Max-SINR Receiver for HMCT Systems over Non-Stationary Doubly Dispersive Channel

In this paper, a maximizing Signal-to-Interference plus-Noise Ratio (Max-SINR) receiver for Hexagonal Multicarrier Transmission (HMCT) system over non-stationary doubly dispersive (NSDD) channel is proposed. The closed-form timing offset expression of the prototype pulse for the proposed Max-SINR HMCT receiver over NSDD channel is derived. Simulation results show that the proposed Max-SINR receiver outperforms traditional projection scheme and obtains an approximation to the theoretical upper bound SINR performance within all the local stationarity regions (LSRs). Meanwhile, the SINR performance of the proposed Max-SINR HMCT receiver is robust to the estimation error between the estimated value and the real value of root mean square (RMS) delay spread.Comment: This paper has been accepted by URSI GASS 2014 and will be presented in the proceeding of URSI GASS 201

Characterization, sub-cellular localization and expression profiling of the isoprenylcysteine methylesterase gene family in Arabidopsis thaliana

Background: Isoprenylcysteine methylesterases (ICME) demethylate prenylated protein in eukaryotic cell. Until now, knowledge about their molecular information, localization and expression pattern is largely unavailable in plant species. One ICME in Arabidopsis, encoded by At5g15860, has been identified recently. Over-expression of At5g15860 caused an ABA hypersensitive phenotype in transgenic Arabidopsis plants, indicating that it functions as a positive regulator of ABA signaling. Moreover, ABA induced the expression of this gene in Arabidopsis seedlings. The current study extends these findings by examining the sub-cellular localization, expression profiling, and physiological functions of ICME and two other ICME-like proteins, ICME-LIKE1 and ICME-LIKE2, which were encoded by two related genes At1g26120 and At3g02410, respectively. Results: Bioinformatics investigations showed that the ICME and other two ICME-like homologs comprise a small subfamily of carboxylesterase (EC 3.1.1.1) in Arabidopsis. Sub-cellular localization of GFP tagged ICME and its homologs showed that the ICME and ICME-like proteins are intramembrane proteins predominantly localizing in the endoplasmic reticulum (ER) and Golgi apparatus. Semi-quantitative and real-time quantitative PCR revealed that the ICME and ICME-like genes are expressed in all examined tissues, including roots, rosette leaves, cauline leaves, stems, flowers, and siliques, with differential expression levels. Within the gene family, the base transcript abundance of ICME-LIKE2 gene is very low with higher expression in reproductive organs (flowers and siliques). Time-course analysis uncovered that both ICME and ICME-like genes are up-regulated by mannitol, NaCl and ABA treatment, with ICME showing the highest level of up-regulation by these treatments. Heat stress resulted in up-regulation of the ICME gene significantly but down-regulation of the ICME-LIKE1 and ICME-LIKE2 genes. Cold and dehydration stimuli led to no significant change of both ICME and ICME-like gene expression. Mutant icme-like2-1 showed increased sensitivity to ABA but slightly decreased sensitivity to salt and osmotic stresses during seed germination. Conclusions: It is concluded that the ICME family is involved in stress and ABA signaling in Arabidopsis, probably through mediating the process of demethylating prenylated proteins. Identification of these prenylated proteins will help to better understand the significance of protein prenylation in Planta

Hyperspectral Target Detection Based on Low-Rank Background Subspace Learning and Graph Laplacian Regularization

Hyperspectral target detection is good at finding dim and small objects based on spectral characteristics. However, existing representation-based methods are hindered by the problem of the unknown background dictionary and insufficient utilization of spatial information. To address these issues, this paper proposes an efficient optimizing approach based on low-rank representation (LRR) and graph Laplacian regularization (GLR). Firstly, to obtain a complete and pure background dictionary, we propose a LRR-based background subspace learning method by jointly mining the low-dimensional structure of all pixels. Secondly, to fully exploit local spatial relationships and capture the underlying geometric structure, a local region-based GLR is employed to estimate the coefficients. Finally, the desired detection map is generated by computing the ratio of representation errors from binary hypothesis testing. The experiments conducted on two benchmark datasets validate the effectiveness and superiority of the approach. For reproduction, the accompanying code is available at https://github.com/shendb2022/LRBSL-GLR.Comment: 4 pages, 3 figures, 1 tabl

Quantized cooperative output regulation of continuous-time multi-agent systems over switching graph

summary:This paper investigates the problem of quantized cooperative output regulation of linear multi-agent systems with switching graphs. A novel dynamic encoding-decoding scheme with a finite communication bandwidth is designed. Leveraging this scheme, a distributed protocol is proposed, ensuring asymptotic convergence of the tracking error under both bounded and unbounded link failure durations. Compared with the existing quantized control work of MASs, the semi-global assumption of initial conditions is not required, and the communication graph is only required to be jointly connected. Finally, two simulation examples demonstrate the effectiveness of the proposed distributed protocol for bounded and unbounded link failure durations

VGLL4 inhibits stemness and cisplatin resistance in non-small cell lung cancer via the COL3A1/NF-κB pathway

Previous studies have confirmed that vestigial-like protein 4 (VGLL4) can inhibit the malignant progression of lung cancer cells. However, its impact on cisplatin resistance and stemness in lung cancer cells remains unclear. In this study, we established cisplatin-resistant cells and transfected them with VGLL4 overexpression plasmid and siRNA. Their 50% inhibitory concentration (IC50) values were determined via Cell Counting Kit-8 (CCK-8) assay, cell proliferation was assessed via clone formation assay, apoptosis rate was measured by flow cytometry, sphere formation was quantified, and protein expression of collagen type III alpha 1 (COL3A1) and p-p65/p65 was analyzed using Western blot. Our findings demonstrate that VGLL4 enhances the sensitivity of cisplatin-resistant cells to cisplatin, inhibits cell proliferation, and promotes apoptosis. Moreover, VGLL4 suppresses sphere formation and the expression of stemness markers Nanog and Oct4 in cisplatin-resistant cells. Mechanistically, VGLL4 regulates the nuclear transcription factor-κB (NF-κB) pathway through COL3A1, thereby influencing the sensitivity and stemness characteristics of cisplatin-resistant cells. In conclusion, this study shows that VGLL4 can augment treatment sensitivity and suppress stemness of cisplatin-resistant cells, thereby proposing a potential therapeutic target for cisplatin-resistant lung cancer

Using cloud-assisted body area networks to track people physical activity in mobility

This paper describes a novel BSN-based integrated system for detecting, monitoring, and securely recording human physical activities using wearable sensors, a personal mobile device, and a Cloud-computing infrastructure supported by the BodyCloud platform. An integration with a smart-wheelchair system is also presented. BSNs are a key enabling technology for the revolution of personal-health services and their integration with Cloud infrastructure can effectively supports the diffusion of such services in our daily life. Many of these personal-health systems - regardless of their final aim - are based, use or are supported by contextual information on user's physical activity (body posture, movement or action) being performed. This work, hence, aims at providing a basic physical activity service that is capable of supporting personal, mobile-Health applications with real-time activity recognition and labeling both on the personal mobile device and on the Cloud

A Study on the Individualized Training Mode of the Professional Degree Graduate Students of the Army

From the learning motivation, training process, professional direction of the diversified characteristics, this paper analyzes the realistic requirements of personalized training of military professional degree students, analyzes the problem of current personalized training of military professional degree students that training courses teaching lack of humanity, courses research lack of pertinence, and the training standard is not clear, put forward the suggests that graduate student selection protruding flexibility strategy, subject based on the problem oriented, study research subject to the forces demand, and cooperative establish the graduate training standards to improve the personalized professional degree graduate training ability