Sampled-Data Control of Singular Systems with Time Delays

This paper is concerned with sampled-data controller design for singular systems with time delay. It is assumed that the sampling periods are arbitrarily varying but bounded. A time-dependent Lyapunov function is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Combining input delay approach with Lyapunov method, sufficient conditions are derived which guarante that the singular system is regular, impulse free, and exponentially stable. Then, the existence conditions of desired sampled-data controller can be obtained, which are formulated in terms of strict linear matrix inequality. Finally, numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

Synthesis and characterization of aligned ZnO/BeO core/shell nanocable arrays on glass substrate

By sequential hydrothermal growth of ZnO nanowire arrays and thermal evaporation of Be, large-scale vertically aligned ZnO/BeO core/shell nanocable arrays on glass substrate have been successfully synthesized without further heat treatment. Detailed characterizations on the sample morphologies, compositions, and microstructures were systematically carried out, which results disclose the growth behaviors of the ZnO/BeO nanocable. Furthermore, incorporation of BeO shell onto ZnO core resulted in distinct improvement of optical properties of ZnO nanowire, i.e., significant enhancement of near band edge (NBE) emission as well as effective suppression of defects emission in ZnO. In particular, the NBE emission of nanocable sample shows a noticeable blue-shift compared with that of pristine ZnO nanowire, which characteristics most likely originate from Be alloying into ZnO. Consequently, the integration of ZnO and BeO into nanoscale heterostructure could bring up new opportunities in developing ZnO-based device for application in deep ultraviolet region

GPS: a comprehensive www server for phosphorylation sites prediction

Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is labor-intensive and often limited by the availability and optimization of enzymatic reactions. Recently, large-scale analysis of the phosphoproteome by the mass spectrometry (MS) has become a popular approach. But experimentally, it is still difficult to distinguish the kinase-specific sites on the substrates. In this regard, the in silico prediction of phosphorylation sites with their specific kinases using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of MS phosphoproteomic data. A variety of such tools exists over the Internet and provides the predictions for at most 30 PK subfamilies. We downloaded the verified phosphorylation sites from the public databases and curated the literature extensively for recently found phosphorylation sites. With the hypothesis that PKs in the same subfamily share similar consensus sequences/motifs/functional patterns on substrates, we clustered the 216 unique PKs in 71 PK groups, according to the BLAST results and protein annotations. Then, we applied the group-based phosphorylation scoring (GPS) method on the data set; here, we present a comprehensive PK-specific prediction server GPS, which could predict kinase-specific phosphorylation sites from protein primary sequences for 71 different PK groups. GPS has been implemented in PHP and is available on a www server at

Effectiveness of a multimodal standard nursing program on health-related quality of life in Chinese mainland female patients with breast cancer: protocol for a single-blind cluster randomized controlled trial.

BACKGROUND Breast cancer and its treatment-related adverse effects are harmful to physical, psychological, and social functioning, leading to health-related quality of life (HRQoL) impairment in patients. Many programs have been used with this population for HRQoL improvement; however, few studies have considered the physical, psychological, and social health domains comprehensively, and few have constructed multimodal standard nursing interventions based on specific theories. The purpose of this trial is to examine the effect of a health belief model (HBM)-based multimodal standard nursing program (MSNP) on HRQoL in female patients with breast cancer. METHODS This is a two-arm single-blind cluster randomized controlled trial (cRCT) in clinical settings. Twelve tertiary hospitals will be randomly selected from the 24 tertiary hospitals in Xi'an, China, and allocated to the intervention arm and control arm using a computer-generated random numbers table. Inpatient female patients with breast cancer from each hospital will receive either MSNP plus routine nursing care immediately after recruitment (intervention arm), or only routine nursing care (control arm). The intervention will be conducted by trained nurses for 12 months. All recruited female patients with breast cancer, participating clinical staff, and trained data collectors from the 12 hospitals will be blind with respect to group allocation. Patients of the control arm will not be offered any information about the MSNP during the study period to prevent bias. The primary outcome is HRQoL measured through the Functional Assessment of Cancer Therapy-Breast version 4.0 at 12 months. Secondary outcomes include pain, fatigue, sleep, breast cancer-related lymphedema, and upper limb function, which are evaluated by a visual analogue scale, the circumference method, and the Constant-Murley Score. DISCUSSION This trial will provide important evidence on the effectiveness of multimodal nursing interventions delivered by nurses in clinical settings. Study findings will inform strategies for scaling up comprehensive standard intervention programs on health management in the population of female patients with breast cancer. TRIAL REGISTRATION Chictr.org.cn ChiCTR-IOR-16008253 (April 9, 2016)

DGI: Easy and Efficient Inference for GNNs

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. On the other hand, layer-wise inference avoids the neighbor explosion problem by conducting inference layer by layer such that the nodes only need their one-hop neighbors in each layer. However, implementing layer-wise inference requires substantial engineering efforts because users need to manually decompose a GNN model into layers for computation and split workload into batches to fit into device memory. In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution. DGI is general for various GNN models and different kinds of inference requests, and supports out-of-core execution on large graphs that cannot fit in CPU memory. Experimental results show that DGI consistently outperforms layer-wise inference across different datasets and hardware settings, and the speedup can be over 1,000x.Comment: 10 pages, 10 figure

Sampled-Data Control for Singular Neutral System

This study is concerned with the ∞ control problem for singular neutral system based on sampled-data. By input delay approach and a composite state-derivative control law, the singular system is turned into a singular neutral system with time-varying delay. Less conservative result is derived for the resultant system by incorporating the delay decomposition technique, Wirtinger-based integral inequality, and an augmented Lyapunov-Krasovskii functional. Sufficient conditions are derived to guarantee that the resulting system is regular, impulse-free, and asymptotically stable with prescribed ∞ performance. Then, the ∞ sampled-data controller is designed by means of linear matrix inequalities. Finally, two simulation results have shown that the proposed method is effective

Enhancing Event Sequence Modeling with Contrastive Relational Inference

Neural temporal point processes(TPPs) have shown promise for modeling continuous-time event sequences. However, capturing the interactions between events is challenging yet critical for performing inference tasks like forecasting on event sequence data. Existing TPP models have focused on parameterizing the conditional distribution of future events but struggle to model event interactions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.Comment: 6 pages, 2 figure

H

This study is concerned with the H∞ control problem for singular neutral system based on sampled-data. By input delay approach and a composite state-derivative control law, the singular system is turned into a singular neutral system with time-varying delay. Less conservative result is derived for the resultant system by incorporating the delay decomposition technique, Wirtinger-based integral inequality, and an augmented Lyapunov-Krasovskii functional. Sufficient conditions are derived to guarantee that the resulting system is regular, impulse-free, and asymptotically stable with prescribed H∞ performance. Then, the H∞ sampled-data controller is designed by means of linear matrix inequalities. Finally, two simulation results have shown that the proposed method is effective

Serum IL-18 Is Closely Associated with Renal Tubulointerstitial Injury and Predicts Renal Prognosis in IgA Nephropathy

Background. IgA nephropathy (IgAN) was thought to be benign but recently found it slowly progresses and leads to ESRD eventually. The aim of this research is to investigate the value of serum IL-18 level, a sensitive biomarker for proximal tubule injury, for assessing the histopathological severity and disease progression in IgAN. Methods. Serum IL-18 levels in 76 IgAN patients and 36 healthy blood donors were measured by ELISA. We evaluated percentage of global and segmental sclerosis (GSS) and extent of tubulointerstitial damage (TID). The correlations between serum IL-18 levels with clinical, histopathological features and renal prognosis were evaluated. Results. The patients were 38.85 ± 10.95 years old, presented with 2.61 (1.43∼4.08) g/day proteinuria. Serum IL-18 levels were significantly elevated in IgAN patients. Baseline serum IL-18 levels were significantly correlated with urinary protein excretion (r = 0.494, P = 0.002), Scr (r = 0.61, P < 0.001), and eGFR (r = −0.598, P < 0.001). TID scores showed a borderline significance with serum IL-18 levels (r = 0.355, P = 0.05). During follow-up, 26 patients (34.21%) had a declined renal function. Kaplan-Meier analysis found those patients with elevated IL-18 had a significant poor renal outcome (P = 0.03), and Cox analysis further confirmed that serum IL-18 levels were an independent predictor of renal prognosis (β = 1.98, P = 0.003)

RpoN (σ54) Is Required for Floc Formation but Not for Extracellular Polysaccharide Biosynthesis in a Floc-Forming Aquincola tertiaricarbonis Strain.

Some bacteria are capable of forming flocs, in which bacterial cells become self-flocculated by secreted extracellular polysaccharides and other biopolymers. The floc-forming bacteria play a central role in activated sludge, which has been widely utilized for the treatment of municipal sewage and industrial wastewater. Here, we use a floc-forming bacterium, Aquincolatertiaricarbonis RN12, as a model to explore the biosynthesis of extracellular polysaccharides and the regulation of floc formation. A large gene cluster for exopolysaccharide biosynthesis and a gene encoding the alternative sigma factor RpoN1, one of the four paralogues, have been identified in floc formation-deficient mutants generated by transposon mutagenesis, and the gene functions have been further confirmed by genetic complementation analyses. Interestingly, the biosynthesis of exopolysaccharides remained in the rpoN1-disrupted flocculation-defective mutants, but most of the exopolysaccharides were secreted and released rather than bound to the cells. Furthermore, the expression of exopolysaccharide biosynthesis genes seemed not to be regulated by RpoN1. Taken together, our results indicate that RpoN1 may play a role in regulating the expression of a certain gene(s) involved in the self-flocculation of bacterial cells but not in the biosynthesis and secretion of exopolysaccharides required for floc formation.IMPORTANCE Floc formation confers bacterial resistance to predation of protozoa and plays a central role in the widely used activated sludge process. In this study, we not only identified a large gene cluster for biosynthesis of extracellular polysaccharides but also identified four rpoN paralogues, one of which (rpoN1) is required for floc formation in A. tertiaricarbonis RN12. In addition, this RpoN sigma factor regulates the transcription of genes involved in biofilm formation and swarming motility, as previously shown in other bacteria. However, this RpoN paralogue is not required for the biosynthesis of exopolysaccharides, which are released and dissolved into culture broth by the rpoN1 mutant rather than remaining tightly bound to cells, as observed during the flocculation of the wild-type strain. These results indicate that floc formation is a regulated complex process, and other yet-to-be identified RpoN1-dependent factors are involved in self-flocculation of bacterial cells via exopolysaccharides and/or other biopolymers