
Two full-length aquaporin genes, namely GintAQPF1 and GintAQPF2, were cloned by rapid amplification of cDNA 5'- and 3'-ends from an AMF, Glomus intraradices. Aquaporin localization, activities and water permeability were examined by heterologous expression in yeast. Gene expression during symbiosis was also analyzed by quantitative real-time polymer-ase chain reaction.
GintAQPF1 was localized to the plasma membrane of yeast, whereas GintAQPF2 was localized to both plasma and intracellular membranes. Transformed yeast cells exhibited a signifi-cant decrease in cell volume on hyperosmotic shock and faster protoplast bursting on hypo-osmotic shock. Polyethylene glycol (PEG) stimulated, but glycerol inhibited, the aquaporin activities. Furthermore, the expression of the two genes in arbuscule-enriched cortical cells and extraradical mycelia of maize roots was also enhanced significantly under drought stress.
GintAQPF1 and GintAQPF2 are the first two functional aquaporin genes from AMF reported to date. Our data strongly support potential water transport via AMF to host plants, which leads to a better understanding of the important role of AMF in plant drought tolerance.
Are children less susceptible to COVID-19? Lee, Ping-Ing; Hu, Ya-Li; Chen, Po-Yen ...
Journal of microbiology, immunology and infection,
06/2020, Letnik:
53, Številka:
3
Journal Article
• Under-sampling class imbalance data by the clustering technique is studied.• Cluster centers and their nearest neighbors of the majority class are used individually.• Using the nearest neighbors of ...cluster centers in the majority class performs the best.• This approach combined with the MLP classifier is a better choice for small scale datasets.• This approach combined with C4.5 classifier ensembles is good at large scale datasets.
Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.
Aberrant regulation of microRNAs (miRNAs) has been implicated in the pathogenesis of Alzheimer's disease (AD), but most abnormally expressed miRNAs found in AD are not regulated by synaptic activity. ...Here we report that dysfunction of miR-135a-5p/Rock2/Add1 results in memory/synaptic disorder in a mouse model of AD. miR-135a-5p levels are significantly reduced in excitatory hippocampal neurons of AD model mice. This decrease is tau dependent and mediated by Foxd3. Inhibition of miR-135a-5p leads to synaptic disorder and memory impairments. Furthermore, excess Rock2 levels caused by loss of miR-135a-5p plays an important role in the synaptic disorder of AD via phosphorylation of Ser726 on adducin 1 (Add1). Blocking the phosphorylation of Ser726 on Add1 with a membrane-permeable peptide effectively rescues the memory impairments in AD mice. Taken together, these findings demonstrate that synaptic-related miR-135a-5p mediates synaptic/memory deficits in AD via the Rock2/Add1 signaling pathway, illuminating a potential therapeutic strategy for AD.
Einstein–Gauss–Bonnet theory is a string-generated gravity theory when approaching the low energy limit. By introducing the higher order curvature terms, this theory is supposed to help to solve the ...black hole singularity problem. In this work, we investigate the evaporation of the static spherically symmetric neutral AdS black holes in Einstein–Gauss–Bonnet gravity in various spacetime dimensions with both positive and negative coupling constant
α
. By summarizing the asymptotic behavior of the evaporation process, we find the lifetime of the black holes is dimensional dependent. For
α
>
0
, in
D
⩾
6
cases, the black holes will be completely evaporated in a finite time, which resembles the Schwarzschild-AdS case in Einstein gravity. While in
D
=
4
,
5
cases, the black hole lifetime is always infinite, which means the black hole becomes a remnant in the late time. Remarkably, the cases of
α
>
0
,
D
=
4
,
5
will solve the terminal temperature divergent problem of the Schwarzschild-AdS case. For
α
<
0
, in all dimensions, the black hole will always spend a finite time to a minimal mass corresponding to the smallest horizon radius
r
min
=
2
|
α
|
which coincide with an additional singularity. This implies that there may exist constraint conditions to the choice of coupling constant.
While associations between maternal infections during pregnancy and childhood leukemia in offspring have been extensively studied, the evidence for other types of childhood cancers is limited. ...Additionally, antibiotic exposure during pregnancy could potentially increase the risk of childhood cancers. Our study investigates associations between maternal infections and antibiotic prescriptions during pregnancy and the risk of childhood cancer in Taiwan. We conducted a population‐based cohort study using the Taiwan Maternal and Child Health Database (TMCHD), linked with national health and cancer registries. The study included 2 267 186 mother‐child pairs, and the median follow‐up time was 7.96 years. Cox proportional hazard models were utilized to estimate effects. Maternal infections during pregnancy were associated with a moderate increase in the risk of childhood hepatoblastoma (adjusted hazard ratio HR = 1.34; 95% confidence interval CI: 0.90‐1.98) and a weaker increase in the risk of childhood acute lymphoblastic leukemia (ALL) (adjusted HR = 1.15; 95% CI: 0.99‐1.35). Antibiotic prescriptions during pregnancy were also associated with an elevated risk of childhood ALL (adjusted HR = 1.30; 95% CI: 1.04‐1.63), particularly with tetracyclines (adjusted HR = 2.15; 95% CI: 1.34‐3.45). Several specific antibiotics were also associated with an increased risk of hepatoblastoma and medulloblastoma. Children exposed in utero to antibiotic prescription or both infections and antibiotics during pregnancy were at higher risk of developing ALL. Our findings suggest that there are associations between maternal infections, antibiotic use during pregnancy and the risk of several childhood cancers in addition to ALL and highlight the importance of further research in this area.
What's new?
Maternal infection and antibiotic exposure during pregnancy are potential risk factors for childhood cancer. Previous studies of possible associations between these factors and childhood cancer risk, however, have focused mainly on European and U.S. populations. Here, relationships between childhood cancer and medically diagnosed maternal infection and antibiotic use during pregnancy were explored in a Taiwanese population. Analyses reveal moderate associations between maternal infection during pregnancy and childhood hepatoblastoma risk and risk of childhood acute lymphoblastic leukemia (ALL). Maternal use of certain antibiotics during pregnancy increased childhood hepatoblastoma and ALL risk, with ALL risk especially linked to maternal tetracycline use.
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