Optimized kernel minimum noise fraction transformation for hyperspectral image classification

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy

Influence of diabetes on cardiac resynchronization therapy in heart failure patients: a meta-analysis

BACKGROUND: Diabetes mellitus is an independent risk factor of increased morbidity and mortality in patients with heart failure. Cardiac resynchronization therapy (CRT), a pacemaker-based therapy for dyssynchronous heart failure, improves cardiac performance and quality of life, but its effect on mortality in patients with diabetes is uncertain. METHODS: We performed a meta-analysis of results from randomized controlled trials (RCTs) of the long-term outcome of cardiac resynchronization therapy for heart failure in diabetic and non-diabetic patients. Literature search of MEDLINE via Pubmed for reports of randomized controlled trials of Cardiac resynchronization for chronic symptomatic left-ventricular dysfunction in patients with and without diabetes mellitus, with death as the outcome. Relevant data were analyzed by use of a random-effects model. Reports published from 1994 to 2011 that described RCTs of CRT for treating chronic symptomatic left ventricular dysfunction in patients with and without diabetes, with all-cause mortality as an outcome. RESULTS: A total of 5 randomized controlled trials met the inclusion criteria, for 2,923 patients. The quality of studies was good to moderate. Cardiac resynchronization significantly reduced the mortality for heart failure patients with or without diabetes mellitus. Mortality was 24.3% for diabetic patients with heart failure and 20.4 % for non-diabetics (odds ratio 1.28, 95% confidence interval 1.06–1.55; P = 0.010). CONCLUSIONS: Cardiac resynchronization therapy (CRT) may reduce mortality from progressive heart failure in patients with or without diabetes mellitus, but mortality may be higher for patients with than without diabetes after CRT for heart failure

The Design of PID Controller Based On Hopfield Neural Network

With the complexity increase in industrial production process, the traditional Proportion-Integration-Differentiation(PID) control can not meet the requirements of the control system performance. Because neural network has the ability of adaptive, self-learning and nonlinear function approximation, control equality of system is improved if it is combined with traditional PID. In the paper, Hopfield neural network based on Hebb rules is used to identify the parameters of system, and then the state space model is established. Hopfield Neural network has the function of optimal calculation, PID controller based on Hopfield neural network is designed for a system can optimize the parameter of PID in real-time and improve control accuracy. Simulation result show the performance index is greatly improved. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.492

Small Object Tracking in LiDAR Point Cloud: Learning the Target-awareness Prototype and Fine-grained Search Region

Single Object Tracking in LiDAR point cloud is one of the most essential parts of environmental perception, in which small objects are inevitable in real-world scenarios and will bring a significant barrier to the accurate location. However, the existing methods concentrate more on exploring universal architectures for common categories and overlook the challenges that small objects have long been thorny due to the relative deficiency of foreground points and a low tolerance for disturbances. To this end, we propose a Siamese network-based method for small object tracking in the LiDAR point cloud, which is composed of the target-awareness prototype mining (TAPM) module and the regional grid subdivision (RGS) module. The TAPM module adopts the reconstruction mechanism of the masked decoder to learn the prototype in the feature space, aiming to highlight the presence of foreground points that will facilitate the subsequent location of small objects. Through the above prototype is capable of accentuating the small object of interest, the positioning deviation in feature maps still leads to high tracking errors. To alleviate this issue, the RGS module is proposed to recover the fine-grained features of the search region based on ViT and pixel shuffle layers. In addition, apart from the normal settings, we elaborately design a scaling experiment to evaluate the robustness of the different trackers on small objects. Extensive experiments on KITTI and nuScenes demonstrate that our method can effectively improve the tracking performance of small targets without affecting normal-sized objects

Microbiological safety of chicken litter or chicken litter-based organic fertilizer: A review.

Abstract: Chicken litter or chicken litter-based organic fertilizers are usually recycled into the soil to improve the structure and fertility of agricultural land. As an important source of nutrients for crop production, chicken litter may also contain a variety of human pathogens that can threaten humans who consume the contaminated food or water. Composting can inactivate pathogens while creating a soil amendment beneficial for application to arable agricultural land. Some foodborne pathogens may have the potential to survive for long periods of time in raw chicken litter or its composted products after land application, and a small population of pathogenic cells may even regrow to high levels when the conditions are favorable for growth. Thermal processing is a good choice for inactivating pathogens in chicken litter or chicken litter-based organic fertilizers prior to land application. However, some populations may become acclimatized to a hostile environment during build-up or composting and develop heat resistance through cross-protection during subsequent high temperature treatment. Therefore, this paper reviews currently available information on the microbiological safety of chicken litter or chicken litter-based organic fertilizers, and discusses about further research on developing novel and effective disinfection techniques, including physical, chemical, and biological treatments, as an alternative to current methods

OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds

Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they usually utilized heavy correlation operations to capture category-level characteristics only, and overlook the inherent merit of arbitrariness in contrast to multiple object tracking. In this work, we propose a radically novel one-stream network with the strength of the instance-level encoding, which avoids the correlation operations occurring in previous Siamese network, thus considerably reducing the computational effort. In particular, the proposed method mainly consists of a Template-aware Transformer Module (TTM) and a Multi-scale Feature Aggregation (MFA) module capable of fusing spatial and semantic information. The TTM stitches the specified template and the search region together and leverages an attention mechanism to establish the information flow, breaking the previous pattern of independent \textit{extraction-and-correlation}. As a result, this module makes it possible to directly generate template-aware features that are suitable for the arbitrary and continuously changing nature of the target, enabling the model to deal with unseen categories. In addition, the MFA is proposed to make spatial and semantic information complementary to each other, which is characterized by reverse directional feature propagation that aggregates information from shallow to deep layers. Extensive experiments on KITTI and nuScenes demonstrate that our method has achieved considerable performance not only for class-specific tracking but also for class-agnostic tracking with less computation and higher efficiency.Comment: 12 pages,9 figure

Puerarin Improves Diabetic Aorta Injury by Inhibiting NADPH Oxidase-Derived Oxidative Stress in STZ-Induced Diabetic Rats

Objective. Puerarin is a natural flavonoid isolated from the TCM lobed kudzuvine root. This study investigated the effect and mechanisms of puerarin on diabetic aorta in rats. Methods. Streptozotocin- (STZ-) induced diabetic rats were administered with puerarin for 3 weeks. Levels of serum insulin (INS), PGE2, endothelin (ET), glycated hemoglobin (GHb), H2O2, and nitric oxide (NO) in rats were measured by ELISA and colorimetric assay kits. The aortas were stained with H&E. Moreover, the mRNA expression of ICAM-1, LOX-1, NADPH oxidase 2 (NOX2), and NOX4 and the protein expression of ICAM-1, LOX-1, NF-κB p65, E-selectin, NOX2, and NOX4 in aorta tissues were measured by real-time PCR and Western blot, respectively. The localization of ICAM-1, NF-κB p65, NOX2, and NOX4 in the aorta tissues was also determined through immunohistochemistry. Results. Puerarin treatment exerted no effect on fasting blood glucose levels but significantly reduced the serum levels of INS, GHb, PGE2, ET, H2O2, and NO. In addition, puerarin improved the pathological alterations and inhibited the expression of ICAM-1, LOX-1, NOX2, and NOX4 at both mRNA and protein levels. Puerarin also significantly reduced the number of cells showing positive staining for ICAM-1, NOX2, NOX4, and NF-κB p65. Conclusion. Puerarin demonstrated protective effect on the STZ-induced diabetic rat aorta. The protective mechanisms may include regulation of NF-κB and inhibition of NOX2 and NOX4 followed by inhibition of cell adhesion molecule expression

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN