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Given global agricultural challenges such as population growth, climate change, and limitations on resources and the environment, as well as increasing diversity in breeding goals, relying on traditional breeding methods is inadequate to provide food security requirements and promote sustainable development. Genetic transformation technology has become an effective tool for performing functional genomics research and molecular breeding. In this study, we conducted an in-depth analysis of 1669 literary works to investigate the potential of developmental regulators (DRs) in enhancing the efficiency of plant genetic transformation, with a concentration on their use in maize. Through multi-omics data analysis, we identified 12 homologous DRs from various species that are potentially applicable to maize. We identified a total of 41 possible disease resistances (DRs) for maize genetic transformation. Further experimental verification of ZmWIND1, a novel regulator belonging to the ERF/AP2 transcription factor (TF) family, showed that it significantly improved the efficiency of plant regeneration and transformation efficiency in maize. Specifically, compared to the control group, the callus induction rates for the pG3GB411-ZmWIND1 vector increased to 60.22 % and 47.85 % in Xiang249 and Zheng58, respectively. Transformation efficiency increased to 37.5 % in Xiang249 and 16.56 % in Zheng58, both significantly surpassing the control group. These findings have the potential to broaden the range of transformable maize varieties and lines, as well as introduce new genetic transformation methods in agricultural biotechnology, underscoring the immense potential to enhance genetic transformation efficiency through systematic exploration and application of DRs in maize.

Federated learning (FL) is now considered a critical method for breaking down data silos. However, data encryption can significantly increase computing time, limiting its large-scale deployment. While hardware acceleration can be an effective solution, existing research has largely focused on a single hardware type, which hinders the acceleration of FL across the various heterogeneous hardware of the participants. In light of this challenge, this paper proposes a novel FL acceleration framework that supports diverse types of hardware. Firstly, we conduct an analysis of the key elements of FL to clarify our accelerator design goals. Secondly, a unified acceleration framework is proposed, which divides FL into four layers, providing a basis for the compatibility and implementation of heterogeneous hardware acceleration. After that, based on the physical properties of three mainstream acceleration hardware, i.e., GPU, ASIC and FPGA, the architecture design of corresponding heterogeneous accelerators under the framework is detailed. Finally, we validate the effectiveness of the proposed heterogeneous hardware acceleration framework through experiments. For specific algorithms, our implementation achieves a state of the art acceleration effect compared to previous work. For the end-to-end acceleration performance, we gain $12\times $ , $7.7\times $ and $2.2\times $ improvement on GPU, ASIC and FPGA respectively, compared to CPU in large-scale vertical linear regression training tasks.

Radical chemistry is one of the most important methods used in modern polymer science and industry. Over the past century, new knowledge on radical chemistry has both promoted and been generated from the emergence of polymer synthesis and modification techniques. In this review, we discuss radical chemistry in polymer science from four interconnected aspects. We begin with radical polymerization, the most employed technique for industrial production of polymeric materials, and other polymer synthesis involving a radical process. Post-polymerization modification, including polymer crosslinking and polymer surface modification, is the key process that introduces functionality and practicality to polymeric materials. Radical depolymerization, an efficient approach to destroy polymers, finds applications in two distinct fields, semiconductor industry and environmental protection. Polymer chemistry has largely diverged from organic chemistry with the fine division of modern science but polymer chemists constantly acquire new inspirations from organic chemists. Dialogues on radical chemistry between the two communities will deepen the understanding of the two fields and benefit the humanity.

This study investigates the skid resistance performance of asphalt mixtures containing composite aggregates of basalt and limestone. The research aimed to predict the service life of the asphalt mixtures and identify the optimal basalt content for enhanced performance. Using an accelerated friction tester, friction indices such as the British pendulum number (BPN), mean texture depth (MTD), and dynamic friction coefficient (Dµ) were measured. The study conducted accelerated wear tests on mixtures with varying basalt contents under different water flow rates and loads. Results indicate that anti-skid performance decreased with increasing water flow, load, and wear cycles, initially showing a sharp decline followed by a gradual stabilization. Orthogonal experiments determined that basalt content had the most significant impact on skid resistance, followed by load and water flow rate. By converting skid resistance and MTD values into IFI values, a four-area diagram was created to illustrate skid resistance deterioration. The four-area IFI diagram also demonstrated that higher basalt content significantly enhances the skid resistance and service life of asphalt mixtures. Cost analysis based on life prediction showed that a 40% basalt content mixture is cost effective while maintaining excellent skid resistance. A test section study further validated that a 40% basalt content ensures good skid resistance, with indoor test predictions aligning closely with field data. Although the test section has been operational for only two years, ongoing monitoring will provide further insights into long-term skid resistance performance.

The construction of public buildings involves a significant amount of carbon emissions, particularly during the materialization phase of underground construction, which is characterized by a rapid and intense release of carbon emissions. In response to this issue, this study aims to develop a Building Information Modeling (BIM)-based method for assessing the factors influencing carbon emissions during the materialization phase of underground construction in public buildings. To achieve this goal, a process-based quantitative and qualitative method was established to evaluate carbon emissions at various stages, including raw material extraction and processing, building material/equipment production, and on-site processing of components, as well as construction and installation. The proposed method may serve as a valuable tool for selecting design schemes in the early stages of building design. By analyzing 125 actual cases in Xi’an, China and utilizing a carbon emission calculation model, accurate carbon emission data was obtained. This study’s findings indicate a correlation between the embodied carbon emissions and the area of underground space in the cases analyzed. Specifically, larger underground spaces were associated with higher embodied carbon emissions in underground engineering. Furthermore, this study categorized the cases into two groups based on the presence or absence of underground space, resulting in the identification of 16 factors for cases without underground space and 19 factors for cases with underground space. These findings provide valuable insights for understanding and mitigating the impact of carbon emissions during the materialization phase of underground construction in public buildings.

Abstract Background Janus kinase 1 (JAK1) plays a critical role in most cytokine-mediated inflammatory, autoimmune responses and various cancers via the JAK/STAT signaling pathway. Inhibition of JAK1 is therefore an attractive therapeutic strategy for several diseases. Recently, high-performance machine learning techniques have been increasingly applied in virtual screening to develop new kinase inhibitors. Our study aimed to develop a novel layered virtual screening method based on machine learning (ML) and pharmacophore models to identify the potential JAK1 inhibitors. Methods Firstly, we constructed a high-quality dataset comprising 3834 JAK1 inhibitors and 12,230 decoys, followed by establishing a series of classification models based on a combination of three molecular descriptors and six ML algorithms. To further screen potential compounds, we constructed several pharmacophore models based on Hiphop and receptor-ligand algorithms. We then used molecular docking to filter the recognized compounds. Finally, the binding stability and enzyme inhibition activity of the identified compounds were assessed by molecular dynamics (MD) simulations and in vitro enzyme activity tests. Results The best performance ML model DNN-ECFP4 and two pharmacophore models Hiphop3 and 6TPF 08 were utilized to screen the ZINC database. A total of 13 potentially active compounds were screened and the MD results demonstrated that all of the above molecules could bind with JAK1 stably in dynamic conditions. Among the shortlisted compounds, the four purchasable compounds demonstrated significant kinase inhibition activity, with Z-10 being the most active (IC50 = 194.9 nM). Conclusion The current study provides an efficient and accurate integrated model. The hit compounds were promising candidates for the further development of novel JAK1 inhibitors.

Heavy metals migrate easily and are difficult to degrade in the soil environment, which causes serious harm to the ecological environment and human health. Thus, soil heavy metal pollution has become one of the main environmental issues of global concern. Plant-growth-promoting rhizobacteria (PGPR) is a kind of microorganism that grows around the rhizosphere and can promote plant growth and increase crop yield. PGPR can change the bioavailability of heavy metals in the rhizosphere microenvironment, increase heavy metal uptake by phytoremediation plants, and enhance the phytoremediation efficiency of heavy-metal-contaminated soils. In recent years, the number of studies on the phytoremediation efficiency of heavy-metal-contaminated soil enhanced by PGPR has increased rapidly. This paper systematically reviews the mechanisms of PGPR that promote plant growth (including nitrogen fixation, phosphorus solubilization, potassium solubilization, iron solubilization, and plant hormone secretion) and the mechanisms of PGPR that enhance plant–heavy metal interactions (including chelation, the induction of systemic resistance, and the improvement of bioavailability). Future research on PGPR should address the challenges in heavy metal removal by PGPR-assisted phytoremediation.

Rationale Asthma is a complex, heterogeneous disease strongly associated with type 2 inflammation, and blood eosinophil counts guide therapeutic interventions in moderate and severe asthma. Eosinophils are leukocytes involved in type 2 immune responses. Despite these critical associations between asthma and blood eosinophil counts, the shared genetic architecture of these two traits remains unknown. The objective of the present study was to characterise the genetic architecture of blood eosinophil counts and asthma in the UK Biobank. Methods We performed genome-wide association studies (GWAS) of doctor-diagnosed asthma, blood eosinophil, neutrophil, lymphocyte and monocyte counts in the UK Biobank. Genetic correlation analysis was performed on GWAS results and validated in the Trans-National Asthma Genetic Consortium (TAGC) study of asthma. Results GWAS of doctor-diagnosed asthma and blood eosinophil counts in the UK Biobank identified 585 and 3429 significant variants, respectively. STAT6, a transcription factor involved in interleukin-4 signalling, was a key shared pathway between asthma and blood eosinophil counts. Genetic correlation analysis demonstrated a positive correlation between doctor-diagnosed asthma and blood eosinophil counts (r=0.38±0.10, correlation±se; p=4.7×10−11). As a validation of this association, we found a similar correlation between TAGC and blood eosinophil counts in the UK Biobank (0.37±0.08, correlation±se; p=1.2×10−6) Conclusions These findings define the shared genetic architecture between blood eosinophil counts and asthma risk in subjects of European ancestry and point to a genetic link to the STAT6 signalling pathway in these two traits.

Abstract An in-fiber Mach-Zehnder interferometer is proposed for the discrimination of strain and temperature. The sensor is based on two cascaded standard single mode fibers using three peanut tapers fabricated by simple splicing. The cascaded structure excites more frequency components, which induce four sets of interference dips in the transmission spectrum. One set of the spectrum dips have different sensitivities to temperature and strain from those of the other three. The sensor can discriminate strain and temperature by monitoring the wavelength shifts of two spectrum dips. Repeated experiments are taken both for strain and temperature increasing and decreasing scenarios. Experimental results show that Dip 1 has an average strain sensitivity of −0.911 pm/µε and an average temperature sensitivity of 49.98 pm/°C. The strain sensitivity for Dip 2 is negligible and its average temperature sensitivity is 60.52 pm/°C The strain and temperature resolutions are ±3.82 µε and ±0.33 °C.

Glioblastoma is acknowledged as the most aggressive cerebral tumor in adults. However, the efficacy of current standard therapy is seriously undermined by drug resistance and suppressive immune microenvironment. Ferroptosis is a recently discovered form of iron-dependent cell death that may have excellent prospect as chemosensitizer. The utilization of ferropotosis inducer Erastin could significantly mediate chemotherapy sensitization of Temozolomide and exert anti-tumor effects in glioblastoma. In this study, a combination of hydrogel-liposome nanoplatform encapsulated with Temozolomide and ferroptosis inducer Erastin was constructed. The αvβ3 integrin-binding peptide cyclic RGD was utilized to modify codelivery system to achieve glioblastoma targeting strategy. As biocompatible drug reservoirs, cross-linked GelMA (gelatin methacrylamide) hydrogel and cRGD-coated liposome realized the sustained release of internal contents. In the modified intracranial tumor resection model, GelMA-liposome system achieved slow release of Temozolomide and Erastin in situ for more than 14 d. The results indicated that nanoplatform (T+E@LPs-cRGD+GelMA) improved glioblastoma sensitivity to chemotherapeutic temozolomide and exerted satisfactory anti-tumor effects. It was demonstrated that the induction of ferroptosis could be utilized as a therapeutic strategy to overcome drug resistance. Furthermore, transcriptome sequencing was conducted to reveal the underlying mechanism that the nanoplatform (T+E@LPs-cRGD+GelMA) implicated in. It is suggested that GelMA-liposome system participated in the immune response and immunomodulation of glioblastoma via interferon/PD-L1 pathway. Collectively, this study proposed a potential combinatory therapeutic strategy for glioblastoma treatment.

Abstract Background In previous studies, the difficulty of surgery has rarely been used as a research object. Our study aimed to develop a predictive model to enable preoperative prediction of the technical difficulty of video-assisted thoracoscopic lobectomy and mediastinal lymph node dissection using retrospective data and to validate our findings prospectively. Methods Collected data according to the designed data table and took the operation time as the outcome variable. A nomogram to predict the difficulty of surgery was established through Lasso logistic regression. The prospective datasets were analyzed and the outcome was the operation time. Results This retrospective study enrolled 351 patients and 85 patients were included in the prospective datasets. The variables in the retrospective research were selected by Lasso logistic regression (only used for modeling and not screening), and four significantly related influencing factors were obtained: FEV1/FVC (forced expiratory volume in the first second/forced vital capacity) (p

Federated learning has solved the problems of data silos and data fragmentation on the premise of satisfying privacy. However, cryptographic algorithms in federated learning brought significant increase in computational complexity, which limited the speed of model training. In this paper, we propose a hardware/software (HW/SW) co-designed field programmable gate array (FPGA) accelerator for federated learning. Firstly, we analyzed the time consumption of each stage in federated learning and the involved cryptographic algorithms, and found the performance bottleneck. Secondly, a HW/SW co-designed architecture is introduced, which can speed up encryption, decryption and ciphertext-space computation at the same time without reconfiguring FPGA circuit. In the HW part, we proposed a Hardware-aware Montgomery Algorithm (HWMA) which utilized data parallelism and pipeline, and designed an FPGA architecture to decouple data access and computation. In the SW part, an Operator Scheduling Engine (OSE) is designed, which can flexibly resolve the target algorithm into multiple HWMA calls, and complete other non-computation-intensive calculations. Finally, evaluations for both specific algorithms and practical applications are implemented. Experimental results show that when deployed on Intel Stratix 10 FPGA, our accelerator can increase the throughput of 2048-bit modular multiplication, modular exponentiation and Paillier algorithm to more than 3x of the CPU. When integrated into a industrial grade federated learning open source framework, the end-to-end training time of linear regression and logistic regression can be shortened by 2.28x and 3.30x respectively, which is more than 2x faster than the reported best results of FPGA accelerator.

Background. High morbidity of nonspecific low back pain (NLBP) and large consumption of medical resources caused by it have become a heavy social burden. There are many factors inducing NLBP, among which the damage and atrophy of multifidus (MF) are most closely related to NLBP. Scraping therapy can have significant treatment effects on NLBP with fewer adverse reactions and less medical fund input than other modalities or medications. However, the mechanism of scraping therapy treating NLBP remains unclarified. Here, we wanted to investigate the effects of scraping therapy on promoting MF regeneration and the underlying mechanisms. Methods. A total of 54 male rats (SD, 6-7 weeks old) were randomly divided into nine groups, namely, K, M6h, M1d, M2d, M3d, G6h, G1d, G2d, and G3d, with six rats in each group. They were injected with bupivacaine (BPVC) to intentionally induce MF injury. We then performed scraping therapy on the rats that had been randomly chosen and compared treatment effects at different time points. In vitro data including skin temperature and tactile allodynia threshold were collected and histological sections were analyzed. mRNA sequencing was applied to distinguish the genes or signaling pathways that had been altered due to scraping therapy, and the results were further verified through reverse transcription polymerase chain reaction and Western blot analysis. Results. Transitory petechiae and ecchymosis both on and beneath the rats’ skin raised by scraping therapy gradually faded in about 3 d. Cross-sectional area (CSA) of MF was significantly smaller 30 h, 2 d, and 4 d after modeling (P=0.007, P=0.001, and P=0.015, respectively, vs. the blank group) and was significantly larger in the scraping group 1 d after treatment (P=0.002 vs. the model 1d group). Skin temperature significantly increased immediately after scraping (P

BackgroundSmall-cell lung cancer (SCLC) is a recalcitrant tumor with a poor prognosis. With the rise of SCLC research in the past decade, this study aims to analyze the foundation and frontiers of SCLC research through bibliometric analysis.MethodsRelevant publications from the Web of Science Core Collection were retrieved on January 3, 2022. R package bibliometrix and EXCEL2019 were used to analyze quantitative variables. Bibliometric mapping was constructed by VOS viewer and CiteSpace software to visualize citation, co-authorship, co-occurrence, and co-citation analysis of countries/regions, organizations, authors, references, and keywords.ResultsA total of 2,361 publications related to SCLC were identified with the total amount of articles steadily increasing, where China is the most productive country with 859 papers. Scholars and organizations from the United States, China, and Europe are primary sources of this research, among which the University of Texas MD Anderson Cancer Center made the most contribution to the field with 122 papers. Lung Cancer published the highest number of SCLC-related articles with a total of 121, while the Journal of Thoracic Oncology received the most citations totaling 3,098. Rudin, Charles M., and Sage, Julien are the most creative author. Leora, Horn, 2018, New Engl J Med and Rudin, Charles M., Nat Genet, 2012 can be categorized as classic literature owing to their high citations or strong sigma value. “Heterogeneity & Subtypes” and “Immunotherapy” may be the new frontiers in the SCLC domain.ConclusionResearch on SCLC showed an upward trend based on the current global situation. Moreover, the current scope of collaboration in SCLC research is chiefly regional, which should further focus on transnational cooperation in the future. More attention should be devoted to “Heterogeneity & Subtypes” and “Immunotherapy”, which will be the hotspots in future research.

The BTN2/3 subfamilies are overexpressed in many cancers, including pan-glioma (low- and high-grade gliomas). However, the expression and prognosis of BTN2/3 subfamilies and tumor-infiltrating lymphocytes in pan-glioma remain unknown. In the present study, we systematically explored and validated the expression and prognostic value of BTN2/3 subfamily members in pan-glioma [The Cancer Genome Atlas–glioblastoma and low-grade glioma (TCGA-GBMLGG) merge cohort] using multiple public databases. We used clinical specimens for high-throughput verification and cell lines for qRT-PCR verification, which confirmed the expression profiles of BTN2/3 subfamilies. In addition, the function of the BTN2/3 subfamily members and the correlations between BTN2/3 subfamily expression and pan-glioma immune infiltration levels were investigated. We found that BTN2/3 subfamily members were rarely mutated. BTN2/3 subfamilies were overexpressed in pan-glioma; high expression of BTN2/3 subfamily members was correlated with poor prognosis. In addition, BTN2/3 subfamilies might positively regulate proliferation, and the overexpression of BTN2/3 subfamilies influenced cell cycle, differentiation, and glioma stemness. In terms of immune infiltrating levels, BTN2/3 subfamily expression was positively associated with CD4+ T-cell, B-cell, neutrophil, macrophage, and dendritic cell infiltrating levels. These findings suggest that BTN2/3 subfamily expression is correlated with prognosis and immune infiltration levels in glioma. Therefore, the BTN2/3 subfamilies can be used as biomarkers for pan-glioma and prognostic biomarkers for determining the prognosis and immune infiltration levels in pan-glioma.

Most of the recent works on optical flow estimation focus on introducing more effective CNNs or even the Transformer architecture that is quite popular in today’s CV field, but they all still follow the traditional two-frame input structure. In this paper, we argue that optical flow estimation should be more focused on the dynamic information in the image sequence. To that end, we present a straightforward but effective streaming framework equipped with a novel prediction module for capturing optical flow trends, which is a point that has not yet been discussed in previous works. By employing the backbone of the strong baseline to extract the stream of features from three consecutive frames as inputs, this module can predict the current state and update the previous changing trend of optical flow for feature fusion at the next stage. Additionally, we redesign the training loss, providing two new cost functions, in particular, to measure the variations in the optical flow trend. At the forward stage, we deploy a historical buffer to maintain the inference speed so that it is unaffected by additional input frames. Compared with the strong baseline, our method improves the average end-point error by 21.12% on Sintel Clean and 12.94% on Sintel Final. In addition, it also outperforms existing methods in real-world scenarios with low brightness or fast-moving targets.

AbstractBackground Previous studies have reported a negative relationship between thyroid-stimulating hormone (TSH) and renal function in euthyroid individuals, but others have found that higher free thyroxine (FT4) was associated with an increased risk of chronic kidney disease. This study was designed to analyze the relationship between thyroid and renal function from a new perspective of sensitivity to thyroid hormone.Methods This retrospective study included 2831 euthyroid individuals who underwent a health examination at the First Hospital of China Medical University between January 2017 and December 2018. Parametric Thyroid Feedback Quantile-based Index (PTFQIFT4), TSH index (TSHI), thyrotroph T4 resistance index (TT4RI), free triiodothyronine to FT4 ratio (FT3/FT4), the secretory capacity of the thyroid gland (SPINA-GT) and the sum activity of peripheral deiodinases (SPINA-GD) were calculated. We also innovated the TT3RI and PTFQIFT3 indices based on FT3 and TSH. Renal function was assessed by estimated glomerular filtration rate (eGFR) CKD-EPI and creatinine-cystatin C-KDIGO equations.Results After adjustment of basic characteristics and comorbidities, linear regression showed that eGFR CKD-EPI was positively associated with FT3/FT4 (β = 23.31), and inversely correlated to PTFQI FT4 (β= −2.69) (both p

Convolutional neural network (CNN) based object detection algorithms are becoming dominant in many application fields due to their superior accuracy advantage over traditional schemes. Among them, You Look Only Once (YOLO) is one of the most popular detection frameworks that show best trade-offs between speed and accuracy. However, due to the intrinsic high computational workload of CNN, it is still challenging when targeting high-throughput processing with low cost in energy consumption. In this paper, we propose a hardware/software (HW/SW) co-design methodology targeting CPU+FPGA-based heterogeneous platforms. Firstly, we extend a novel sparse convolution algorithm to the YOLOv2 framework, and then develop a resource-efficient FPGA accelerator architecture based on asynchronously executed parallel convolution cores. Secondly, algorithm-level optimization schemes, including hardware-aware neural network pruning, clustering and quantization are introduced, which successfully save the computational workload of the YOLOv2 algorithm by 7 times. Finally, an end-to-end design space exploration flow for FPGA-based accelerator design is presented and two HW/SW partition strategies are studied and implemented. Experimental results show that our design can achieve a peak throughput of 2.13 TOPS (72.5 fps) on an Intel Arria-10 GX1150 FPGA under the working frequency of 211 MHz, while the detection accuracy is 74.45 on the PASCAL VOC2007 dataset.

In recent decades, moisture evaluation of transformer oil-immersed cellulose insulation has been widely studied by using the frequency domain spectroscopy (FDS) technique. However, due to the difficulties in sample preparation, most of the existing evaluation methods are based on a small number of samples, which greatly limits the evaluation accuracy of these methods. To accurately evaluate moisture inside transformer oil-immersed cellulose insulation, a novel method combining the FDS technique and the exponential decay model is proposed. In current work, the exponential decay model is introduced to analyze the FDS data, which can be used to assess the quantitative relationship between moisture and extracted parameters. Then, the FDS curves with different moisture can be predicted by using these parameters to form a curve database. Findings reveal the relative error of evaluation results in lab condition is less than 10.57% when using the proposed curve database. In that respect, the feasibility of the proposed database is proved for moisture evaluation of transformer oil-immersed cellulose insulation.

Frequency domain spectroscopy (FDS) technique is widely applied in the condition assessment of the oil-paper system in power transformers. However, the synergistic effect generated by moisture and temperature on the FDS data cannot be analyzed by the existing model since the single independent variable (moisture or temperature) is considered in the construction of the model. To quantify such the synergistic effect, a novel method that utilized for normalizing (or standardizing) the FDS curve is reported based on the theory of the power series and fitting analysis. The present findings reveal that the reported method is capable of predicting the dielectric loss (tanδ) curve under diverse test conditions, in which the average error is less than 7%. The synergistic effect can be also explored by using the extracted feature parameters. The potential application is then proved to make up for the measurement errors during the FDS test, the findings are expected to promote the moisture analysis of the transformer insulation.

Growing evidence indicates that postnatal immune activation (PIA) can adversely increase the lifetime risk for several neuropsychiatric disorders, including anxiety and depression, which involve the activation of glial cells and early neural developmental events. Several glia-targeted agents are required to protect neonates. Folic acid (FA), a clinical medication used during pregnancy, has been reported to have neuroprotective properties. However, the effects and mechanisms of FA in PIA-induced neonatal encephalitis and mood disorders remain unclear. Here, we investigated the roles of FA in a mouse model of PIA, and found that FA treatment improved depressive- and anxiety-like behaviors in adults, accompanied by a decrease in the number of activated microglia and astrocytes, as well as a reduction in the inflammatory response in the cortex and hippocampus of neonatal mice. Furthermore, we offer new evidence describing the functional differences in FA between microglia and astrocytes. Our data show that epigenetic regulation plays an essential role in FA-treated glial cells following PIA stimulation. In astrocytes, FA promoted the expression of IL-10 by decreasing the level of EZH2-mediated H3K27me3 at its promoter, whereas FA promoted the expression of IL-13 by reducing the promoter binding of H3K9me3 mediated by KDM4A in microglia. Importantly, FA specifically regulated the expression level of BDNF in astrocytes through H3K27me3. Overall, our data supported that FA may be an effective treatment for reducing mood disorders induced by PIA, and we also demonstrated significant functional differences in FA between the two cell types following PIA stimulation.

Complex transcriptional gene regulation allows for multifaceted isoform production during retinogenesis, and novel isoforms transcribed from a single locus can have unlimited potential to code for diverse proteins with different functions. In this study, we explored the CTBP2/RIBEYE gene locus and its unique repertoire of transcripts that are conserved among vertebrates. We studied the transcriptional coregulator (CTBP2) and ribbon synapse-specific structural protein (RIBEYE) in the chicken retina by performing comprehensive histochemical and sequencing analyses to pinpoint cell and developmental stage-specific expression of CTBP2/RIBEYE in the developing chicken retina. We demonstrated that CTBP2 is widely expressed in retinal progenitors beginning in early retinogenesis but becomes limited to GABAergic amacrine cells in the mature retina. Inversely, RIBEYE is initially epigenetically silenced in progenitors and later expressed in photoreceptor and bipolar cells where they localize to ribbon synapses. Finally, we compared CTBP2/RIBEYE regulation in the developing human retina using a pluripotent stem cell derived retinal organoid culture system. These analyses demonstrate that similar regulation of the CTBP2/RIBEYE locus during chick and human retinal development is regulated by different members of the K50 homeodomain transcription factor family.

BackgroundPrevious studies on the association between thyroid function and non‐alcoholic fatty liver disease (NAFLD) have contradicted. Acquired resistance to thyroid hormone theory might provide a reasonable explanation for these contradictions. We aimed to analyze the association between sensitivity to thyroid hormone indices with NAFLD.MethodsA total of 4,610 individuals from the health medical center of the First Hospital of China Medical University were included in this study. The previously used thyroid feedback quantile-based index (TFQIFT4) was calculated. Also, we substituted free triiodothyronine (FT3) into the TFQI formulas to get the TFQIFT3 index. NAFLD was defined using abdominal ultrasound.ResultsStudy results showed that FT3/FT4 and TFQIFT3 were positively correlated with the triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) levels (P

Studying the single-molecule behavior of host-guest complexes can provide fundamental insights into their supramolecular interactions. Here, the authors use the scanning tunneling microscopy break junction technique to show that encapsulation of a C60 molecule significantly enhances the conductance of an organoplatinum metallocycle; mechanical stretching of the junction releases the guest, returning the conductance to free-host level.

During wound induced hair follicle neogenesis (WIHN), stem cells regenerate hair follicles but how this arises is unclear. Here, the authors show that self-noncoding dsRNA activates the antiviral receptor TLR3 to induce intrinsic retinoic acid, which stimulates WIHN in mice, and in isolated human keratinocyte cells.

Plasmonically induced transparency (PIT) in hexagon boron nitride (h-BN)-graphene-silica grating-based structure is proposed and demonstrated. By tuning the chemical potential of graphene and changing the geometry parameters of the proposed structure, PIT can be realized both within the upper reststrahlen (RS) band and outside the RS band. The group delay of the PIT window is analyzed and can be up to ~0.15 ps within the upper RS band via changing the chemical potential of graphene and the parameters of the silica grating. Apart from that, a refractive index (RI) sensor with a sensitivity of up to 0.336 μm per RI unit outside the RS band is proposed by changing the RI of the sensor media. The proposed structure may have the application in the graphene-h-BN-based slow light devices and optical sensors.

ContextThyroid hormone influences glucose homeostasis through central and peripheral regulations. To date, the link between sensitivity to thyroid hormones and prediabetes remains unknown. We aimed to investigate the association between thyroid hormones sensitivity and risk of prediabetes in both general and euthyroid populations.MethodsParticipants with serum free triiodothyronine (FT3), free thyroxine (FT4), and thyrotropin (TSH) measurements from the health checkup programs of the First Hospital of China Medical University were collected. We measured the parameters representing central and peripheral sensitivities to thyroid hormones (central sensitivity, assessed by calculating Thyroid Feedback Quantile-based Index (TFQI), TSH Index (TSHI), and Thyrotroph Thyroxine Resistance Index (TT4RI); peripheral sensitivity, evaluated by FT3/FT4 ratio). Associations between thyroid hormones sensitivities and risk of prediabetes were assessed with logistic regression.ResultsA total of 4378 participants (mean age ± SD, 49 ± 11 years) were included, with 1457 (33%) subjects had prediabetes. The risk of prediabetes was negatively associated with levels of TSHI (odds ratio [OR] 0.91; 95% confidence interval [CI], 0.85–0.97), TT4RI (OR 0.91; 95% CI, 0.84–0.99) and Parametric TFQI (PTFQI) (OR 0.89; 95% CI, 0.83–0.95) among all subjects. The association remained significant in euthyroid subjects and euthyroid subjects with negative thyroid autoimmunity. Higher FT3/FT4 ratio was associated with a mild increased risk of prediabetes (95% CI 1.09; 1.02–1.16). Compared with subjects in the lowest quartile of PTFQI, those in the highest quartile had lower risk of prediabetes (0.70; 95% CI, 0.58–0.84).ConclusionsDecreased central sensitivity to thyroid hormones is associated with lower risk of prediabetes. This demonstrates the complex interaction between thyroid system and glucose metabolism. Future studies are warranted to confirm our findings and underlying mechanisms.

This research investigates small-scale tectonic activity in the Jiujing region in Beishan, northwest China through the application of persistent scatterer (PS) Interferometric synthetic aperture radar (InSAR). PS InSAR is an effective monitoring tool in this unpopulated, arid, and unvegetated rural area, whose surface geology is dominated by a single large granitic intrusion, and which represents a candidate site for a geological disposal facility (GDF) for high-level radioactive waste (HLW) in China. This research demonstrates that faults F16-2, F17, F18, and F20-2 are still active, producing dip-slip motions along the fault planes. The lithological variations in weathering and erosion can be discounted as the cause for these small-scale displacement variations. The work has also identified 11 previously unknown faults, characterising them from vertical (DU) and eastward horizontal (DE) displacements along and across the faults. These newly discovered structures demonstrate how PS InSAR can be used to monitor and measure micro-scale movements on regional-scale faults, which, in many cases, were previously considered to be inactive. Thus, this also improves our understanding of local stress regimes in this area. The Jiujing region is part of a convergent fault zone dominated by NE-SW compression, leading to NE-SW crustal shortening and NW-SE elongation. Through determination of the sense of ground movement measured at irregularly distributed PS points, some faults are reverse and trending NW-SE, while others are normal and trending NE-SW, highlighting how InSAR can be used to resolve fault type and relative movements to monitor tectonic fault blocks at a regional scale.

The purpose of the study is to solve problems, i.e., increasingly significant processing delay of massive monitoring data and imbalanced tasks in the scheduling and monitoring center for a railway network. To tackle these problems, a method by using a smooth weighted round-robin scheduling based on backpressure flow control (BF-SWRR) is proposed. The method is developed based on a model for message queues and real-time streaming computing. By using telemetry data flow as input data sources, the fields of data sources are segmented into different sets by using a distributed model of stream computing parallel processing. Moreover, the round-robin (RR) scheduling method for the distributed server is improved. The parallelism, memory occupancy, and system delay are tested by taking a high-speed train section of a certain line as an example. The result showed that the BF-SWRR method for clusters can control the delay to within 1 s. When the parallelism of distributed clusters is set to 8, occupancy rates of the CPU and memory can be decreased by about 15%. In this way, the overall load of the cluster during stream computing is more balanced.

To meet the mission requirements of long-endurance and unmanned marine environment observation, the natural energy-driven unmanned surface vehicle (NSV) usually takes special sailing paths to increase energy capture to achieve the purpose of improving endurance. Aiming at the route planning problems of the “Wave Rider” NSV in the time-varying ocean wind field, this paper is organized as follows. Firstly, a visual modeling method of the real-time-varying ocean wind field for NSV is proposed. Then, through the wind energy capture experiment, the NSV system energy net output model is calculated, and a Dynamic Dijkstra algorithm considering wind energy capture (DW–Dijkstra) has been proposed in this paper based on a Dijkstra algorithm, of which weight function has been improved. Accordingly, the NSV long-endurance dynamic path planning method is designed. Finally, the DW–Dijkstra algorithm has been verified through a set of comparison simulations and a set of semi-physical comparison simulations. The results show that the DW–Dijkstra algorithm can plan a collision-free and high-efficiency energy capture path in the real-time-varying ocean wind field environment in the southern waters of China. Compared with the traditional A* algorithm and the Wind_A* algorithm, the proposed method can save energy by between 15.07% and 6.50%, respectively, which effectively increases the endurance of the NSV.

The anticorrosion properties of polymer emulsion modified cement-based coating with healing effect on carbon steel were investigated. The change of linear polarization resistance (Rp) and electrochemical impedance spectroscopy (EIS) have been used to measure the inhibitive and curative effect of electrodes painted with the coating in 3.5% NaCl solution. The results indicated the high alkalinity of filler (cement) in coating can obviously improve the anticorrosion performance of coated steel electrodes. And the inhibitive ability of cement-based coating with fluoride acrylic emulsion is superior to that of the coating with epoxy emulsion. Moreover, by adding antirust pigments and rust converters in the fluoride acrylic emulsion modified cement-based coating, the inhibitive property was improved and the curative effect can be observed.