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Abstract The impact of volatile organic compounds (VOCs) on type 2 diabetes (T2D) among older adults is unknown. The multiple linear regression model and the multiple binary logistic regression were used to evaluate the relationships between mVOCs and glucose homeostasis/T2D, respectively. Among the 19 mVOCs, the higher levels of urinary N-acetyl-S-(2-hydroxypropyl)-L-cysteine (2HPMA, compound CID:44146439) and N-acetyl-S-(2-hydroxypropyl)-L-cysteine (HPMMA, compound CID:107774684) were significantly associated with higher odds of T2D (OR = 1.16, 95% confidence interval [CI]:1.01–1.34 for 2HPMA; and OR = 1.27, 95% CI:1.04–1.54 for HPMMA). In addition, higher concentrations of multiple mVOCs in urine were significantly correlated with glucose homeostasis biomarkers, including 2HPMA and 2-thioxothiazolidine-4-carboxylic acid (TTCA, compound CID:3034757) with fasting glucose, HPMMA and mandelic acid (MA, compound CID:1292) with HbA1c, phenylglyoxylic acid (PGA, compound CID:11915) with serum insulin, HbA1c and HOMA-IR. Our findings suggested that exposure to VOCs were associated with increased odds of T2D in older adults, which might be mediated by impaired glucose homeostasis. Mitigating VOCs should be a necessary component of public health strategies aimed at reducing the burden of type 2 diabetes.

The Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway play a pivotal role in innate immunity. Among invertebrates, Domeless receptors serve as the key upstream regulators of this pathway. In our study on Bactrocera dorsalis, we identified three cytokine receptors: BdDomeless1, BdDomeless2, and BdDomeless3. Each receptor encompasses five fibronectin-type-III-like (FN III) extracellular domains and a transmembrane domain. Furthermore, these receptors exhibit the increased responsiveness to diverse pathogenic challenges. Notably, only BdDomeless3 is upregulated during symbiont-like viral infections. Moreover, silencing BdDomeless3 enhanced the infectivity of Bactrocera dorsalis cripavirus (BdCV) and B. dorsalis picorna-like virus (BdPLV), underscoring BdDomeless3’s crucial role in antiviral defense of B. dorsalis. Following the suppression of Domeless3 expression, six antimicrobial peptide genes displayed decreased expression, potentially correlating with the rise in viral infectivity. To our knowledge, this is the first study identifying cytokine receptors associated with the JAK/STAT pathway in tephritid flies, shedding light on the immune mechanisms of B. dorsalis.

Abstract Cellulose-degrading microorganisms hold immense significance in utilizing cellulose resources efficiently. The screening of natural cellulase bacteria and the optimization of fermentation conditions are the hot spots of research. This study meticulously screened cellulose-degrading bacteria from mixed soil samples adopting a multi-step approach, encompassing preliminary culture medium screening, Congo red medium-based re-screening, and quantification of cellulase activity across various strains. Particularly, three robust cellulase-producing strains were identified: A24 (MT740356.1 Brevibacillus borstelensis), A49 (MT740358.1 Bacillus cereus), and A61 (MT740357.1 Paenibacillus sp.). For subsequent cultivation experiments, the growth curves of the three obtained isolates were monitored diligently. Additionally, optimal CMCase production conditions were determined, keeping CMCase activity as a key metric, through a series of single-factor experiments: agitation speed, cultivation temperature, unit medium concentration, and inoculum volume. Maximum CMCase production was observed at 150 rpm/37 °C, doubling the unit medium addition, and a 5 mL inoculation volume. Further optimization was conducted using the selected isolate A49 employing response surface methodology. The software model recommended a 2.21fold unit medium addition, 36.11 °C temperature, and 4.91 mL inoculant volume for optimal CMCase production. Consequently, three parallel experiments were conducted based on predicted conditions consistently yielding an average CMCase production activity of 15.63 U/mL, closely aligning with the predicted value of 16.41 U/mL. These findings validated the reliability of the model and demonstrated the effectiveness of optimized CMCase production conditions for isolate A49.

Abstract Saving energy is crucial for utilizing membrane technology, but there is no energy parameter for understanding the relationships among membrane performance and energy. Here, φ is defined as the energy transfer efficiency of the membrane, and its numerical expression of membrane performance is poor (e.g., in the range of 10−23). The method of modifying membranes is a crucial determinant for developing membrane science, but researchers using current parameters to evaluate modification methods might lead to erroneous conclusions. Hence, the newly established system θ is used to analyze the influence of different modification methods on energy consumption, which not only establish the relationship between different modification methods but also provide the research routes for future optimization methods. The main conclusions are as follows: (1) The current modification methods influence on the energy transfer efficiency of the pristine membrane by about 0.4902–3.278 × 104 times; (2) Using scientific data certifies that the modified support layer of the membranes is a more effective method for reducing the energy consumption than the modified activity layer of the membranes; (3) The establishment of this system provides data support for analyzing the advantages and disadvantages of modification methods, and provides guidance for how to optimize the modification methods of membranes. Therefore, this study not only fills key knowledge gaps in membrane science, but also provides theoretical support for how to optimize membrane modification methods.

Abstract Long-term potentiation (LTP) is a key cellular mechanism for learning and memory, and recent studies in the hippocampus found that LTP was impaired in aged animals. Previous studies of cortical LTP have focused primarily on the homosynaptic plasticity in adult mice, while fewer studies have looked at heterosynaptic plasticity—such as synaptic tagging in aged mice. In the present study, we investigated synaptic tagging in adult and middle-aged mice's anterior cingulate cortex (ACC) using the 64-channel multielectrode dish (MED64) recording system. We found that synaptic tagging was impaired in the ACC of middle-aged male mice as compared to adult mice. Both the network late-phase LTP (L-LTP) and the recruitment of inactive responses were reduced in the ACC of middle-aged male mice. Similar results were found in female middle-aged mice, indicating that there is no gender difference. Furthermore, bath application of brain-derived neurotrophic factor (BDNF) or systemic treatment with newly developed TrkB receptor agonists R13, was shown to rescue both synaptic tagging, and L-LTP, in middle-aged mice. To determine the distribution of synaptic LTP within the ACC, a new visualization method was developed to map the Spatio-temporal variation of LTP in the ACC. Our results provide strong evidence that cortical potentiation and synaptic tagging show an age-dependent reduction, and point to the TrkB receptor as a potential drug target for the treatment of memory decline.

Abstract Background Fibroblasts are important structural cells in synovium and play key roles in maintaining the synovial homeostasis. By single-cell RNA sequencing (scRNA-seq), subpopulation of synovium-resident cells has been reported to protect intra-articular structures from chronic inflammation and promote tissue repair. However, a significant number of researchers have concentrated on the role of fibroblasts in the progress of rheumatoid arthritis (RA) while few reports had described the contribution of distinct fibroblast subsets in the RA remission. It is helpful to understand the role of fibroblast subpopulations in the RA process to provide predictive biomarkers and address RA remission mechanisms. Here, we found HBEGF+ fibroblasts that contributed to RA remission by integrating scRNA-seq datasets and bulk RNA sequencing (bulk RNA-seq) datasets. Method Three single-cell RNA datasets of cells harvested from RA patients were processed and integrated by Seurat and Harmony R packages. After identifying cell types by classic marker genes, the integrated dataset was used to run CellChat for analysis of cell-cell communication. Specially, EGF signaling pathway was found and HBEGF+ fibroblasts were identified based on HBEGF expression. Differential expressed genes of HBEGF+ were shown in heatmap and volcano plot and used to run gene ontology (GO) enrichment analysis. Next, bulk RNA-seq datasets of synovium under different conditions (health, osteoarthritis (OA), rheumatoid arthritis, before and after classical treatment) were compared to show expression change of HBEGF and gene markers that are mainly expressed by HBEGF+ fibroblasts such as CLIC5, PDGFD, BDH2, and ENPP1. Finally, two single-cell RNA sequencing datasets of synovial cells from mice were integrated to identify Hbegf+ fibroblasts and calculate the population of Hbegf+ fibroblasts under different joint conditions (health, K/BxN serum transfer arthritis (STA), and remission of STA). Result After integrating three single-cell RNA sequencing datasets, we identified 11 clusters of synovial cells, such as fibroblasts, mural cells, endothelial cells, CD4+ T cells, CD8+ T cells, natural killer cells, synovium macrophage, peripheral blood macrophages, plasma cells, B cells, and STMN1+ cells. We found fibroblasts had an extensive communication network with other clusters and interacted with synovial macrophages through EGF signaling pathway via analysis of cell-cell communication between synovial cells. HBEGF, ligand to EGF signaling pathway, was highly expressed by a subset of fibroblasts and macrophages, and EGFR, receptor to EGF signaling pathway, was highly expressed by fibroblasts and meniscus cells. Moreover, HBEGF was downregulated under RA state and it had an increase after classical treatment. We then defined fibroblasts with high expression of HBEGF as HBEGF+ fibroblasts. In addition, we also found that HBEGF+ fibroblasts highly expressed CRTAC1, ITGB8, SCARA5, THBS4, and ITGBL1, genes relative to encoding extracellular matrix proteins and engaged in positive regulation of cell migration and motility, cellular component movement, and cell growth by GO enrichment analysis. We eventually identified HBEGF+ fibroblasts specially expressed CLIC5, PDGFD, BDH2, and ENPP1, which positively correlated with the expression of HBEGF. Moreover, the expression of CLIC5, PDGFD, BDH2, and ENPP1 was downregulated under RA state and elevated by classical therapy. On the contrary, the HBEGF+ macrophages specially expressed SLAMF8, GK, L1RN, and JAK2, which negatively correlated with the expression of HBEGF. The expression was upregulated in SLAMF8, GK, L1RN, and JAK2 under the RA state, whereas it was decreased after classical treatment. In mice, the number of Hbegf+ fibroblasts was reduced in the RA synovium but increased in the RA remitting synovium. Conclusions HBEGF+ fibroblasts play a role in the remission of rheumatoid arthritis, and HBEGF has potential to become a novel biomarker for prediction of RA progress.

To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage.

Many deep learning methods for image inpainting rely on the encoder-decoder architecture to estimate missing contents. When guidance information from uncorrupted regions cannot be adequately represented or utilized, the encoder may have difficulty handling the rich surrounding or background pixels, and the decoder could not recover visually sophisticated or realistic content. This paper proposes an effective multi-scale optimization network to alleviate these issues and generate coherent results with fine details. It adaptively encodes multi-receptive fields feature maps and puts multi-scale outputs into a discriminator to guide training. Specifically, we propose a Multi-Receptive feature maps & masks Selective Fusion (MRSF) operator that can adaptively extract features in different receptive fields to handle sophisticated destroyed images. Then a multi-gradient discriminator (MGD) module uses the intermediate features of the discriminator to guide the generator to produce results with natural textures and semantically real contents. Experiments on several benchmark datasets demonstrate that the proposed method can synthesize more realistic and coherent image content.

Primary biliary cirrhosis (PBC) is an organ-specific autoimmune disease that eventually develops into cirrhosis and even liver cancer. In recent years, the incidence rate has been increasing, and the early diagnosis and treatment of PBC are crucial. In the early diagnosis method of PBC, anti-mitochondrial antibodies (AMAs) are an important diagnostic basis, especially the M2 subtype (AMA-M2) with almost 100% specificity. We selected the BCOADC-E2 protein, a mitochondrial autoantigen that reacts specifically with AMA-M2 antibodies, and carried out DNA recombination and protein mutation experiments by cloning in vitro the homologous target gene sequence BCKD that expresses the antigenic epitope of BCOADC-E2 protein, to provide experience for later exploring the effect of mutations of amino acids around the lysine in the active center of BCOADC-E2 protein on its specific binding to AMA-M2, and to lay the foundation for determining the key amino acids of BCOADC-E2 for the diagnosis and treatment of PBC. In addition, we apply this scientific research content to graduate course teaching. Experimental technology of microbial molecular ecology is a course with the cross-integration of multidisciplinary knowledge and experimental skills offered at our college since 2018. This article derives from the part of this course on the construction of recombinant plasmids. The students first constructed the recombinant plasmid pGEX-BCKD using the vector plasmid pGEX-4T1 and the target gene fragment BCKD provided by the laboratory and used this as a template to construct the pGEX-BCKD-E4A point mutation plasmid by the overlap extension PCR (SOE PCR) technique to achieve the effect of mutating the fifth amino acid glutamate in front of lysine, the active centre of the BCOADC-E2 lipid acyl binding domain, to alanine for subsequent studies. Through the research experiment, combining theoretical knowledge and experimental operation, we aim to deepen the student's understanding of DNA recombination technology, let them feel the practical application prospect of experimental technology, stimulate students' interest in professional knowledge learning, and cultivate students' scientific thinking and innovation consciousness. We examined the quality of the teaching through the process and summative evaluation of the students. In this study, the students successfully completed the construction of pGEX-BCKD-E4A point mutant plasmid, and the average test score increased from 40.4% before teaching to 91.1%. The teaching effect was remarkable. This kind of research experimental teaching mode has good application prospects, and other education and teachers can refer to and reference it.

Two-dimensional time-resolved particle image velocimetry (TR-PIV) and stereographic particle image velocimetry (SPIV) techniques were used to investigate the effect of leading-edge tubercles on the flow over low-aspect-ratio wing models. The angle of attack is fixed at 10°, and the Reynolds number based on chord length is 5.8 × 103. It is shown that the leading-edge tubercles can effectively mitigate flow separation in the model and also reduce the contribution of wake vortex to the fluctuating energy of flow. Counter-rotating vortex pairs (CVPs) initiated from the peak of leading-edge tubercles can promote nearby momentum exchange, enhance mixing of the flow and increase the energy contained in the boundary layer, which results in resisting the larger adverse pressure gradient. Therefore, it is concluded that CVPs play an important role in mitigating the flow separation for wings with leading-edge tubercles.

Abstract Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained. In general, approximate solutions can be obtained by iterative optimization methods. In the current work, a practical particle reconstruction method based on a convolutional neural network (CNN) with geometry-informed features is proposed. The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique (ART) based methods. Compared with available ART-based algorithms, the novel technique makes significant improvements in terms of reconstruction quality, robustness to noise, and at least an order of magnitude faster in the offline stage.

Primary biliary cholangitis (PBC) is a cholestatic liver disease primarily featured by autoimmune-mediated damage of intrahepatic small- and medium-sized bile ducts. Elevated serum proinflammatory cytokines, serum anti-mitochondrial antibodies (AMAs), liver inflammation, and fibrosis are also hallmarks of PBC disease. However, whether the elevated proinflammatory cytokines play a role in autoimmune cholangitis remains unknown. Herein, we utilized the p40-/-IL-2Rα-/- PBC mouse model to investigate the roles of proinflammatory cytokines IL-18, IL-21, and IFN-γ in the onset and progression of PBC. IL-18-/-, IFN-γ-/-, and IL-21-/- mice were crossed with p40-/-IL-2Ra+/- mice, respectively, to produce corresponding cytokine-deficient PBC models. Autoantibody level, liver inflammation, and bile duct injury were analyzed. We found that livers from p40-/-IL-2Rα-/- mice exhibit similar transcriptomic characters of PBC patients. In p40-/-IL-2Rα-/- mice, deletion of IL-18 has no remarkable effect on disease progression, while deletion of IL-21 indicates that it is necessary for AMA production but independent of liver inflammation and cholangitis. IFN-γ is responsible for both AMA production and liver inflammation in our model. Our results demonstrate that different proinflammatory cytokines can regulate different effector functions in PBC pathogenesis and need to be considered in PBC treatment.

The reconstruction of missing pixels is essential for remote sensing images, as they often suffer from problems such as covering, dead pixels, and scan line corrector (SLC)-off. Image inpainting techniques can solve these problems, as they can generate realistic content for the unknown regions of an image based on the known regions. Recently, convolutional neural network (CNN)-based inpainting methods have integrated the attention mechanism to improve inpainting performance, as they can capture long-range dependencies and adapt to inputs in a flexible manner. However, to obtain the attention map for each feature, they compute the similarities between the feature and the entire feature map, which may introduce noise from irrelevant features. To address this problem, we propose a novel adaptive attention (Ada-attention) that uses an offset position subnet to adaptively select the most relevant keys and values based on self-attention. This enables the attention to be focused on essential features and model more informative dependencies on the global range. Ada-attention first employs an offset subnet to predict offset position maps on the query feature map; then, it samples the most relevant features from the input feature map based on the offset position; next, it computes key and value maps for self-attention using the sampled features; finally, using the query, key and value maps, the self-attention outputs the reconstructed feature map. Based on Ada-attention, we customized a u-shaped adaptive-attention completing network (AACNet) to reconstruct missing regions. Experimental results on several digital remote sensing and natural image datasets, using two image inpainting models and two remote sensing image reconstruction approaches, demonstrate that the proposed AACNet achieves a good quantitative performance and good visual restoration results with regard to object integrity, texture/edge detail, and structural consistency. Ablation studies indicate that Ada-attention outperforms self-attention in terms of PSNR by 0.66%, SSIM by 0.74%, and MAE by 3.9%, and can focus on valuable global features using the adaptive offset subnet. Additionally, our approach has also been successfully applied to remove real clouds in remote sensing images, generating credible content for cloudy regions.

Due to sensor malfunctions and poor atmospheric conditions, remote sensing images often miss important information/pixels, which affects downstream tasks, therefore requiring reconstruction. Current image reconstruction methods use deep convolutional neural networks to improve inpainting performances as they have a powerful modeling capability. However, deep convolutional networks learn different features with the same group of convolutional kernels, which restricts their ability to handle diverse image corruptions and often results in color discrepancy and blurriness in the recovered images. To mitigate this problem, in this paper, we propose an operator called Bilateral Convolution (BC) to adaptively preserve and propagate information from known regions to missing data regions. On the basis of vanilla convolution, the BC dynamically propagates more confident features, which weights the input features of a patch according to their spatial location and feature value. Furthermore, to capture different range dependencies, we designed a Multi-range Window Attention (MWA) module, in which the input feature is divided into multiple sizes of non-overlapped patches for several heads, and then these feature patches are processed by the window self-attention. With BC and MWA, we designed a bilateral convolution network for image inpainting. We conducted experiments on remote sensing datasets and several typical image inpainting datasets to verify the effectiveness and generalization of our network. The results show that our network adaptively captures features between known and unknown regions, generates appropriate content for various corrupted images, and has a competitive performance compared with state-of-the-art methods.

Video smoke detection is a promising method for early fire prevention. However, it is still a challenging task for application of video smoke detection in real-world detection systems, as the limitations of smoke image samples for training and lack of efficient detection algorithm. This paper proposes a method based on two state-of-the-art fast detectors, a single-shot multi-box detector, and a multi-scale deep convolutional neural network, for smoke detection using synthetic smoke image samples. The virtual data can automatically offer rich samples with ground truth annotations. However, the learning of smoke representation in the detectors will be restricted by the appearance gap between real and synthetic smoke samples, which will cause a significant performance drop. To train a strong detector with synthetic smoke samples, we incorporate the domain adaptation into the fast detectors. A series of branches with the same structure as the detection branches are integrated into the fast detectors for domain adaptation. We design an adversarial training strategy to optimize the model of the adapted detectors, to learn a domain-invariant representation for smoke detection. The domain discrimination, domain confusion, and detection are combined in the iterative training procedure. The performance of the proposed approach surpasses the original baseline in our experiments.

Solutions to most electromagnetic problems use numerical methods, such as the finite element method (FEM), element free method (EFM), and soft computing method. To decrease the computational complexities of these methods, this paper presents an approach for solving electromagnetic problems, called radial basis function (RBF) network-based finite element least square support vector machine (FE-LSSVM). First, the expansion of approximate solutions by the proposed method uses the same structure as the RBF network method. Second, governing equations of electromagnetic problems are transformed to weak integral forms and variational formulations of FEM. Finally, Dirichlet boundary conditions are handled in the LS-SVM framework, which are considered as constraints of an optimization problem. By the Lagrange multiplier method, the quadratic programming problem can be transformed to a problem requiring the solution of a system of equations. The advantages of the proposed method are to directly satisfy the natural boundary conditions of the electromagnetic equations and remarkably improve the calculation accuracy. To verify the efficiency of the method, four categories of electromagnetic problems are investigated by using the proposed method. An analytical method and FEM are also carried out as comparisons to prove the advantages of the method proposed in this paper.

The Asian tramp snail, Bradybaena similaris (Ferusssac), is an invasive land snail species and has been a rising agricultural pest in south of China. As a pest, it also plays a role in transmission of Angiostrongylus cantonensis. However, present studies on this species are rare and the molecular information is limited. For this purpose, we sequenced the transcriptome and small RNA of B. similaris collected from citrus orchards. In total, 89,747 unigenes with an N50 size of 1287 bp and an average length of 817 bp were generated from ∼8.9 Gb transcriptome and 31 Mb clean reads were generated from ∼36 Mb small RNA library. To demonstrate the usefulness of these two datasets, we analyzed a series of genes associated with xenobiotic metabolism and core RNAi machinery. Analysis of the transcripts resulted in annotation of 126 putative genes encoding cytochrome P450 monooxygenases (CYP, 45), carboxyl/cholinesterases (CCE, 13), glutathione-S-transferases (GST, 24), and ATP-binding cassette transporters (ABC, 44). Analysis of the small RNA detected 42 miRNAs. In addition, four genes involved in small RNA pathways (miRNA, piRNA, and siRNA) were identified, and a total of 430 genes that can be targeted by miRNAs were predicted. Moreover, we found that a few miRNAs could target certain genes involved in xenobiotic metabolism. Therefore, we believe that these two datasets and the characterization of the identified/predicted genes will facilitate the molecular study of this species as well as other land snails with agricultural importance.

Early detection of smoke having indistinguishable pixel intensities in digital images is a difficult task. To better maintain fire surveillance, early smoke detection is crucial. To solve the problem, we have integrated the principal component analysis (PCA) as a pre-processing module with the improved version of You Only Look Once (YOLOv3). The ordinary YOLOv3 structure has been improved after inserting one extra detection scale at stride-4 specifically to detect immense small smoke instances in the wild. The improved network design establishes a sequential relation between feature maps of lower spatial information and fine-grained semantic information in up-sampled maps via skip connections and concatenation operations. The testing of the improved model is carried out on self-prepared smoke datasets. In digital images, the smoke instances are captured in various complicated environments, for example, the mountains and fog in the background. A principal component analysis (PCA) helps in useful features selection and abandons the involvement of redundant features in the testing of the trained network hence, overcoming the latency at inference stage. In addition, to process small smoke images as positive samples during training, new sizes of anchors are calculated on small smoke data at a specified Intersection over Union (IoU) threshold. The experimental results show the improvement in precision rate, recall rate, and mean harmonic (F1-score) by 2.67, 3.06, and 5.59 percentages. The respective improvements in average precision (AP) and mean average precision (mAP) are 1.66 and 2.78 percentages.

Objective(s): The aim of this study was to observe the impact of sirolimus on proteinuria in streptozotocin (STZ) induced diabetic rats. Materials and Methods: Rats were given a single injection of STZ to induce diabetic rat model. Rats’ 24 hr urine was collected to test, urinary and the kidney tissues were harvested at the 8th and 20th weeks, respectively. Podocyte morphological changes were examined by electron microscopy and the ZO-1, podocin expressions in kidneys were detected by immunohistochemistry; the protein levels of Raptor and pS6 were measured by Western blot assay. Results: In the early stage of diabetic nephropathy (DN), sirolimus reduced the proteinuria significantly (P

The complete mitochondrial DNA genome of the Oberea diversipes was reported. The genome was a circular molecule of 15,499 base pairs (bp), with 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and an estimated 830 bp A + T-rich control region. The total base composition of the mitogenome was A–T bias, with 40.9% for A, 37.0% for T, 13.4% for C, and 8.7% for G. The molecular data presented here would be useful for further study of O. diversipes.

Tubular-interstitial nephritis (TIN) is characterized by tubular cell damage and inflammatory lesions of kidneys. Baicalein (BAI) is a flavonoid compound found in the roots of Scutellaria baicalensis Georgi. The present study was undertaken to explore the anti-inflammatory and anti-oxidative effects of BAI on TIN patients and a lipopolysaccharide (LPS)-induced TIN cell model. The expression levels of interleukin-6 (IL-6), IL-10, and tumor necrosis factor α in serum samples of TIN patients and culture supernatants of renal proximal tubular epithelial cells (RPTECs) were evaluated using enzyme-linked immunosorbent assay. Creatinine clearance was calculated using the Cockcroft-Gault equation. Activities of malondialdehyde, superoxide dismutase, and glutathione peroxidase were also determined. Viability and apoptosis of RPTECs were measured using MTT assay and Guava Nexin assay, respectively. qRT-PCR was performed to determine the expressions of Bax, Bcl-2, nuclear factor kappa B (IκBα), and p65. Protein levels of Bax, Bcl-2, IκBα, p65, c-Jun N-terminal kinase, extracellular regulated protein kinases, and p38 were analyzed using western blotting. We found that BAI reduced inflammation and oxidative stress in vivo and in vitro. Moreover, BAI alleviated the LPS-induced RPTECs viability inhibition and apoptosis enhancement, as well as nuclear factor kappa B (NF-κB), and mitogen-activated protein kinase (MAPK) activation. Phorbol ester, an activator of NF-κB, attenuated the effects of BAI on LPS-induced inflammatory cytokine expressions in RPTECs. In conclusion, BAI had anti-inflammatory and anti-oxidative effects on TIN patients and LPS-induced RPTECs by down-regulating NF-κB and MAPK pathways.

Low-speed streaks in wall-bounded turbulence are the dominant structures in the near-wall turbulent self-sustaining cycle. Existing studies have well characterized their spanwise spacing in the buffer layer and below. Recent studies suggested the existence of these small-scale structures in the higher layer where large-scale structures usually receive more attention. The present study is thus devoted to extending the understanding of the streak spacing to the log layer. An analysis is taken on two-dimensional (2D) wall-parallel velocity fields in a smooth-wall turbulent boundary layer with R e τ = 440∼2400, obtained via either 2D Particle Image Velocimetry (PIV) measurement taken here or public Direct Numerical Simulation (DNS). Morphological-based streak identification analysis yields a R e -independent log-normal distribution of the streak spacing till the upper bound of the log layer, based on which an empirical model is proposed to account for its wall-normal growth. The small-scale part of the spanwise spectra of the streamwise fluctuating velocity below y + = 100 is reasonably restored by a synthetic simulation that distributes elementary streak units based on the proposed empirical streak spacing model, which highlights the physical significance of streaks in shaping the small-scale part of the velocity spectra beyond the buffer layer.

This paper presents a doubly fed induction generator (DFIG) wind power system with hydrogen energy storage, with a focus on its virtual inertia adaptive control. Conventionally, a synchronous generator has a large inertia from its rotating rotor, and thus its kinetic energy can be used to damp out fluctuations from the grid. However, DFIGs do not provide such a mechanism as their rotor is disconnected with the power grid, owing to the use of back-to-back power converters between the two. In this paper, a hydrogen energy storage system is utilized to provide a virtual inertia so as to dampen the disturbances and support the grid’s stability. An analytical model is developed based on experimental data and test results show that: (1) the proposed method is effective in supporting the grid frequency; (2) the maximum power point tracking is achieved by implementing this proposed system; and, (3) the DFIG efficiency is improved. The developed system is technically viable and can be applied to medium and large wind power systems. The hydrogen energy storage is a clean and environmental-friendly technology, and can increase the renewable energy penetration in the power network.

Decisions to help those in need are essential for human development and survival. Previous studies have demonstrated the "identified effect", in which one identifiable individual typically invokes stronger feelings of compassion and receives greater aid than statistical victim. However, this preference might be influenced by cultural differences. In the current study, Chinese respondents' ratings of distress and sympathy and their willingness to contribute are greater for a group of sick children than an individual. In the U.S., greater willingness to help and sympathy are elicited by an identified victim in comparison with an unidentified one. The different results may demonstrate the importance of cultural differences when trying to understand people's prosocial behavior.

BACKGROUND: The citrus red mite is a worldwide citrus pest and a common sensitizing allergen of asthma and rhinitis. It has developed strong resistance to many registered acaricides, However, the molecular mechanisms of resistance remain unknown. we therefore used next generation sequencing technology to investigate the global transcriptomes between resistant strains and susceptible strains. RESULTS: We obtained 34,159, 30,466 and 32,217 unigenes by assembling the SS reads, RS reads and SS&RS reads respectively. There are total 17,581 annotated unigenes from SS&RS reads by BLAST searching databases of nr, the Clusters of Orthologous Groups (COGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) with an E-value ≤ 1e-5, in which 7,075 unigenes were annotated in the COG database, 12, 712 unigenes were found in the KEGG database and 3,812 unigenes were assigned to Gene ontology (GO). Moreover, 2,701 unigenes were judged to be the differentially expressed genes (DEGs) based on the uniquely mapped reads. There are 219 pathways in all annotated unigenes and 198 pathways in DEGs that mapped to the KEGG database. We identified 211 metabolism genes and target genes related to general insecticide resistance such as P450 and Cytochrome b, and further compared their differences between RS and SS. Meanwhile, we identified 105 and 194 genes related to growth and reproduction, respectively, based on the mode of action of Hexythiazox. After further analyses, we found variation in sequences but not in gene expression related to mite growth and reproduction between different strains. CONCLUSION: To our knowledge, this is the first comparative transcriptome study to discover candidate genes involved in phytophagous mite resistance. This study identified differential unigenes related to general pesticide resistance and organism growth and reproduction in P. citri. The assembled, annotated transcriptomes provide a valuable genomic resource for further understanding the molecular basis of resistance mechanisms.