CdS Quantum Dots as Fluorescent Probes for Detection of Naphthalene

A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect

CdS Quantum Dots as Fluorescent Probes for Detection of Naphthalene

A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect

Variable structure attitude control for a rolling aerial vehicle via extended state observer

A novel attitude control scheme is proposed for a rolling aerial vehicle (RAV) with large uncertainties. Firstly, the RAV highly coupled nonlinear system is separated into attitude loop and angular loop via backstepping technique. The nominal states are calculated based on the procedure of trajectory linearization control (TLC). Then, extended state observers (ESO) are applied to estimate the uncertainties in the RAV system. Meanwhile, a feedback linearization-based controller is synthesized for the attitude loop using the estimated uncertainties, and an ESO-based sliding mode controller is synthesized for the angular rate loop. The stability of the closed-loop system is studied. Simulation results with comparisons are presented to demonstrate the feasibility of the proposed control scheme

SCFIA: a statistical corresponding feature identification algorithm for LC/MS

<p>Abstract</p> <p>Background</p> <p>Identifying corresponding features (LC peaks registered by identical peptides) in multiple Liquid Chromatography/Mass Spectrometry (LC-MS) datasets plays a crucial role in the analysis of complex peptide or protein mixtures. Warping functions are commonly used to correct the mean of elution time shifts among LC-MS datasets, which cannot resolve the ambiguity of corresponding feature identification since elution time shifts are random. We propose a Statistical Corresponding Feature Identification Algorithm(SCFIA) based on both elution time shifts and peak shape correlations between corresponding features. SCFIA first trains a set of statistical models, and then, all candidate corresponding features are scored by the statistical models to find the maximum likelihood solution.</p> <p>Results</p> <p>We test SCFIA on publicly available datasets. We first compare its performance with that of warping function based methods, and the results show significant improvements. The performance of SCFIA on replicates datasets and fractionated datasets is also evaluated. In both cases, the accuracy is above 90%, which is near optimal. Finally the coverage of SCFIA is evaluated, and it is shown that SCFIA can find corresponding features in multiple datasets for over 90% peptides identified by Tandem MS.</p> <p>Conclusions</p> <p>SCFIA can be used for accurate corresponding feature identification in LC-MS. We have shown that peak shape correlation can be used effectively for improving the accuracy. SCFIA provides high coverage in corresponding feature identification in multiple datasets, which serves the basis for integrating multiple LC-MS measurements for accurate peptide quantification.</p

Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks

<p>Abstract</p> <p>Background</p> <p>Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task.</p> <p>Results</p> <p>We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method.</p> <p>Conclusions</p> <p>The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.</p

3D Model-based Zero-Shot Pose Estimation Pipeline

Most existing learning-based pose estimation methods are typically developed for non-zero-shot scenarios, where they can only estimate the poses of objects present in the training dataset. This setting restricts their applicability to unseen objects in the training phase. In this paper, we introduce a fully zero-shot pose estimation pipeline that leverages the 3D models of objects as clues. Specifically, we design a two-step pipeline consisting of 3D model-based zero-shot instance segmentation and a zero-shot pose estimator. For the first step, there is a novel way to perform zero-shot instance segmentation based on the 3D models instead of text descriptions, which can handle complex properties of unseen objects. For the second step, we utilize a hierarchical geometric structure matching mechanism to perform zero-shot pose estimation which is 10 times faster than the current render-based method. Extensive experimental results on the seven core datasets on the BOP challenge show that the proposed method outperforms the zero-shot state-of-the-art method with higher speed and lower computation cost

Histone Deacetylase 3-Directed PROTACs Have Anti-inflammatory Potential by Blocking Polarization of M0-like into M1-like Macrophages

Macrophage polarization plays a crucial role in inflammatory processes. The histone deacetylase 3 (HDAC3) has deacetylase-independent function that can activate pro-inflammatory gene expression in LPS-stimulated M1-like macrophages and cannot be blocked by traditional small-molecule HDAC3 inhibitors. Here we employ the proteolysis targeting chimera (PROTAC) technology to target the deacetylase-independent function of HDAC3. We developed a potent and selective HDAC3-directed PROTAC, denoted P7, which induces nearly complete HDAC3 degradation at low micromolar concentrations in both THP-1 cells and human primary macrophages. P7 increases the anti-inflammatory cytokine secretion in THP-1 derived M1-like macrophages. Importantly, P7 decreases the secretion of pro-inflammatory cytokines in M1-like macrophages derived from human primary macrophages. This can be explained by the observed inhibition of macrophage polarization from M0-like into M1-like macrophage. In conclusion, we demonstrate that the HDAC3-directed PROTAC P7 has anti-inflammatory activity and blocks macrophage polarization, which demonstrates that this molecular mechanism can be targeted with small molecule therapeutics.</p

Proteome characterization of cassava (Manihot esculenta Crantz) somatic embryos, plantlets and tuberous roots

<p>Abstract</p> <p>Background</p> <p>Proteomics is increasingly becoming an important tool for the study of many different aspects of plant functions, such as investigating the molecular processes underlying in plant physiology, development, differentiation and their interaction with the environments. To investigate the cassava (<it>Manihot esculenta </it>Crantz) proteome, we extracted proteins from somatic embryos, plantlets and tuberous roots of cultivar SC8 and separated them by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE).</p> <p>Results</p> <p>Analysis by liquid chromatography-electrospray ionisation-tandem mass spectrometry (LC-ESI-MS/MS) yielded a total of 383 proteins including isoforms, classified into 14 functional groups. The majority of these were carbohydrate and energy metabolism associated proteins (27.2%), followed by those involved in protein biosynthesis (14.4%). Subsequent analysis has revealed that 54, 59, 74 and 102 identified proteins are unique to the somatic embryos, shoots, adventitious roots and tuberous roots, respectively. Some of these proteins may serve as signatures for the physiological and developmental stages of somatic embryos, shoots, adventitious roots and tuberous root. Western blotting results have shown high expression levels of Rubisco in shoots and its absence in the somatic embryos. In addition, high-level expression of α-tubulin was found in tuberous roots, and a low-level one in somatic embryos. This extensive study effectively provides a huge data set of dynamic protein-related information to better understand the molecular basis underlying cassava growth, development, and physiological functions.</p> <p>Conclusion</p> <p>This work paves the way towards a comprehensive, system-wide analysis of the cassava. Integration with transcriptomics, metabolomics and other large scale "-omics" data with systems biology approaches can open new avenues towards engineering cassava to enhance yields, improve nutritional value and overcome the problem of post-harvest physiological deterioration.</p

Different forms of nitrogen contents and their vertical variations of transformation modes of the sediments of Lake Yuehu, Wuhan

Vertical distributions of nitrogen contents, net nitrification rates, net N-mineralization rates and nitrate reductase activities in sediments of Lake Yuehu in June, 2005, were described on basis of four samples from the lake. The results showed that there was a critical layer in which exchangeable nitrate contents were the highest. Exchangeable ammonium and available nitrogen contents were the lowest in the Subsurface sediment (5-10cm). Available nitrogen was stored mainly in the form of exchangeable ammonium in both surface (0-5cm deep) and deeper layers (>10cm deep) where their contents were higher. The pattern of this distribution can be explained by anaerobic conditions. The surface sediment not only showed higher contents of total nitrogen and organic nitrogen, rates of net nitrification, N-mineralization and nitrate reductase activities, but also displayed the highest ammonium and the lowest nitrate concentrations in interstitial water. Therefore, based on a nitrogen cycling mode, we proposed that organic nitrogen was re-mineralized to ammonium and nitrate with processes of the former being nitrified into the later, resulting in anaerobic conditions that contributed to ammonium accumulation by the production of its own and nitrate reduction in interstitial water of surface sediment. In general, the surface sediment in eutrophic lakes, enriched by organic nitrogen, is the most active dimension for the biogeochemical cycling of nitrogen with ammonium being the major and most effective forms