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Abstract Background The mortality rate of COVID-19 patients with critical symptoms is reported to be 40.5%. Early identification of patients with poor progression in the critical cohort is essential to timely clinical intervention and reduction of mortality. Although older age, chronic diseases, have been recognized as risk factors for COVID-19 mortality, we still lack an accurate prediction method for every patient. This study aimed to delve into the cell-free DNA fragmentomics of critically ill patients, and develop new promising biomarkers for identifying the patients with high mortality risk. Methods We utilized whole genome sequencing on the plasma cell-free DNA (cfDNA) from 33 COVID-19 patients with critical symptoms, whose outcomes were classified as survival (n = 16) and death (n = 17). Mitochondrial DNA (mtDNA) abundance and fragmentomic properties of cfDNA, including size profiles, ends motif and promoter coverages were interrogated and compared between survival and death groups. Results Significantly decreased abundance (~ 76% reduction) and dramatically shorter fragment size of cell-free mtDNA were observed in deceased patients. Likewise, the deceased patients exhibited distinct end-motif patterns of cfDNA with an enhanced preference for “CC” started motifs, which are related to the activity of nuclease DNASE1L3. Several dysregulated genes involved in the COVID-19 progression-related pathways were further inferred from promoter coverages. These informative cfDNA features enabled a high PPV of 83.3% in predicting deceased patients in the critical cohort. Conclusion The dysregulated biological processes observed in COVID-19 patients with fatal outcomes may contribute to abnormal release and modifications of plasma cfDNA. Our findings provided the feasibility of plasma cfDNA as a promising biomarker in the prognosis prediction in critically ill COVID-19 patients in clinical practice.

Abstract In military contexts, synthetic aperture radar (SAR) automatic target recognition (ATR) models frequently encounter the challenge of imbalanced data, resulting in a noticeable degradation in the recognition performance. Therefore, the authors propose a cost‐sensitive awareness generative adversarial network (CAGAN) model, aiming to improve the robustness of ATR models under imbalanced data. Firstly, the authors introduce a convolutional neural network (DCNN) to extract features. Then, the synthetic minority over‐sampling technique (SMOTE) is applied to achieve feature‐level balancing for the minority category. Finally, a CAGAN model is designed to perform the final classification task. In this process, the GAN‐based adversarial training mechanism enriches the diversity of training samples, making the ATR model more comprehensive in understanding different categories. In addition, the cost matrix increases the penalty for misclassification results and further improves the classification accuracy. Simultaneously, the cost‐sensitive awareness can accurately adjust the cost matrix through training data, thus reducing dependence on expert knowledge and improving the generalisation performance of the ATR model. This model is an end‐to‐end ATR scheme, which has been experimentally validated on the MSTAR and OpenSARship datasets. Compared to other methods, the proposed method exhibits strong robustness in dealing with various imbalanced scenarios and significant generalisation capability across different datasets.

Frequency agile technology provides full play to the advantage of radars for adopting electronic countermeasures actively, which can effectively enhance the antinoise suppression jamming performance of radars. However, with the increasing complexity of the interference environment, developing an online decision-making method for frequency-agile radar with dynamic adaptability and without foresight of the nature of the environment is a demanding task. According to the features of the jamming strategy, suppression jamming scenarios are divided into three categories, and an online decision-making method for frequency-agile radar based on Multi-Armed Bandit (MAB) is developed to maximize the radar’s detection probability. This approach is an online learning algorithm that does not need to interfere with the foresight of the environment and offline training process and realizes remarkable learning performance from noninterference scenarios to adaptive interference scenarios. The simulation results and theoretical analysis demonstrate that compared with the classical algorithm and stochastic agile strategy, the proposed method has stronger flexibility and can effectively improve the antijamming and target detection performances of the frequency-agile radar for various jamming scenarios.

Objective Generally, anterior lumbar interbody fusion (ALIF) was believed superior to transforaminal lumbar interbody fusion (TLIF) in induction of fusion. However, many studies have reported comparable results in lumbosacral fusion rate between the two approaches. This study aimed to evaluate the realistic lumbosacral arthrodesis rates following ALIF and TLIF in patients with degenerative spondylolisthesis as measured by CT and radiology. Methods Ninety‐six patients who underwent single‐level L5‐S1 fusion through ALIF (n = 48) or TLIF (n = 48) for degenerative spondylolisthesis at the Spine Center, University of California San Francisco, between October 2014 and December 2017 were retrospectively evaluated. Fusion was independently evaluated and categorized as solid fusion, indeterminate fusion, or pseudarthroses by two radiologists using the modified Brantigan–Steffee–Fraser (mBSF) grade. Clinical data on sex, age, body mass index, Meyerding grade, smoking status, follow‐up times, complications, and radiological parameters including disc height, disc angle, segmental lordosis, and overall lumbar lordosis were collected. The fusion results and clinical and radiographic data were statistically compared between the ALIF and TLIF groups by using t‐test or chi‐square test. Results The mean follow‐up period was 37.5 (ranging from 24 to 51) months. Clear, solid radiographic fusions were higher in the ALIF group compared with the TLIF group at the last follow‐up (75% vs 47.9%, p = 0.006). Indeterminate fusion occurred in 20.8% (10/48) of ALIF cases and in 43.8% (21/48) of TLIF cases (p = 0.028). Radiographic pseudarthrosis was not significantly different between the TLIF and ALIF groups (16.7% vs 8.3%; p = 0.677). In subgroup analysis of the patients without bone morphogenetic protein (BMP), the solid radiographic fusion rate was significantly higher in the ALIF group than that in the TLIF group (78.6% vs 45.5%; p = 0.037). There were no differences in sex, age, body mass index, Meyerding grade, smoking status, or follow‐up time between the two groups (p > 0.05). The ALIF group had more improvement in disc height (7.8 mm vs 4.7 mm), disc angle (5.2° vs 1.5°), segmental lordosis (7.0° vs 2.5°), and overall lumbar lordosis (4.7° vs 0.7°) compared with the TLIF group (p 0.999). Conclusions With a minimum 2‐year radiographic analysis of arthrodesis at lumbosacral level by radiologists, the rate of solid radiographic fusions was higher in the ALIF group compared with the TLIF group, whereas the TLIF group had a higher rate of indeterminate fusion. Radiographic pseudarthrosis did not differ significantly between the TLIF and ALIF groups.

Shallow marine shelf sedimentation is a hot and difficult topic in today’s reservoir sedimentology research, and it is widely present in the world. The shallow marine shelf sedimentation is not only affected by complex hydrodynamic effects such as tides and waves, but also controlled by bottom tectonic features, forming a complex and varied sedimentation pattern. During the Middle Jurassic period, the northern part of West Siberian Basin was characterized by a shallow marine shelf sedimentary environment. In the central reion of this basin, a typical tectonic uplift zone developed, forming a tectonic background of “one uplift zone between two depressions”. Simultaneously, the dominant influence of tides in the shallow marine shelf environment facilitated the formation of a typical shelf-type tidal delta sedimentation system in the Jurassic strata of the northern part of West Siberian Basin. This sedimentation constitutes a significant natural gas reservoir, and it is important to investigate the sedimentary evolution of shelf-type tidal deltas and to clarify the internal structure and distribution of sedimentary sand bodies and interlayers in shelf-type tidal deltas, which is the basis for the fine development of this type of reservoir. This paper takes the Jurassic strata in the Y region of northern part of West Siberian Basin as the research object, and conducts numerical simulation based on sedimentary dynamics for the shelf-type tidal delta sedimentation formed under the tectonic background of “one uplift zone between two depressions”. In addition, tidal amplitude and initial water level were selected for different hydrodynamic factors to study the main controlling factors of shelf-type tidal delta sedimentation. The simulation results show that tidal amplitude is positively correlated with three-dimensional configuration characteristic parameters of the sedimentary sand bodies, and the development of tidal bars becomes more and more limited as the initial water level increases. This paper systematically investigates the sedimentary evolution of shelf-type tidal delta under the tectonic background of “one uplift zone between two depressions” by the sedimentary dynamics method, which deepens the understanding of the shelf-type tidal delta sedimentation process and provides a new thinking for the development of this sedimentary reservoir type (School of Geosciences China University of Petroleum (East China)).

Purpose: The purpose of this study was to clarify the effect of ZC3H13 on the growth of papillary thyroid carcinoma (PTC). Methods: Firstly, we used qRT-PCR and Western blot to compare the difference in the expression of ZC3H13 between normal thyroid epithelial cells and PTC cell lines. Then, ZC3H13 overexpression/knockout thyroid cancer cells were constructed by lentivirus transfection, and the effects of overexpression of ZC3H13 on the proliferation, migration and invasion of PTC cells were detected by CCK8 and transwell experiments. Lastly, MeRIP-qPCR, RIP and o Actinomycin D were used to verify that ZC3H13 regulated the expression of downstream target gene IQGAP1 through m6A modification. Results: ZC3H13 expression was decreased in PTC cell lines BCPAP, KTC-1, k1, HTH83, and TPC-1. Proliferation, invasion, and migration of PTC cells were inhibited by overexpressed ZC3H13 but increased by knockdown of ZC3H13. IQGAP1 expression was suppressed by ZC3H13 overexpression but enhanced by ZC3H13 knockdown. In ZC3H13-overexpressed PTC cells, the m6A level of IQGAP1 mRNA was increased, and the IQGAP1 mRNA expression was decreased with the increasing time of Actinomycin D treatment. YTHDF2 enriched more IQGAP1 mRNA than IgG and knockdown of YTHDF2 reversed the effect of ZC3H13 overexpression on IQGAP1 mRNA stability. The xenograft tumor experiment in nude mice confirmed that the overexpression of ZC3H13 inhibited tumor growth, while overexpression of IQGAP1 could reverse the inhibitory effect of ZC3H13 overexpression on tumor growth. Conclusion: ZC3H13 mediates IQGAP1 mRNA degradation by promoting m6A modification of IQGAP1 mRNA, this provides a prospective therapeutic target for PTC.

Abstract Background DNA damage and DNA damage repair (DDR) are important therapeutic targets for triple-negative breast cancer (TNBC), a subtype with limited chemotherapy efficiency and poor outcome. However, the role of microRNAs in the therapy is emerging. In this study, we explored whether miR-26a-5p could act as BRCAness and enhance chemotherapy sensitivity in TNBC. Methods Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to detect the expression of miR-26a-5p in breast cancer tissues and cell lines. CCK-8 was used to measure drug sensitivity in concentration gradient and time gradient. Comet assay was used to detect DNA damage. Flow cytometry was performed to examine apoptosis. Moreover, we used western blot and immunofluorescence to detect biomarkers. Luciferase reporter assay was performed to verify the combination of miR-26a-5p and 3’UTR of target gene. Hormone deprivation and stimulation assay were used to validate the effect of hormone receptors on the expression of miR-26a-5p. Chromatin immunoprecipitation (ChIP) assays were used to verify the binding sites of ER-a or PR with the promoter of miR-26a-5p. Animal experiments were performed to the effect of miR-26a-5p on Cisplatin treatment. Results The expression of miR-26a-5p was significantly downregulated in TNBC. Overexpressing miR-26a-5p enhanced the Cisplatin-induced DNA damage and following apoptosis. Interestingly, miR-26a-5p promoted the expression of Fas without Cisplatin stimulating. It suggested that miR-26a-5p provided a hypersensitivity state of death receptor apoptosis and promoted the Cisplatin sensitivity of TNBC cells in vitro and in vivo. Besides, miR-26a-5p negatively regulated the expression of BARD1 and NABP1 and resulted in homologous recombination repair defect (HRD). Notably, overexpressing miR-26a-5p not only facilitated the Olaparib sensitivity of TNBC cells but also the combination of Cisplatin and Olaparib. Furthermore, hormone receptors functioned as transcription factors in the expression of miR-26a-5p, which explained the reasons that miR-26a-5p expressed lowest in TNBC. Conclusions Taken together, we reveal the important role of miR-26a-5p in Cisplatin sensitivity and highlight its new mechanism in DNA damage and synthetic lethal.

Diabetes could directly induce cardiac injury, leading to cardiomyopathy. However, treatment strategies for diabetic cardiomyopathy remain limited. ZNF593-AS knockout and cardiomyocyte-specific transgenic mice were constructed. In addition, high-fat diet (HFD)-induced diabetic mouse model and db/db mice, another classic diabetic mouse model, were employed. ZNF593-AS was silenced using GapmeR, a modified antisense oligonucleotide, while overexpressed using a recombinant adeno-associated virus serotype 9-mediated gene delivery system. Transcriptome sequencing, RNA pull-down assays, and RNA immunoprecipitation assays were also performed to investigate the underlying mechanisms. ZNF593-AS expression was decreased in diabetic hearts. ZNF593-AS attenuated the palmitic acid-induced apoptosis of cardiomyocytes in vitro. In HFD-induced diabetic mice, ZNF593-AS deletion aggravated cardiac dysfunction and enhanced cardiac apoptosis and inflammation. In contrast, HFD-induced cardiac dysfunction was improved in ZNF593-AS transgenic mice. Consistently, ZNF593-AS exerted the same cardioprotective effects in db/db mice. Mechanistically, ZNF593-AS directly interacted with the functional domain of interferon regulatory factor 3 (IRF3), and suppressed fatty acid-induced phosphorylation and activation of IRF3, contributing to the amelioration of cardiac cell death and inflammation. In conclusion, our results identified the protective role of ZNF593-AS in diabetic cardiomyopathy, suggesting a novel potential therapeutic target.

Radar forward-looking imaging is important in many fields, such as precision guidance, autonomous landing, and terrain mapping. Due to the constraints of actual radar aperture, obtaining high-resolution images using the traditional forward-looking imaging method based on real beam scanning is challenging. Compared with the entire imaging scene, the objects of interest usually occupy only a small part of the area. This sparsity enables the use of Compressed Sensing(CS) to reconstruct high-resolution forward-looking images. However, the high noise in the radar echo affects the quality of the image generated by the compressed sensing method. Inspired by the low-rank property of the final image, this paper proposes a forward-looking super-resolution imaging model that combines sparse and low-rank properties. To effectively solve the dual constraint optimization problem in the proposed model, a forward-looking image reconstruction method based on an Augmented Lagrange Multiplier(ALM) within the framework of the Alternating Direction Multiplier Method(ADMM) was proposed. Finally, the experimental results from simulation and real data show that the proposed method can effectively improve the azimuth resolution of radar forward-looking imaging while also being noise-robust.

Intensive glycemic control is insufficient for reducing the risk of heart failure among patients with diabetes mellitus (DM). While the “hyperglycemic memory” phenomenon is well documented, little is known about its underlying mechanisms. In this study, a type 1 DM model was established in C57BL/6 mice using streptozotocin (STZ). Leptin receptor-deficient (db/db) mice were used as a model of type 2 DM. A type 9 adeno-associated virus was used to overexpress or knock down miR-320 in vivo. Diastolic dysfunction was observed in the type 1 DM mice with elevated miR-320 expression. However, glycemic control using insulin failed to reverse diastolic dysfunction. miR-320 knockdown protected against STZ-induced diastolic dysfunction. Similar results were observed in the type 2 DM mice. In vitro, we found that miR-320 promoted CD36 expression, which in turn induced further miR-320 expression. CD36 was rapidly induced by hyperglycemia at protein level compared with the much slower induction of miR-320, suggesting a positive feedback loop of CD36/miR-320 with CD36 protein induction as the initial triggering event. In conclusion, in DM-induced cardiac injury, miR-320 and CD36 mutually enhance each other’s expression, leading to a positive feedback loop and a sustained hyperlipidemic state in the heart.

Enhancing yield and achieving environmental goals represent challenges for the future of agriculture. Rational nitrogen (N) management is one of the most promising ways to meet this challenge. However, complicated nitrogen management strategies and considerable input requirements still exist in rice–ratoon rice production. To address this issue, field experiments were conducted with two main high-yield rice crop genotypes and five fertilization treatments at six sites in Southwest China from 2018 to 2020. The results showed the following: (1) the yield of the main rice crop was extremely significantly affected by the year, location, and fertilization, but not by genotype; (2) the yield of the ratoon rice was extremely significantly affected by year, genotype, location, and fertilization; and (3) the total plant N content (TPN) and leaf SPAD value at the full heading stage of the main crop were significantly positively correlated with the total soil N content (TSN) and soil available N (SAN) content of the basic soil. The highly efficient N application rate of grain- and bud-promoting fertilizer for ratoon rice was 60–120 kg ha−1. The TSN, SAN, TPN, and SPAD values higher than 0.247 kg N kg−1, 298 mg N kg−1, 2.159 kg N kg−1, and 49.94 were, respectively, considered the reference values when not applying grain- and bud-promoting fertilizer. A regression equation was established to predict the amount of high-efficiency grain- and bud-promoting fertilizer based on the TSN and SPAD. Overall, the yield of rice–ratoon rice was significantly affected by year, genotype, location, fertilization, and their interactions. The use of the predicted grain- and bud-promoting fertilizer regression equation can achieve high yields under simplified and reduced N input practices in the rice–ratoon rice systems.

BackgroundTraditional Chinese exercises (TCEs) have played a significant role in treating various diseases. However, there is limited research assessing the efficacy of TCEs in treating Lumbar disc herniation (LDH). This study aimed to systematically evaluate the effects of four commonly used TCEs (Baduanjin, Yijinjing, Taichi, and Wuqinxi) on pain and disability in elderly patients with LDH.ObjectivesTo assess the quality of relevant randomized controlled trials (RCTs) to provide evidence support for the treatment of LDH.MethodsRCTs were identified through eight databases. Meta-analysis and trial sequence analysis (TSA) were conducted using RevMan 5.4, Stata 17.0, and TSA 0.9.ResultsA total of 22 RCTs, involving 1931 patients, were included in the analysis. TCEs exhibited a superior effectiveness in treating LDH compared to the control group. However, the TSA analysis suggested the possibility of false positives, indicating the need for more high-quality RCT evidence. Nevertheless, TCEs showed reliable results in significantly improving the VAS score and JOA score of LDH patients.ConclusionCurrent evidence indicates that the four TCEs have advantages in treating LDH in middle-aged and elderly individuals. However, considering the limitations of this study, we need to exercise caution in drawing conclusions, and further research is required to validate these findings.Systematic Review Registrationhttp://www.crd.york.ac.uk/PROSPERO, identifier [CRD42023431633].

BackgroundStudies have revealed that there were significant changes in intestinal flora composition in patients with coronavirus disease 2019 (COVID-19) compared to non-COVID-19 patients, regardless of whether they were treated with medication. Therefore, a comprehensive study of the intestinal flora of COVID-19 patients is needed to further understand the mechanisms of COVID-19 development.MethodsIn total, 20 healthy samples and 20 COVID-19 samples were collected in this study. Firstly, alpha diversity and beta diversity were analyzed to assess whether there were difference in species richness and diversity as well as species composition between COVID-19 and control groups. The observed features index, Evenness index, PD index, and Shannon index were utilized to measure alpha diversity. The principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) were performed to analyzed beta diversity. Linear discriminant analysis Effect Size (LEfSe) was utilized to analyze the variability in the abundance of bacterial taxa from different classification levels. The random forest (RF), Least absolute shrinkage and selection operator (LASSO), and univariate logistic regression were utilized to identify key Amplicon Sequence Variant (ASVs). Finally, the relevant networks of bacterial taxa were created in COVID-19 and control groups, separately.ResultsThere were more species in the control group than in COVID-19 group. The observed features index, Shannon index, and Evenness index in the control groups were markedly higher than in the COVID-19 group. Therefore, there were marked variations in bacterial taxa composition between the COVID-19 and control groups. The nine bacterial taxa were significantly more abundant in the COVID-19 group, such as g-Streptococcus, f-Streptococcaceae, o-Lactobacillales, c-Bacilli and so on. In the control group, 26 bacterial taxa were significantly more abundant, such as c-Clostrjdia, o-Oscillospirales, f-Ruminococcaceae, etc. The 5 key ASVs were obtained through taking the intersection of the characteristic ASVs obtained by the three algorithms, namely ASV6, ASV53, ASV92, ASV96, and ASV105, which had diagnostic value for COVID-19. The relevance network in the control group was more complex compared to the COVID-19 group.ConclusionOur findings provide five key ASVs for diagnosis of COVID-19, providing a scientific reference for further studies of COVID-19.

For the closed Brayton cycle using carbon heaters, working fluid contains some solid particles generally. These impurities will enter turbine along with gas, influence aerodynamic performance, and even make turbine work under off-design condition. Therefore, it is necessary to study the influence of particles on turbine. In this paper, a turbine using argon with carbon particles as working fluid is investigated. Particles are assumed to have no volume and are evenly divided into ten different sizes. Based on the discrete phase model (DPM), CFD method is adopted to simulate turbine flow field, and influences of carbon particle mass fraction, particle diameter and incident velocity on aerodynamic performance are analyzed. The results indicate that as particle mass fraction increases, total pressure, static pressure and Mach number decrease significantly, isentropic efficiency decreases slightly, while temperature increases. Collision and rebound of particles in flow field are more intense with a larger particle diameter, but flow field is less influenced under the same mass fraction due to decrease of particle number. Incident velocity has little effect on aerodynamic performance; however, with increase of incident velocity, diameter of particles on blade surface is larger and collision of particles is more intense especially in nozzle. These results will help understand the influence of solid particles on turbines.

In order to improve the resolution and accuracy of seismic inversion, this study constructs a multi-scale super-asymmetric network (Cycle-JNet). In this model, wavelet analysis is used to capture the multi-scale data characteristics of well-seismic data, thereby improving the machine’s ability to learn details. Using the UNet neural network from Convolutional Neural Network (CNN), we modified the network structure by adding several convolution kernel layers at the output end to expand generated data, solving the problem of mismatched resolutions in well-seismic data, thus improving the resolution of seismic inversion and achieving the purpose of accurately identifying thin sandstone layers. Meanwhile, a cycle structure of Recurrent Neural Network (RNN) was designed for the secondary learning of the seismic data generated by JNet. By comparing the data transformed through inverse wavelet transform with the original data again, the accuracy of machine learning can be improved. After optimization, the Cycle-JNet model significantly outperforms traditional seismic inversion methods in terms of resolution and accuracy. This indicates that this method can provide more precise inversion results in more complex data environments, providing stronger support for seismic analysis.

A complete telomere-to-telomere (T2T) genome has been a longstanding goal in the field of genomic research. By integrating high-coverage and precise long-read sequencing data using multiple assembly strategies, we present here the first T2T gap-free genome assembly of Ganoderma leucocontextum strain GL72, a Tibetan medicinal mushroom. The T2T genome, with a size of 46.69 Mb, consists 13 complete nuclear chromosomes and typical telomeric repeats (CCCTAA)n were detected at both ends of 13 chromosomes. The high mapping rate, uniform genome coverage, a complete BUSCOs of 99.7%, and base accuracy exceeding 99.999% indicate that this assembly represents the highest level of completeness and quality. Regions characterized by distinct structural attributes, including highest Hi-C interaction intensity, high repeat content, decreased gene density, low GC content, and minimal or no transcription levels across all chromosomes may represent potential centromeres. Sequence analysis revealed the first Copia centromeric retrotransposon in macro-fungi genome. Phylogenomic analysis identified that G. leucocontextum and G. tsugae diverged from the other Ganoderma species approximately 9.8–17.9 MYA. The prediction of secondary metabolic clusters confirmed the capability of this fungus to produce a substantial quantity of metabolites. This T2T gap-free genome will contribute to the genomic ‘dark matter’ elucidation and server as a great reference for genetics, genomics, and evolutionary studies of G. leucocontextum.

Abstract Background Microbial electricity production has received considerable attention from researchers due to its environmental friendliness and low price. The increase in the number of intracellular electrons in a microbial fuel cell (MFC) helps to improve the MFC performance. Results In this study, we accumulated excess electrons intracellularly by knocking out the gene related to intracellular electron consumption in Saccharomyces cerevisiae, and the elevated intracellular electron pool positively influenced the performances of MFCs in terms of electricity production, while helping to increase ethanol production and achieve ethanol and electricity co-production, which in turn improved the utilization of substrates. The final knockout strain reached a maximum ethanol yield of 7.71 g/L and a maximum power density of 240 mW/m2 in the MFC, which was 12 times higher than that of the control bacteria, with a 17.3% increase in energy utilization. Conclusions The knockdown of intracellular electron-consuming genes reported here allowed the accumulation of excess electrons in cells, and the elevated intracellular electron pool positively influenced the electrical production performance of the MFC. Furthermore, by knocking out the intracellular metabolic pathway, the yield of ethanol could be increased, and co-production of ethanol and electricity could be achieved. Thus, the MFC improved the utilization of the substrate.

Jurassic strata in the ST gas field of the northern West Siberia Basin have been regarded as a potential exploration target with undiscovered hydrocarbon resources. However limited research has been performed on the sequence stratigraphy of the Jurassic strata, as well as its sandstone distribution controlled by variable sea level change and sediment input. In this paper, four third-order sequences (SQ1, SQ2, SQ3, and SQ4) and nine fourth-order sequences for the Jurassic strata are interpreted based on seismic facies analysis and the lithology stacking patterns of seven wells. SQ1 is characterized by the special Bazhenov Formation which is featured by regionally distributed deep marine shales. SQ2 (J1) is composed of a coarsening upward sequence, the base of which is an unconformable surface that can be recognizable in both seismic profiles and well logging data. SQ3 (J2-J8) is composed of a complete fining-upward and coarsening-upward sequence, showing a series of transgressive and regressive successions. A complete SQ4 has not been drilled through by all the seven wells, only showing a coarsening upward succession on its top (J9) which evolves into a fining upward succession at the base of SQ3. Combined with the seismic inversion result, which predicts sandstone distribution, a sequence evolution model was built for SQ3 showing a full unit of transgressive system tract and highstand system tract (TST-HST) which often occurs in shallow marine shelves. During sequence development, most reservoir sandstones are deposited in the shelf and tidal delta environment at the bottom of the TST and the top of HST, and mudstones are deposited as shelf mudstones, especially at maximum flooding surface. That is controlled by both accommodation and sediment input. Generally, under this sequence framework, the depositional architecture can be further analyzed with implications for source rock, reservoir sandstones, and sealing rock, which may guide future gas exploration and exploitation in this area.

For the automatic target recognition (ATR) based on synthetic aperture radar (SAR) images, enough training data are required to effectively characterize target features and obtain good recognition performance. However, in practical applications, it is difficult to collect sufficient training data. To tackle the limitation, a novel end-to-end expansion method, called conditional Wasserstein deep convolutional generative adversarial network with gradient penalty (CWDCGAN), is proposed to achieve SAR image expansion with specified category. To be specific, the CWDCGAN innovatively designed a generative adversarial network architecture based on convolutional and deconvolution networks to improve the quality of generated images. At the same time, conditional information is introduced to control the categories of generated images, and Wasserstein distance and gradient penalty are used to modify the loss function, which makes the network training more stable. Besides, feature extraction and classifier design in a typical ATR system often rely heavily on subjective expert knowledge, which seriously affects its generalization performance. Therefore, a joint recognition method of Resnet18 and support vector machine (Renset18-SVM) is adopted to improve the generalization capacity and the recognition performance. Experimental results with public measured data show that the CWDCGAN can generate higher quality SAR images, and by feeding expanded data to Renset18-SVM, the recognition accuracy is improved under different proportions of training samples.

A recognition method combining Cameron decomposition and fusing Reduced Kernel Extreme Learning Machine (RKELM) is proposed for the Full Polarimetric (FP) High Resolution Range Profile (HRRP)-based radar target recognition task. In the feature extraction phase, Cameron decomposition is exploited to define the projection component of the target on the standard scatterers. Through analysis, the projection components on three scattering bases, i.e., trihedral, dihedral, and 1/4 wave device, are selected as target features, which achieve more detailed descriptions of the target characteristics. In the classification phase, considering the instability of the recognition performance of the RKELM algorithm, the RKELM based on prototype clustering preprocessing is first proposed. Then, to improve the recognition performance, we proposed the feature level fusing RKELM and the decision level fusing RKELM to fuse the three projection components of the targets. The experiments compared the performance of the proposed recognition method and the state-of-the-art methods using the FP HRRP data from 10 civilian vehicles. The results demonstrate that the projection features by Cameron decomposition exhibit higher separability and better noise robustness, and that the feature level fusing RKELM has better generalization performance with a large number of training samples, but the decision level fusing RKELM was better with a small number of training samples.

Axin1 is a key regulator of canonical Wnt signaling pathway. Roles of Axin1 in skeletal development and in disease occurrence have not been fully defined. Here, we report that Axin1 is essential for lower limb development. Specific deletion of Axin1 in limb mesenchymal cells leads to fibular hemimelia (FH)-like phenotype, associated with tarsal coalition. Further studies demonstrate that FH disease is associated with additional defects in Axin1 knockout (KO) mice, including decreased osteoclast formation and defects in angiogenesis. We then provide in vivo evidence showing that Axin1 controls limb development through both canonical β-catenin and BMP signaling pathways. We demonstrate that inhibition of β-catenin or BMP signaling could significantly reverse the FH phenotype in mice. Together, our findings reveal that integration of β-catenin and BMP signaling by Axin1 is required for lower limb development. Defect in Axin1 signaling could lead to the development of FH disease.

BackgroundTo curb the spread of the coronavirus disease 2019 (COVID-19) epidemic, the Chinese government shut down Wuhan city from January 23rd to April 8th, 2020. The COVID-19 epidemic not only leads to widespread illness but also affects the diagnosis and treatment of hematopoietic stem-cell transplant (HSCT) recipients.ObjectiveTo investigate the medical-seeking pattern and daily behavior changes in Hubei Province during the COVID-19 epidemic in Hubei Province during the lockdown.MethodsWe conducted a multicenter, cross-sectional, web-based investigation among 325 HSCT recipients by online questionnaires in Hubei Province during the COVID-19 epidemic.ResultsA total of 145 complete responses were collected both before and during the epidemic questionnaires. The participants from pre-epidemic group preferred to go to hospital (68.29%) when they experienced influenza-like symptoms. The majority of the patients elected to take oral drugs by themselves (40%) or consulted their attending physicians online or by telephone during the lockdown (23.33%). 64.83% had difficulties in purchasing drugs during the lockdown, which was significantly higher than the proportion of the pre-epidemic group (24.83%) (P < 0.05). The participants preferred to purchase drugs online (23.40%) and decrease or withdraw drugs (18.09%) during the epidemic. The number of participants received regular re-examinations during the epidemic decreased sharply. The proportion of wearing masks and isolating themselves at home increased significantly during the epidemic. No statistic difference was observed in the incidence of graft-versus-host disease (GVHD)complications in participants between the during the epidemic group and the pre-epidemic group. In our study, six patients were confirmed to have COVID-19, and half of them died due to COVID-19-related complications.ConclusionThe medical-seeking pattern and daily behavior of HSCT recipients changed during the lockdown; the methods of self-protection, online consultation and drug delivery can help patients receive necessary follow-up and reduce the occurrence of COVID-19.

General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods perform well under standard operation conditions (SOCs). However, they are not effective in extended operation conditions (EOCs). To improve the robustness of the ATR system under various EOCs, an ATR method for SAR images based on the scattering parameter Gaussian mixture model (GMM) is proposed in this paper. First, an improved active contour model (ACM) is used for target–background segmentation, which is more robust against noise than the constant false alarm rate (CFAR) method. Then, as the extracted attributed scattering center (ASC) is sensitive to noise and resolution, the GMM is constructed using the extracted ASC set. Next, the weighted Gaussian quadratic form distance (WGQFD) is adopted to measure the similarity of GMMs for the recognition task, thereby avoiding false alarms and missed alarms caused by the varying number of scattering centers. Moreover, adaptive aspect–frame division is employed to reduce the number of templates and improve recognition efficiency. Finally, based on the public measured MSTAR dataset, different EOCs are constructed under noise, resolution change, model change, depression angle change, and occlusion of different proportions. The experimental results under different EOCs demonstrate that the proposed method exhibits excellent robustness while maintaining low computation time.

Abstract Accumulated evidence shows that OGT-mediated O-GlcNAcylation plays an important role in response to DNA damage repair. However, it is unclear if the “eraser” O-GlcNAcase (OGA) participates in this cellular process. Here, we examined the molecular mechanisms and biological functions of OGA in DNA damage repair, and found that OGA was recruited to the sites of DNA damage and mediated deglycosylation following DNA damage. The recruitment of OGA to DNA lesions is mediated by O-GlcNAcylation events. Moreover, we have dissected OGA using deletion mutants and found that C-terminal truncated OGA including the pseudo HAT domain was required for the recruitment of OGA to DNA lesions. Using unbiased protein affinity purification, we found that the pseudo HAT domain was associated with DNA repair factors including NONO and the Ku70/80 complex. Following DNA damage, both NONO and the Ku70/80 complex were O-GlcNAcylated by OGT. The pseudo HAT domain was required to recognize NONO and the Ku70/80 complex for their deglycosylation. Suppression of the deglycosylation prolonged the retention of NONO at DNA lesions and delayed NONO degradation on the chromatin, which impaired non-homologus end joining (NHEJ). Collectively, our study reveals that OGA-mediated deglycosylation plays a key role in DNA damage repair.

Spasticity after brain injury is a neurological sequela caused by damage to upper motor neurons. The primary symptoms are involuntary muscle activity, decreased muscle strength, and joint contracture. Acupuncture as a therapeutic method to regulate central nervous system function has been studied widely in recent years. Many clinical experiments have proved that acupuncture has positive effects on spasticity after brain injury. In this review, we discuss recent research of acupuncture treatment and the need for large randomized controlled trials.

Soil microorganisms play vital roles in ecosystem functions, and soil microbial communities might be affected by heavy metal contamination caused by the anthropogenic activities associated with the coal-based industry. This study explored the effects of heavy metal contamination on soil bacterial and fungal communities surrounding different coal-based industrial fields (the coal mining industry, coal preparation industry, coal-based chemical industry, and coal-fired power industry) in Shanxi province, North China. Moreover, soil samples from farmland and parks away from all the industrial plants were collected as references. The results showed that the concentrations of most heavy metals were greater than the local background values, particularly for arsenic (As), lead (Pb), cadmium (Cd), and mercury (Hg). There were significant differences in soil cellulase and alkaline phosphatase activities among sampling fields. The composition, diversity, and abundance of soil microbial communities among all sampling fields were significantly different, particularly for the fungal community. Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria were the predominant bacterial phyla, while Ascomycota, Mortierellomycota, and Basidiomycota dominated the studied fungal community in this coal-based industrially intensive region. A redundancy analysis, variance partitioning analysis, and Spearman correlation analysis revealed that the soil microbial community structure was significantly affected by Cd, total carbon, total nitrogen, and alkaline phosphatase activity. This study profiles the basic features of the soil physicochemical properties, the multiple heavy metal concentrations, and the microbial communities in a coal-based industrial region in North China.

Supercritical water gasification (SCWG) of coal is a promising clean coal technology, which discards the traditional coal combustion and oxidation reaction to release carbon dioxide and other pollutants and replaces coal with a gasification reduction reaction in supercritical water to finally convert coal into a hydrogen-rich gas product with no net carbon dioxide emissions and no pollutant emissions, and thus has received much attention in recent years. However, the experimental conditions of coal to the hydrogen reactor are harsh, costly, and not easy to visualize and analyze, so numerical calculation and simulation analysis are important for the design, optimization, and industrial scaling-up of the reactor. In order to study the effect of the temperature field on the hydrogen production rate of the coal supercritical water gasification hydrogen production reactor, a numerical simulation calculation model is developed for this reactor in this paper. Comparing the experimental data in the literature, the maximum relative error of the gasification product yield per kg of coal between the two is less than 5%, which verifies the accuracy of the model built and the numerical method adopted in this paper. On this basis, the effects of supercritical water temperature and coal slurry temperature on the reactor’s gasification products and reaction rate were investigated in depth. The results show that increasing the supercritical water temperature is beneficial to improve the reactor hydrogen production efficiency, while the high coal slurry temperature is not conducive to adequate reaction, thus reducing the hydrogen production efficiency. For the laboratory coal supercritical water gasification to hydrogen reactor studied in this paper, the ideal temperature of supercritical water is 850~900 K, and the ideal temperature of coal slurry is 400–450 K. The conclusions of this paper can provide some reference for subsequent industrial scale-up studies of the reactor.

New CMOS imaging sensor (CIS) techniques in smartphones have helped user-generated content dominate our lives over traditional DSLRs. However, tiny sensor sizes and fixed focal lengths also lead to more grainy details, especially for zoom photos. Moreover, multi-frame stacking and post-sharpening algorithms would produce zigzag textures and over-sharpened appearances, for which traditional image-quality metrics may over-estimate. To solve this problem, a real-world zoom photo database is first constructed in this paper, which includes 900 tele-photos from 20 different mobile sensors and ISPs. Then we propose a novel no-reference zoom quality metric which incorporates the traditional estimation of sharpness and the concept of image naturalness. More specifically, for the measurement of image sharpness, we are the first to combine the total energy of the predicted gradient image with the entropy of the residual term under the framework of free-energy theory. To further compensate for the influence of over-sharpening effect and other artifacts, a set of model parameters of mean subtracted contrast normalized (MSCN) coefficients are utilized as the natural statistics representatives. Finally, these two measures are combined linearly. Experimental results on the zoom photo database demonstrate that our quality metric can achieve SROCC and PLCC over 0.91, while the performance of single sharpness or naturalness index is around 0.85. Moreover, compared with the best tested general-purpose and sharpness models, our zoom metric outperforms them by 0.072 and 0.064 in SROCC, respectively.

Abstract Background The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. Results By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the outbreak scale. Specifically, we collected the historical information of clinic visits caused by HFMD infection between years 2009 and 2018 respectively from public records of Tokyo, Hokkaido, and Osaka, Japan. When applied to the city networks we modelled, our method successfully identified pre-outbreak signals in an average 5 weeks ahead of the HFMD outbreak. Moreover, from the performance comparisons with other methods, it is seen that the L-DNM based system performs better when given only the records of clinic visits. Conclusions The study on the dynamical changes of clinic visits in local district networks reveals the dynamic or landscapes of HFMD spread at the network level. Moreover, the results of this study can be used as quantitative references for disease control during the HFMD outbreak seasons.

Intra prediction is the key technology to reduce spatial redundancy in the modern video coding standard. Recently, deep learning based methods that directly generate the intra prediction by neural network achieve superior performance than traditional directional based intra prediction. However, these methods lack the ability to handle complex blocks which contain mixed directional textures or recurrent patterns since they only use the neighboring reference samples of the current one. The other intermediate information denoted as reference priors in this paper generated during the coding process is not exploited. In this paper, a Current Frame Priors assisted Neural Network (CFPNN) is presented to improve the intra prediction efficiency. Specifically, we utilize the local contextual information provided by the neighboring multiple references as the primary inference source. In addition to the neighboring references, we additionally use the other two reference priors within the current frame – the predictor searched by intra block copy (IntraBC) and the corresponding residual component. The IntraBC predictor provides useful nonlocal information to help generate more accurate prediction for complex blocks together with neighboring local information. While the residual component contains unique information that reflects the characteristics of the block to some extent is utilized to reduce the noise contained in the reconstructed reference samples. Moreover, we investigate the best way to integrate the proposed method into the codec. Experimental results demonstrate that compared to HEVC, our proposed CFPNN achieves an average of 4.1% BD-rate reduction for the luma component under the All Intra configuration.

ObjectiveTo investigate the clinical effects of time window on hyperbaric oxygen treatment (HBOT) in patients with disorders of consciousness (DOC).MethodsAll the clinical research literature regarding HBOT for DOC published between January 2000 and November 2020 were retrieved from China National Knowledge Infrastructure (CNKI), Wanfang Standards Database (WFSD) and VIP Database using Chinese key words disorders of consciousness, the vegetable state, minimally conscious state, or hyperbaric oxygen followed by a comprehensive meta-analysis.ResultsThe query gave rise to 348 results, in which 21 articles were eventually selected for meta-analysis. Among the selected 21 articles, 18 articles involved a time window comparison. All the patients were classified into Z = 11.28, P = 0.000, odds ratio = 3.80, 95% CI = 3.02-4.80). Additionally, the adverse reaction rate of the Z = 10.01, P = 0.000, odds ratio = 4.82, 95% CI = 3.54-6.56). The funnel diagram in articles related to curative effect analysis and time window evaluation is inverted and symmetrical, indicating that publication bias was not significant.ConclusionsThe clinical curative effect of the HBOT group is higher compared with that of the control group. However, the conclusions based on meta-analysis are limited because of the methodological problems of some studies. Therefore, the clinical efficacy needs to be further tested using carefully designed large sample trials (multicenter, randomized, controlled, and double-blind).

BackgroundPrevious studies have shown that estrogen, kidney function, and homocysteine (Hcy) or hyperhomocysteinemia (HHcy) are related to each other. However, the underlying biological mechanisms still remain unclear. We aimed to explore the association between estradiol (E2) and HHcy in the female population, and to further evaluate the mediating role of renal function indicators.MethodsThis unmatched case–control study consisted of 1,044 female participants who were 60.60 ± 12.46 years old. Data on general demographic characteristics, such as age, smoking and drinking status, menopause and so on were collected in a personal interview, and laboratory examinations were performed by well-trained personnel. The mediating effect model was applied to analyze the direct and indirect effects of E2 on Hcy.ResultsThe average levels of Hcy and E2 of the participants were 12.6 μmol/L and 14.95 pg/ml. There were statistical differences in renal indexes blood urea nitrogen (BUN), serum creatinine (Scr), uric acid (UA), glomerular filtration rate (GFR) and E2 between HHcy group and non-HHcy group. The logistic regression models showed that UA was risk factor for HHcy (P

Background The aim of the study was to identify biomarkers that can facilitate early diagnosis and treatment of fulminant myocarditis (FM) in order to reduce mortality. Methods and Results First, the expression profiles of circulating cytokines were determined in the plasma samples from 4 patients with FM and 4 controls using human cytokine arrays. The results showed that 39 cytokines from patients with FM were changed at admission. Among them, 8 cytokines returned to normal levels at discharge, including soluble ST2 (sST2), which showed the most marked dynamic changes from disease onset to resolution. Then, in a cohort of 76 patients with FM, 57 patients with acute hemodynamic dysfunction attributable to other causes, and 56 patients with non‐FM, receiver operating characteristic curve analyses suggested that plasma sST2 level was able to differentiate FM from non‐FM or other FM‐unrelated acute heart failure more robustly N‐terminal pro‐B‐type natriuretic peptide or cardiac troponin I. Moreover, longitudinal analysis of plasma sST2 was performed in 10 patients with FM during hospitalization and 16 patients with FM during follow‐up. Finally, the diagnostic value was validated in an additional 26 patients with acute onset of unstable hemodynamics. The cutoff value of plasma sST2 for optimal diagnosis of FM was established at 58.39 ng/mL, where a sensitivity of 85.7% and specificity of 94.7% were achieved. Conclusions Elevated sST2 level was associated with mechanical stress or inflammation. Especially, sST2 might be used as a potential biomarker for the rapid diagnosis of FM, which was characterized by strong mechanical stretch stimulation and severe inflammatory response. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03268642.

Abstract Background The prognostic value of Neutrophil-to-Lymphocyte Ratio (NLR) for the outcome of acute cervical traumatic spinal cord injury (tSCI) patients has rarely been studied by now throughout the world. Methods We performed a single-center retrospective cohort study to evaluate the prognostic value of NLR from peripheral whole blood count in patients with acute cervical tSCI. Patients within 6 h of acute cervical tSCI treated between Dec 2008 and May 2018 in Huashan Hospital of Fudan University were enrolled. Outcomes of patients with tSCI were assessed using American spinal injury association Impairment Scale (AIS). 6-month outcomes were dichotomized into poor outcome group (AIS A to C) and good outcome group (AIS D and E). Uni- and multivariate analyses were performed to assess the independent predictors of 6-month outcome. Two prediction models based on admission characteristics were built to evaluate the prognostic value of NLR. The discriminative ability of predictive models was evaluated using the area under the curve (AUC). Results A total of 377 patients were identified from our single center in China PR. Multivariate analysis showed that age, AIS grade at admission, NLR (p

Background: Viral myocarditis could initiate various immune response to the myocardium, resulting in myocyte damage and subsequent cardiac dysfunction. The expression profile and functions of circRNAs in this process are unknown.Methods: Fulminant myocarditis (FM) and non-FM models were induced by coxsackie B3 virus (CVB3) infection in A/J mice and C57BL/6 mice, respectively. CircRNAs expression profile was identified by RNA-seq. Quantitative RT-PCR, Spearman rank correlation, KEGG pathway, GO analysis, Western blot and flow cytometry were performed for functional analysis.Results: Severer inflammatory cell infiltration and cardiomyocyte necrosis were presented in CVB3-treated A/J mice than those in C57BL/6 mice. The dysregulated circRNAs in both of the mouse strains displayed strong correlation with the immune response, but dysregulated circRNAs in A/J mice were more prone to cardiac dysfunction. KEGG analysis indicated that the target genes of dysregulated circRNAs in A/J mice were mainly involved in viral infection, T cell and B cell receptor signaling pathways, while the target genes of dysregulated circRNAs in C57BL/6 mice were unrelated to immune pathways. Furthermore, knockdown of circArhgap32 that was downregulated in CVB3-treated A/J mice promoted cardiomyocyte apoptosis in vitro.Conclusion: Our data showed that cardiac circRNAs dysregulation is an important characteristic of viral myocarditis.

Supercritical water fluidized beds (SCWFBs) are promising and efficient reactors for the gasification of coal in supercritical water. The understanding and investigation of multi-phase flows as well as the gasification process usually rely on time-consuming experiments or numerical simulations, which prohibit fast and full exploration of the single and coupled effects of the operation and geometric parameters. To this end, this paper builds an efficient surrogate-assisted parameter analysis framework for the SCWFB reactor. Particularly, (1) it establishes a steady numerical simulation model of the SCWFB reactor for the subsequent analysis; and (2) it employs a Gaussian process surrogate modeling via efficient adaptive sampling to serve as an approximation for predicting the carbon conversion efficiency (CE) of the reactor. Based on this parameter analysis framework, this paper investigates the effects of five independent parameters (the mass flow rate of supercritical water, mass flow rate of the coal slurry, temperature of supercritical water, temperature of the outer wall and reactor length) and their interactions on the reaction performance in terms of the carbon conversion efficiency (CE). We found that the CE increases as a function of the temperature of supercritical water, the temperature of the outer wall and the reactor length; while it decreases as a function of the mass flow rate of supercritical water and the mass flow rate of the coal slurry. Additionally, the global sensitivity analysis demonstratesthat the influence of the temperature of the outer wall exerts a stronger effect than all the other factors on the CE, and the coupled interaction among parameters has a slight effect on the CE. This research provides useful guidance for scaled-up designs and optimization of the SCWFB reactor.

The development of MFC using electroactive industrial microorganisms has seen a surge of interest because of the co-generation for bioproduct and electricity production. Vibrio natriegens as a promising next-generation industrial microorganism chassis and its application for microbial fuel cells (MFC) was first studied. Mediated electron transfer was found in V. natriegens MFC (VMFC), but V. natriegens cannot secrete sufficient electron mediators to transfer electrons to the anode. All seven electron mediators supplemented are capable of improving the electronic transfer efficiency of VMFC. The media and carbon sources switching study reveals that VMFCs have excellent bioelectricity generation performance with feedstock flexibility and high salt-tolerance. Among them, 1% glycerol as the sole carbon source produced the highest power density of 111.9 ± 6.7 mW/cm2. The insight of the endogenous electronic mediators found that phenazine-1-carboxamide, phenazine-1-carboxylic acid, and 1-hydroxyphenazine are synthesized by V. natriegens via the shikimate pathway and the phenazine synthesis and modification pathways. This work provides the first proof for emerging industrial biotechnology chassis V. natriegens as a novel high salt-tolerant and feedstock flexibility electroactive microorganism for MFC, and giving insight into the endogenous electron mediator biosynthesis of VMFC, paving the way for the application of V. natriegens in MFC and even microbial electrofermentation (EF).

Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods.

Although the interactions between biotic and geomorphic processes usually occur on small spatial and short temporal scales, many of the mechanisms remain to be investigated. This study provides the first direct evidence of the interaction between biotic burrowing and loess cave formation in the Chinese Loess Plateau (CLP). The study area is the Qingshui Valley in the western CLP, near Lanzhou. We surveyed the target site (with an area of ∼13,367 m2) four times from Jul 2019 to Dec 2020, using an unmanned aerial vehicle (UAV). High resolution UAV images enabled us to determine the temporal and spatial dynamics of biotic burrowing and loess caves. The results show that loess caves tended to develop down valley below collapses, while animal burrows were preferentially located upslope away from collapses. Despite the distinct “topographic niches” for both biotic and abiotic processes, we observed an interaction between the two processes in space when tracking their temporal dynamics. Three out of seven new loess caves were in the process of formation at typical “topographic niches” of animal burrows and there was a significantly high animal burrow density around these three caves before their initiation. These results indicate that the three caves were directly initiated from animal burrows and/or developed under the influence of biotic activities. Therefore, biotic burrowing promotes the spatial heterogeneity of loess cave distribution. We also found significant decreases in animal burrow density surrounding the newly-formed loess caves after their initiation. This may reflect a risk avoidance strategy of animal burrowing, which causes animals to avoid areas of recent mass movement (i.e., collapses and new caves). The formation and expansion of loess caves can dictate the distribution of active areas of biotic disturbance. Our results demonstrate a clear interaction between biotic burrowing and loess cave formation, and they emphasize the role of biological agents as a mechanism for the formation of loess caves, which enrich the understanding of searching fingerprints of life during landscape evolution.

Glioma remains the toughest brain tumor among all primary central nervous system (CNS) tumors [...]

Summary: The COVID-19 pandemic has caused over 220 million infections and 4.5 million deaths worldwide. Current risk factor cannot fully explain the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients' laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory traits and used Mendelian randomization (MR) analysis to further investigate the causality of traits on disease severity. Two causal traits, WBC counts and cholesterol levels, were identified based on MR study, and their functional genes are located at genes MHC complex and ApoE, respectively. Our gene-based analysis and GSEA revealed four interferon pathways, including type I interferon receptor binding and SARS coronavirus and innate immunity. We hope that our work will contribute to studying the genetic mechanisms of disease and serve as a useful reference for COVID-19 diagnosis and treatment.

Abstract Background Most colorectal cancer (CRC) patients are diagnosed at an advanced or metastatic stage with poor prognosis. Ubiquitin-specific protease 6 N-terminal-like protein (USP6NL) with high expression in CRC tissues regulates CRC cell proliferation via Wnt/β-catenin pathway. We hypothesized that USP6NL impacts CRC growth and inhibition of USP6NL may be a novel treatment strategy to improve CRC therapy. Methods USP6NL level in human CRC tissues and its association with tumor growth and metastasis were examined. Its roles and potential mechanisms in regulating tumor growth were studied by genetic and pharmacological manipulation of CRC cells in vitro and in vivo. Results Herein, we found that USP6NL was up-regulated in tumorous tissues of CRC patients. Our data suggested that knockdown of USP6NL in human CRC cell lines (HCT116 and LOVO cells) inhibited cell proliferation, induced G0/G1 cell cycle arrest, and prevented the tumorigenicity of HCT116 cells in nude mice, and which was associated with the prevention of Wnt/β-catenin pathway. On the contrary, USP6NL overexpression in human CRC cells (SW480) showed the opposite result. Our data suggested that the promoted cell proliferation, G1/S cell cycle progression, and the enhanced expression of β-catenin Cyclin D1 and C-myc while reduced P27 induced by the overexpression of USP6NL were significantly reversed by additional treatment of XAV939, indicating that activating Wnt/β-catenin pathway was the mechanism, by which USP6NL exerted carcinogenesis in CRC in vitro. Besides, our data suggested that knockdown of USP6NL increased the ubiquitination of β-catenin, indicating that USP6NL may serve as a deubiquitinase that regulated β-catenin accumulation in this process. Furthermore, 10058-F4 down-regulated USP6NL, inhibited CRC cell proliferation and induced cell cycle arrest. The result demonstrated a possible feedback loop between USP6NL, β-catenin and C-myc in regulating CRC cell growth. Conclusion USP6NL was an oncogene in CRC, and it may be a potential target for the treatment of CRC.

A forward-biased silicon-based light-emitting device (Si-LED) was designed and fabricated by standard 0.18 μm complementary metal-oxide-semiconductor (CMOS) technology without any modification. For the Si-LED, wedge-shaped electrodes and needle-like tip were designed to enhance the intensity of electric field. When the Si-LED works at high forward current, the output optical power increases rapidly with the increase of forward current, which is a non-linear growth similar to exponential. To the best of our knowledge, such a phenomenon has never been reported before. Moreover, when the forward current is at 200 mA, the optical power density reaches 32.7 nW·μm 2, which is two or three orders higher than that of other ever reported forward-biased Si-LED fabricated by standard CMOS technology. In addition, the red shift of the main peak in the spectra of Si-LED was observed and analyzed, and also the light-emission mechanism of each peak in the spectra was given.

In this paper, a fault-tolerant synchronization control (FTSC) algorithm is proposed to deal with the position tracking control problem for a dual redundant hydraulic actuation system (DRHAS) working on active/active (A/A) mode and suffering from internal leakage faults, large disturbances, and force fighting between actuators. Specifically, two reference trajectories are introduced and a novel nonlinear model for the DRHAS is developed to facilitate the synthesis of position tracking control and force synchronization control. Then, a nonlinear FTSC algorithm is proposed by incorporating adaptive control and disturbance rejection control into the backstepping design. In which, a simple reconfiguration mechanism based on faulty parameters online adaptation is adopted to accommodate the faults. The matched and unmatched disturbances in both actuators are estimated by constructing four extended state observers (ESOs) and are compensated in a feedforward way. The stability analysis indicates that the proposed control algorithm can ensure prescribed tracking performance for the system under internal leakage faults and time-varying disturbances, and can make the tracking error of the faulty system converge to zero asymptotically under constant disturbances. Finally, the effectiveness of the proposed control algorithm is verified through comparative simulations.

Background: We aim to examine the relationship between age at menarche and hyperhomocysteinemia in women in Hunan Province. Methods: Participants were required to complete a questionnaire that included age at menarche, lifestyle habits, other baseline information, and blood biochemical parameters in a cross-sectional study. The association between hyperhomocysteinemia and age at menarche was examined by Multivariable adjusted logistic regression. Results: A cohort of 2008 women with a mean age of 60.11 years (aged from 18.0 to 88.0 years) was included in this study. After adjustment for confounding factors such as age, the results showed that the risk of hyperhomocysteinemia among women whose age at menarche were over 16 years was 2.543 (1.849, 3.469) times higher than the risk among women whose age at atmenarche were less than 14 years, and 2.656 (1.882, 3.748) times more likely to have hypertension than women with menarche at 14 years. Besides, the odds ratios of diabetes, hyperlipidemia, and obesity were elevated in women older than 16 years of age at menarche (OR = 1.924, p < 0.001; OR = 1.491, p = 0.014; OR = 1.670, p = 0.022). Conclusions: Our findings suggest that late menarche tends to be associated with a high risk of hyperhomocysteinemia and its associated set of diseases such as hypertension, diabetes, hyperlipidemia, and obesity in women in Hunan, China. This association tends to differ across birth cohorts. Therefore, adequate attention of menarcheal age may be able to predict diseases in elderly females.

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method.

We report a case of coronavirus disease 2019 (COVID-19) after haploidentical transplantation with acute graft-versus-host disease (aGVHD). COVID-19 and aGVHD were improved under treatment with arbidol, remdesivir, methylprednisolone, and ruxolitinib. However, eventually, the patient died of septic shock and multiple organ failure. It was concluded that the disease condition of this COVID-19 patient after transplantation was serious, complex, and variable, with poor prognosis.

Moving target imaging in high-squint synthetic aperture radar (SAR) shows great potential for reconnaissance and surveillance tasks. For the desired resolution, high-squint SAR has a long-time coherent processing interval (CPI). In this case, the maneuvering motion of the moving target usually causes high-order phase terms in the echoed data, which cannot be neglected for precise focusing. Many ground moving target imaging (GMTIm) algorithms have been proposed in the literature, but some high-order phase terms remain uncompensated in high-squint SAR. For this problem, a high-order phase correction-based GMTIm (HPC-GMTIm) method is proposed in this paper. We assumed that the target of interest has a constant velocity in the subaperture CPI, but maneuvering motion parameters for the whole CPI. Within the short subaperture CPI, the target signal can be simplified as a three-order phase expression, and the instantaneous Doppler frequency (DF) was estimated by some time–frequency analysis tools, including the Hough transform and the fractional Fourier transform. For the whole CPI, the subaperture, the instantaneous DF was combined to form a total least-squares problem, outputting the undetermined phase coefficients. Using the proposed local-to-global processing chain, all high-order phase terms can be estimated and corrected, which outperforms existing methods. The effectiveness of the HPC-GMTIm method is demonstrated by real measured high-squint SAR data.

Triple negative breast cancer (TNBC) is characterized by lack of expression of the estrogen and progesterone receptors and HER2, which are common therapeutic targets. CDK4/6 inhibitor Palbociclib has been approved as an anti-cancer agent for breast cancer. However, identifying biomarkers that predict the response to Palbociclib has always been a challenge for molecular targeted therapy. In this study, we identify microRNA as a hallmark in TNBC patients and explore if miR-3613-3p might serve as a tumor suppressor biomarker for triple negative breast cancer patients and if overexpression of miR-3613-3p could enhance the sensitivity of TNBC cells to Palbociclib. We show that the expression of miR3613-3p was down-regulated in TNBC tumors and cells, and the overexpression of miR-3613-3p in patients’ tumor tissues was clinically and pathologically correlated with favorable prognosis, such as smaller tumor size and the lower Ki-67. In vitro, overexpression of miR-3613-3p inhibited cell proliferation, induced G1 cell-cycle arrest, and enhanced the sensitivity of TNBC cells to Palbociclib treatment. In vivo study revealed that overexpression of miR-3613-3p inhibited TNBC tumorigenesis and exerted a significant inhibitory effect of Palbociclib on MDA-MB-231 cells. Mechanically, SMAD2 and EZH2 were found to be two direct targets of miR-3613-3p and mediate the proliferation of TNBC cells and the sensitivity of the cells to Palbociclib through inducing cellular senescence. Our findings suggested that miR-3613-3p acts as a cancer-suppressor miRNA in TNBC. Moreover, our study showed that miR-3613-3p might be used as a predictive biomarker for the response of TNBC to Palbociclib.