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Inhalation anesthesia stands as a pivotal modality within clinical anesthesia practices. Beyond its primary anesthetic effects, inhaled anesthetics have non-anesthetic effects, exerting bidirectional influences on the physiological state of the body and disease progression. These effects encompass impaired cognitive function, inhibition of embryonic development, influence on tumor progression, and so forth. For many years, inhaled anesthetics were viewed as inhibitors of stem cell fate regulation. However, there is now a growing appreciation that inhaled anesthetics promote stem cell biological functions and thus are now regarded as a double-edged sword affecting stem cell fate. In this review, the effects of inhaled anesthetics on self-renewal and differentiation of neural stem cells (NSCs), embryonic stem cells (ESCs), and cancer stem cells (CSCs) were summarized. The mechanisms of inhaled anesthetics involving cell cycle, metabolism, stemness, and niche of stem cells were also discussed. A comprehensive understanding of these effects will enhance our comprehension of how inhaled anesthetics impact the human body, thus promising breakthroughs in the development of novel strategies for innovative stem cell therapy approaches. [ABSTRACT FROM AUTHOR]

Mastitis typically arises from bacterial invasion, where host cell apoptosis significantly contributes to the inflammatory response. Gram-positive bacteria predominantly utilize the virulence factor lipoteichoic acid (LTA), which frequently leads to chronic breast infections, thereby impacting dairy production and animal husbandry adversely. This study employed LTA to develop models of mastitis in cow mammary gland cells and mice. Transcriptomic analysis identified 120 mRNAs associated with endocytosis and apoptosis pathways that were enriched in the LTA-induced inflammation of the Mammary Alveolar Cells-large T antigen (MAC-T), with numerous differential proteins also concentrated in the endocytosis pathway. Notably, actin-related protein 2/3 complex subunit 3 (ARPC3), actin-related protein 2/3 complex subunit 4 (ARPC4), and the heat shock protein 70 (HSP70) are closely related. STRING analysis revealed interactions among ARPC3, ARPC4, and HSP70 with components of the apoptosis pathway. Histological and molecular biological assessments confirmed that ARPC3, ARPC4, and HSP70 were mainly localized to the cell membrane of mammary epithelial cells. ARPC3 and ARPC4 are implicated in the mechanisms of bacterial invasion and the initiation of inflammation. Compared to the control group, the expression levels of these proteins were markedly increased, alongside the significant upregulation of apoptosis-related factors. While HSP70 appears to inhibit apoptosis and alleviate inflammation, its upregulation presents novel research opportunities. In conclusion, we deduced the development mechanism of ARPC3, ARPC4, and HSP70 in breast inflammation, laying the foundation for further exploring the interaction mechanism between the actin-related protein 2/3 (ARP2/3) complex and HSP70. [ABSTRACT FROM AUTHOR]

Natural gas transmission pipelines installed in seismic and permafrost regions are vulnerable to cyclic loads with a large strain amplitude. Under these conditions, the pipe may fail in extremely low cycles, a situation which is also known as extremely low cycle fatigue (ELCF) failure. The fracture mechanism of ELCF shows significant difference to that of low cycle fatigue, and the ELCF life usually deviates from the Coffin–Manson law. Thus, it is essential to develop an effective model to predict ELCF failure of the pipeline. In this study, a series of ELCF tests is performed on pipeline steel (X70). A damage coupled mixed hardening model is developed to simulate the fracture behaviors. Continuum damage law under monotonic load is extended to cyclic load by introducing the effective equivalent plastic strain. By assuming the cyclic softening is induced by the damage accumulation, the damage parameters are fitted directly from the peak stress in each cycle. Then, the model is input into commercial software ABAQUS with a user material subroutine to simulate the fracture behaviors of these specimens. The simulation results show good agreements with the test results both under cyclic and monotonic load, which verifies the reliability of the model. [ABSTRACT FROM AUTHOR]

Land use change is one of the key elements leading to carbon emission changes, and is of great significance to the process of achieving the goals of carbon peaking and carbon neutrality. In this study, we calculated the land-use carbon emissions (LCE) in the Guanzhong area (GZA) of Shaanxi province from 2000 to 2019 by using an improved LCE measurement model. Meanwhile, the spatial and temporal changes of LCE were analyzed and the driving forces were investigated based on the correlation analysis and multi-scale geographical weighting regression (MGWR). The results showed that the total amount of LCE showed a significant increasing trend from 2000 to 2019. Regions where the LCE significantly increased occupied 71.20% of the total area; these regions were distributed in the central and eastern parts of the study area. The LCE showed a significant positive spatial correlation and had a remarkable aggregation state. The H-H agglomeration area of LCE was distributed in the central urban agglomeration. The L-L agglomeration areas were always distributed in the southwest part of the GZA with low carbon emissions. The average correlation coefficients between LCE and nighttime light (NTL), population density (PD), and gross primary productivity (GPP) were 0.13, 0.21, and −0.05, respectively. The NLT and PD had obvious positive effects on LCE, while GPP has obvious negative effects on carbon emissions, which can be ascribed to the carbon sink effect of forests and grasslands. The results of this study have important reference value regarding the formulation of carbon emission reduction policies and the development of a low-carbon social economy. [ABSTRACT FROM AUTHOR]

Development of low-cost, high-temperature-resistant and salt-resistant fracturing fluids is a hot and difficult issue in reservoir fluids modification. In this study, an organic zirconium crosslinker that was synthesized and crosslinked with partially hydrolyzed polyacrylamide (HPAM) was employed as a cost-effective polymer thickener to synthesize a high-temperature-resistant and salt-resistant fracturing fluid. The rheological properties of HPAM in tap water solutions and 2 × 104 mg/L salt solutions were analyzed. The results demonstrated that addition of salt reduced viscosity and viscoelasticity of HPAM solutions. Molecular dynamics (MD) simulation results indicated that, due to electrostatic interaction, the carboxylate ions of HPAM formed an ionic bridge with metal cations, curling the conformation, decreasing the radius of rotation and thus decreasing viscosity. However, optimizing fracturing fluids formulation can mitigate the detrimental effects of salt on HPAM. The rheological characteristics of the HPAM fracturing fluid crosslinking process were analyzed and a crosslinking rheological kinetic equation was established under small-amplitude oscillatory shear (SAOS) test. The results of a large-amplitude oscillation shear (LAOS) test indicate that the heating effect on crosslinking is stronger than the shear effect on crosslinking. High-temperature-resistant and shear-resistant experiments demonstrated good performance of fracturing fluids of tap water and salt solution at 200 °C and 180 °C. [ABSTRACT FROM AUTHOR]

Traditional optics usually studies the uniform polarization state of light. Compared with uniform vector beams, non-uniform vector beams have more polarization information. Most of the research on generating cylindrical vector beams using metasurfaces focuses on generating transmitted beams using the geometric phase. However, the geometric phase requires the incident light to be circularly polarized, which limits the design freedom. Here, an all-dielectric reflective metasurface is designed to generate different output light according to the different polarization states of the incident light. By combining the two encoding arrangements of the dynamic phase and the geometric phase, the output light is a radial vector beam when the linearly polarized light is incident along the x-direction. Under the incidence of linearly polarized light along the y-direction, the generated output light is an azimuthal vector beam. Under the incidence of left-handed circularly polarized light, the generated output light is a vortex beam with a topological charge of −1. Under the incidence of right-handed circularly polarized light, the generated output light is a vortex beam with a topological charge of +1. The proposed reflective metasurface has potential applications in generating vector beams with high integration. [ABSTRACT FROM AUTHOR]

A novel nonlinear dynamic modeling approach is proposed for the T-shaped beam structures widely used in the field of aerospace. All of the geometrical nonlinearities including the terms in the deformation of the beams, the terms at the connections, and the free ends of beams are considered in the dynamic modeling process. The global mode method is employed to determine the natural frequencies and global mode shapes of the linearized system. The validity and accuracy of the derived model are verified by comparing the natural frequencies obtained with those calculated from FEM. Adopting the Galerkin truncation procedure, a set of reduced-order nonlinear ODEs is obtained for the structure. A study on the variation of dynamic responses taking the different numbers of global modes into account is performed to determine the number of modes taken in nonlinear vibration analysis. A comparison between the responses of the system with linear or nonlinear matching and boundary conditions is given to evaluate the importance of neglecting and reserving the nonlinear terms in matching and boundary conditions. It is shown that ignoring the nonlinear terms in both matching and boundary conditions may significantly alter the responses while developing the discretized governing ODEs of the structure. [ABSTRACT FROM AUTHOR]

The lactonase activity of paraoxonase 1 (PON1) has a crucial antiatherogenic function, and also serves as an important biochemical marker in human blood because the aberrant lactonase activity of PON1 is a key indicator for a number of diverse human diseases. However, no sensitive fluorescence assays that detect PON1 lactonase activity are available. We report the synthesis of two fluorescence turn-on chemical probes 16a and 16b (16) able to quantify PON1 lactonase activity. The chemical probes were constructed utilizing a disulfide-containing bicyclononyne, derivatives of rhodamine B and carboxyfluorescein, and reactions including copper-free azide–alkyne cycloaddition. Fluorescence quenching in 16 was characterized by spectroscopic studies and was mainly attributed to the effect of contact quenching. Kinetic analysis of 16b confirmed the outstanding reactivity and specificity of 16b with thiols in the presence of general base catalysts. The 16b-based assay was employed to determine PON1 lactonase activity, with a linear range of 10.8–232.1 U L−1 and detection limit (LOD) of 10.8 U L−1, to quantify serum PON1 activity in human sera, and to determine the Ki of 20.9 μM for the 2-hydroxyquinoline inhibition of PON1 lactonase. We are employing 16b to develop high-throughput assays for PON1 lactonase activity. [ABSTRACT FROM AUTHOR]

Prunus tomentosa (Thunb.) Wall has high nutritional value and medicinal effects. It is widespread in China; however, most plants growing in the wild are near extinction in many places. Predicting the potential distribution of P. tomentosa under climate change is helpful for cultivating and protecting wild germplasm resources. We used two general circulation models (CCSM4 and MIROC-ESM) and two future climate scenarios (RCP4.5 and RCP8.5) to predict P. tomentosa's present and future geographical distribution. A total of 137 distribution data points and 19 bioclimatic variables were imported into the maximum entropy model (MaxEnt). The optimal parameter combination (feature class LQHPT, regularized multiplier 3.0) was selected with corrected Akaike Information Criterion as the index. The results showed that at present and in the future, P. tomentosa was distributed across the northern provinces, with Gansu, Shanxi, Shaanxi, and Henan being the most suitable regions. Compared with the current climatic conditions, the potential growing area of P. tomentosa will move north, and the growing area will increase, especially in Xinjiang, where the low-impact zone area decreases. Temperature and humidity were the main variables affecting the potential distribution of the plant, including the average temperature in the coldest season (Bio11) and precipitation in the warmest season (Bio18). [ABSTRACT FROM AUTHOR]

Broadband resonant scattering in a visible region that can be obtained by coupled multiple silicon nanocylinders. For a single high refractive index silicon nanocylinder, the electric dipole and magnetic dipole resonances can be observed. By constructing a silicon nanocylinder dimer, the interaction between the particles plays an important role in broadband scattering. Interestingly, due to magnetic-magnetic dipole interaction, a splitting phenomenon of magnetic resonance mode is revealed. A new magnetic resonant mode emerges at a longer wavelength in dimer and trimer by changing the diameter of one nanocylinder in dimer or trimer, and the gap size between nanocylinders. The scattering bandwidth can further increase with the effect of substrate, which is attributed to the extension of resonant mode into substrate. The broadband optical response can be revealed by the calculated scattering resonant spectra and the spatial electromagnetic field distributions. Furthermore, the transmission of periodic nanocylinder structure, including single nanocylinder and dimer, is demonstrated. By decreasing the gap between nanocylinders in dimer for periodic array structure, a new electric resonant mode occurs. These results can provide a guideline to realize broadband resonant optical elements. [ABSTRACT FROM AUTHOR]

Despite the significant progress that has been made in the genome sequencing of Prunus, this area of research has been lacking a systematic description of the mitochondrial genome of this genus for a long time. In this study, we assembled the mitochondrial genome of the Chinese plum (Prunus salicina) using Illumina and Oxford Nanopore sequencing data. The mitochondrial genome size of P. salicina was found to be 508,035 base pair (bp), which is the largest reported in the Rosaceae family to date, and P. salicina was shown to be 63,453 bp longer than sweet cherry (P. avium). The P. salicina mitochondrial genome contained 37 protein-coding genes (PCGs), 3 ribosomal RNA (rRNA) genes, and 16 transfer RNA (tRNA) genes. Two plastid-derived tRNA were identified. We also found two short repeats that captured the nad3 and nad6 genes and resulted in two copies. In addition, nine pairs of repeat sequences were identified as being involved in the mediation of genome recombination. This is crucial for the formation of subgenomic configurations. To characterize RNA editing sites, transcriptome data were used, and we identified 480 RNA editing sites in protein-coding sequences. Among them, the initiation codon of the nad1 gene confirmed that an RNA editing event occurred, and the genomic encoded ACG was edited as AUG in the transcript. Combined with previous reports on the chloroplast genome, our data complemented our understanding of the last part of the organelle genome of plum, which will facilitate our understanding of the evolution of organelle genomes. [ABSTRACT FROM AUTHOR]

Breast cancer is now the most common type of cancer worldwide, surpassing lung cancer. This issue is further worsened by the lack of effective therapies for the disease. Recent reports indicate that the inhibition of ubiquitin-like modifier-activating enzyme 5 (UBA5) can impede tumor development. However, there have been few reports regarding UBA5-inhibiting compounds. This work studied usenamine A, a natural product from the lichen Usnea longissimi that exhibits UBA5-inhibitory effects. Bioinformatics analysis was performed using public databases, and the anti-proliferative ability of usenamine A in breast cancer cells was examined through MTS and colony formation assays. Flow cytometry and western blot analysis were also conducted to examine and analyze cell cycle arrest and apoptosis. In addition, LC3B-RFP and UBA5 expression plasmids were used for the analysis of usenamine A-induced autophagy. According to the bioinformatics analysis results, UBA5 was upregulated in breast cancer. According to in vitro studies, usenamine A displayed prominent anti-proliferative activity and resulted in G2/M phase arrest in MDA-MB-231 cells. Moreover, usenamine A induced autophagy and endoplasmic reticulum stress in MDA-MB-231 cells. In conclusion, the findings support the potential of usenamine A as an agent that can attenuate the development and progression of breast cancer. [ABSTRACT FROM AUTHOR]

As the fastest growing trend in big data analysis, deep learning technology has proven to be both an unprecedented breakthrough and a powerful tool in many fields, particularly for image segmentation tasks. Nevertheless, most achievements depend on high-quality pre-labeled training samples, which are labor-intensive and time-consuming. Furthermore, different from conventional natural images, coastal remote sensing ones generally carry far more complicated and considerable land cover information, making it difficult to produce pre-labeled references for supervised image segmentation. In our research, motivated by this observation, we take an in-depth investigation on the utilization of neural networks for unsupervised learning and propose a novel method, namely conditional co-training (CCT), specifically for truly unsupervised remote sensing image segmentation in coastal areas. In our idea, a multi-model framework consisting of two parallel data streams, which are superpixel-based over-segmentation and pixel-level semantic segmentation, is proposed to simultaneously perform the pixel-level classification. The former processes the input image into multiple over-segments, providing self-constrained guidance for model training. Meanwhile, with this guidance, the latter continuously processes the input image into multi-channel response maps until the model converges. Incentivized by multiple conditional constraints, our framework learns to extract high-level semantic knowledge and produce full-resolution segmentation maps without pre-labeled ground truths. Compared to the black-box solutions in conventional supervised learning manners, this method is of stronger explainability and transparency for its specific architecture and mechanism. The experimental results on two representative real-world coastal remote sensing datasets of image segmentation and the comparison with other state-of-the-art truly unsupervised methods validate the plausible performance and excellent efficiency of our proposed CCT. [ABSTRACT FROM AUTHOR]

This study aimed to construct an effective model to estimate the recreation value and total annual recreation benefits of the Alishan National Forest Recreation Area (ANFR) through the travel cost method. Negative binomial regression was also employed to estimate the demand function. This study sorted participants based on gender, age, level of education, personal monthly income, occupation, and place of residence to examine their differences in perceived recreation value and recreation demand. The survey responses revealed that most tourists were mainly middle aged, and took family vacations. The findings indicate that the recreation benefits of traveling to Alishan per person each year equaled NTD 1703. Furthermore, the annual recreation benefits of Alishan were approximately between NTD 2,157,121,944 and NTD 2,452,136,112 based on a total of 1,353,276 visits traveling to Alishan in 2019. Recommendations and suggestions are drawn for ANFR based the results. [ABSTRACT FROM AUTHOR]

Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. Although adversaries-based domain adaptation methods have been proposed to address this issue, they always implement distribution alignment via a global discriminator while ignoring the data structure. Additionally, the low inter-class variances and intricate spatial details of coastal objects may entail poor presentation. Therefore, this paper proposes a category-space constrained adversarial method to execute category-level adaptive CLCM. Focusing on the underlying category information, we introduce a category-level adversarial framework to align semantic features. We summarize two diverse strategies to extract category-wise domain labels for source and target domains, where the latter is driven by self-supervised learning. Meanwhile, we generalize the lightweight adaptation module to multiple levels across a robust baseline, aiming to fine-tune the features at different spatial scales. Furthermore, the self-supervised learning approach is also leveraged as an improvement strategy to optimize the result within segmented training. We examine our method on two converse adaptation tasks and compare them with other state-of-the-art models. The overall visualization results and evaluation metrics demonstrate that the proposed method achieves excellent performance in the domain adaptation CLCM with high-resolution remotely sensed images. [ABSTRACT FROM AUTHOR]

Low inter-class variance and complex spatial details exist in ground objects of the coastal zone, which leads to a challenging task for coastal land cover classification (CLCC) from high-resolution remote sensing images. Recently, fully convolutional neural networks have been widely used in CLCC. However, the inherent structure of the convolutional operator limits the receptive field, resulting in capturing the local context. Additionally, complex decoders bring additional information redundancy and computational burden. Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. Among them, lightweight global feature attention modules are employed to aggregate multi-scale spatial details in the decoding stage. Meanwhile, position and channel attention modules with long-range dependencies are embedded to enhance feature representations of specific categories by capturing the multi-dimensional global context. Additionally, multiple objective functions are introduced to supervise and optimize feature information at specific scales. We apply the proposed method in CLCC tasks of two study areas and compare it with other state-of-the-art approaches. Experimental results indicate that the proposed method achieves the optimal performances in encoding long-range context and recognizing spatial details and obtains the optimum representations in evaluation indexes. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence technology has prompted an immense amount of researches on improving the performance of change detection approaches. Existing deep learning-driven methods generally regard changes as a specific type of land cover, and try to identify them relying on the powerful expression capabilities of neural networks. However, in practice, different types of land cover changes are generally influenced by environmental factors at different degrees. Furthermore, seasonal variation-induced spectral differences seriously interfere with those of real changes in different land cover types. All these problems pose great challenges for season-varying change detection because the real and seasonal variation-induced changes are technically difficult to separate by a single end-to-end model. In this paper, by embedding a convolutional long short-term memory (ConvLSTM) network into a conditional generative adversarial network (cGAN), we develop a novel method, named progressive domain adaptation (PDA), for change detection using season-varying remote sensing images. In our idea, two cascaded modules, progressive translation and group discrimination, are introduced to progressively translate pre-event images from their own domain to the post-event one, where their seasonal features are consistent and their intrinsic land cover distribution features are retained. By training this hybrid multi-model framework with certain reference change maps, the seasonal variation-induced changes between paired images are effectively suppressed, and meanwhile the natural and human activity-caused changes are greatly emphasized. Extensive experiments on two types of season-varying change detection datasets and a comparison with other state-of-the-art methods verify the effectiveness and competitiveness of our proposed PDA. [ABSTRACT FROM AUTHOR]

Butyrylcholinesterase (BChE) is a biomarker in human blood. Aberrant BChE activity has been associated with human diseases. Here we developed a fluorescence resonance energy transfer (FRET) chemical probe to specifically quantify BChE activity in serum, while simultaneously discriminating against glutathione (GSH). The FRET chemical probe 11 was synthesized from a key trifunctional bicyclononyne exo-6 and derivatives of 5-(2-aminoethylamino)-1-naphthalenesulfonic acid (EDANS) and 4-[4-(dimethylamino)phenylazo]benzoic acid (DABCYL). EDANS fluorescence visualization and kinetic analysis of 11 in the presence of diverse compounds confirmed the outstanding reactivity and specificity of 11 with thiols. The thiol-dependent fluorescence turn-on property of 11 was attributed to a general base-catalyzed SN2 nucleophilic substitution mechanism and independent of metal ions. Moreover, all thiols, except GSH, reacted swiftly with 11. Kinetic studies of 11 in the presence of covalently modified GSH derivatives corroborated that the steric hindrance of 11 imposing on GSH was the likely cause of the distinguished reactivity. Since GSH commonly interferes in assays measuring BChE activity in blood samples, the 11-based fluorescent assay was employed to directly quantify BChE activity without GSH interference, and delivered a linear range of 4.3–182.2 U L−1 for BChE activity with detection limit of 4.3 U L−1, and accurately quantified serum BChE activity in the presence of 10 μM GSH. Finally, the 11-based assay was exploited to determine Ki of 5 nM for tacrine inhibition on BChE catalysis. We are harnessing the modulated characteristics of 6 to synthesize advanced chemical probes able to more sensitively screen for BChE inhibitors and quantify BChE activity in serum. [ABSTRACT FROM AUTHOR]

Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node identification, in both calculation and richer application scope. In this paper, we propose a two-step node embedding-based solution for the social influence maximization problem (IMP). The solution employs a revised network-embedding algorithm to map input nodes into vector space in the first step. In the second step, the solution clusters the vector space nodes into subgroups and chooses the subgroups' centers to be the influential spreaders. The proposed approach is a simple but effective IMP solution because it takes both the social reinforcement and homophily characteristics of the social network into consideration in node embedding and seed spreaders selection operation separately. The information propagation simulation experiment of single-point contact susceptible-infected-recovered (SIR) and full-contact SIR models on six different types of real network data sets proved that the proposed social influence maximization (SIM) solution exhibits significant propagation capability. [ABSTRACT FROM AUTHOR]

The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as "seed nodes", the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability. [ABSTRACT FROM AUTHOR]

Tensile creep is an important factor affecting the early cracking resistance of concrete. The tensile creep model can effectively predict the development of tensile creep. In order to establish an appropriate tensile creep model, a temperature–stress testing machine (TSTM) was employed to test the development of temperature, deformation and restraint stress of benchmark concrete and concrete mixed with the MgO under different temperature curing modes. The development law of early age stress, strain and creep was analyzed via the test data of the TSTM. The early age tensile creep of concrete was predicted with the existing Kelvin creep model. The effect of variable temperature on creep was considered in this study, and an improved Kelvin creep model was proposed. The prediction accuracy of the two models was compared and analyzed. The results indicate that MgO has little influence on the creep and specific creep of concrete. The early age cracking resistance of MgO concrete is better than benchmark concrete. The improved Kelvin model based on the microprestress-solidification (MPS) theory predicts the early tensile creep of concrete more accurately in variable temperature conditions. These are significantly helpful for the application of the MgO expansion agent in dam engineering. [ABSTRACT FROM AUTHOR]

Since manually labeling aerial images for pixel-level classification is expensive and time-consuming, developing strategies for land cover mapping without reference labels is essential and meaningful. As an efficient solution for this issue, domain adaptation has been widely utilized in numerous semantic labeling-based applications. However, current approaches generally pursue the marginal distribution alignment between the source and target features and ignore the category-level alignment. Therefore, directly applying them to land cover mapping leads to unsatisfactory performance in the target domain. In our research, to address this problem, we embed a geometry-consistent generative adversarial network (GcGAN) into a co-training adversarial learning network (CtALN), and then develop a category-sensitive domain adaptation (CsDA) method for land cover mapping using very-high-resolution (VHR) optical aerial images. The GcGAN aims to eliminate the domain discrepancies between labeled and unlabeled images while retaining their intrinsic land cover information by translating the features of the labeled images from the source domain to the target domain. Meanwhile, the CtALN aims to learn a semantic labeling model in the target domain with the translated features and corresponding reference labels. By training this hybrid framework, our method learns to distill knowledge from the source domain and transfers it to the target domain, while preserving not only global domain consistency, but also category-level consistency between labeled and unlabeled images in the feature space. The experimental results between two airborne benchmark datasets and the comparison with other state-of-the-art methods verify the robustness and superiority of our proposed CsDA. [ABSTRACT FROM AUTHOR]

As a fundamental and profound task in remote sensing, change detection from very-high-resolution (VHR) images plays a vital role in a wide range of applications and attracts considerable attention. Current methods generally focus on the research of simultaneously modeling and discriminating the changed and unchanged features. In practice, for bi-temporal VHR optical remote sensing images, the temporal spectral variability tends to exist in all bands throughout the entire paired images, making it difficult to distinguish none-changes and changes with a single model. In this paper, motivated by this observation, we propose a novel hybrid end-to-end framework named dual learning-based Siamese framework (DLSF) for change detection. The framework comprises two parallel streams which are dual learning-based domain transfer and Siamese-based change decision. The former stream is aimed at reducing the domain differences of two paired images and retaining the intrinsic information by translating them into each other's domain. While the latter stream is aimed at learning a decision strategy to decide the changes in two domains, respectively. By training our proposed framework with certain change map references, this method learns a cross-domain translation in order to suppress the differences of unchanged regions and highlight the differences of changed regions in two domains, respectively, then focus on the detection of changed regions. To the best of our knowledge, the idea of incorporating dual learning framework and Siamese network for change detection is novel. The experimental results on two datasets and the comparison with other state-of-the-art methods verify the efficiency and superiority of our proposed DLSF. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the relationship between bladder urothelial cancer (BLCA) and immunity, to screen prognosis-related immune genes (PIGs), and to construct an immune-related prognosis model (IRPM). We processed the relevant data of The Cancer Genome Atlas (TCGA-BLCA) and GSE13507 using R software and Perl. We divided BLCA into high-immunity and low-immunity subtypes. There were significant differences in the two subtypes. In addition, we identified 13 PIGs of BLCA by jointly analyzing the gene expression data and survival information of GSE13507 and TCGA-BLCA, and constructed IRPM through nine of them. The low-risk group had better survival outcome than the high-risk group. We also constructed a nomogram based on clinicopathological information and risk scores of the patients. Moreover, the prognosis of BLCA patients was significantly impacted by the expression of almost every gene used to calculate the risk score. The result of real-time fluorescence quantitative polymerase chain reaction revealed that all the genes used to calculate the risk score were differentially expressed between BLCA and adjacent normal tissues, except PDGFRA. Our research provided potential targets for the treatment of BLCA and a reference for judging the prognosis of BLCA.

Exposure to polycyclic aromatic hydrocarbons (PAHs) may lead to adverse health risks. To understand the potential sources and carcinogenic risks of PAHs in Tangshan, 40 PM 2.5 samples were collected for analysis of eighteen PM 2.5 -bound PAHs during non-heating period and heating period. The results display a significant variation. The median concentration of ∑ 18 PAHs during the heating period (282 ng/m 3 ) was higher than during the non-heating period (185 ng/m 3 ). Especially, the median concentration of Benzopyrene (BaP) during the heating period (61.6 ng/m 3 ) was 16.9-fold that during the non-heating period (3.64 ng/m 3 ). It exceeded BaP annual average limit of China (1 ng/m 3 ). Diagnostic ratios (DRs) and principal component analysis (PCA) both indicated that vehicle emissions and coal and biomass combustion were the dominant contributors of PAHs pollution in Tangshan. The incremental lifetime cancer risk (ILCR) of three age groups (children, teenagers, and adults) ranged from 2.56 × 10 -6 to 5.26 × 10 -5 during the entire sampling periods. The 95% risk values of adults exceeded 10 -4 during the heating periods, indicating a potential health risk from PAHs., Competing Interests: The authors declare no conflict of interest.

In a rat model of traumatic brain injury (TBI), we investigated changes in cognitive function and S100A6 expression in the hippocampus. TBI-associated changes in this protein have not previously been reported. Rat S100A6 was studied via immunohistochemical staining, Western blot, and reverse transcription-polymerase chain reaction (RT-PCR) after either lateral head acceleration or sham. Reduced levels of S100A6 protein and mRNA were observed 1 h after TBI, followed by gradual increases over 6, 12, 24, and 72 h, and then a return to sham level at 14 day. Morris water maze (MWM) test was used to evaluate animal spatial cognition. TBI- and sham-rats showed an apparent learning curve, expressed as escape latency. Although TBI-rats displayed a relatively poorer cognitive ability than sham-rats, the disparity was not significant early post-injury. Marked cognitive deficits in TBI-rats were observed at 72 h post-injury compared with sham animals. TBI-rats showed decreased times in platform crossing in the daily MWM test; the performance at 72 h post-injury was the worst. In conclusion, a reduction in S100A6 may be one of the early events that lead to secondary cognitive decline after TBI, and its subsequent elevation is tightly linked with cognitive improvement. S100A6 may play important roles in neuronal degeneration and regeneration in TBI.

In nanoscale structure sizes, the surface-to-bulk energy ratio is high and the surface effects must be taken into account. Surface effect plays a key role in accurately predicting the vibration behavior of nanostructures. In this paper, the wave behaviors of a single-walled carbon nanotube (CNT) conveying fluid are studied. The nonlocal Timoshenko beam theory is used and the surface effect is taken into account. It is found that the fluid can flow at a very high flow velocity and the wave propagates in the terahertz frequency range. The surface effects can significantly enhance the propagating frequency. This finding is different from the classical model where the surface effect is neglected.

Ischemic preconditioning has been reported to protect against spinal cord ischemia-reperfusion (I-R) injury, but the underlying mechanisms are not fully understood. To investigate this, Japanese white rabbits underwent I-R (30 min aortic occlusion followed by reperfusion), ischemic preconditioning (three cycles of 5 min aortic occlusion plus 5 min reperfusion) followed by I-R, or sham surgery. At 4 and 24 h following reperfusion, neurological function was assessed using Tarlov scores, blood spinal cord barrier permeability was measured by Evan's Blue extravasation, spinal cord edema was evaluated using the wet-dry method, and spinal cord expression of zonula occluden-1 (ZO-1), matrix metalloproteinase-9 (MMP-9), and tumor necrosis factor-α (TNF-α) were measured by Western blot and a real-time polymerase chain reaction. ZO-1 was also assessed using immunofluorescence. Spinal cord I-R injury reduced neurologic scores, and ischemic preconditioning treatment ameliorated this effect. Ischemic preconditioning inhibited I-R-induced increases in blood spinal cord barrier permeability and water content, increased ZO-1 mRNA and protein expression, and reduced MMP-9 and TNF-α mRNA and protein expression. These findings suggest that ischemic preconditioning attenuates the increase in blood spinal cord barrier permeability due to spinal cord I-R injury by preservation of tight junction protein ZO-1 and reducing MMP-9 and TNF-α expression.