A new route to produce starch-based fiber mesh scaffolds by wet spinning and subsequent surface modification as a way to improve cell attachment and proliferation

This study proposes a new route for producing fiber mesh scaffolds from a starch-polycaprolactone (SPCL) blend. It was demonstrated that the scaffolds with 77% porosity could be obtained by a simple wet-spinning technique based on solution/precipitation of a polymeric blend. To enhance the cell attachment and proliferation, Ar plasma treatment was applied to the scaffolds. It was observed that the surface morphology and chemical composition were significantly changed because of the etching and functionalization of the fiber surfaces. XPS analyses showed an increase of the oxygen content of the fiber surfaces after plasma treatment (untreated scaffolds O/C:0.32 and plasma-treated scaffolds O/C:0.41). Both untreated and treated scaffolds were examined using a SaOs-2 human osteoblast-like cell line during 2 weeks of culture. The cell seeded on wet-spun SPCL fiber mesh scaffolds showed high viability and alkaline phosphatase enzyme activity, with those values being even higher for the cells seeded on the plasma-treated scaffolds.Contract grant sponsor: FCT Foundation for Science and Technolog

The Pathogenic Properties of a Novel and Conserved Gene Product, KerV, in Proteobacteria

Identification of novel virulence factors is essential for understanding bacterial pathogenesis and designing antibacterial strategies. In this study, we uncover such a factor, termed KerV, in Proteobacteria. Experiments carried out in a variety of eukaryotic host infection models revealed that the virulence of a Pseudomonas aeruginosa kerV null mutant was compromised when it interacted with amoebae, plants, flies, and mice. Bioinformatics analyses indicated that KerV is a hypothetical methyltransferase and is well-conserved across numerous Proteobacteria, including both well-known and emerging pathogens (e.g., virulent Burkholderia, Escherichia, Shigella, Vibrio, Salmonella, Yersinia and Brucella species). Furthermore, among the 197 kerV orthologs analyzed in this study, about 89% reside in a defined genomic neighborhood, which also possesses essential DNA replication and repair genes and detoxification gene. Finally, infection of Drosophila melanogaster with null mutants demonstrated that KerV orthologs are also crucial in Vibrio cholerae and Yersinia pseudotuberculosis pathogenesis. Our findings suggested that KerV has a novel and broad significance as a virulence factor in pathogenic Proteobacteria and it might serve as a new target for antibiotic drug design

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

A Study on Harmonious Rural Tourism Environment Under the Construction of the Socialism New Rural

Rural environment is the base for the development of rural tourism. At the construction of socialism new rural, the rural tourism environment has been turned to a radical change. The paper analysed from the meaning and the composing of ruroal tourism environment, applyed to discuss the changes and the characcteristics of harmonious rural tourism environment under the construction of socialism new rural, and wish to advance the development of harmonious rural tourism

Improvement of Image Stitching Using Binocular Camera Calibration Model

Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax

Improvement of Image Stitching Using Binocular Camera Calibration Model

Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax

SlJAZ10 and SlJAZ11 mediate dark-induced leaf senescence and regeneration.

During evolutionary adaptation, the mechanisms for self-regulation are established between the normal growth and development of plants and environmental stress. The phytohormone jasmonate (JA) is a key tie of plant defence and development, and JASMONATE-ZIM DOMAIN (JAZ) repressor proteins are key components in JA signalling pathways. Here, we show that JAZ expression was affected by leaf senescence from the transcriptomic data. Further investigation revealed that SlJAZ10 and SlJAZ11 positively regulate leaf senescence and that SlJAZ11 can also promote plant regeneration. Moreover, we reveal that the SlJAV1-SlWRKY51 (JW) complex could suppress JA biosynthesis under normal growth conditions. Immediately after injury, SlJAZ10 and SlJAZ11 can regulate the activity of the JW complex through the effects of electrical signals and Ca2+ waves, which in turn affect JA biosynthesis, causing a difference in the regeneration phenotype between SlJAZ10-OE and SlJAZ11-OE transgenic plants. In addition, SlRbcs-3B could maintain the protein stability of SlJAZ11 to protect it from degradation. Together, SlJAZ10 and SlJAZ11 not only act as repressors of JA signalling to leaf senescence, but also regulate plant regeneration through coordinated electrical signals, Ca2+ waves, hormones and transcriptional regulation. Our study provides critical insights into the mechanisms by which SlJAZ11 can induce regeneration

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations

Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments prevent widespread application of DRL agents in real-world scenarios. Although advances have been made recently, most prior works assume sufficient online interaction on training environments, which can be costly in practical cases. To this end, we focus on an offline-training-online-adaptation setting, in which the agent first learns from offline experiences collected in environments with different dynamics and then performs online policy adaptation in environments with new dynamics. In this paper, we propose Policy Adaptation with Decoupled Representations (PAnDR) for fast policy adaptation. In offline training phase, the environment representation and policy representation are learned through contrastive learning and policy recovery, respectively. The representations are further refined by mutual information optimization to make them more decoupled and complete. With learned representations, a Policy-Dynamics Value Function (PDVF) [Raileanu et al., 2020] network is trained to approximate the values for different combinations of policies and environments from offline experiences. In online adaptation phase, with the environment context inferred from few experiences collected in new environments, the policy is optimized by gradient ascent with respect to the PDVF. Our experiments show that PAnDR outperforms existing algorithms in several representative policy adaptation problems.Comment: Accepted on IJCAI 2022 and a previous version of this work is presented at the Generalizable Policy Learning in the Physical World Workshop (ICLR 2022