We examined the effects of nitrate (NO
-N) and ammonium (NH
-N) input at different concentrations (0, 1, 5 and 25 mg·kg
) on N
O production rate from the surface sediment (0-5 cm) of Luoshijiang ...Wetland, located upstream from Lake Erhai. The contribution of nitrification, denitrification, nitrifier denitrification, and other factors to the N
O production rate in sediments was studied by the inhibitor method. The relationships between N
O production and the activities of hydroxylamine (HyR), nitrate (NAR), nitric oxide (NOR), and nitrous oxide (NOS) reductases in sediments were analyzed. We found that NO
-N input significantly increased total N
O production rate (1.51-11.35 nmol·kg
·h
), which led to N
O release, whereas NH
-N input decreased that (-0.80 to -0.54 nmol·kg
·h
), causing N
O absorption. NO
-N input did not change the dominant roles of nitrification and nitrifier denitrification in N
O production in sediments, but increased the contributions of these two factors to 69.5% and 56.5%, respectively. T
Ground Penetrating Radar (GPR) has emerged as a pivotal tool for subsurface explorations, particularly in detecting subsurface defects that might endanger structural integrity. While GPR B-scan data ...visually depict underground conditions, it represents the time delay and amplitude of the returned Electromagnetic (EM) waves, making them complex to interpret due to both their image- like appearance and their inherent waveform changes. To address this complexity, this paper introduces the novel Temporal-Spatial Synthesis Network (TSSNet), designed to harness both temporal and spatial features for enhanced subsurface defect detection in GPR B-scan data. The Echo State Network (ESN), underpinned by Reservoir Computing, is utilized to fit the GPR data and capture its "temporal features", emphasizing the temporal variations present in the EM waves. Concurrently, the Convolutional Neural Network (CNN) focuses on discerning "spatial features" from the B-scan images, spotlighting spatial patterns that possibly indicate subsurface defects. After extracting these temporal and spatial features, they are synthesized to form a comprehensive representation of the GPR data. The enhanced synthesized feature facilitates precise classification, resulting in heightened differentiation between normal and defect-contained subsurface areas. Experiments on real-world GPR datasets are conducted, with the results underscoring the efficacy of the proposed approach.
Background The intermediate-conductance Ca.sup.2+-activated K.sup.+ channel KCa3.1 was recently shown to control the phenotype switch of reactive astrogliosis (RA) in Alzheimer's disease (AD). ...Methods KCa3.1 channels expression and cell localization in the brains of AD patients and APP/PS1 mice model were measured by immunoblotting and immunostaining. APP/PS1 mice and KCa3.1.sup.-/-/APP/PS1 mice were subjected to Morris water maze test to evaluate the spatial memory deficits. Glia activation and neuron loss was measured by immunostaining. Fluo-4AM was used to measure cytosolic Ca.sup.2+ level in beta-amyloid (Abeta) induced reactive astrocytes in vitro. Results KCa3.1 expression was markedly associated with endoplasmic reticulum (ER) stress and unfolded protein response (UPR) in both Abeta-stimulated primary astrocytes and brain lysates of AD patients and APP/PS1 AD mice. The KCa3.1 channel was shown to regulate store-operated Ca.sup.2+ entry (SOCE) through an interaction with the Ca.sup.2+ channel Orai1 in primary astrocytes. Gene deletion or pharmacological blockade of KCa3.1 protected against SOCE-induced Ca.sup.2+ overload and ER stress via the protein kinase B (AKT) signaling pathway in astrocytes. Importantly, gene deletion or blockade of KCa3.1 restored AKT/mechanistic target of rapamycin signaling both in vivo and in vitro. Consistent with these in vitro data, expression levels of the ER stress markers 78-kDa glucose-regulated protein and CCAAT/enhancer-binding protein homologous protein, as well as that of the RA marker glial fibrillary acidic protein were increased in APP/PS1 AD mouse model. Elimination of KCa3.1 in KCa3.1.sup.-/-/APP/PS1 mice corrected these abnormal responses. Moreover, glial activation and neuroinflammation were attenuated in the hippocampi of KCa3.1.sup.-/-/APP/PS1 mice, as compared with APP/PS1 mice. In addition, memory deficits and neuronal loss in APP/PS1 mice were reversed in KCa3.1.sup.-/-/APP/PS1 mice. Conclusions Overall, these results suggest that KCa3.1 is involved in the regulation of Ca.sup.2+ homeostasis in astrocytes and attenuation of the UPR and ER stress, thus contributing to memory deficits and neuronal loss. Keywords: Alzheimer's disease, Endoplasmic reticulum stress, Mouse model, Unfolded protein response, KCa3.1
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Synonymous codon usage bias (SCUB) of both nuclear and organellar genes can mirror the evolutionary specialization of plants. The polyploidization process exposes the nucleus to genomic shock, a ...syndrome which promotes, among other genetic variants, SCUB. Its effect on organellar genes has not, however, been widely addressed. The present analysis targeted the chloroplast genomes of two leading polyploid crop species, namely cotton and bread wheat. The frequency of codons in the chloroplast genomes ending in either adenosine (NNA) or thymine (NNT) proved to be higher than those ending in either guanidine or cytosine (NNG or NNC), and this difference was conserved when comparisons were made between polyploid and diploid forms in both the cotton and wheat taxa. Preference for NNA/T codons was heterogeneous among genes with various numbers of introns and was also differential among the exons. SCUB patterns distinguished tetraploid cotton from its diploid progenitor species, as well as bread wheat from its diploid/tetraploid progenitor species, indicating that SCUB in the chloroplast genome partially mirrors the formation of polyploidies.
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Walnut shell is a very potential biochar precursor because of its wide source, low cost, and easy structure modification. In this paper, the co-activation method of FeCl3, ZnCl2 and H2O(g) was ...adopted to prepare walnut shell-based biochar with high microporosity and the effect of pore structure on CO2 adsorption performance at different temperatures was investigated. The prepared biochar had a larger specific surface area (2647.8 m2 g−1), satisfactory micropore area (2008.7 m2 g−1) and high total pore volume (2.58 cm3 g−1). At 273 K and 298 K, its CO2 adsorption capacity was 4.79 mmol g−1 and 3.20 mmol g−1, respectively. Particularly, CO2 adsorbed uptake on biochar was strongly sensitive to their narrow micropore volume, instead of the total specific surface area, total pore volume, and micropore specific surface area. The optimal pore size beneficial for CO2 adsorption was 0.33–0.82 nm at 273 K, but the optimal pore size was 0.33–0.39 nm at 298 K. It provides theoretical guidance for future material preparation and selection, and FeCl3, ZnCl2 and H2O(g) may be effective biochar activators.
A rigid polyurethane foam (RPUF) composite was prepared by compounding phytic acid (PA)-functionalized Graphite oxide (PA-GO) with flame-retardant poly (Ammonium phosphate) (APP) and expandable ...graphite (EG). The effects of PA-GO on the thermal, flame-retardant, and mechanical properties of RPUF were studied using a thermogravimetric analyzer, a limiting oxygen index (LOI) tester, a UL-94 vertical combustion tester, a cone calorimeter, scanning electron microscopy, and a universal tensile testing machine. The results indicated that there was a significant synergistic flame-retardant effect between PA-GO and the intumescent flame retardants (IFR) in the RPUF matrix. Compared with RPUF-1, the addition of 0.3 wt% PA-GO could increase LOI from 25.7% to 26.5%, increase UL-94 rating from V-2 to V-0, and reduce the peak heat release rate (PHRR) and total heat release rate (THR) by 28.5% and 22.2%, respectively. Moreover, the amount of residual char increased from 22.2 wt% to 24.6 wt%, and the char layer was continuous and dense, with almost no holes. Meanwhile, the loss of mechanical properties was apparently lightened.
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High-resolution networks have demonstrated significant advantages in multi-scale feature extraction for human pose estimation task. However, this often leads to the problems of large network ...parameter quantity and high computational complexity. On the other hand, some existing lightweight high-resolution networks have lower model parameter quantity and computational complexity, but they lack accuracy in handling keypoint position information. To address these issues, this study proposes a lightweight High-resolution network called DB-HRNet (Dual Branch High-Resolution Network) for human pose estimation. Specifically. based on HRNet, a more efficient network framework is designed as the backbone framework of DB-HRNet. Additionally, we propose a position-sensitive basic module called DBBlock (Dual Branch Block) to enhance the model's ability to capture positional information accurately, as the basic block of DB-HRNet. Finally, we propose a Pose Refine Module to establish the connections between keypoints and make the final adjustments to the model's output. To validate the effectiveness of DB-HRNet, we conduct experimental studies on the COCO2017 and MPII human pose estimation datasets, comparing DB-HRNet with several advanced existing lightweight human pose estimation models. The results demonstrate that the proposed network model achieves a 1.8 points improvement in detection accuracy on the COCO2017 dataset compared to the advanced lightweight high-resolution network Dite-HRNet, while also achieving a 38% increase in model inference speed.
Micro-expressions are brief, involuntary facial movements that reveal genuine emotions. However, extracting and learning features from micro-expressions poses challenges due to their short duration ...and low intensity. To address this problem, we propose the ADMME (Action Decouple Multi-tasking for Micro-Expression Recognition) method. In our model, we adopt a pseudo-siamese network architecture and leverage contrastive learning to obtain a better representation of micro-expression motion features. During model training, we utilize focal loss to handle the class imbalance issue in micro-expression datasets. Additionally, we introduce an AU (Action Unit) detection task, which provides a new inductive bias for micro-expression detection, enhancing the model's generalization and robustness. Through five-class classification experiments conducted on the CASMEII and SAMM datasets, we achieve accuracy rates of 86.34% and 81.28%, with F1 scores of 0.8635 and 0.8168, respectively. These results validate the effectiveness of our method in micro-expression recognition tasks. Furthermore, we validate the effectiveness of each component of our approach through a series of ablation experiments.
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