PilG is Involved in the Regulation of Twitching Motility and Antifungal Antibiotic Biosynthesis in the Biological Control Agent \u3ci\u3eLysobacter enzymogenes\u3c/i\u3e

Lysobacter enzymogenes strain C3 is a gliding bacterium which produces the antifungal secondary metabolite heat-stable antifungal factor (HSAF) and type IV pilus (T4P) as important mechanisms in biological control activity against fungal pathogens. To date, the regulators that control HSAF biosynthesis and T4P-dependent twitching motility in L. enzymogenes are poorly explored. In the present study, we addressed the role of pilG in the regulation of these two traits in L. enzymogenes. PilG of L. enzymogenes was found to be a response regulator, commonly known as a component of a two-component transduction system. Mutation of pilG in strain C3 abolished its ability to display spreading colony phenotype and cell movement at the colony margin, which is indicative of twitching motility; hence, PilG positively regulates twitching motility in L. enzymogenes. Mutation of pilG also enhanced HSAF production and the transcription of its key biosynthetic gene hsaf pks/nrps, suggesting that PilG plays a negative regulatory role in HSAF biosynthesis. This finding represents the first demonstration of the regulator PilG having a role in secondary metabolite biosynthesis in bacteria. Collectively, our results suggest that key ecological functions (HSAF production and twitching motility) in L. enzymogenes strain C3 are regulated in opposite directions by the same regulatory protein, PilG

Synthesis and anti-myocarditis activity in a multifunctional lanthanide microporous metal-organic framework with 1D helical chain building units

A new microporous lanthanide metal-organic framework, {[Yb(BTB)(H2O) (DEF)2}n (1, DEF=N,N-Diethylformamide), with 1D nano-sized channels has been constructed by bridging helical chain secondary building units with 1,3,5-benzenetrisbenzoic acid (H3BTB) ligand. Structural characterization suggests that this complex crystallizes in the hexagonal space group P6122 and possesses 1D triangular channels with coordinated water molecules pointing to the channel center. In addition, anti-myocarditis properties of compound 1 were evaluated in vivo. The results showed that compound 1 can improve hemodynamic parameters of, and it may be a good therapeutic option for heart failure in the future

Scale-aware network with modality-awareness for RGB-D indoor semantic segmentation

This paper focuses on indoor semantic segmentation based on RGB-D images. Semantic segmentation is a pixel-level classification task that has made steady progress based on fully convolutional networks (FCNs). However, we find there is still room for improvements in the following three aspects. The first is related to multi-scale feature extraction. Recent state-of-the-art works forcibly concatenate multi-scale feature representations extracted by spatial pyramid pooling, dilated convolution or other architectures, regardless of the spatial extent for each pixel. The second is regarding RGB-D modal fusion. Most successful methods treat RGB and depth as two separate modalities and force them to be joined together regardless of their different contributions to the final prediction. The final aspect is about the modeling ability of extracted features. Due to the “local grid” defined by the receptive field, the learned feature representation lacks the ability to model spatial dependencies. In addition to these modules, we design a depth estimation module to encourage the RGB network to extract more effective features. To solve the above challenges, we propose four modules to address them: scale-aware module, modality-aware module, attention module and depth estimation module. Extensive experiments on the NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method is effective against RGB-D indoor semantic segmentation

Dopamine Transporter, Depression and Anxiety Differences Between Tremor-Predominant and Non-Tremulous PD are Already Evident Within 2 Years of PD Diagnosis

Objective: PD patients can be partitioned into two groups, denoted Tremor Dominant (PD-TD) and non-tremor-dominant (PD-NTD) based on their MDS-UPDRS III subscores. Is this distinction correlated with the outcomes of cognitive tests, biomarkers, and dopaminergic imaging within 2 years of the PD diagnosis? Background: The classification of Parkinson’s disease (PD) patients into tremor-dominant (PD-TD) and non-tremor-dominant (PD-NTD) is commonly used clinically. The distinction is based on the relative difficulty the patient has with tremor vs akinetic or rigid aspects of PD, as reflected in scores on specific questions in the MDS-UPDRS III. The Parkinson’s Progression Markers Initiative (PPMI) cohort is a large observational clinical study aimed at verifying progression markers of Parkinson’s disease. This cohort provides us with the data for answering this question. Indeed, it contains 400 patients with a diagnosis of PD within 2 years of the baseline visit and an exhaustive collection of assessments. Methods: We have studied a comprehensive collection of PPMI measurements, including neurological and psychological tests, biofluid-based biomarkers, and dopamine transporter Imaging acquired at the baseline visit. In each case, we conducted a Wilcoxon test for the difference of the median within the two groups PD-TD and PD-NTD and corrected for multiple testing. Results: PD-NTD patients were clinically more severe in regards to their motor and non-motor symptoms of PD, as measured with the MDS-UPDRS I and II, as well as more likely to have depression and anxiety symptomatology. In addition, there were very significant dopamine transporter imaging (DaTscan) differences between the PD subtypes: PD-TD patients had less of a reduction in uptake in the striatum, caudate, and putamen compared to PD-NTD. Conclusions: The distinction between PD-TD and PD-NTD is well established using neurological exams. However, we have shown that the depression, anxiety and DaTscan measurements are also significantly different between these 2 groups at the earliest stages of the disease

Electrical Tuning of the SERS Enhancement by Precise Defect Density Control

International audienceSurface-enhanced Raman scattering (SERS) has been widely established as a powerful analytical technique in molecular fingerprint recognition. Although conventional noble metal-based SERS substrates show admirable enhancement of the Raman signals, challenges on reproducibility, biocompatibility, and costs limit their implementations as the preferred analysis platforms. Recently, researches on SERS substrates have found that some innovatively prepared metal oxides/chalcogenides could produce noble metal comparable SERS enhancement, which profoundly expanded the material selection. Nevertheless, to tune the SERS enhancement of these materials, careful experimental designs and sophisticated processes were needed. Here, an electrically tunable SERS substrate based on tungsten oxides (WO3–x) is demonstrated. An electric field is used to introduce the defects in the oxide on an individual substrate, readily invoking the SERS detection capability, and further tuning the enhancement factor is achieved through electrical programming of the oxide leakage level. Additionally, by virtue of in situ tuning the defect density and enhancement factor, the substrate can adapt to different molecular concentrations, potentially improving the detection range. These results not only help build a better understanding of the chemical mechanism but also open an avenue for engaging non-noble metal materials as multifunctional SERS substrates

Intraday multi-objective hierarchical coordinated operation of a multi-energy system

An intraday, multi-objective, hierarchical and coordinated operation scheduling method for a multi-energy system (MES), which uses 15-min and 5-min scheduling intervals for different energy subsystems, is proposed. According to the characteristics of MES and the response time of energy conversion equipment, energy subsystems are dispatched on different dispatch intervals instead of unified dispatch intervals to dispatch energy subsystems. In a case study, the prediction error rate for a load is 5% and 2% when the dispatch interval is 15 min and 5 min, respectively; the prediction error rates for wind and solar energy output are 10% and 5%, respectively. Dispatching different subsystems with different intervals according to their characteristics reduces the impact of source and load uncertainties, which improves energy management. Multi-energy power dispatch considers exergy efficiency and the operation cost to improve the utilization energy efficiency. The Tchebycheff method, which considers the fuzzy entropy weight, is employed to balance the objectives between highest exergy efficiency and lowest operation cost. Numerical studies demonstrate that the operation cost of a multi-objective system is ¥500.8877 higher than that of a system with the objective of lowest operation cost. The exergy efficiency is increased by 3.94%, and the operation cost, which is reduced by ¥1706.9606, is 2.13% lower than that for the objective of highest exergy efficiency. Thus, the proposed method can balance the objectives between highest exergy efficiency and lowest operation cost. These findings provide a multi-dimensional dispatch scheme for dispatchers and highlight the development potential of an MES