Robust Adaptive Fuzzy Control for a Class of Uncertain MIMO Nonlinear Systems with Input Saturation

This paper studies the robust adaptive fuzzy control design problem for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear systems in the presence of actuator amplitude and rate saturation. In the control scheme, fuzzy logic systems are used to approximate unknown nonlinear systems. To compensate the effect of input saturations, an auxiliary system is constructed and the actuator saturations then can be augmented into the controller. The modified tracking error is introduced and used in fuzzy parameter update laws. Furthermore, in order to deal with fuzzy approximation errors for unknown nonlinear systems and external disturbances, a robust compensation control is designed. It is proved that the closed-loop system obtains H∞ tracking performance through Lyapunov analysis. Steady and transient modified tracking errors are analyzed and the bound of modified tracking errors can be adjusted by tuning certain design parameters. The proposed control scheme is applicable to uncertain nonlinear systems not only with actuator amplitude saturation, but also with actuator amplitude and rate saturation. Detailed simulation results of a rigid body satellite attitude control system in the presence of parametric uncertainties, external disturbances, and control input constraints have been presented to illustrate the effectiveness of the proposed control scheme

Multiplexed Serum Biomarkers for the Detection of Lung Cancer

AbstractCurrently, there is no available biomarker for lung cancer diagnosis. Here we recruited 844 lung cancer patients and 620 healthy participants from six hospitals. A total of four serum proteins was identified and subsequently assessed in the training and validation cohorts. The concentrations of four serum proteins were found to be significantly higher in lung cancer patients compared with healthy participants. The area under the curve (AUC) for the 4-biomarker were 0.86 in the training cohort, and 0.87 in the validation cohort. The classification improved to a corrected AUC of 0.90 and 0.89 respectively following addition of sex, age and smoking status. Similar results were observed for early-stage lung cancer. Remarkably, in a blinded test with a suspicious pulmonary nodule, the adjusted prediction model correctly discriminated the patients with 86.96% sensitivity and 98.25% specificity. These results demonstrated the 4-biomarker panel improved lung cancer prediction beyond that of known risk factors. Moreover, the biomarkers were valuable in differentiating benign nodules which will remain indolent from those that are likely to progress and therefore might serve as an adjuvant diagnosis tool for LDCT scanning

Development of a multiplex autoantibody test for detection of lung cancer.

Lung cancer is the leading cause of cancer-related deaths for both men and women. Early diagnosis of lung cancer has a 5-year survival rate of 48.8%, however, nearly 35% of stage I patients relapses after surgical resection, thus portending a poor prognosis. Therefore, detecting lung cancer in early stage and further identifying the high-risk patients would allow the opportunity to provide adjuvant therapy and possibly increase survival. There is considerable evidence that the immune system produces an autoantibody response to neoplastic cells. The detection of such autoantibodies has been shown to have diagnostic and prognostic value. Here we took advantage of the high-throughput Luminex technique to multiplex a total of 14 tumor-associated autoantigens to detect the autoantibody from the patients sera. The 14 antigens were expressed by in vitro transcription/translation system with HaloTag at N-terminus. The fusion proteins were then covalently immobilized onto the Luminex microspheres conjugated by the halo-link ligand, thus eliminating the protein purification procedure. Sera samples from cancer patients and healthy controls were interacted with the microsphere-antigen complex to measure the autoantibodies. We have developed a quick multiplex detection system for measuring autoantibody signature from patient sera with minimal cross-reaction. A panel of seven autoantibody biomarkers has generated an AUC>80% in distinguishing the lung cancers from healthy controls. This study is the first report by combining Luminex platform and HaloTag technology to detect humoral immune response in cancer patients. Due to the flexibility of the Luminex technology, this approach can be applied to others conditions such as infectious, neurological, and metabolic diseases. One can envision that this multiplex Luminex system as well as the panel of seven biomarkers could be used to screen the high-risk population with subsequent CT test based on the blood test result

A study of the role of myeloid-derived growth factor on acute lung injury in vitro and In vivo

Objectives: Bone marrow-derived mesenchymal stem cells (BMSCs) are considered to have potential clinical application value in the treatment of acute lung injury (ALI). Myeloid-derived growth factor (MYDGF) can promote the proliferation of stem cell. We hypothesized that MYDGF may play a role in reducing lung injury in vitro and in vivo through bone marrow mesenchymal stem cells. Methods: An in vitro model of lipopolysaccharide (LPS)(MLE-12) was established, which was divided into five groups: A: MLE-12; B: MLE-12+LPS; C: MLE-12+LPS + BMSCs; D: MLE-12+LPS + MYDGF; and E: MLE-12+LPS + BMSCs + MYDGF. A Cell Counting Kit-8 was used to detect the OD value. And an ALI model was constructed by inducing mice with a lipopolysaccharide. Forty male Balb/c mice were randomly divided into five groups: A control group; B: model group; C: LPS + BMSCs; D: LPS + MYDGF; E: LPS +BMSCs +MYDGF. Specimens were collected after 24 h. Hematoxylin-eosin (HE)-staining was performed on the tissue sections. The protein concentration in the alveolar lavage fluid was measure by bicinchoninic acid (BCA). The NF-κB, p-Akt, Bax, and Bcl-2 protein expression was detected through Western blotting, and Enzyme linked immunosorbent assay (ELISA) was used to measure the expression of serum interleukin-6, interleukin-10, and TNF-α. Results: Compared with the model group, BMSCs and MYDGF can alleviate the ALI induced by lipopolysaccharide in vitro and vivo ( p < .05). Conclusion: We found that the combined treatment effect of MYDGF and BMSCs was better than using MYDGF or BMSCs alone. We speculate that a pretreatment with MYDGF after ALI in mice may improve the survival and growth of transplanted MSCs, thereby improving the curative effect of cell transplantation

Risk factors analysis and survival prediction model establishment of patients with lung adenocarcinoma based on different pyroptosis-related gene subtypes

Abstract Background Lung adenocarcinoma (LUAD) is a common cancer with a poor prognosis. Pyroptosis is an important process in the development and progression of LUAD. We analyzed the risk factors affecting the prognosis of patients and constructed a nomogram to predict the overall survival of patients based on different pyroptosis-related genes (PRGs) subtypes. Methods The genomic data of LUAD were downloaded from the TCGA and GEO databases, and all data were filtered and divided into TCGA and GEO cohorts. The process of data analysis and visualization was performed via R software. The data were classified based on different PRGs subtypes using the K-means clustering method. Then, the differentially expressed genes were identified between two different subtypes, and risk factors analysis, survival analysis, functional enrichment analysis, and immune cells infiltration landscape analysis were conducted. The COX regression analysis was used to construct the prediction model. Results Based on the PRGs of LUAD, the patients were divided into two subtypes. We found the survival probability of patients in subtype 1 is higher than that in subtype 2. The results of the logistics analysis showed that gene risk score was closely associated with the prognosis of LUAD patients. The results of GO analysis and KEGG analysis revealed important biological processes and signaling pathways involved in the differentially expressed proteins between the two subtypes. Then we constructed a prediction model of patients’ prognosis based on 13 genes, including IL-1A, P2RX1, GSTM2, ESYT3, ZNF682, KCNF1, STK32A, HHIPL2, GDF10, NDC80, GSTA1, BCL2L10, and CCR2. This model was strongly related to the overall survival (OS) and also reflects the immune status in patients with LUAD. Conclusion In our study, we examined LUAD heterogeneity with reference to pyroptosis and found different prognoses between the two subtypes. And a novel prediction model was constructed to predict the OS of LUAD patients based on different PRGs signatures. The model has shown excellent predictive efficiency through validation

Optimizing nitrogen fertilizer application for achieving high yield with low environmental risks in apple orchard

The great challenges of food security, climate change and environmental degradation resulting from unreasonable nitrogen fertilizer (NF) inputs necessitate the optimization of fertilizer application to ensure fruit production and environmental sustainability. However, there is a lack of systematic analysis on the effects of NF inputs on apple yield and reactive nitrogen losses (N2O emission, NH3 volatilization, and nitrate leaching) at the global scale. Therefore, we used a meta-analysis of 159 observations from 31 published studies to evaluate the response of apple yield and reactive nitrogen losses to NF. We aim to identify suitable NF rates, environmental conditions, and planting factors that improve apple yield while minimizing reactive nitrogen losses. Our results showed that NF significantly increased apple yield, N2O emission, NH3 volatilization, and nitrate leaching by 17.1%, 255.7%, 236.4%, and 68.7% compared with no nitrogen fertilizer (NNF), respectively. The effects of NF rates and environmental factors on the response of N2O emission, NH3 volatilization, and nitrate leaching to NF were prior to those of planting factors, but the result of apple yield was the opposite. Apple production under NF in regions with MAT (mean annual air temperature) ≥ 10 °C and MAP (mean annual precipitation) 450 kg ha−1). Our findings provide guidance for optimizing NF management in apple orchard to achieve high crop yields, reduce emissions, and mitigate pollution