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Information on changes in lipid composition of seed oils under biotic stresses is scare. The camellia weevil, Curculio chinensis (Coleoptera: Curculionidae) as a notorious seed predator of Camellia species, has caused huge economic losses in China. Lipidomics is used in this study to reveal the lipid composition of camellia oil and its changes after insect damage. 278 lipids including glycerolipids (GL) (221), glycerophospholipids (GP) (34), fatty acyls (FA) (13), sphingolipids (SP) (8), prenol lipids (PR) (1) and sterol lipids (ST) (1) were determined in camellia oils. Insect damage had a significant impact on lipids, particularly FA and GL. Ten significantly different lipids [FFA(18:2), FFA(24:6), TG(14:1/18:2/18:2), TG(16:0/23:0/18:2), TG(20:1/24:1/18:2), TG(18:2/24:0/18:2), TG(16:3/18:2/22:5), PI(16:1/18:1), PE(16:0/18:1), PE(18:1/18:2)] were identified as potential biomarkers for distinguishing oil extracted from non-infested oilseeds and oil from infested oilseeds. We also detected four most important metabolic pathways by bioinformatics analysis to explore the mechanisms underlying changes. Our findings may be useful for future camellia oil production and may provide new insight into improving of nutritional quality of camellia oil.

Aspirin, also known as acetylsalicylic acid, is widely consumed as a pain reliever and an anti-inflammatory as well as anti-platelet agent. Recently, our studies using the animal model of Drosophila demonstrated that the dietary supplementation of aspirin renovates age-onset intestinal dysfunction and delays organismal aging. Nevertheless, it remains probable that aspirin plays functional roles in other biological activities, for instance antiviral defense reactions. Intriguingly, we observed that the replications of several types of viruses were drastically antagonized in Drosophila macrophage-like S2 cells with the addition of aspirin. Further in vivo experimental approaches illustrate that adult flies consuming aspirin harbor higher resistances to viral infections with respect to flies without aspirin treatment. Mechanistically, aspirin positively contributes to the Drosophila antiviral defense largely through mediating the STING (stimulator of interferon genes) but not the IMD (immune deficiency) signaling pathway. Collectively, our studies uncover a novel biological function of aspirin in modulating Drosophila antiviral immunity and provide theoretical bases for exploring new antiviral treatments in clinical trials.

China is the world’s largest walnut producer. There is still a scarcity of comprehensive information on the nutritional quality of Chinese walnuts and the influence of soil on walnut quality. In this study, 273 samples and the corresponding soils from the main English walnut (Juglans regia L.)-producing areas in China were collected. The results revealed that walnut oil contained a high percentage of linoleic acid and γ-tocopherol, with an average content of 62.36% and 77.70%, respectively. Walnut flour is rich in nutritional elements, with the highest content of macro-element K at 3961.29 mg kg−1. In addition, 12 micro-elements with concentrations ranging from 0.01 to 42.34 mg/kg were discovered in walnut flour. The total amino acid profile of walnut flour contains 27.23% of the seven essential amino acids, with the highest amount allocated to leucine at 2.07 g 100 g−1. The Pearson correlation analysis revealed that almost all amino acids showed a significant positive correlation (p < 0.01, r > 0.70). The soil’s content in terms of three trace elements (Fe, Cu, and Zn) has the greatest influence on the nutritional qualities of walnuts. Furthermore, the pH of the soil played a role in determining the qualities of the walnut. These findings provide fundamental information for the consumer selection and trade of walnuts, as well as guidance for the development of high-quality walnuts in China.

Objective This study aims to investigate the effect of the pretreatment of S-ketamine on postoperative depression (POD) for breast cancer patients with mild/moderate depression. Methods The present randomized, double-blinded controlled trial included 303 breast cancer patients with mild/moderate depression from June 2017 to June 2018. All patients were randomly divided into three groups: (1) control group, patients treated with normal saline; (2) racemic ketamine group, patients treated with racemic ketamine; (3) S-ketamine group, patients treated with S-ketamine. Operation time, blood loss and hospital stay and complications were recorded. The Visual Analog Scale (VAS) score was recorded, and the Hamilton Rating Scale for Depression (HAMD-17) scores, serum brain-derived neurotrophic factor (BDNF) and 5-hydroxytryptamine (5-HT) were measured at three days, one week, one month and three months after surgery. Results No significant difference was found in operation time, bleeding volume and complication rate. In both groups, the VAS scores at one day and three days after surgery were significantly lower. The HAMD-17 scores were significantly lower, and the serum levels of both BDNF and 5-HT were remarkably higher at three days, one week and one month after surgery. Meanwhile, the HAMD-17 scores were remarkably lower, while the serum levels of BDNF and 5-HT were remarkably higher in the S-ketamine group. The BDNF and 5-HT levels were negatively correlated with the HAMD-17 score. Conclusion S-ketamine is more effective for reducing POD for breast cancer patients.

Congenital limb malformations (CLMs) affect 1 in 500 live births. However, the value of exome sequencing (ES) for CLM is lacking. The purpose of this study was to decipher the mutational signature of CLM on an exome level. We enrolled a cohort of 66 unrelated probands (including 47 families) with CLM requiring surgical correction. ES was performed for all patients and available parental samples. A definite molecular diagnosis was achieved in 21 out of 66 (32%) patients. We identified 19 pathogenic or likely pathogenic single-nucleotide variants and three copy number variants, of which 11 variants were novel. We identified four variants of uncertain significance. Additionally, we identified RPL9 and UBA2 as novel candidate genes for CLM. By comparing the detailed phenotypic features, we expand the phenotypic spectrum of diastrophic dysplasia and chromosome 6q terminal deletion syndrome. We also found that the diagnostic rate was significantly higher in patients with a family history of CLM (p = 0.012) or more than one limb affected (p = 0.034). Our study expands our understanding of the mutational and phenotypic spectrum of CLM and provides novel insights into the genetic basis of these syndromes.

AimsTo evaluate the predictive value of mobile magnetic resonance imaging (MRI) in screening stroke.MethodsThis was a prospective case-control study performed on healthy residents over 40 years old in remote rural areas of northern China between May 2019 and May 2020. Multivariate logistic regression and receiver operator characteristic curve (ROC) analysis were used to evaluate the screening model.ResultsA total of 1,224 patients (500 [40.8%] men) enrolled, including 56 patients who suffered from stroke (aged 64.05 ± 7.27). The individuals who developed stroke were significantly older (P < 0.001), had a significantly higher occurrence of heart disease (P = 0.015), diabetes (P = 0.005), dyslipidemia (P = 0.009), and significantly increased waist circumference (P = 0.02), systolic blood pressure (SBP) (P = 0.003), glycosylated hemoglobin (HbA1c) level (P = 0.007), triglyceride (TG) level (P = 0.025), low density lipoprotein cholesterol (LDL-c) level (P = 0.04), and homocysteine (HCY) level (P < 0.001). Multivariate logistic regression analysis showed that age (OR = 1.055, 95% CI: 1.017–1.094, P = 0.004), HCY (OR = 1.029, 95% CI: 1.012–1.047, P = 0.001) and mobile MRI (OR = 4.539, 95% CI: 1.726–11.939, P = 0.002) were independently associated with stroke. The area under the curve (AUC) of the combined model including national screening criteria, mobile MRI results, and stroke risk factors was 0.786 (95% CI: 0.721–0.851), with a sensitivity of 69.6% and specificity of 80.4%.ConclusionMobile MRI can be used as a simple and easy means to screen stroke.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle paralysis, which is followed by degeneration of motor neurons in the motor cortex of the brainstem and spinal cord. The etiology of sporadic ALS (sALS) is still unknown, limiting the exploration of potential treatments. Ferroptosis is a new form of cell death and is reported to be closely associated with Alzheimer’s disease (AD), Parkinson’s disease (PD), and ALS. In this study, we used datasets (autopsy data and blood data) from Gene Expression Omnibus (GEO) to explore the role of ferroptosis and ferroptosis-related gene (FRG) alterations in ALS. Gene set enrichment analysis (GSEA) found that the activated ferroptosis pathway displayed a higher enrichment score, and the expression of 26 ferroptosis genes showed obvious group differences between ALS and controls. Using weighted gene correlation network analysis (WGCNA), we identified FRGs associated with ALS, of which the Gene Ontology (GO) analysis displayed that the biological process of oxidative stress was the most to be involved in. KEGG pathway analysis revealed that the FRGs were enriched not only in ferroptosis pathways but also in autophagy, FoxO, and mTOR signaling pathways. Twenty-one FRGs (NR4A1, CYBB, DRD4, SETD1B, LAMP2, ACSL4, MYB, PROM2, CHMP5, ULK1, AKR1C2, TGFBR1, TMBIM4, MLLT1, PSAT1, HIF1A, LINC00336, AMN, SLC38A1, CISD1, and GABARAPL2) in the autopsy data and 16 FRGs (NR4A1, DRD4, SETD1B, MYB, PROM2, CHMP5, ULK1, AKR1C2, TGFBR1, TMBIM4, MLLT1, HIF1A, LINC00336, IL33, SLC38A1, and CISD1) in the blood data were identified as target genes by least absolute shrinkage and selection operator analysis (LASSO), in which gene signature could differentiate ALS patients from controls. Finally, the higher the expression of CHMP5 and SLC38A1 in whole blood, the shorter the lifespan of ALS patients will be. In summary, our study presents potential biomarkers for the diagnosis and prognosis of ALS.

Background T-cell receptor-engineered T cells (TCR-Ts) have achieved encouraging success in anticancer clinical trials. The antigenic targets, however, were primarily focused on human leukocyte antigen (HLA) A*02:01 restricted epitopes from a few cancer/testis antigens (CTAs) which are not widely expressed in common solid cancers; the tested T-cell receptors (TCRs) were frequently from tumor-infiltrating lymphocytes of old patients and were not assured to have higher avidity. Here, we propose the isolation of high-avidity TCRs against CTAs that are frequently expressed in common solid cancers.Methods We selected the CT83 protein, which is frequently expressed in common solid cancers, as a model antigen for screening of its specific TCR. The predicted CT83 epitopes with strong or weak binding to HLA-I molecules, popular in the Chinese population, were integrated into three synthetic long peptides. CT83 reactive CD8+ T cells were stimulated with peptide-loaded dendritic cells (DCs) and sorted using the CD137 biomarker for single-cell sequencing to obtain the paired TCRαβ sequence. The higher frequency TCRs were reconstructed for characterization of the CT83 epitope and for assessment of in vitro and in vivo antitumor activities.Results CT83 reactive T cells from young healthy donors (YHDs) were generated by repeated stimulation with DCs and peptides. The single-cell TCR sequencing results of reactive T cells indicated that a single TCR clonotype dominated the paired TCRs. T cells engineered with this dominant TCR led to HLA-A*11:01-restricted recognition of the CT8314-22 epitope, with higher avidity. Functional assays showed powerful cytotoxicity in vitro against the targets of several CT83-positive solid cancer cell lines. Furthermore, TCR-Ts showed therapeutic efficacy in three xenograft solid tumor models. The meta-analysis of gene expression of 92 CTAs indicated that most CTAs did not or at low levels in the thymus, which suggested that those CTAs may experience incomplete thymic central tolerance.Conclusions High-avidity TCR against CT83 could be isolated from YHDs and efficiently mediate regression of well-established xenograft common solid tumors. The high-avidity TCR repertoire in the peripheral blood of some donors for CT83 and other CTAs provides the basis for the efficient isolation of high-avidity TCRs to target numerous solid cancers.

Infectious bronchitis virus (IBV) belongs to the gamma-coronavirus genus of Coronaviridae and causes serious infectious diseases in the poultry industry. However, only a few IBV strains can infect avian passage cell lines, seriously hindering the progress of basic research on IBV pathogenesis. Whereas IBV field strains can replicate in tracheal ring organ culture (TOC) without any previous adaptation in chicken embryos or primary cells. In this study, to investigate the potential use of TOC as an in vitro infection model for the study of IBV-host interaction, we first established a chicken embryo TOC culture system and carried out an investigation on the IBV replication kinetics in the system. We found that the selected strains of the IBV GI-1, GI-7, GI-13, GI-19, and GI-22 genotypes could successfully replicate in TOC and bring about damage to the infected trachea. Next, we identified host proteins of the chicken embryo trachea that interact with the IBV S1 protein by immunoprecipitation and protein mass spectrometry. A total of 127 candidate proteins were initially identified with major involvement in cell adhesion pathways and apoptosis- and autophagy-related pathways. The heat shock protein 70 (HSP70) was selected for further investigation in the interaction with IBV viral proteins. Our results showed that HSP70 interacted with IBV S1 in both TOC and CEK cells, whereas HSP70 overexpression inhibited viral replication. This study indicates that TOC is a good system for the elucidation of IBV-host interactions and HSP70 is a potential host antiviral factor.

In this study, a solar single/double-effect switching LiBr-H2O absorption refrigeration system was investigated to make full use of solar energy and give full play to the advantages of solar refrigeration systems. A corresponding thermodynamic dynamic mathematical model was developed. The operation characteristics of the system operating continuously for one week were analyzed. In order to highlight the advantages of the solar single/double-effect switching absorption refrigeration system, it was compared with other forms of solar refrigeration systems and compression refrigeration systems. The practical application potential of the single/double-effect switching LiBr-H2O absorption refrigeration system was evaluated from the perspective of economy and environmental effect. The results showed that the system could achieve the switching operation between single-effect mode and double-effect mode under weather conditions of high solar radiation intensity, and the daily cooling efficiency on such days was relatively high. After an auxiliary heater was added, the primary energy savings of the solar single/double-effect switching LiBr-H2O absorption refrigeration system were 25–52%, depending on the area of the collector and the volume of the storage tank. The solar fraction of the system was about 71.99% for continuous operation during the whole refrigeration season. However, the initial investment cost of the system equipment accounted for 89.66% of the total cost. Compared with the traditional compression refrigeration system, the initial investment cost of the solar single/double-effect switching LiBr-H2O absorption refrigeration system was higher, but it had a better environmental protection effect.

In traditional image processing, the Fourier transform is often used to transform an image from the spatial domain to the frequency domain, and frequency filters are designed from the perspective of the frequency domain to sharpen or blur the image. In the field of remote sensing change detection, deep learning is beginning to become a mainstream tool. However, deep learning can still refer to traditional methodological ideas. In this paper, we designed a new convolutional neural network (MFGFNet) in which multiple global filters (GFs) are used to capture more information in the frequency domain, thus sharpening the image boundaries and better preserving the edge information of the change region. In addition, in MFGFNet, we use CNNs to extract multi-scale images to enhance the effects and to better focus on information about changes in different sizes (multi-scale combination module). The multiple pairs of enhancements are fused by the difference method and then convolved and concatenated several times to obtain a better difference fusion effect (feature fusion module). In our experiments, the IOUs of our network for the LEVIR-CD, SYSU, and CDD datasets are 0.8322, 0.6780, and 0.9101, respectively, outperforming the state-of-the-art model and providing a new perspective on change detection.

Amino acid salt (AAS) solvents with equimolar base (used to obtain the salt) to amino acid (AA) have been well studied for CO2 absorption, however, very little is known about the reactions when more base is applied. This study determined the reaction mechanisms of AAS solvents with base to AA ratio over equimolar and the potential benefits of such solvents for CO2 absorption. The CO2 loading capacity was found to be dictated by the molar concentration of the base and was approximately half of the base applied. The reaction mechanisms were investigated based on the compositions of carbamate, carbonate/bicarbonate, and AA using 13C-quantitative nuclear magnetic resonance (13C qNMR). An innovative cycling reaction mechanism of CO2 absorption was proposed and experimentally confirmed for AAS solvents with base/AA > 1. Besides all steps of the current widely accepted Zwitterion mechanism, this reaction pathway also contains two cycles: 1) The extra base (i.e., OH−) may react with the protonated AA generated during CO2 absorption to form deprotonated AA which further absorbs CO2 to form more carbamate. 2) The carbamate, as a product of reaction cycle 1, undergoes hydrolysis to yield bicarbonate and deprotonated AA, which further absorbs CO2. As a result, high CO2 loadings were achieved. Compared to the use of alkaline solutions like KOH alone for CO2 absorption, AAS solvents with base to AA ratio over equimolar resulted in lower pH and temperature while maintaining high CO2 loading. These innovative cyclic mechanisms will enable advanced CO2 management approaches using AAS solvents with base/AA > 1.

Abstract An amorphous Ni nanoparticles‐loaded ZnO nanowire array is controllably constructed on stainless steel mesh through combined electrodeposition and hydrothermal reaction. Its nano‐arrayed structure not only provides a platform to achieve stable and good dispersion of Ni nanoparticles, but also helps improve their ability to adsorb 4‐nitrophenolate ions and to capture hydrogen radicals, thereby accelerating the hydrogen transfer from metal hydride complex to 4‐nitrophenol. Benefiting from the abovementioned unique features, it outperforms most Ni‐based catalysts. While a 30‐s re‐electrodeposition of Ni can provide it extra 150 min of high catalytic activity towards the reduction of waste 4‐nitrophenol to valuable 4‐aminophenol, further demonstrating its outstanding reusability. The highly general methodology reported here paves the way to economically and effectively synthesize various other metal nanoparticles‐loaded ZnO nanowire arrays on different conductive substrates, which not only enrich the nanowire‐arrayed catalysts, but also significantly promote their practical utilizations in a wide range of environment‐related fields.

Coal and biomass are two important carbonaceous feedstock and their co-pyrolysis has been proven to modify the fuel properties of resulting oil and char. Nevertheless, how do the volatiles from coal impact the property of biochar from biomass is largely unknown. In this study, sequential pyrolysis of coal in upper bed with sawdust, cellulose, or lignin in lower bed at 600 °C was investigated. The volatiles from the pyrolysis of coal did react with the biochar from biomass, which promoted cracking reactions and resulted in the formation of more gases at expense of biochar. The coal-derived volatiles enhanced the cracking or gasification of sugar derivatives and impacted the evolution of aromatic phenols. Furthermore, coal-derived volatiles also promoted the cracking or deoxygenation of oxygen-containing functionalities, making the biochar carbon-rich with higher crystallinity but reduced the thermal stability. Additionally, the hydrophilicity and the evolution of functionalities on surface biochar was also significantly affected by the volatiles from pyrolysis of coal.

Congenital contractural arachnodactyly (CCA) is a rare autosomal dominant disorder of connective tissue characterized by crumpled ears, arachnodactyly, camptodactyly, large joint contracture, and kyphoscoliosis. The nature course of CCA has not been well-described. We aim to decipher the genetic and phenotypic spectrum of CCA. The cohort was enrolled in Beijing Jishuitan Hospital and Peking Union Medical College Hospital, Beijing, China, based on Deciphering disorders Involving Scoliosis and COmorbidities (DISCO) study (http://www.discostudy.org/). Exome sequencing was performed on patients’ blood DNA. A recent published CCA scoring system was validated in our cohort. Seven novel variants and three previously reported FBN2 variants were identified through exome sequencing. Two variants outside of the neonatal region of FBN2 gene were found. The phenotypes were comparable between patients in our cohort and previous literature, with arachnodactyly, camptodactyly and large joints contractures found in almost all patients. All patients eligible for analysis were successfully classified into likely CCA based on the CCA scoring system. Furthermore, we found a double disease-causing heterozygous variant of FBN2 and ANKRD11 in a patient with blended phenotypes consisting of CCA and KBG syndrome. The identification of seven novel variants broadens the mutational and phenotypic spectrum of CCA and may provide implications for genetic counseling and clinical management.

Abstract Background One major mission of microbial breeding is high-level production of desired metabolites. Overproduction of intermediate metabolites in core pathways is challenging as it may impair cell growth and viability. Results Here we report that aconitic acid, an intermediate metabolite in tricarboxylic acid (TCA) cycle, can be overproduced by an engineered CRISPR interference (CRISPRi) system in Escherichia coli. This CRISPRi system was designed to simultaneously target pyruvate kinase (PK) and isocitrate dehydrogenase (IDH), two enzymes in glycolytic pathway and TCA cycle, respectively. Reverse transcription and quantitative PCR and enzyme activity assays showed that this engineered CRISPRi system significantly repressed the genes encoding IDH and PK, resulting in simultaneous reduction in the activities of IDH and PK. In shake-flask and fed-batch cultivation, this CRISPRi strain produced 60-fold (362.80 ± 22.05 mg/L) and 15-fold (623.80 ± 20.05 mg/L) of aconitic acid relative to the control strain, respectively. In addition, this two-target CRISPRi strain maintained low levels of acetate and lactate, two problematic byproducts. Conclusions This work demonstrates that CRISPRi system can improve aconitic acid production by coordinating glycolysis and TCA cycle. This study provides insights for high-level production of the intermediate metabolites in central pathways.

The exploration of deep and subtle oil and gas reservoirs is currently an important means of increasing production in older oil fields. How to effectively identify weak signals with noise is a common problem faced in such reservoirs. Especially for deep seismic reflection data, the application of traditional denoising methods is limited due to the weak energy and small difference frequency band between the effective signals and the noise. A novel method of noise attenuation for weak seismic signals based on compressed sensing and CEEMD (complementary ensemble empirical mode decomposition) was proposed in this work. This method consists of three steps: First, the CEEMD algorithm, a time-frequency analysis method, was introduced into the classic CS (compressed sensing) denoising method to overcome the non-adaptability of CS. CEEMD decomposed the raw seismic signals into sets of IMFs (finite intrinsic mode functions). The IMFs with noise were reconstructed and denoised by CS. In the second step, an enhancement operator was introduced into the penalty term to ensure that the effective signals could be extracted. Finally, the OMP algorithm is adopted to reconstruct the seismic weak signal to prevent the iterative threshold method from damaging weak effective signals. It was demonstrated through the synthetic seismic record and the field seismic data that (a) the CS method can identify the weak signals submerged in the noise by selecting the basic function that is most similar to the effective signals, but it cannot adaptively suppress the high frequency and high wavenumber noise of the complex seismic records; (b) the proposed method overcome the non-adaptability of CS and enhance the edge information described by the curved wave rather than the noises, as a result, the high frequency noise is suppressed while the middle/low frequency noise and weak effective signals are also effectively separated.

Cyclophilins, a type of peptidyl-prolyl cis-trans isomerase, function as important molecular chaperones in a series of biological processes. However, the expression pattern and signal transduction pathway of cyclophilins are still unclear. Here, we showed that the promoter of OsCYP2 could function as a tissue-specific promoter by GUS staining. Moreover, we found that the promoter sequence contained not only core elements but also inducible elements. Then, the ABA-responsive element was used for cDNA library screening, and the transcription factor MYC2-like was identified by a yeast one-hybrid assay and confirmed through an electrophoretic mobility shift assay. Furthermore, the relative expression showed that MYC2-like was induced by abscisic acid. In addition, MYC2-like overexpression enhanced salt tolerance in transformants and partially restored the cyp2-RNAi line. In summary, we explored a novel transcriptional signal mediated by MYC2-like, a potential regulator of salt stress-related physiological processes in rice.

Walnut is a natural source of antioxidants. Its antioxidant capacity is determined by the distribution and composition of phenolics. The key phenolic antioxidants in various forms (free, esterified, and bound) in walnut kernel (particularly seed skin) are unknown. The phenolic compounds in twelve walnut cultivars were analyzed using ultra-performance liquid chromatography coupled with a triple quadrupole mass spectrometer in this study. A boosted regression tree analysis was used to identify the key antioxidants. Ellagic acid, gallic acid, catechin, ferulic acid, and epicatechin were abundant in the kernel and skin. The majority of phenolic acids were widely distributed in the free, esterified, and bound forms in the kernel but more concentrated in bound phenolics in the skin. The total phenolic levels of the three forms were positively correlated with antioxidant activities (R = 0.76–0.94, p < 0.05). Ellagic acid was the most important antioxidant in the kernel, accounting for more than 20%, 40%, and 15% of antioxidants, respectively. Caffeic acid was responsible for up to 25% of free phenolics and 40% of esterified phenolics in the skin. The differences in the antioxidant activity between the cultivars were explained by the total phenolics and key antioxidants. The identification of key antioxidants is critical for new walnut industrial applications and functional food design in food chemistry.

In the application of change detection satellite remote sensing images, synthetic aperture radar (SAR) images have become a more important data source. This paper proposes a new end-to-end SAR image change network architecture—TransUNet++SAR—that combines Transformer with UNet++. First, the convolutional neural network (CNN) was used to obtain the feature maps of the single time SAR images layer by layer. Tokenized image patches were encoded to extract rich global context information. Using improved Transformer for effective modeling of global semantic relations can generate rich contextual feature representations. Then, we used the decoder to upsample the encoded features, connected the encoded multi-scale features with the high-level features by sequential connection to learn the local-global semantic features, recovered the full spatial resolution of the feature map, and achieved accurate localization. In the UNet++ structure, the bitemporal SAR images are composed of two single networks, which have shared weights to learn the features of the single temporal image layer by layer to avoid the influence of SAR image noise and pseudo-change on the deep learning process. The experiment results show that the experimental effect of TransUNet++SAR on the Beijing, Guangzhou, and Qingdao datasets were significantly better than other deep learning SAR image change detection algorithms. At the same time, compared with other Transformer related change detection algorithms, the description of the changed area edge was more accurate. In the dataset experiments, the model had higher indices than the other models, especially the Beijing building change datasets, where the IOU was 9.79% higher and F1-score was 4.38% higher.

Rare diseases affect the life of a tremendous number of people globally. The CRISPR-Cas system emerged as a powerful genome engineering tool and has facilitated the comprehension of the mechanism and development of therapies for rare diseases. This review focuses on current efforts to develop the CRISPR-based toolbox for various rare disease therapy applications and compares the pros and cons of different tools and delivery methods. We further discuss the therapeutic applications of CRISPR-based tools for fighting different rare diseases.

Regression modelling has always been a key process in unlocking the relationships between independent and dependent variables that are held within data. In recent years, machine learning has uncovered new insights in many fields, providing predictions to previously unsolved problems. Generative Adversarial Networks (GANs) have been widely applied to image processing producing good results, however, these methods have not often been applied to non-image data. Seeing the powerful generative capabilities of the GANs, we explore their use, here, as a regression method. In particular, we explore the use of the Wasserstein GAN (WGAN) as a multi-output regression method. The resulting method we call Multi-Output Regression GANs (MOR-GANs) and its performance is compared to a Gaussian Process Regression method (GPR)—a commonly used non-parametric regression method that has been well tested on small datasets with noisy responses. The WGAN regression model performs well for all types of datasets and exhibits substantial improvements over the performance of the GPR for certain types of datasets, demonstrating the flexibility of the GAN as a model for regression.

The high-precision geometric positioning of optical remote sensing satellites is the prerequisite to determine the application capability of satellite image products. Its positioning accuracy is related to the observation accuracy of each link in the imaging process, including satellite attitude, orbit measurement accuracy, time synchronization accuracy, camera measurement accuracy, and so on. Untimely and inaccurate on-orbit calibration will lead to great geometric positioning errors. To optimize the positioning accuracy of satellite images with the rational function model (RFM) under low positioning accuracy, our paper proposes an improved geometric quality model based on the reorientation of internal and external orientation elements in the RFM model of remote sensing images. By establishing the rational function positioning model, the external orientation model, and the internal orientation model, the original image can be reorientated. Then, we use the improved model to generate uniformly distributed virtual ground control points. By analyzing and verifying the relationship between each rational polynomial coefficient (RPC) and its influence on geometric positioning accuracy, we propose an RPC coefficients optimization method based on image offset correction and positioning dominant coefficients. Finally, we use the small satellite “MN200Sar-1” with low geometric accuracy for experimental verification. The results show that the model can effectively eliminate the errors of internal and external elements in the on-orbit calibration, and the positioning accuracy is improved from one hundred pixels to one pixel. At the same time, the rational polynomial dominant coefficient optimization method can improve geometric positioning accuracy without introducing additional compensation parameters.

This article reports a novel method for producing ZnO nanowire film, achieved through in situ growth on nanocrystalline zinc electrodeposit using a low-temperature hydrothermal process at 90 °C without utilization of catalyst or template. The ZnO nanowire film has an approximate wire diameter of 50 nm, showing strong response to UV illumination, leading to large UV emission and high optoelectronic activity. This alternative process for synthesis of ZnO nanowire film is simple, efficient, and easy to be extended to massive production. Keywords: ZnO nanowire film, Nanocrystalline zinc substrate, In situ growth, Hydrothermal reaction

Here, by utilizing crystal structure analysis through the particle swarm optimization (CALYPSO) structural searching method with density functional theory (DFT), we investigate the systemic structures and electronic properties of Ca2Mgn (n = 1–15) clusters. Structural searches found that two Ca atoms prefer to occupy the external position of magnesium-doped systems at n = 2–14. Afterward, one Ca atom begins to move from the surface into the internal of the caged skeleton at n = 15. Calculations of the average binding energy, second-order difference of energies, and HOMO–LUMO gaps indicated that the pagoda construction Ca2Mg8 (as the magic cluster) has higher stability. In addition, the simulated IR and Raman spectra can provide theoretical guidance for future experimental and theoretical investigation. Last, further electronic properties were determined, including the charge transfer, density of states (DOS) and bonding characteristics. We hope that our work will provide theoretical and experimental guidance for developing magnesium-based nanomaterials in the future.

An adaptive dynamic surface controller (ADSC), which comprises an online parameter estimator and a robust control law, is developed for the pneumatic servo systems driven by the proportional directional control valves. Departing from the use of time-consuming offline fitting of the orifice area to accommodate the effect of valve dead zone, this paper employs the least-square-type indirect parameter estimation algorithm with online condition monitoring to estimate the dead-zone parameters and some other important model parameters. These accurate estimates of model parameters are utilized in the development of a precise position tracking controller for the single-rod pneumatic actuator. By using the dynamic surface control technique to synthesize the robust control law, the problem of “explosion of complexity” in traditional backstepping design method is avoided. The stability of the closed-loop system is proved by the means of the Lyapunov theory. The obtained extensive comparative experimental results verify the effectiveness of the proposed valve dead-zone compensation strategy and the high-performance nature of the ADSC in practical implementation.

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (CFAR) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use CFAR to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods.

In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.

The Orbita hyperspectral satellite (OHS) is the first hyperspectral satellite with surface coating technology for sensors in the world. It includes 32 bands from visible to near-infrared wavelengths. However, technology such as the fabricating process of complementary metal−oxide−semiconductor (CMOS) sensors makes the image contain a lot of random and unsystematic stripe noise, which is so bad that it seriously affects visual interpretation, object recognition and the application of the OHS data. Although a large number of stripe removal algorithms have been proposed, very few of them take into account the characteristics of OHS sensors and analyze the causes of OHS data noise. In this paper, we propose a destriping algorithm for OHS data. Firstly, we use both the adaptive moment matching method and multi-level unidirectional total variation method to remove stripes. Then a model based on piecewise linear least squares fitting is proposed to restore the vertical details lost in the first step. Moreover, we further utilize the spectral information of the OHS image, and extend our 2-D destriping method to the 3-D case. Results demonstrate that the proposed method provides the optimal destriping result on both qualitative and quantitative assessments. Moreover, the experimental results show that our method is superior to the existing single-band and multispectral destriping methods. Also, we further use the algorithm to the stripe noise removal of other real remote sensing images, and excellent image quality is obtained, which proves the universality of the algorithm.

In this paper, the needle insertion motion control of a magnetic resonance imaging (MRI) compatible robot, which is actuated by a pneumatic cylinder with long transmission lines, is considered and a pressure observer based adaptive dynamic surface controller is proposed. The long transmission line is assumed to be an intermediate chamber connected between the control valve and the actuator in series, and a nonlinear first order system model is constructed to characterize the pressure losses and time delay brought by it. Due to the fact that MRI-compatible pressure sensors are not commercially available, a globally stable pressure observer is employed to estimate the chamber pressure. Based on the model of the long transmission line and the pressure observer, an adaptive dynamic surface controller is further designed by using the dynamic surface control technique. Compared to the traditional backstepping design method, the proposed controller can avoid the problem of “explosion of complexity” since the repeated differentiation of virtual controls is no longer required. The stability of the closed-loop system is analytically proven by employing the Lyapunov theory. Extensive experimental results are presented to demonstrate the effectiveness and the performance robustness of the proposed controller.

A novel strategy for large-scale synthesis of ZnO nanowire film is reported, which inherits the advantages of the solution-phase method and seeded growth process, such as low-temperature, efficient, economical, facile and flexible. It is easy to implement on various metals through room-temperature electrodeposition, followed by hydrothermal treatment at 90 °C, and suitable for industrialized production. The ZnO nanowires with an average wire diameter about 40 nm are in situ grown from and on nanocrystalline zinc coating, which forms a strong metallurgical bonding with the substrates. The p-type ZnO nanowire film has a well-preferred orientation along the (100) direction and a wurtzite structure, thereby displaying an effective photocatalytic capability for carcinogenic Cr6+ ions and CO2 greenhouse gas reduction under visible light irradiation. In addition to these features, the ZnO nanowire film is easy to recycle and, therefore, it has broad application prospects in contaminant degradation and renewable energy.

Sharing drafts of scientific manuscripts on preprint hosting services for early exposure and pre-publication feedback is a well-accepted practice in fields such as physics, astronomy, or mathematics. The field of neuroscience, however, has yet to adopt the preprint model. A reason for this reluctance might partly be the lack of central preprint services for the field of neuroscience. To address this issue, we announce the launch of Preprints of the R-fMRI Network (PRN), a community funded preprint hosting service. PRN provides free-submission and free hosting of manuscripts for resting state functional magnetic resonance imaging (R-fMRI) and neuroscience related studies. Submissions will be peer viewed and receive feedback from readers and a panel of invited consultants of the R-fMRI Network. All manuscripts and feedback will be freely available online with citable permanent URL for open-access. The goal of PRN is to supplement the “peer reviewed” journal publication system – by more rapidly communicating the latest research achievements throughout the world. We hope PRN will help the field to embrace the preprint model and thus further accelerate R-fMRI and neuroscience related studies, eventually enhancing human mental health.

Introduction: Autoimmune encephalitis (AE) comprises a group of inflammatory brain disorders mediated by autoimmune responses. Anti-N-methyl-Daspartate receptor (NMDAR) encephalitis, anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis, and anti-g-aminobutyric acid-B receptor (GABABR) encephalitis are the most prevalent forms, characterized by the presence of antibodies against neuronal cell-surface antigens. Efgartigimod, an antagonist of the neonatal Fc receptor, has proven efficacy in myasthenia gravis treatment. This clinical case report describes the clinical progression and functional outcomes of AE in three patients who received efgartigimod treatment. Case presentations: Case 1 was a 60-year-old man exhibiting memory impairment and psychiatric disturbances over 11 days. Case 2 was a 38-yearold man with a 1-month history of rapid cognitive decline and seizures. Case 3 was a 68-year-old woman with mental behavioral changes and seizures for 4 months. Anti-GABABR, anti-LGI1, and anti-NMDAR antibodies were confirmed in the respective patients' cerebrospinal fluid or serum. All three patients experienced marked and swift symptomatic relief after four cycles of efgartigimod treatment, with no complication. Conclusion: Current first-line and second-line treatments for AE have limitations, and efgartigimod has demonstrated potential in the rapid and efficacious treatment of AE, emerging as a promising option for the management of this disease. [ABSTRACT FROM AUTHOR]