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The Tibetan Plateau (TP) is often referred to as 'the Third Pole' and plays an essential role in the global climate. However, it remains challenging for most global and regional models to realistically simulate the characteristics of climate over the TP. In this study, two Weather Research and Forecasting model (WRF) experiments using spectral nudging with gray-zone (GZ9) and convection-permitting (CP3) resolution are conducted for summers from 2009 to 2018. The surface air temperature (T2m) and precipitation from the two simulations and the global reanalysis ERA5 are evaluated against in-situ observations. The results show that ERA5 has a general cold bias over southern TP, especially in maximum T2m (Tmax), and wet bias over whole TP. Both experiments can successfully capture the spatial pattern and daily variation of T2m and precipitation, though cold bias for temperature and dry bias for precipitation exist especially over the regions south of 35° N. Compared with ERA5, the added value of the two WRF experiments is mainly reflected in the reduced cold bias especially for Tmax with more improvement found in CP3 and the reduced wet bias. However, the ability of the convection-permitting WRF experiment in improving the simulation of precipitation seems limited when compared to the gray-zone WRF experiment, which may be related to the biases in physical parameterization and lack of representativeness of station observation. Further investigation into surface radiation budget reveals that the underestimation of net shortwave radiation contributes a lot to the cold bias of T2m over the southeastern TP in GZ9 which is improved in CP3. Compared with GZ9, CP3 shows that larger specific humidity at low-level (mid-high level) coexists with more precipitation (clouds) over the southern TP. This improvement is achieved by better depiction of topographic details, underlying surface and atmospheric processes, land–atmosphere interactions and so on, leading to stronger northward water vapor transport (WVT) in CP3, providing more water vapor for precipitation at surface and much wetter condition in the mid-high level. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) holds a pivotal position in regional and global climate, whereas the atmospheric characteristics vary greatly in the simulation with different physical parameterization schemes at the gray-zone scale (around 9 km). In this study, 24 sets of experiments were set up with different cumulus parameterization schemes (CPSs), microphysics parameterization schemes (MPSs) and planetary boundary layer parameterization schemes (PBLSs) using the WRF model driven by the ERA5 reanalysis to explore the sensitivity and try to summarize a combination of parameterization schemes suitable for the simulation over TP. Based on comparisons against in-situ observations, most CPS experiments showed large wet bias and overestimated occurrence of precipitation events, and CTL without CPS and CU6 with Tiedtke CPS show better performance at gray-zone scale. The influence of MPS and PBLS is less than CPS on the simulation, and Thompson MPS and UW PBLS showed a better performance. Most of the CPS experiments reach an earlier and more intensive peak compared to the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, except CTL, CU11, and CU6, which show closer but weaker peaks. CPS experiments simulate more precipitation associated with water vapor transporting into TP and anomalous cyclonic circulation against CTL. The less water vapor transport from the south edge of TP in the CTL against CU14, CU16, and CU2, leads to more local precipitation. Graupel particles and the warm-rain process play a crucial role in the precipitation simulation with different MPSs. Although with less water vapor in the low atmosphere, MP2 simulates more precipitation due to more graupel particles and warm-rain processes against CTL. PBLSs have a substantial impact on latent heat, then influence evaporation and precipitation. The lower PBL height usually leads to more latent heat and more precipitation. • Thompson, UW and Tiedtke schemes perform better based on sensitivity analysis • CPSs affect precipitation mainly through low-level circulation and related WVT • MPSs modulate the VIWV and hydrometer particles to affect precipitation • PBLSs exert indirect influence on precipitation through surface latent heat flux [ABSTRACT FROM AUTHOR]

Two regional climate simulation experiments (spectral nudging and re-initialization) at convection-permitting scale are conducted using the WRF model driven by the 4th generation Global Reanalysis data (ERA5) from European Centre for Medium-Range Weather Forecasts over the Tibetan Plateau (TP). The surface air temperature (T2m) and the precipitation in summer during 2016–2018 are evaluated against the in-situ station observations and the Global Satellite Mapping of Precipitation (GSMaP) dataset. The results show that both experiments as well as ERA5 can successfully capture the spatial distribution and the daily variation of T2m and precipitation, with reasonable cold bias for temperature and dry bias for precipitation when compared with the station observations. In addition, the diurnal cycle of precipitation is investigated, indicating that both experiments tend to simulate the afternoon precipitation peak in advance and postpone the night precipitation peak. The precipitation bias is reduced by using the spectral nudging technique, especially at night and early morning. Both WRF experiments outperform ERA5 in reproducing the diurnal cycle of precipitation amount. Possible causes for the differences between the two experiments are also analyzed. In the re-initialization experiment, the daytime net shortwave radiation contributes a lot to the cold biases of Tmax, and the stronger vertically integrated moisture flux convergence leads to more precipitation compared with the spectral nudging experiment over the central and southeastern TP. These results can provide valuable guidance for further fine-scale simulation studies over the TP. [ABSTRACT FROM AUTHOR]

Arbuscular mycorrhizal fungi (AMF) can form a symbiotic relationships with most terrestrial plants and play an important role in plant growth and adaptation to various stresses. To study the role of AMF in regulating drought resistance in apple, the effects of drought stress on Malus hupehensis inoculated with AMF were investigated. Inoculation of AMF enhanced apple plants growth. Mycorrhizal plants had higher total chlorophyll concentrations but lower relative electrolyte leakage under drought stress. Mycorrhizal plants increased net photosynthetic rate, stomatal conductance, and transpiration rate under drought stress, however, they showed lower inhibition in the quantum yield of PSII photochemistry. Mycorrhizal plants also had higher superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) enzyme activities under drought conditions. Thus, mycorrhizal plants had lower accumulated MDA, H 2 O 2 , and O 2 − than non-mycorrhizal seedlings. Total sugar and proline concentrations also significantly increased, helping maintain the osmotic balance. Furthermore, mitogen-activated protein kinase (MAPK) cascades, which participate in the regulation of responses of plants and microorganisms to biotic and abiotic stress, were up-regulated in apple plants and AMF during drought. We saw that there were at least two motifs that were identical in MAPK proteins and many elements that responded to hormones and stress from these MAPK genes. In summary, our results showed that mycorrhizal colonization enhanced apple drought tolerance by improving gas exchange capacity, increasing chlorophyll fluorescence parameters, creating a greater osmotic adjustment capacity, increasing scavenging of reactive oxygen species (ROS), and using MAPK signals for interactions between AMF and their apple plant hosts. • a better ability to balance the distribution of light energy in photochemical and non-photochemical processes. • Increased antioxidant enzyme activity to scavenge ROS and alleviate oxidative stress injury. • More soluble substances to improve osmotic regulation. • Higher expressions of fungal and apple MAPK genes between the two symbiotic parties that cooperated to regulate the mycorrhizal apple seedlings under drought stress. [ABSTRACT FROM AUTHOR]

Liquid organic hydrogen carriers (LOHC) are regarded as a promising hydrogen storage and transportation technology. However, problems of incomplete dehydrogenation and the high dehydrogenation catalyst costs have hindered the development and application of this technology. Therefore, the research and development of a dehydrogenation catalyst with low cost and high reactivity have become the focus of current research in this field. In this paper, we report on a dehydrogenation catalyst with low noble metal loading prepared using amino silane coupling agent. The catalyst showed excellent catalytic performance on dodecahydro-N-ethylcarbazole (12H-NECZ). At 473 K, the hydrogen release of 0.3% Pd–NH2(1) catalyst prepared by the amino modification method is 4.44 wt%, which is 2.1 times that of Pd catalyst prepared using the traditional impregnation method. The amino-modified catalysts with increasing Pd loading of 0.5, 0.8, and 1.0 wt% displayed hydrogen release of 4.96, 5.14 and 5.21 wt%, respectively. The characterization of amnio-modified Pd catalysts showed that Pd was uniformly distributed on the SiO2 support, and the Pd particle size of the 0.3% Pd–NH2(1) catalyst was only 1.6 nm. The reaction kinetic parameters of two catalysts in the 12H-NECZ dehydrogenation were calculated, and the results indicated that the activation energy of the rate-limiting step (4H-NECZ to NECZ) in dehydrogenation was reduced with amnio-modified catalysts compared to impregnated catalysts. By analyzing the relationship between the Pd particle size and turnover frequency (TOF), the optimal Pd particle size in the overall dehydrogenation reaction and each step of the 12H-NECZ dehydrogenation was obtained. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) has undergone significant climate warming with a stronger amplitude than that experienced elsewhere in the Northern Hemisphere during the past years, but it is still challenging for most regional climate models to realistically simulate the present-day climate and promisingly project the future climate over the TP. In this study, high-resolution simulation using the Weather Research and Forecasting model (WRF) driven by bias-corrected CESM is conducted from 1979 to 2100, with the period from 2006 to 2100 under RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios. The simulated present-day climate is evaluated firstly and then the future climate is studied secondly. The results show that compared with station observation, WRF successfully captures the spatial pattern of annual mean surface air temperature (T2m) and precipitation over the TP, with the spatial correlation coefficients larger than 0.95 for T2m and larger than 0.70 for precipitation. However, great underestimation of T2m over the southeastern TP is found in the cold season which is related to the underestimation of snow there, and the snow-temperature positive feedback develops. WRF shows limited ability in reducing the dry bias in summer, which is related to the simulated weaker water vapor transport over the southern and eastern TP. For the future changes, substantial warming, general increase in precipitation and decrease in snow are projected under RCP8.5. The warming magnitude is greater over the western TP where the more significant decrease of snow will occur by the end of 21st century. Projected precipitation tends to consistently decrease over the western TP and along the south flank of TP which is related to the low-level circulation change. The occurring frequency of light precipitation will decrease while that of non-precipitation and extreme precipitation will increase especially in the far future, with the more obvious change occurring under RCP8.5. Overall, WRF shows high ability in simulating the present-day climate and thus reliable performance in future climate projection. • High-resolution WRF historical simulation can successfully reproduce the annual and seasonal mean climate over the TP. • The cold bias is related to the overestimated snow, and the dry bias is related to the weaker water vapor transport. • Substantial warming and decrease in snow are projected over the western TP by the end of 21st century under RCP8.5. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is an important component of the global climate system, while the characteristics of its climate are poorly represented in most regional climate models at coarse resolutions. In this study, a 20-year (2000–2019) dynamical downscaling simulation at the gray-zone resolution (9 km) using the WRF model driven by the ERA5 reanalysis is conducted over the TP. Based on comparisons against in-situ observations and the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, the assessment of basic climate variables, such as near-surface air temperature (T2m) and precipitation, is performed to evaluate the model's performance and understand its added value better. Results show that both WRF and ERA5 can successfully reproduce the spatial patterns of annual mean and seasonal mean surface air temperature. However, significant cold and wet biases are found especially over the southeastern TP in ERA5, which are greatly improved in WRF with reduced RMSEs. Not only the climatological characteristics, but also the inter-annual variability and seasonal variation of T2m and precipitation are well captured by WRF which reduces the cold and wet biases especially in winter and summer compared to ERA5, respectively. Besides, at daily scale, the overestimation of precipitation in WRF and ERA5 is mainly caused by the overestimated precipitation frequency when precipitation intensity changed slightly. Furthermore, WRF outperforms ERA5 in capturing the diurnal variation of precipitation with more realistic peak time in all sub-regions over the TP. Further investigation into the mechanism of model bias reveals that less simulated snow cover fraction plays a crucial role in increasing the surface net energy by affecting surface albedo over the southeastern TP in WRF, leading to higher T2m. In addition, less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF, indicating that the added value in dynamical downscaling at gray-zone resolution is obtained by representing water vapor transport more realistically. [ABSTRACT FROM AUTHOR]

Ovarian granulosa cells (GC) play an essential role in the development and atresia of follicles. Emerging studies suggest that non-coding RNAs are involved in the regulation of GC apoptosis. Here, we aimed to analyze the function of ssc-circINHA-001, coded by the first exon of the inhibin subunit α gene (INHA), in resisting GC apoptosis and follicular atresia by enhancing the expression of the inhibin subunit β A (INHBA) through a cluster of miRNAs. A higher expression of ssc-circINHA-001 in healthy follicles compared to early atretic follicles was detected by qRT-PCR. Its circular structure was confirmed by RNase R treatment and reversed PCR. The function of ssc-circINHA-001 in GC resistance to apoptosis was detected by in vitro transfection of its si-RNA. Furthermore, the dual-luciferase reporter assay suggested that ssc-circINHA-001 adsorbed three miRNAs, termed miR-214-5p, miR-7144-3p, and miR-9830-5p, which share the common target INHBA. A low expression of ssc-circINHA-001 increased the levels of the free miRNAs, inhibited INHBA expression, and thus raised GCs apoptosis through a shift from the secretion of activin to that of inhibin. Our study demonstrated the existence of a circRNA–microRNAs–INHBA regulatory axis in follicular GC apoptosis and provides insight into the relationship between circRNA function and its coding gene in inhibin/activin balance and ovarian physiological functions. [ABSTRACT FROM AUTHOR]

Results from eight regional climate models (RCMs) participating in the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) initiative of the Global Energy and Water Exchanges (GEWEX) are examined and compared with observations over the Tibetan Plateau (TP). The RCM common domain covers most areas of East Asia with a horizontal resolution of 20–30 km. The model simulation covers a period from April to September in each year between 1991 and 2015. This study explores the RCMs' ability for seasonal climate simulation over the TP, focusing on the summer monsoon climate as part of the LS4P initiative. An intercomparison is made among eight RCMs for precipitation, surface air temperature, mid-troposphere atmospheric circulation, moisture conditions, and surface energy fluxes. It shows that the downscaling characteristics differ significantly between two major RCM types. The RegCM4 models show positive precipitation biases over the entire TP, especially over the south and southeast TP, while the WRF models mostly show both positive and negative precipitation biases over the TP with relatively high spatial correlation between simulated and observed precipitation. The multi-model ensemble mean produces overall smaller precipitation biases than most individual RCMs, with the largest biases over the southeastern TP, and smaller surface air temperature biases over most areas of the TP, especially over the central and southwestern TP. Moreover, the ensemble mean can better reproduce the inter-annual variation of precipitation and surface air temperature than most RCMs with proper magnitude. Sensitivity analyses using RegCM4 with different physics parameterizations show that varying land and cumulus schemes may induce large precipitation differences over the TP by affecting moisture and atmospheric circulation conditions in the lower and upper troposphere, respectively. Moreover, turbulent heat and radiation fluxes differences are associated with the temperature differences between different RegCM4 models. [ABSTRACT FROM AUTHOR]

Follicular atresia is an inevitable degenerative process that occurs in mammalian ovarian follicles. The molecular events involved in atresia, particularly granulosa cell apoptosis, have long attracted researchers' attention. Vascular endothelial growth factor A (VEGFA) is downregulated during follicular atresia in porcine ovaries and serves as an inhibitor of apoptosis in granulosa cells. In addition, transforming growth factor (TGF)-βsignaling has been considered a central trigger in granulosa cell apoptosis. However, the link between TGF-β signaling and VEGFA is unknown. We proved that miR-361-5p is significantly upregulated during the atresia process and that it promotes GC apoptosis by directly targeting the VEGFA 3′UTR. In addition, we revealed that the miR-361-5p coding gene MIR361 was significantly downregulated by SMAD4, the central intracellular mediator of TGF-β signaling, that bound to the MIR361 promoter. In conclusion, our findings expanded what is known about VEGFA posttranscriptional regulation and revealed a complete SMAD4/miR-361-5p/VEGFA regulatory network in ovarian granulosa cell apoptosis. These data provide useful references for follicular atresia and ovarian physiological function studies. [ABSTRACT FROM AUTHOR]

Text classification is an important tasks in natural language processing. Multilayer attention networks have achieved excellent performance in text classification tasks, but they also face challenges such as high temporal and spatial complexity levels and low-rank bottleneck problems. This paper incorporates spatial attention into a neural network architecture that utilizes fewer encoder layers. The proposed model aims to enhance the spatial information of semantic features while addressing the high temporal and spatial demands of traditional multilayer attention networks. This approach utilizes spatial attention to selectively weigh the relevance of the spatial locations in the input feature maps, thereby enabling the model to focus on the most informative regions while ignoring the less important regions. By incorporating spatial attention into a shallower encoder network, the proposed model achieves improved performance on spatially oriented tasks while reducing the computational overhead associated with deeper attention-based models. To alleviate the low-rank bottleneck problem of multihead attention, this paper proposes a variable multihead attention mechanism, which changes the number of attention heads in a layer-by-layer manner with the encoder, achieving a balance between expression power and computational efficiency. We use two Chinese text classification datasets and an English sentiment classification dataset to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model. [ABSTRACT FROM AUTHOR]

To address the problem that single-channel neural networks cannot fully extract text semantic features in traditional user portrait construction methods, this paper proposes a dual-channel user portrait model based on DPCNN-BIGRU and attention mechanism. The model first uses Bidirectional Encoder Representation from Transformers(Bert) and CK-means+ to obtain the fusion vector of semantic features and topic features, and then feeds the vector into Deep Pyramid Convolutional Neural Networks (DPCNN) and Bidirectional Gated Recurrent Unit (BiGRU). Deep features and global features of the text are obtained simultaneously, and then weights are assigned by the attention mechanism. Finally, the output features of the dual channels are fused and classified. It is tested on the Sogou user portrait datasets, and the experimental results prove that the dual-channel model outperforms the baseline model. [ABSTRACT FROM AUTHOR]

Integrated electrical, natural gas and district heating (IENGDH) systems considering solar thermal CHP (STCHP) plants and electric boilers, are considered one of the effective way to alleviate the problem of wind and solar curtailments. With the randomness of wind, solar and load taken into account, this paper proposes a risk assessment method for analyzing the IENGDH systems with STCHP plants and electric boilers. In the proposed method, unified steady-state power flow considering the coupling conversion characteristics of STCHP plants and electric boilers is analyzed. Then, an optimization model is achieved to minimize the integrated wind, solar and load curtailments by controlling the characteristics of STCHP plants and electric boilers. The risk levels of wind, solar and load curtailments are effectively assessed and considered in the proposed method. Finally, seven risk indices are proposed to quantify the severity of wind, solar and load curtailments in IENGDH systems. The simulation results of two case studies demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Through soil moisture-temperature feedback, soil moisture significantly impacts heatwaves, which have detrimental effects on ecosystems, society, and human health. This study analyzed the spatiotemporal features of heatwave indices, including heatwave magnitude (HWM) and frequency (HWF) over eastern China during 1998–2010, and their relationships with antecedent soil moisture based on both observations and model simulations. To reasonably capture the land-atmosphere feedback, the convection-permitting (4 km grid spacing) Weather Research and Forecast model (WRF_CPM) was adopted for the investigation. Results show that WRF_CPM can reasonably simulate the observed spatial and temporal features of heatwave indices over most of the six chosen subregions of eastern China, except HWM in Jiangnan Hills (JNH). As a good indicator of soil moisture anomaly (SMA), the standardized precipitation evapotranspiration index (SPEI) was applied to illustrate the soil moisture-heatwave relationship. SPEI is negatively correlated with both HWF and HWM over JNH, Yangtze-Huai River basin (YHR), Sichuan Basin (SCB), and North China Plain (NCP). Such negative correlations are well reproduced by WRF_CPM, except for HWM in southeast China and HWF in NCP. It is indicated that increased soil moisture suppresses HWM in most areas, especially for the high quantiles, and such relationship is pronounced in YHR, JNH, NCP, Loess Plateau (LP), and SCB. Similarly, HWF was more dependent on SPEI for the higher quantiles, especially in YHR, JNH, SCB, and NCP. WRF can well capture the heterogeneous responses of heatwave indices to antecedent SPEI over most subregions except for underestimate the high-quantile HWF responses in NCP, and slightly overestimate HWF responses in JNH, respectively. The relationship between SPEI and soil moisture is consistent with the theoretical considerations. Additionally, the results illustrate the advantages of a high-resolution (4 km) convection-permitting model in simulating the impacts of antecedent soil moisture on heatwaves in China, which are also important implications for land-atmosphere coupling research as well as heatwave monitoring and forecasting. • The convection-permitting regional model well simulate heatwave features and their relationship with soil moisture. • The impacts of antecedent soil moisture on frequency and magnitude of heatwaves are stronger at higher quantiles. • Soil moisture have different behavior in modulating heatwaves in subregions over eastern China. • The standardized precipitation evapotranspiration index could reasonably represent soil moisture anomaly. [ABSTRACT FROM AUTHOR]

Background: Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in current research.Methods: This study proposed a multi-label classification method based on the graph convolutional network (GCN), so as to detect 8 types of fundus lesions in color fundus images. We collected 7459 fundus images (1887 left eyes, 1966 right eyes) from 2282 patients (1283 women, 999 men), and labeled 8 types of lesions, laser scars, drusen, cup disc ratio ([Formula: see text]), hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates. We constructed a specialized corpus of the related fundus lesions. A multi-label classification algorithm for fundus images was proposed based on the corpus, and the collected data were trained.Results: The average overall F1 Score (OF1) and the average per-class F1 Score (CF1) of the model were 0.808 and 0.792 respectively. The area under the ROC curve (AUC) of our proposed model reached 0.986, 0.954, 0.946, 0.957, 0.952, 0.889, 0.937 and 0.926 for detecting laser scars, drusen, cup disc ratio, hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates, respectively.Conclusions: Our results demonstrated that our proposed model can detect a variety of lesions in the color images of the fundus, which lays a foundation for assisting doctors in diagnosis and makes it possible to carry out rapid and efficient large-scale screening of fundus lesions. [ABSTRACT FROM AUTHOR]

Background: Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions.Methods: In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network.Results: Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly.Conclusion: In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists' reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed.Methods: In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture's traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results.Results: On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance.Conclusion: The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG. [ABSTRACT FROM AUTHOR]

Most land plant species have the ability to establish a symbiosis with arbuscular mycorrhizal (AM) fungi. These fungi penetrate into root cortical cells and form branched structures (known as arbuscules) for nutrient exchange. We cloned the MdIAA24 from apple (Malus domestica) following its up-regulation during AM symbiosis. Results demonstrate the positive impact of the overexpression (OE) of MdIAA24 in apple on AM colonization. We observed the strigolactone (SL) synthesis genes, including MdD27 , MdCCD7 , MdCCD8a , MdCCD8b and MdMAXa , to be up-regulated in the OE lines. Thus, the OE lines exhibited both a higher SL content and colonization rate. Furthermore, we observed that the OE lines were able to maintain better growth parameters under AM inoculation conditions. Under drought stress with the AM inoculation, the OE lines were less damaged, which was demonstrated by a higher relative water content, a lower relative electrolytic leakage, a greater osmotic adjustment, a higher reactive oxygen species scavenging ability, an improved gas exchange capacity and an increased chlorophyll fluorescence performance. Our findings demonstrate that the OE of MdIAA24 in apple positively regulates the synthesis of SL and the formation of arbuscules as a drought stress coping mechanism. [ABSTRACT FROM AUTHOR]

The design of satisfied catalytic activity and stable catalysts for the oxygen evolution reaction (OER) is crucial in the production of hydrogen in electrolyzed water. Herein, S-doping NiCo-metal organic framework (NiCoS-MOF) were prepared successfully in situ on a nickel foamed (NF) substrate by used terephthalic acid (TPA) as an organic ligand via the original one-step solvothermal process, NiCoS-MOF reveals the electrocatalytic performance by the low overpotential of 71 mV at 10 mA cm−2 and the tafel slope of 74.4 mV dec−1 under alkaline condition (1 M KOH) served as OER electrodes directly. The i-t curve for 48 h at 1.50 and 2.14 V (vs.REH) showed excellent stability of NiCoS-MOF and the structure had no significant collapse after 1000 cycles. Superior OER activity and excellent stability could ascribed to the fluffy and orderly petal-like structure, which exhibited dispersal of the active sites and facilitated the evolution of oxygen during the catalysis process. The work enriched exploration of heteroatom-doped bimetallic MOFs in energy storage and conversion. • S-doped NiCo bimetallic MOF synthesized by One-step facile solvothermal method. • The OER overpotential at 100 mA/cm−2 is 247 mv. • The robust petal-like fluffy ordered structure enhanced the stability. [ABSTRACT FROM AUTHOR]

Highlights: [•] Prenatal exposure to TiO2 NPs decreased the level of CAT, GSH-PX and T-AOC. [•] Prenatal exposure to TiO2 NPs increased the level of MDA. [•] Prenatal exposure to TiO2 NPs induced oxidative damage to nucleic acids. [•] Prenatal exposure to TiO2 NPs enhanced the depressive-like behavior in adult rats. [Copyright &y& Elsevier]

• Overexpressed MdTYDC enhanced drought tolerance by increasing cellular ABA levels in apple plants. • MdTYDC overexpression improved ASA/DHA and GSH/GSSG ratios under drought. • MdTYDC overexpression modulated stomatal movement by increasing ABA levels and ABA sensitivity. • Transcriptomic analyses indicated that MdTYDC overexpression regulated the expression of drought-related transcription factors. Dopamine is produced in plants and plays a vital role in improving plant responses to abiotic stress. Tyrosine decarboxylase (TYDC) synthesizes tyramine from tyrosine, thereby providing the substrate for dopamine production. However, the function of TYDC in response to drought stress in apple plants remains unclear. In this study, transgenic apple plants that overexpressed MdTYDC were exposed to drought, in order to explore the biological function of TYDC in drought stress. Overexpression of MdTYDC significantly increased apple dopamine content. Transgenic apple plants that overexpressed MdTYDC exhibited less electrolyte leakage, a lower water loss rate, higher photosynthetic pigment contents, and enhanced photosynthetic performance under drought conditions. MdTYDC overexpression also improved reducing power by increasing the ratios of ascorbate to dehydroascorbate and reduced to oxidized glutathione. It influenced leaf water retention capacity and stomatal movement by modulating cellular abscisic acid (ABA) levels and ABA sensitivity. Transcriptomic analyses indicated that MdTYDC overexpression enhanced drought tolerance by regulating the expression of ABA- and drought-related transcription factors, including those from the NAC, ERF, and WRKY families. These results provide evidence for the involvement of MdTYDC and dopamine in enhancing the drought tolerance of apple plants. [ABSTRACT FROM AUTHOR]

Arbuscular mycorrhizal fungi (AMF) can form a symbiotic relationship with most terrestrial plant roots, promote plant growth, and heavy metal (HM) tolerance and thus plays a crucial role in phytoremediation. However, research on the relationship between colonization level and HM tolerance is limited. In this study, apple (Malus domestica) Gretchen Hagen3 genes MdGH3-2/12 silencing plants were treated with four AMF and Cd combination treatments to determine AMF colonization levels, biomass, Cd accumulation, photosynthesis, fluorescence, reactive oxygen species (ROS) and antioxidant substance accumulation, and Cd uptake, transport and detoxification gene expression levels. Results indicate the greater sensitivity of transgenic plants under AMF inoculation and Cd treatment compared with wild type (WT) via lower AMF colonization levels, biomass accumulation, photosynthetic parameters, and the accumulation and clearance homeostasis of ROS, as well as lower detoxification expression levels and higher Cd uptake and transport expression levels. Our study essentially demonstrates that MdGH3-2 / 12 plays an important role in Cd stress tolerance by regulating AM colonization in apple. Image 1 • Silencing MdGH3-2/12 in apple had a negative impact on AM colonization. • The transgenic plants showed greater sensitivity under AMF inoculation and Cd treatment compared with wild type. • The transgenic plants exhibited lower biomass accumulation, photosynthetic parameters, and clearance of ROS. • Lower detoxification and higher Cd uptake and transport expression levels were observed in transgenic plants. [ABSTRACT FROM AUTHOR]