NHE3 inhibitor tenapanor maintains intestinal barrier function, decreases visceral hypersensitivity, and attenuates TRPV1 signaling in colonic sensory neurons.

The pathogenesis of irritable bowel syndrome (IBS) is multifactorial, characterized in part by increased intestinal permeability, and visceral hypersensitivity. Increased permeability is associated with IBS severity and abdominal pain. Tenapanor is FDA-approved for the treatment of IBS with constipation (IBS-C) and has demonstrated improvements in bowel motility and a reduction in IBS-related pain; however, the mechanism by which tenapanor mediates these functions remains unclear. Here, the effects of tenapanor on colonic pain signaling and intestinal permeability were assessed through behavioral, electrophysiological, and cell culture experiments. Intestinal motility studies in rats and humans demonstrated that tenapanor increased luminal sodium and water retention and gastrointestinal transit versus placebo. A significantly reduced visceral motor reflex (VMR) to colonic distension was observed with tenapanor treatment versus vehicle in two rat models of visceral hypersensitivity (neonatal acetic acid sensitization and partial restraint stress; both P < 0.05), returning VMR responses to that of nonsensitized controls. Whole cell voltage patch-clamp recordings of retrogradely labeled colonic dorsal root ganglia (DRG) neurons from sensitized rats found that tenapanor significantly reduced DRG neuron hyperexcitability to capsaicin versus vehicle (P < 0.05), an effect not mediated by epithelial cell secretions. Tenapanor also attenuated increases in intestinal permeability in human colon monolayer cultures caused by incubation with proinflammatory cytokines (P < 0.001) or fecal supernatants from patients with IBS-C (P < 0.005). These results support a model in which tenapanor reduces IBS-related pain by strengthening the intestinal barrier, thereby decreasing permeability to macromolecules and antigens and reducing DRG-mediated pain signaling.NEW & NOTEWORTHY A series of nonclinical experiments support the theory that tenapanor inhibits IBS-C-related pain by strengthening the intestinal barrier. Tenapanor treatment reduced visceral motor responses to nonsensitized levels in two rat models of hypersensitivity and reduced responses to capsaicin in sensitized colonic nociceptive dorsal root ganglia neurons. Intestinal permeability experiments in human colon monolayer cultures found that tenapanor attenuates increases in permeability induced by either inflammatory cytokines or fecal supernatants from patients with IBS-C.

MicroRNA-200 Loaded Lipid Nanoparticles Promote Intestinal Epithelium Regeneration in Canonical MicroRNA-Deficient Mice.

Intestinal epithelium undergoes regeneration after injuries, and the disruption of this process can lead to inflammatory bowel disease and tumorigenesis. Intestinal stem cells (ISCs) residing in the crypts are crucial for maintaining the intestinal epithelium's homeostasis and promoting regeneration upon injury. However, the precise role of DGCR8, a critical component in microRNA (miRNA) biogenesis, in intestinal regeneration remains poorly understood. In this study, we provide compelling evidence demonstrating the indispensable role of epithelial miRNAs in the regeneration of the intestine in mice subjected to 5-FU or irradiation-induced injury. Through a comprehensive pooled screen of miRNA function in Dgcr8-deficient organoids, we observe that the loss of the miR-200 family leads to the hyperactivation of the p53 pathway, thereby reducing ISCs and impairing epithelial regeneration. Notably, downregulation of the miR-200 family and hyperactivation of the p53 pathway are verified in colonic tissues from patients with active ulcerative colitis (UC). Most importantly, the transient supply of miR-200 through the oral delivery of lipid nanoparticles (LNPs) carrying miR-200 restores ISCs and promotes intestinal regeneration in mice following acute injury. Our study implies the miR-200/p53 pathway as a promising therapeutic target for active UC patients with diminished levels of the miR-200 family. Furthermore, our findings suggest that the clinical application of LNP-miRNAs could enhance the efficacy, safety, and acceptability of existing therapeutic modalities for intestinal diseases.

Flow and Heat-Transfer Characteristics of Droplet Impingement on Hydrophilic Wires.

It is a common phenomenon that droplets collide with wires in industrial production, and their flow and heat-transfer behavior significantly impact the production efficiency. This article presents an experimental and numerical study on the impact of pure water droplets on hydrophilic stainless-steel wires. The dynamic behavior and solid-liquid heat-transfer law of droplet impacting the wire are emphatically analyzed. The impact position of the droplets has a significant effect on their morphology. Under the condition of low Weber number (We), eccentric impacts tend to cause droplets to separate from the wire. Additionally, both We and wire/droplet size ratio have noticeable effects on the droplet morphology. The smaller the We, the larger the wire/droplet size ratio, and the easier it is for droplets to be captured by wires. Conversely, as We increases and the wire-to-droplet size ratio decreases, some droplets become detached from the wire, primarily exhibiting a single-film falling mode. Furthermore, the impact morphology of droplets is influenced by the Ohnesorge number (Oh). The higher the Oh, the more inclined the droplet to develop a double-film falling mode. There is obvious field synergy in the process of droplet impacting on wire. The maximum heat flux is located at the three-phase contact line, while the minimum heat flux is observed at the bubble interface. The impact position of droplets influences the temperature distribution, although its impact on the magnitude of temperature variation is minimal.

Control strategy and mechanism for satellite droplet reduction.

Satellite droplets accompanying the formation of monodispersed particles have serious negative effects in the fields of medicine and food, especially in pill preparation. Therefore, it is of great significance to study control strategy and mechanism for satellite droplet reduction. This paper proposes a simple and efficient device, Drainage Assisted Dropper (DAD), which adds a stainless-steel needle to the center of General Dropper (GD). Experimental and numerical results show the number and volume of satellite droplets of the dripping formed by DAD are significantly reduced compared to those formed by GD. DAD can reduce the liquid volume of satellite droplets with a reduction rate of 87 %, while reducing the size of the primary droplet and increasing the interval between the adjacent primary droplets. Compared with GD, DAD has a smaller cross-sectional area and a larger wetted area, which results in a smaller downward velocity of the liquid. The drainage assisted needle of DAD changes the dripping flow pattern at the outlet of the dropper near the breaking time, causing the residual liquid to be subjected to a higher additional pressure. Less liquid is replenished to the filament, resulting in the filament with a shorter length and a smaller liquid volume. DAD proposed here has a clear development potential and application value in the fields of pharmaceuticals, food, agriculture, and manufacturing.

Transforming Growth Factor Beta Promotes Inflammation and Tumorigenesis in Smad4-Deficient Intestinal Epithelium in a YAP-Dependent Manner.

Transforming growth factor beta (TGF-ß), a multifunctional cytokine, plays critical roles in immune responses. However, the precise role of TGF-ß in colitis and colitis-associated cancer remains poorly defined. Here, it is demonstrated that TGF-ß promotes the colonic inflammation and related tumorigenesis in the absence of Smad family member 4 (Smad4). Smad4 loss in intestinal epithelium aggravates colitis and colitis-associated neoplasia induced by dextran sulfate sodium (DSS) and azoxymethane/dextran sulfate sodium (AOM/DSS), leading to over-activated immune responses and increased TGF-ß1 levels. In Smad4-deficient organoids, TGF-ß1 stimulates spheroid formation and impairs intestinal stem cell proliferation and lineage specification. YAP, whose expression is directly upregulated by TGF-ß1 after Smad4 deletion, mediates the effect of TGF-ß1 by interacting with Smad2/3. Attenuation of YAP/TAZ prevents TGF-ß1-induced spheroid formation in Smad4-/- organoids and alleviates colitis and colitis-associated cancer in Smad4-deficient mice. Collectively, these results highlight an integral role of the TGF-ß/Smad4 axis in restraining intestinal inflammation and tumorigenesis and suggest TGF-ß or YAP signaling as therapeutic targets for these gastrointestinal diseases intervention.

Rh(III)-Catalyzed [4 + 1] Annulation of Sulfoximines with Maleimides: Access to Benzoisothiazole Spiropyrrolidinediones.

Rh(III)-catalyzed synthesis of benzoisothiazole spiropyrrolidinediones using sulfoximine as a directing group under a C-H activation and [4 + 1] annulation strategy with maleimides as a coupling partner is reported. The cyclization reaction was compatible with various substituted sulfoximine and maleimides. The deuterium-labeling studies were performed to investigate the mechanism of the reaction.

Cp*RhIII-Catalyzed Cascade Annulation of Arylimidates with Pyridotriazoles toward Isoquinolin-3-ol Derivatives.

A Cp*RhIII-catalyzed efficient synthesis of isoquinolin-3-ol derivatives bearing a pyridinyl ring using imidate as a directing group under C-H activation strategy with pyridotriazoles as carbene reagents is reported. In this reaction, cascade C-H activation, regioselective cyclization, and elimination occur in one pot. The present methodology featured a good range of functional group tolerance and furnished the target products in moderate-to-excellent yields.

The Effect of Serine Protease Inhibitors on Visceral Pain in Different Rodent Models With an Intestinal Insult.

Background: Serine proteases are believed to play a key role in the origin of abdominal pain in IBD and IBS. We previously demonstrated a reduction of visceral pain in a post-inflammatory IBS rat model after a single intraperitoneal or intracolonic administration of a serine protease inhibitor. The aim of this study was to investigate the efficacy of serine protease inhibition on visceral pain in two different animal models involving a colonic insult based either on acute inflammation or on neonatal irritation. Moreover, protease profiling was explored in the acute colitis model. Methods: An acute 2,4,6-trinitrobenzenesulphonic acid (TNBS) colitis rat model and a chronic neonatal acetic acid mouse model were used in this study. Visceral sensitivity was quantified by visceromotor responses (VMRs) to colorectal distension, 30 min after intraperitoneal administration of the serine protease inhibitors nafamostat, UAMC-00050 or their vehicles. Colonic samples from acute colitis rats were used to quantify the mRNA expression of a panel of serine proteases and mast cell tryptase by immunohistochemistry. Finally, proteolytic activities in colonic and fecal samples were characterized using fluorogenic substrates. Key Results: We showed a significant and pressure-dependent increase in visceral hypersensitivity in acute colitis and neonatal acetic acid models. UAMC-00050 and nafamostat significantly reduced VMRs in both animal models. In acute colitis rats, the administration of a serine protease inhibitor did not affect the inflammatory parameters. Protease profiling of these acute colitis animals revealed an increased tryptase immunoreactivity and a downregulation of matriptase at the mRNA level after inflammation. The administration of UAMC-00050 resulted in a decreased elastase-like activity in the colon associated with a significantly increased elastase-like activity in fecal samples of acute colitis animals. Conclusion: In conclusion, our results suggest that serine proteases play an important role in visceral hypersensitivity in an acute TNBS colitis model in rats and a neonatal acetic acid model in mice. Moreover, we hypothesize a potential mechanism of action of UAMC-00050 via the alteration of elastase-like proteolytic activity in acute inflammation. Taken together, we provided fundamental evidence for serine protease inhibitors as a promising new therapeutic strategy for abdominal pain in gastrointestinal diseases.

Discovery and construction of prognostic model for clear cell renal cell carcinoma based on single-cell and bulk transcriptome analysis.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common malignant kidney tumor in adults. Single-cell transcriptome sequencing can provide accurate gene expression data of individual cells. Integrated single-cell and bulk transcriptome data from ccRCC samples provide comprehensive information, which allows the discovery of new understandings of ccRCC and the construction of a novel prognostic model for ccRCC patients. METHODS: Single-cell transcriptome sequencing data was preprocessed by using the Seurat package in R software. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were used to perform cluster classification. Two subtypes of cancer cells were identified, pseudotime trajectory analysis and gene ontology (GO) analysis were conducted with the monocle and clusterProfiler packages. Two novel cancer cell biomarkers were identified according to the single-cell sequencing and were confirmed by The Cancer Genome Atlas (TCGA) data. T cell-related marker genes according to single-cell sequencing were screened by a combination of Kaplan-Meier (KM) analysis, univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox analysis of TCGA data. Four survival predicting genes were screened out to develop a risk score model. A nomogram consisting of the risk score and clinical information was constructed to predict the prognosis for ccRCC patients. RESULTS: A total of 5,933 cells were included in the study after quality control. Fifteen cell clusters were classified by PCA and t-SNE algorithm. Two clusters of cancer cells with distinct differentiation status were identified. Besides, GO analysis revealed that biological processes were different between the two subgroups. Egl-9 family hypoxia-inducible factor 3 (EGLN3) and nucleolar protein 3 (NOL3) were specifically expressed in cancer cell clusters, bulk RNA sequencing data from TCGA confirmed their high expression in ccRCC tissues. GTSE1, CENPF, SMC2 and H2AFV were screened out and applied to the construction of risk score model. A nomogram was generated to predict prognosis of ccRCC by combing the risk score and clinical parameters. CONCLUSIONS: We integrated single-cell and bulk transcriptome data from ccRCC in this study. Two subtypes of ccRCC cells with different biological characteristics and two potential biomarkers of ccRCC were discovered. A novel prognostic model was constructed for clinical application.

Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification.

As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures.

A review of biosensor technology and algorithms for glucose monitoring.

Diabetes mellitus (DM) has become a serious illness in the whole world. Until now, there is no effective cure for patients with DM. It is well known that the glucose level is one key factor to determine the progress of DM. It is also an important reference to carry out the accurate and timely treatment for patients with DM. In this article, the related biosensors technology that can be utilized to identify and predict glucose level are reviewed in detail, including the algorithms that can help to achieve numerical value of glucose level. Firstly, the biosensor technology based on the physiological fluids are illustrated, including blood, sweat, interstitial fluid, ocular fluid, and other available fluids. Secondly, the algorithms for achieving numerical value of glucose level are investigated, including the physiological model-based method and the machine learning-based method. Finally, the future development trend and challenges of glucose level monitoring are given and the conclusions are drawn.

Overexpression of MAX dimerization protein 3 (MXD3) predicts poor prognosis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of malignant kidney tumor. The molecular mechanism of ccRCC is complicated, and few effective prognostic predictors have been applied to clinical practice. MAX dimerization protein 3 (MXD3) is generally considered a transcription factor of the MYC/MAX/MAD transcriptional network. This study aimed to investigate the impact of MXD3 in ccRCC. METHODS: Gene expression profiles and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. MXD3 expression levels between tumors and adjacent normal tissues were compared. The influence of MXD3 on overall survival (OS) was evaluated using the Kaplan-Meier method. Associations between MXD3 expression and clinical features were assessed with the Kruskal test and Wilcoxon test. Univariate and multivariate Cox analyses were performed to observe the impact of MXD3 expression and clinical features on prognosis. The correlation between MXD3 and ccRCC immune infiltration was estimated with TIMER. The DNA methylation levels of the MXD3 promoter were obtained from UALCAN. Gene set enrichment analysis (GSEA) was conducted to explore the biological signaling pathways. RESULTS: MXD3 was overexpressed in ccRCC tumor tissues compared with adjacent normal kidney tissues. High expression of MXD3 was significantly correlated with poor prognosis. MXD3 expression levels were associated with tumor grade, tumor stage, tumor (T) classification and metastasis (M) classification. Univariate and multivariate Cox analyses showed that high expression of MXD3 was an independent risk factor for OS in ccRCC. MXD3 expression was positively correlated with the infiltrating levels of B cells and myeloid dendritic cells, and negatively correlated with macrophages. The MXD3 promoter region tended to be hypomethylated in ccRCC compared with normal tissues. GSEA identified homologous recombination, base excision repair, and glycerophospholipid metabolism as differentially enriched in ccRCC with high MXD3 expression. CONCLUSIONS: This study suggests that high expression of MXD3 is an independent risk factor for poor prognosis in ccRCC. MXD3 expression potentially contributes to regulation of immune infiltration and cell proliferation in ccRCC, and the aberrant expression of MXD3 in tumor tissues could be caused by hypomethylation of gene promoter. MXD3 could be an effective prognostic biomarker and potential therapeutic target for ccRCC.

Vagal gut-brain signaling mediates amygdaloid plasticity, affect, and pain in a functional dyspepsia model.

Functional dyspepsia (FD) is associated with chronic gastrointestinal distress and with anxiety and depression. Here, we hypothesized that aberrant gastric signals, transmitted by the vagus nerve, may alter key brain regions modulating affective and pain behavior. Using a previously validated rat model of FD characterized by gastric hypersensitivity, depression-like behavior, and anxiety-like behavior, we found that vagal activity - in response to gastric distention - was increased in FD rats. The FD phenotype was associated with gastric mast cell hyperplasia and increased expression of corticotrophin-releasing factor (Crh) and decreased brain-derived neurotrophic factor genes in the central amygdala. Subdiaphragmatic vagotomy reversed these changes and restored affective behavior to that of controls. Vagotomy partially attenuated pain responses to gastric distention, which may be mediated by central reflexes in the periaqueductal gray, as determined by local injection of lidocaine. Ketotifen, a mast cell stabilizer, reduced vagal hypersensitivity, normalized affective behavior, and attenuated gastric hyperalgesia. In conclusion, vagal activity, partially driven by gastric mast cells, induces long-lasting changes in Crh signaling in the amygdala that may be responsible for enhanced pain and enhanced anxiety- and depression-like behaviors. Together, these results support a "bottom-up" pathway involving the gut-brain axis in the pathogenesis of both gastric pain and psychiatric comorbidity in FD.

Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network.

Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT images, a denoising algorithm based on a convolutional neural network is proposed in this paper. Unlike traditional algorithms that directly obtain denoised images, the algorithm model proposed in this paper learns the speckle noise distribution through the constructed trainable OCT dataset and indirectly obtains the denoised result image. In order to verify the performance of the model, we compare the denoising results of the algorithm proposed in this paper with several state-of-the-art algorithms from three perspectives: qualitative evaluation from the subjective visual perspective, quantitative evaluation from objective parameter indicators, and running time. The experimental results show that the proposed algorithm has a good denoising effect on different OCT images of the anterior segment and has good generalization ability. Besides, it retains the relevant details and texture information in the image, and it has strong edge preserving ability. The image of OCT speckle removal can be obtained within 0.4 s, which meets the time limit requirement of clinical application.

White learning methodology: A case study of cancer-related disease factors analysis in real-time PACS environment.

BACKGROUND AND OBJECTIVE: Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure. DL functions like a black box. As a result, many medical practitioners are reductant to use deep learning as the only tool for critical machine learning application, such as aiding tool for cancer diagnosis. METHODS: In this paper, a framework of white learning is being proposed which takes advantages of both black box learning and white box learning. Usually, black box learning will give a high standard of accuracy and white box learning will provide an explainable direct acyclic graph. According to our design, there are 3 stages of White Learning, loosely coupled WL, semi coupled WL and tightly coupled WL based on degree of fusion of the white box learning and black box learning. In our design, a case of loosely coupled WL is tested on breast cancer dataset. This approach uses deep learning and an incremental version of Naïve Bayes network. White learning is largely defied as a systemic fusion of machine learning models which result in an explainable Bayes network which could find out the hidden relations between features and class and deep learning which would give a higher accuracy of prediction than other algorithms. We designed a series of experiments for this loosely coupled WL model. RESULTS: The simulation results show that using WL compared to standard black-box deep learning, the levels of accuracy and kappa statistics could be enhanced up to 50%. The performance of WL seems more stable too in extreme conditions such as noise and high dimensional data. The relations by Bayesian network of WL are more concise and stronger in affinity too. CONCLUSION: The experiments results deliver positive signals that WL is possible to output both high classification accuracy and explainable relations graph between features and class.

Palladium-Catalyzed Oxidative Annulation of Sulfoximines and Arynes by C-H Functionalization as an Approach to Dibenzothiazines.

This work reports a novel and efficient palladium-catalyzed synthesis of tricyclic dibenzothiazines using easily prepared aryl sulfoximines and aryne precursors via C-H functionalization and cyclization. A mechanistic investigation indicated that the C-H bond cleavage at the position ortho to the sulfoximine group is the rate-determining step.

Altered Brain Regional Homogeneity Following Contralateral Acupuncture at Quchi (LI 11) and Zusanli (ST 36) in Ischemic Stroke Patients with Left Hemiplegia: An fMRI Study.

OBJECTIVE: To study the effect of contralateral acupuncture (CAT) at acupoints of Quchi (LI 11) and Zusanli (ST 36) on the unaffected limbs of ischemic stroke patients with left hemiplegia based on regional homogeneity (ReHo) indices. METHODS: Ten ischemic stroke patients with left hemiplegia received CAT on right side at LI 11 and ST 36. Functional magnetic resonance imaging (fMRI) was performed before and after acupuncture. A ReHo analytical method was used to compare brain responses of patients before and after CAT operated by REST software. RESULTS: The stimulation at both LI 11 and ST 36 on the unaffected limbs produced significantly different neural activities. CAT elicited increased ReHo values at the right precentral gyrus and superior frontal gyrus, decreased ReHo value at right superior parietal lobule, left fusiform gyrus and left supplementary motor area. CONCLUSIONS: Acupuncture at one side could stimulate bilateral regions. CAT could evoke the gyrus which was possibly related to motor recovery from stroke. A promising indicator of neurobiological deficiencies could be represented by ReHo values in post-stroke patients.

Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine.

The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.

Slow pyrolysis characteristics of bamboo subfamily evaluated through kinetics and evolved gases analysis.

The objective of this study is to investigate the pyrolysis and kinetic characteristics of three varieties of the bamboo subfamily via thermogravimetry/Fourier transform infrared spectrometry (TG-FTIR) coupling technologies. The pyrolysis process can be divided into three stages of dehydration, volatilization, and carbonization. TG-FTIR analysis revealed that evolved gas is constituted by CO2, CO, CH4, H2O, NO, NO2, formic acid, HCN, and CO functional groups as major pyrolysis products. The kinetic parameters of pyrolysis were calculated using model-free methods of distributed activation energy (DAEM). With an increase in conversion, the activation energy of each bamboo subfamily exhibited distinct variations. The average values of activation energy for moso bamboo, bambusa multiplex, and black bamboo determined by DAEM were 201.59, 220.49, and 224.47 kJ/mol, respectively. Results of thermodynamic and kinetic analysis indicate that the bamboo subfamily shows great potential as an alternative fuel by pyrolysis.

UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm.

Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.