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In this study, both adaptive and robust one is proposed, considering that the properties of a classic unscented Kalman filter (UKF) can be degraded severely by the outliers measured in the contamination distribution and the effects of time-varying noise. An adaptive and robust UKF approach on Gaussian process regression- assisted Variational Bayesian is proposed. The Variable Bayesian (VB) method is used to statistically approximate the time-varying noise due to the strong nonlinearity and inaccuracy of the system model. At the same time, Gaussian Process Regression (GPR) has the advantage of machine learning. The observation information trained by the GPR model performs real-time prediction of the sliding window. Based on the above two points, the output source of the measurement information is estimated to realize the robustness of the filter measurement update. When the noise is uncertain and there are outliers in the measurement information, simulation and SINS/GPS integrated navigation of actual tests were performed, respectively. The test outcomes indicate that the algorithm has better nonlinear estimation performance than the traditional method, the effectiveness of the filtering algorithm proposed is verified.

In this paper, a uniform form for the nonlinear error model is proposed for the inertial navigation system (INS). A modified quaternion-based nonlinear error model is derived using the uniform coordinate frame for the INS. Based on the modified quaternion-based nonlinear error model, a uniform form of the nonlinear error model is proposed based on the Gibbs parameter. The uniform form can present the nonlinear error model based on low-order Rodrigues parameters or Rodrigues-like parameters for the INS. Field tests are carried out to assess the performance of the existing typical nonlinear error models.

Silicon-based cross-coupling has been recognized as one of the most reliable alternatives for constructing carbon–carbon bonds. However, the employment of such reaction as an efficient ring expansion strategy for silacycle synthesis is comparatively little known. Herein, we develop the first intermolecular silacyclization strategy involving Pd-catalyzed silicon-based C(sp2)–C(sp3) cross-coupling. This method allows the modular assembly of a vast array of structurally novel and interesting sila-benzo[b]oxepines with good functional group tolerance. The key to success for this reaction is that silicon atoms have a stronger affinity for oxygen nucleophiles than carbon nucleophiles, and silacyclobutanes (SCBs) have inherent ring-strain-release Lewis acidity., Herein, we develop the first silacyclization between 2-halophenols and SCBs, which allows the modular assembly of sila-benzo[b]oxepines with good functional group tolerance and can be applied for the late-stage modification of biologically active molecules.

In this paper, a modified quaternion-based nonlinear error model is explored using three derivation methods. The modified quaternion-based nonlinear error model can be more reasonable than the traditional one due to the uniform frame problem, which is underlying and fundamental but is easy to overlook. Further, field tests for inertial integrated navigation (INS) are carried out to investigate the benefits and drawbacks of the existing nonlinear model or nonlinear error model based on Euler angles and quaternion for the INS

Genome-wide association studies (GWAS) have identified multiple genomic loci linked to blood cell traits, however understanding the biological relevance of these genetic loci has proven to be challenging. Here, we performed a transcriptome-wide association study (TWAS) integrating gene expression and splice junction usage in neutrophils (N = 196) with a neutrophil count GWAS (N = 173,480 individuals). We identified a total of 174 TWAS-significant genes enriched in target genes of master transcription factors governing neutrophil specification. Knockout of a TWAS candidate at chromosome 5q13.2, TAF9, in CD34+ hematopoietic and progenitor cells (HSPCs) using CRISPR/Cas9 technology showed a significant effect on neutrophil production in vitro. In addition, we identified 89 unique genes significant only for splice junction usage, thus emphasizing the importance of alternative splicing beyond gene expression underlying granulopoiesis. Our results highlight the advantages of TWAS, followed by gene editing, to determine the functions of GWAS loci implicated in hematopoiesis., Yao Yao et al. perform transcriptome-wide association analysis through integration of gene expression data and results of a previously-published genome-wide association study for neutrophil count. They find 34 independent genomic regions associated with expression changes and show that knockout of one candidate gene, TAF9, in CD34+ progenitor cells increases neutrophil maturation in vitro.

Tebuconazole is an effective but potentially carcinogenic fungicide. Tebuconazole residues in loquat and sugarcane were investigated, together with an analysis of their dietary risk. Solid-phase extraction and high-performance liquid chromatography-tandem mass spectrometry showed fortified recoveries of 87% to 98%, with standard deviations ≤ 6.4%. The tebuconazole residues were below 6.34 mg kg

Code for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow projections under changing climatic conditions"

Complex and high levels of various pollutants in high-strength wastewaters hinder efficient and stable biological nutrient removal. In this study, the changes in pollutant removal performance and microbial community structure in a laboratory-scale anaerobic/aerobic sequencing batch reactor (SBR) treating simulated pre-fermented high-strength wastewater were investigated under different influent loading conditions. The results showed that when the influent chemical oxygen demand (COD), total nitrogen (TN), and orthophosphate (PO43−-P) concentrations in the SBR increased to 983, 56, and 20 mg/L, respectively, the COD removal efficiency was maintained above 85%, the TN removal efficiency was 64.5%, and the PO43−-P removal efficiency increased from 78.3% to 97.5%. Partial nitrification with simultaneous accumulation of ammonia (NH4+-N) and nitrite (NO2−-N) was observed, which may be related to the effect of high influent load on ammonia- and nitrite-oxidising bacteria. The biological phosphorus removal activity was higher when propionate was used as the carbon source instead of acetate. The relative abundance of glycogen accumulating organisms (GAOs) increased significantly with the increase in organic load, while Tetrasphaera was the consistently dominant polyphosphate accumulating organism (PAO) in the reactor. Under high organic loading conditions, there was no significant PAO–GAO competition in the reactor, thus the phosphorus removal performance was not affected.

Tissue-specific gene expression requires coordinated control of gene-proximal and -distal cis-regulatory elements (CREs), yet functional analysis of gene-distal CREs such as enhancers remains challenging. Here we describe CRISPR/dCas9-based enhancer-targeting epigenetic editing systems, enCRISPRa and enCRISPRi, for efficient analysis of enhancer function in situ and in vivo. Using dual effectors capable of re-writing enhancer-associated chromatin modifications, we show that enCRISPRa and enCRISPRi modulate gene transcription by remodeling local epigenetic landscapes at sgRNA-targeted enhancers and associated genes. Comparing with existing methods, the improved systems display more robust perturbations of enhancer activity and gene transcription with minimal off-targets. Allele-specific targeting of enCRISPRa to oncogenic TAL1 super-enhancer modulates TAL1 expression and cancer progression in xenotransplants. Single or multi-loci perturbations of lineage-specific enhancers using an enCRISPRi knock-in mouse establish in vivo evidence for lineage-restricted essentiality of developmental enhancers during hematopoiesis. Hence, enhancer-targeting CRISPR epigenetic editing provides opportunities for interrogating enhancer function in native biological contexts., Tissues-specific gene expression requires coordinated cis-regulatory elements. Here the authors use dCas9-based enhancer targeting to remodel local epigenetic landscapes and activate or inactive transcription.

Proteinprotein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying proteinprotein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of proteinprotein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify proteinprotein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights.

Motivation Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology. Results This article proposes a deep learning-based multi-objective computational approach, iHMnBS, to identify which of the seven typical histone modifications a DNA sequence may choose to bind, and which parts of the DNA sequence bind to them. iHMnBS employs a customized dataset that allows the marking of modifications contained in histones that may bind to any position in the DNA sequence. iHMnBS tries to mine the information implicit in this richer data by means of deep neural networks. In comprehensive comparisons, iHMnBS outperforms a baseline method, and the probability of binding to modified histones assigned to a representative nucleotide of a DNA sequence can serve as a reference for biological experiments. Since the interaction between transcription factors and histone modifications has an important role in gene expression, we extracted a number of sequence patterns that may bind to transcription factors, and explored their possible impact on disease. Availability and implementation The source code is available at https://github.com/lennylv/iHMnBS. Supplementary information Supplementary data are available at Bioinformatics online.

Background Phthalates have been widely used as plasticizers in various industries and are widely focused on in the international community as a result of their reproductive toxicity. Exposure of Chinese residents to phthalates via edible vegetable oil occurs often. In the present study, gas chromatography-mass spectrometry was used to detect the two main phthalates bis(2-ethylhexyl) phthalate (DEHP) and dibutyl phthalate (DBP) in four major edible vegetable oil sources: an edible oil blend, soybean oil, peanut oil and rapeseed oil (a total of 1016 samples), as collected throughout China. Furthermore, cumulative risk assessment was used to estimate the reproductive health risk to Chinese residents caused by the phthalates that come from edible vegetable oils. Results Both phthalates were detected in four major edible vegetable oil sources. The phthalate with the highest detection rate was DBP (13.48%), followed by DEHP (7.78%). The results of the cumulative risk assessment showed that the hazard indices of these two phthalates in edible vegetable oils were less than 1, except in soybean oil. Nevertheless, the two phthalates had the lowest detection rates in soybean oil, which were 1.94% (DEHP) and 5.16% (DBP). In China, contamination levels of phthalates in the soils where oil crops are cultivated have a great influence on the phthalate concentrations in edible vegetable oils. Conclusion It is recommended that Chinese residents who are consuming soybean oil choose well-known brands and regularly change their brand of consumption. The phthalates in edible vegetable oils pose a relatively small reproductive health risk to Chinese residents. © 2019 Society of Chemical Industry.

To design a home service robot, the Turtlebot robot based on ROS platform is used to achieve the autonomous navigation and obstacle avoidance functions. The result is shown in simulation experiment. During the experiment, to complete SLAM mapping, the Gmapping algorithm is applied. On the other hand, the Dijkstra algorithm is used for global path planning, and the dynamic window algorithm is used for local path planning. Both of them are used to realize the autonomous navigation function. By carrying the depth camera Astra, the robot can autonomous navigate and avoid obstacles in the indoor environment. In addition, by writing scripts, the SLAM mapping process, which originally required manual control, has also been improved to autonomous SLAM without manual control, making it more convenient in practical applications.

Endovascular interventional therapy is the first choice for the treatment of the traumatic arteriovenous fistula (AVF) because of its small trauma, fast recovery, exact effect, and low incidence of complications. However, surgery is still an important option for patients with contraindications to endovascular treatment or failure of endovascular treatment. We present the case of a 64-year-old male who was carried out with traumatic AVF and given endovascular treatment in other hospital 10 years ago. However, AVF did not disappear after surgery, and symptoms of venous hypertension such as lower limb swelling and ulcers appeared. We removed the stent and repaired the AVF with opening surgery. AVF did not recur after 1-year follow-up.

Efficient reallocation and conjunctive operation of existing water supplies have been receiving increasing attention as the competition among water users are intensified in worldwide. In this paper, a market-based stochastic optimization model is developed to address inherent uncertainties and complexities in water resources planning in arid regions. This model can be used not only to explore the water conservation potential via three strategies as cropping pattern optimization, irrigation infrastructure improvement and water trading, but also to help generate related water policy that promote the local welfare and facilitate local cleaner production. A real case study of the pumping irrigation system located in Yellow River basin is conducted to demonstrate applicability of the proposed model. Our findings indicate that the adjusted cropping pattern significantly increases the irrigator's income by growing economic crops such as herb and wolfberry; meanwhile, it does not sacrifice the food production. With the efficient infrastructural development, substantial water resources are saved and traded to other industries, thereby stimulating the local economy. Optimum subsidization policy is identified to increase the local welfare by allowing seasonal water trading while lessening the financial burden of the local government.

Epigenetic gene regulation and metabolism are highly intertwined, yet little is known about whether altered epigenetics influence cellular metabolism during cancer progression. Here, we show that EZH2 and NRASG12D mutations cooperatively induce progression of myeloproliferative neoplasms to highly penetrant, transplantable, and lethal myeloid leukemias in mice. EZH1, an EZH2 homolog, is indispensable for EZH2-deficient leukemia-initiating cells and constitutes an epigenetic vulnerability. BCAT1, which catalyzes the reversible transamination of branched-chain amino acids (BCAA), is repressed by EZH2 in normal hematopoiesis and aberrantly activated in EZH2-deficient myeloid neoplasms in mice and humans. BCAT1 reactivation cooperates with NRASG12D to sustain intracellular BCAA pools, resulting in enhanced mTOR signaling in EZH2-deficient leukemia cells. Genetic and pharmacologic inhibition of BCAT1 selectively impairs EZH2-deficient leukemia-initiating cells and constitutes a metabolic vulnerability. Hence, epigenetic alterations rewire intracellular metabolism during leukemic transformation, causing epigenetic and metabolic vulnerabilities in cancer-initiating cells. Significance: EZH2 inactivation and oncogenic NRAS cooperate to induce leukemic transformation of myeloproliferative neoplasms by activating BCAT1 to enhance BCAA metabolism and mTOR signaling. We uncover a mechanism by which epigenetic alterations rewire metabolism during cancer progression, causing epigenetic and metabolic liabilities in cancer-initiating cells that may be exploited as potential therapeutics. See related commentary by Li and Melnick, p. 1158. This article is highlighted in the In This Issue feature, p. 1143

The applicability of the traditionally used overall decomposition-based (ODB) sampling technique in the development of forecasting models is controversial. This study first conducts a systematic investigation of the performance of models developed using the ODB sampling technique. A stepwise decomposition-based (SDB) sampling technique that is consistent with actual forecasting practice is then proposed. Moreover, a novel calibration algorithm that couples a two-stage calibration strategy with a shuffled complex evolutionary approach is formulated to help maintain the performance of models. The application of models produced using these different sampling techniques to four gauging stations in China and Canada indicates that (1) the ODB sampling technique that employ the discrete wavelet transform (DWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) as series decomposition techniques do not produce convincing forecasting models because additional information on the future streamflow that is to be predicted is introduced into the explanatory variables of the samples; (2) the SDB sampling technique strictly excludes information on future streamflow from the explanatory variables and is thus as an appropriate alternative for developing forecasting models; (3) the DWT and VMD techniques benefit models by enhancing their performance; on the other hand, the EMD is unsuitable for use in forecasting, due to the variable number of subseries that result from the implementation of the stepwise decomposition strategy. Finally, methods that can be used to enhance the performance of decomposition-based models and the prediction accuracy of nonstationary streamflow are suggested.

With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application of the standard particle swarm optimization algorithm to Wi-Fi fingerprint-based indoor localization, wherein a new two-panel fingerprint homogeneity model is adopted to characterize fingerprint similarity to achieve better performance. In addition, the performance of the localization method is experimentally verified. The proposed localization method outperforms conventional algorithms, with improvements in the localization accuracy of 15.32%, 15.91%, 32.38%, and 36.64%, compared to those of KNN, SVM, LR, and RF, respectively.

Feature importance has been a popular approach for machine learning models to investigate the relative significance of model predictors. In this study, we developed a Wilk's feature importance (WFI) method for hydrological inference. Compared with conventional feature importance methods such as permutation feature importance (PFI) and mean decrease in impurity (MDI), the proposed WFI aims to provide more reliable importance scores that could partially address the equifinality problem in hydrology. To achieve this, the WFI measures the importance scores based on Wilk's Ʌ (a test-statistic that can be used to distinguish the differences between two or more groups of variables) throughout a decision tree. The WFI has an advantage over PFI and MDI as it does not account for predictive accuracy so the risk of overfitting will be greatly reduced. The proposed WFI was applied to three interconnected irrigated watersheds located in the Yellow River Basin, China. By employing the recursive feature elimination approach, our results indicated that the WFI could generate more stable relative importance scores in response to the reduction of irrelevant predictors, as compared with PFI and MDI embedded in three different machine learning algorithms. In addition, the comparative study also shows that the predictors identified by WFI achieved the highest predictive accuracy on the testing dataset, which indicates the proposed WFI could identify more informative predictors among many irrelevant ones. We also extended the WFI to the local importance scores for reflecting the varying characteristics of a predictor in the hydrological processes. The related findings could help to gain insights into different hydrological behaviours.

The control of the coordinated expression of genes is primarily regulated by the interactions between transcription factors (TFs) and their DNA binding sites, which are an integral part of transcriptional regulatory networks. There are many computational tools focused on determining TF binding or unbinding to a DNA sequence. However, other tools focused on further determining the relative preference of such binding are needed. Here, we propose a regression model with deep learning, called SemanticBI, to predict intensities of TF-DNA binding. SemanticBI is a convolutional neural network (CNN)-recurrent neural network (RNN) architecture model that was trained on an ensemble of protein binding microarray data sets that covered multiple TFs. Using this approach, SemanticBI exhibited superior accuracy in predicting binding intensities compared to other popular methods. Moreover, SemanticBI uncovered vectorized sequence-oriented features using its CNN-RNN architecture, which is an abstract representation of the original DNA sequences. Additionally, the use of SemanticBI raises the question of whether motifs are necessary for computational models of TF binding. The online SemanticBI service can be accessed at http://qianglab.scst.suda.edu.cn/semantic/ .

Irrigation systems play vital roles not only in food production but also in supporting ecosystems. Understanding how the ecosystem has evolved in response to human activities is crucial for sustainable food production, especially for arid and semi-arid regions. In this study, we examined the trends of vegetation growth on a monthly basis in the ancient Yellow River irrigation system in Ningxia, China. We used the leaf area index (LAI) to characterize the vegetation growth from 2007 to 2019. The LAI trends were associated with a series of driving forces, explaining the spatial and temporal change of vegetation growth. With the provision of the Wilks feature importance method, 2-month averaged air temperature and irrigation were identified as the two most important variables for monthly LAI simulation. Future climate projections based on the Regional Climate Model system (RegCM) suggested dryer and longer summers under the RCP 8.5 scenario. These changes will increase the crop water demand during the growing months. In the future, water conflict might be further intensified in May, in which the present irrigation water has already led to a decreased crop growth. Our findings demonstrated that the Mann Kendall monthly trend analysis could provide more helpful information for monitoring the vegetation growth than the trend analysis on a yearly and seasonal basis.

MYC stimulates both metabolism and protein synthesis, but how cells coordinate these complementary programs is unknown. Previous work reported that, in a subset of small-cell lung cancer (SCLC) cell lines, MYC activates guanosine triphosphate (GTP) synthesis and results in sensitivity to inhibitors of the GTP synthesis enzyme inosine monophosphate dehydrogenase (IMPDH). Here, we demonstrated that primary MYC(hi) human SCLC tumors also contained abundant guanosine nucleotides. We also found that elevated MYC in SCLCs with acquired chemoresistance rendered these otherwise recalcitrant tumors dependent on IMPDH. Unexpectedly, our data indicated that IMPDH linked the metabolic and protein synthesis outputs of oncogenic MYC. Coexpression analysis placed IMPDH within the MYC-driven ribosome program, and GTP depletion prevented RNA polymerase I (Pol I) from localizing to ribosomal DNA. Furthermore, the GTPases GPN1 and GPN3 were upregulated by MYC and directed Pol I to ribosomal DNA. Constitutively GTP-bound GPN1/3 mutants mitigated the effect of GTP depletion on Pol I, protecting chemoresistant SCLC cells from IMPDH inhibition. GTP therefore functioned as a metabolic gate tethering MYC-dependent ribosome biogenesis to nucleotide sufficiency through GPN1 and GPN3. IMPDH dependence is a targetable vulnerability in chemoresistant MYC(hi) SCLC.

The primary objective of the present clinical trial was to evaluate efficacy and safety of tramadol versus prednisone in Chinese patients with carpal tunnel syndrome (CTS) diagnosed by ultrasonography. A total of 60 patients' diagnosed with moderate CTS based on a clinical and electrophysiological parameters were enrolled in this clinical trial. The patients were randomly assigned to one of two groups in allocation ratio of 1:1. Test group was given controlled release Tramadol (100 mg every 12 hours) and Reference Group received Prednisone 20 mg once daily for 2 weeks (14 days). Ultrasound therapy (UT) was given as adjuvant therapy in both the group. CTS were evaluated before and after treatment through clinical findings, Boston Carpal Tunnel Questionnaire, visual analog scale (VAS) and electrophysiological data. The results were evaluated with Student's t test and chi-squared. A statistically significant difference was observed between both the treatment group regarding Durkan's test, Phalen's test, VAS and electrophysiological data after treatment. Improvement in patients treated with tramadol was significantly higher compared to prednisone group in all clinical and electrophysiological parameters. The Boston Questionnaire showed better results in tramadol groups, with a significant improvement in the symptom severity scale (SSS; p0.005) and functional status scale (FSS; p0.005). The results of this clinical trial suggest that treatment of CTS with tramadol along with UT as adjuvant therapy was associated with a significant improvement of clinical and electrophysiological parameters compared to Prednisone.

Thin copper substrates in precision physical experiments are commonly machined by double-sided lapping to obtain high flatness. However, the flatness is limited by the accuracy of lapping plate, process vibration and so on in double-sided lapping process. Hence, magnetorheological finishing (MRF) with its good performance on profile modification is employed to improve the flatness. Nevertheless, thin copper substrates, which are sensitive to the stress, deformed easily with uneven material removal on the surface. In this paper, MRF is conducted on machining thin copper substrate for the first time considering deformation induced by stress. A finite element model is established to evaluate the deformation by residual stress, and the results show that the deformation tends to be more serious with the increase of the material removal. According to the simulation results, the material removal is optimized considering both deformation and efficiency, and a series of experiments are conducted on a Φ100×2.8 mm workpiece to verify the simulation results. The experimental results show that the flatness is further improved from peak to valley (PV) 6.6 μm to PV 2.3 μm with optimized processing parameters. Hence, the feasibility of magnetorheological finishing on thin copper substrate is demonstrated.

ObjectiveTo analyse the incidence, risk factors and impact of acute kidney injury (AKI) on the prognosis of patients with COVID-19.DesignMeta-analysis.Data sourcesPubMed, Embase, CNKI and MedRxiv of Systematic Reviews from 1 January 2020 to 15 May 2020.Study selectionStudies examining the following demographics and outcomes were included: patients’ age; sex; incidence of and risk factors for AKI and their impact on prognosis; COVID-19 disease type and incidence of continuous renal replacement therapy (CRRT) administration during COVID-19 infection.ResultsA total of 79 research articles, including 49 692 patients with COVID-19, met the systemic evaluation criteria. The mortality rate and incidence of AKI in patients with COVID-19 in China were significantly lower than those in patients with COVID-19 outside China. A significantly higher proportion of patients with COVID-19 from North America were aged ≥65 years and also developed AKI. European patients with COVID-19 had significantly higher mortality and a higher CRRT rate than patients from other regions. Further analysis of the risk factors for COVID-19 combined with AKI showed that age ≥60 years and severe COVID-19 were independent risk factors for AKI, with an OR of 3.53, 95% CI (2.92–4.25) and an OR of 6.07, 95% CI (2.53–14.58), respectively. The CRRT rate in patients with severe COVID-19 was significantly higher than in patients with non-severe COVID-19, with an OR of 6.60, 95% CI (2.83–15.39). The risk of death in patients with COVID-19 and AKI was significantly increased, with an OR of 11.05, 95% CI (9.13–13.36).ConclusionAKI was a common and serious complication of COVID-19. Older age and having severe COVID-19 were independent risk factors for AKI. The risk of in-hospital death was significantly increased in patients with COVID-19 complicated by AKI.

Poplar 84 K (Populus alba x P. tremula var. glandulosa) is a good resource for genetic engineering due to its rapid growth and wide adaptability, and it is also an excellent ornamental tree species. In this study, we used 84 K plantlets grown in the nitrogen-limited medium as experimental materials to explore the molecular mechanism in 84 K leaves under nitrogen deficiency. A total of 5,868 differentially expressed genes (DEGs) were identified using the transcriptional information from RNA-seq data. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment results revealed that the DEGs were mainly involved in energy metabolism and anthocyanin biosynthesis. We then identified differentially expressed transcription factors (TFs) and constructed TF centered gene co-expression networks for chlorophyll and anthocyanin biosynthesis pathway genes. Twenty potential regulators were finally identified. We speculated the transcription factors that control the pigmentation in leaves with the MYB-bHLH-WD40 (MBW) pigment regulatory model. Such identification will clarify the genetic basis of the secondary metabolism in 84 K, and being a source of candidate genes for future plant genetic engineering. Our work broadens the researchers' understanding of the regulation of anthocyanin synthesis in trees and provides new perspectives for ornamental 84 K poplar breeding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-01012-3.

Gastric cancer is the third most common type of tumor associated with death. TRAF6 belongs to the tumor necrosis factor receptor-associated factor family and has been demonstrated to be involved in tumor progression in various cancers. However, the exact effect of TRAF6 on gastric cancer stem cells has not been extensively studied. In this study, abnormal expression of TRAF6 was found in gastric cancer tissues. Overexpression of TRAF6 enhanced proliferation and migration, and TRAF6 knockdown reversed this phenomenon in gastric cancer cells. Moreover, TRAF6 may inhibit differentiation and promote stemness and epithelial-mesenchymal transition (EMT). Transcriptome profiles revealed 701 differentially expressed genes in the wild-type group and the TRAF6 knockout group. Potential molecules associated with cell proliferation and migration were identified, including MAPK, FOXO, and IL-17. In conclusion, TRAF6 is a significant factor promoting proliferation and migration in gastric cancer cells and may provide a new target for the accurate treatment of gastric cancer.

In non–small cell lung cancer (NSCLC), concurrent mutations in the oncogeneKRASand the tumor suppressorSTK11encoding the kinase LKB1 result in aggressive tumors prone to metastasis but with liabilities arising from reprogrammed metabolism. We previously demonstrated perturbed nitrogen metabolism and addiction to an unconventional pathway of pyrimidine synthesis in KRAS/LKB1 co-mutant (KL) cancer cells. To gain broader insight into metabolic reprogramming in NSCLC, we analyzed tumor metabolomes in a series of genetically engineered mouse models with oncogenic KRAS combined with mutations in LKB1 or p53. Metabolomics and gene expression profiling pointed towards an activation of the hexosamine biosynthesis pathway (HBP), another nitrogen-related metabolic pathway, in both mouse and human KL mutant tumors. KL cells contain high levels of HBP metabolites, higher flux through the HBP pathway and elevated dependence on the HBP enzyme Glutamine-Fructose-6-Phosphate Transaminase 2 (GFPT2). GFPT2 inhibition selectively reduced KL cell growth in culture and xenografts. Our results define a new metabolic vulnerability in KL tumors and provide a rationale for targeting GFPT2 in this aggressive NSCLC subtype.

Ultraprecision optical systems such as high-energy laser systems have very strict requirements for surface defects of optical components. Comet-shaped defects are common in magnetorheological finishing, which is one of the main ultraprecision manufacturing technologies. Computational fluid dynamics is used to simulate the pressure and velocity of the magnetorheological fluid in pits during magnetorheological finishing for the first time, to the best of our knowledge. The simulation model analyzes the distribution of velocity and pressure in the process of the formation of comet-shaped defects. After the simulation, magnetorheological polishing experiments were carried out on the fused quartz silica glass. The polishing experiments use the rotation speed and immersion depth of the polishing wheel to verify the velocity and pressure distribution in the simulation experiment. The polishing experiment results are consistent with the simulation results, which show that the high polishing speed will increase the depth of the defect, and the uneven velocity and pressure distribution at the pits make the pits evolve into comet-shaped defects. We explain the cause of comet-shaped defects from the perspective of material removal and provide an optimization method of process parameters for high-efficiency removal of comet-shaped defects.

This paper explores a modified inertial-integrated navigation approach for the strapdown inertial navigation system and Global Positioning System (SINS/GPS). Since every realization of state error must be expressed with respect to the same coordinate basis, how to express the navigation parameter errors is important, especially for inertial measurement unit (IMU) errors. To this respect, the IMU errors including gyro drift and accelerometer bias expressed in common frame are more reasonable than the unframed one. This paper derives the IMU errors defined with respect to the common frame, thereby extending to propose a common frame based unscented quaternion estimator for the inertial-integrated navigation. The simulation test and car-mounted experiments for a low-cost SINS are shown to assess the performance of the new filter compared with that of the traditional filter.

Language regression is observed in a subset of toddlers with autism spectrum disorder (ASD) as initial symptom. However, such a phenomenon has not been fully explored, partly due to the lack of definite diagnostic evaluation methods and criteria. Materials and Methods: Fifteen toddlers with ASD exhibiting language regression and fourteen age-matched typically developing (TD) controls underwent diffusion tensor imaging (DTI). DTI parameters including fractional anisotropy (FA), average fiber length (AFL), tract volume (TV) and number of voxels (NV) were analyzed by Neuro 3D in Siemens syngo workstation. Subsequently, the data were analyzed by using IBM SPSS Statistics 22. Results: Compared with TD children, a significant reduction of FA along with an increase in TV and NV was observed in ASD children with language regression. Note that there were no significant differences between ASD and TD children in AFL of the arcuate fasciculus (AF). Conclusions: These DTI changes in the AF suggest that microstructural anomalies of the AF white matter may be associated with language deficits in ASD children exhibiting language regression starting from an early age.

Recent studies have highlighted super-enhancers (SEs) as important regulatory elements for gene expression, but their intrinsic properties remain incompletely characterized. Through an integrative analysis of Hi-C and ChIP-seq data, here we find that a significant fraction of SEs are hierarchically organized, containing both hub and non-hub enhancers. Hub enhancers share similar histone marks with non-hub enhancers, but are distinctly associated with cohesin and CTCF binding sites and disease-associated genetic variants. Genetic ablation of hub enhancers results in profound defects in gene activation and local chromatin landscape. As such, hub enhancers are the major constituents responsible for SE functional and structural organization., Super-enhancers (SEs) are important regulatory elements for gene expression, but their intrinsic properties remain poorly understood. Here the authors analyse Hi-C and ChIP-seq data and find that a significant fraction of SEs are hierarchically organized, containing both hub and non-hub enhancers.

This study develops a robust Gaussian particle filter (RGPF) based on modifying the likelihood function by Huber's M-estimation theory. In the developed RGPF, the innovations are reweighted based on the Huber's cost function, resulting in a modified likelihood function which is then used to update the weights of the involved particles. In the normal case, the developed RGPF has a comparable performance in terms of accuracy with the original GPF and better filtering consistency. When there are outliers and contaminated distributions in the measurements, the RGPF can outperform the GPF in terms of both accuracy and consistency. The validity of the developed algorithm is demonstrated through numerical simulation studies.

Hydrometeorological teleconnections play a vital role in hydrological processes, as they can reflect the influence of large-scale atmospheric circulations. Understanding and accessing such teleconnections can help reveal the hydrometeorological phenomenon and its associated mechanisms. Therefore, a decomposition-based teleconnection framework (DBTF) is developed to identify the hydrological processes related to the teleconnections under climate change. It consists of three modules, namely, hydrological series decomposition, teleconnection patterns decomposition, and attribution analysis. Specifically, the streamflows are separated into the stationary and nonstationary components using the SSA decomposition technique. Then, the teleconnection patterns (ENSO, AO, PDO, and Sunspots) are decomposed into multi-layer signals by the EEMD decomposition technique. The multiple complex correspondences of different components from hydrological and climate variables are studied by factorial analysis, wavelet coherence transformation, maximum information coefficient, and the partial autocorrelation coefficients. Basins from the humid, semi-humid and semiarid, and arid climate zones are estimated by the framework. The results show that: (1) high-frequency of teleconnection patterns has the highest correlation with the stationary components of streamflow, while low-frequency and trend have the most considerable association with the nonstationary components; (2) the stationary components of streamflow show more pronounced correlations with the teleconnection patterns than the nonstationary components; (3) teleconnection patterns demonstrate more substantial on the decomposed components than the raw streamflow. The contribution is to provide new insight into mechanisms in the effects of hydrometeorological teleconnections on hydrological variations.

High salinity is one of the most common abiotic stresses that plants have to confront, and selecting salt-tolerant plants is becoming a very important task in breeding. In this study, non-transgenic (NT) and three LbDREB-transgenic Populus ussuriensis Kom. (Dr22, Dr8, and Dr2) lines were treated with 150 mM NaCl. The salt tolerance of NT, Dr2, Dr8, and Dr22 were evaluated and compared. Phenotypic observation, chlorophyll content detection and leaf salt injury index calculation suggested NT exhibited more severe salt injury symptoms and weaker photosynthetic capacity than Dr2, Dr8, and Dr22. LbDREB gene exhibited opposite temporal expression patterns with no apical meristem (NAM), trihelix transcription factor (GT-1) and WRKY transcription factor 70 (WRKY70), and consistent expression patterns with Cu–Zn superoxide dismutase and peroxidase-PO1 genes, in Dr2, Dr8, and Dr22 under salt stress. The superoxide dismutase and peroxidase activities of transgenic lines were higher than NT plants during the salt stress treatment. Malonaldehyde accumulation in NT leaves was more significant than in transgenic leaves, while proline accumulation in NT leaves was much less than that in transgenic leaves under salt stress. The root to shoot ratios were significantly increased in Dr2, Dr8, and Dr22 plants, but were slightly decreased in NT plants, 12 days after salt stress treatment. The relative water content of NT leaves decreased more than transgenic leaves, meanwhile, the relative electrolytic leakage of NT leaves increased more significantly than transgenic leaves after salt stress. In summary, Dr2, Dr8, and Dr22 exhibited stronger salt stress tolerance than NT. The transgenic lines Dr2, Dr8, and Dr22 can serve as alternative salt-tolerant germplasm resources in P. ussuriensis breeding.

Metabolic reprogramming by oncogenic signals promotes cancer initiation and progression. The oncogene KRAS and tumour suppressor STK11, which encodes the kinase LKB1, regulate metabolism and are frequently mutated in non-small-cell lung cancer (NSCLC). Concurrent occurrence of oncogenic KRAS and loss of LKB1 (KL) in cells specifies aggressive oncological behaviour. Here we show that human KL cells and tumours share metabolomic signatures of perturbed nitrogen handling. KL cells express the urea cycle enzyme carbamoyl phosphate synthetase-1 (CPS1), which produces carbamoyl phosphate in the mitochondria from ammonia and bicarbonate, initiating nitrogen disposal. Transcription of CPS1 is suppressed by LKB1 through AMPK, and CPS1 expression correlates inversely with LKB1 in human NSCLC. Silencing CPS1 in KL cells induces cell death and reduces tumour growth. Notably, cell death results from pyrimidine depletion rather than ammonia toxicity, as CPS1 enables an unconventional pathway of nitrogen flow from ammonia into pyrimidines. CPS1 loss reduces the pyrimidine to purine ratio, compromises S-phase progression and induces DNA-polymerase stalling and DNA damage. Exogenous pyrimidines reverse DNA damage and rescue growth. The data indicate that the KL oncological genotype imposes a metabolic vulnerability related to a dependence on a cross-compartmental pathway of pyrimidine metabolism in an aggressive subset of NSCLC.

In this paper, a unified form for robust Gaussian information filtering based on M-estimate is proposed, which can incorporate robust weight functions with zero weight for large residues. The proposed information filtering framework can avoid the numerical problem introduced by the zero weight in the Kalman filtering framework. Simulation results show the efficiency and superiority of the proposed robust filters over the nonrobust filter against heavy-tailed measurement noises.