Electronic structure of hollow graphitic carbon nanoparticles made from acetylene carbon black

The electronic structure of hollow graphitic carbon nanoparticles obtained by catalytic graphitization of acetylene carbon black (ACB HGCNs) was studied by ultra-soft X-ray emission spectroscopy (USXES) method. The phases of the carbon powder samples were determined by XRD with monochromatic CuKα1 radiation. Transmission electron microscopy was used to study the ACB HGCN spatial structures and morphologies. The electronic structures of reference Q-graphenes and HGCNs obtained from iron carbide filled carbon nanocapsules (Fe3C@CNCs) which were synthesized by plasma method in hexane were measured for comparison with that of the synthesized ACB HGCNs

Electronic structure of hollow graphitic carbon nanoparticles made from acetylene carbon black

The electronic structure of hollow graphitic carbon nanoparticles obtained by catalytic graphitization of acetylene carbon black (ACB HGCNs) was studied by ultra-soft X-ray emission spectroscopy (USXES) method. The phases of the carbon powder samples were determined by XRD with monochromatic CuKα1 radiation. Transmission electron microscopy was used to study the ACB HGCN spatial structures and morphologies. The electronic structures of reference Q-graphenes and HGCNs obtained from iron carbide filled carbon nanocapsules (Fe3C@CNCs) which were synthesized by plasma method in hexane were measured for comparison with that of the synthesized ACB HGCNs

Deep learning for feature extraction in remote sensing: A case-study of aerial scene classification

Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy of image scene classification in such systems. To classify the scene from areal images, we used a two-stream deep architecture. We performed the first part of the classification, the feature extraction, using pre-trained CNN that extracts deep features of aerial images from different network layers: the average pooling layer or some of the previous convolutional layers. Next, we applied feature concatenation on extracted features from various neural networks, after dimensionality reduction was performed on enormous feature vectors. We experimented extensively with different CNN architectures, to get optimal results. Finally, we used the Support Vector Machine (SVM) for the classification of the concatenated features. The competitiveness of the examined technique was evaluated on two real-world datasets: UC Merced and WHU-RS. The obtained classification accuracies demonstrate that the considered method has competitive results compared to other cutting-edge techniques

Aerial scene classification through fine-tuning with adaptive learning rates and label smoothing

Remote Sensing (RS) image classification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional Neural Networks (CNNs). A common approach in the literature is employing CNNs for feature extraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre-trained models, we apply label smoothing regularization. For the fine-tuning and feature extraction process, we adopt the Inception-v3 and Xception inception-based CNNs, as well the residual-based networks ResNet50 and DenseNet121. We present extensive experiments on two real-world remote sensing image datasets: AID and NWPU-RESISC45. The results show that the proposed method exhibits classification accuracy of up to 98%, outperforming other state-of-the-art methods

Atmospheric pressure plasma and depositions of antibacterial coatings

Healthcare-associated infections (HCAI) are complications of healthcare that result in elevated patient morbidity and mortality. HCAI present a huge financial burden for patients, hospitals and insurers due to extended hospitalisation and associated care. According to the estimations, in the US alone, HCAI affects approximately 2 million patients annually, of whom approximately 90.000 patients die, with an estimated annual cost estimated to range from 28 billion to 45 billion US$. [1] European Union is facing the similar situation, the European Centre for Disease Prevention and control (ECDC) advice that approximately 4.1 million acute care patients acquire a HCAI annually, with 37.000 deaths directly attributed to HCAI. With increasing prevalence of HCAI across European countries and threatening development of antimicrobial resistance to widely used antibiotics, there is a recognised need for novel approach in battle against this healthcare burden [2]. One of the approaches involves a development and fabrication of materials with antimicrobial properties. Usually, these are coatings with integrated antibacterial agent that is responsible for the elimination of microorganisms that come into contact with active surface. There is a variety of different antibacterial compounds integrated in such coatings, such as different antibiotics, chemical compounds, peptides. Recently, metal nanoparticles (NPs) have been increasingly used in designing coatings with antibacterial properties due to their large surface-to-volume ration, physiochemical properties and biological multi-target mechanism of actions. Besides all beneficial properties of NPs their emergence of cytotoxicity is limiting their practical applications in human body. [3-4] To overcome this drawback it is important to design a new class of antibacterial coatings with firmly embedded NPs that allows controlled release of antimicrobial agent into the microenvironment. Atmospheric pressure plasma technology has shown a big promise as an alternative and cost-efficient method for deposition of coatings with antibacterial properties. This contribution explores the potential of plasma-assisted approach for fabrication of antibacterial coatings, containing different metal NPs on medical textiles. Plasma-assisted deposition of coatings was carried out with so-called ˝sandwich technique˝, where nanoparticles were embedded between two layers in order to tailor the desirable ion release and to prolong antibacterial effect of fabrics. Antibacterial effects of different nano-coatings were tested against G+ and G- bacterial species, Staphylococcus aureus and Escherichia coli, respectively. Besides antibacterial properties, potential cytotoxic effects were also studied. The study demonstrates that atmospheric pressure plasma can be an efficient technique for deposition of antibacterial coatings containing metal NPs. Medical textiles with plasma-assisted nano-coatings showed effective antibacterial properties. The choice of proper metal antimicrobial agent and optimal concentration of NPs should be considered in regards to potential cytotoxic effects when these materials would be used in medical environments.info:eu-repo/semantics/publishedVersio

Cmr1/WDR76 defines a nuclear genotoxic stress body linking genome integrity and protein quality control

DNA replication stress is a source of genomic instability. Here we identify ​changed mutation rate 1 (​Cmr1) as a factor involved in the response to DNA replication stress in Saccharomyces cerevisiae and show that ​Cmr1—together with ​Mrc1/​Claspin, ​Pph3, the chaperonin containing ​TCP1 (CCT) and 25 other proteins—define a novel intranuclear quality control compartment (INQ) that sequesters misfolded, ubiquitylated and sumoylated proteins in response to genotoxic stress. The diversity of proteins that localize to INQ indicates that other biological processes such as cell cycle progression, chromatin and mitotic spindle organization may also be regulated through INQ. Similar to ​Cmr1, its human orthologue ​WDR76 responds to proteasome inhibition and DNA damage by relocalizing to nuclear foci and physically associating with CCT, suggesting an evolutionarily conserved biological function. We propose that ​Cmr1/​WDR76 plays a role in the recovery from genotoxic stress through regulation of the turnover of sumoylated and phosphorylated proteins

Population gene introgression and high genome plasticity for the zoonotic pathogen Streptococcus agalactiae

The influence that bacterial adaptation (or niche partitioning) within species has on gene spillover and transmission among bacteria populations occupying different niches is not well understood. Streptococcus agalactiae is an important bacterial pathogen that has a taxonomically diverse host range making it an excellent model system to study these processes. Here we analyze a global set of 901 genome sequences from nine diverse host species to advance our understanding of these processes. Bayesian clustering analysis delineated twelve major populations that closely aligned with niches. Comparative genomics revealed extensive gene gain/loss among populations and a large pan-genome of 9,527 genes, which remained open and was strongly partitioned among niches. As a result, the biochemical characteristics of eleven populations were highly distinctive (significantly enriched). Positive selection was detected and biochemical characteristics of the dispensable genes under selection were enriched in ten populations. Despite the strong gene partitioning, phylogenomics detected gene spillover. In particular, tetracycline resistance (which likely evolved in the human-associated population) from humans to bovine, canines, seals, and fish, demonstrating how a gene selected in one host can ultimately be transmitted into another, and biased transmission from humans to bovines was confirmed with a Bayesian migration analysis. Our findings show high bacterial genome plasticity acting in balance with selection pressure from distinct functional requirements of niches that is associated with an extensive and highly partitioned dispensable genome, likely facilitating continued and expansive adaptation

Functional Importance of the DNA Binding Activity of Candida albicans Czf1p

The human opportunistic pathogen Candida albicans undergoes a reversible morphological transition between the yeast and hyphal states in response to a variety of signals. One such environmental trigger is growth within a semisolid matrix such as agar medium. This growth condition is of interest because it may mimic the growth of C. albicans in contact with host tissue during infection. During growth within a semisolid matrix, hyphal growth is positively regulated by the transcriptional regulator Czf1p and negatively by a second key transcriptional regulator, Efg1p. Genetic studies indicate that Czf1p, a member of the zinc-cluster family of transcriptional regulators, exerts its function by opposing the inhibitory influence of Efg1p on matrix-induced filamentous growth. We examined the importance of the two known activities of Czf1p, DNA-binding and interaction with Efg1p. We found that the two activities were separable by mutation allowing us to demonstrate that the DNA-binding activity of Czf1p was essential for its role as a positive regulator of morphogenesis. Surprisingly, however, interactions with Efg1p appeared to be largely dispensable. Our studies provide the first evidence of a key role for the DNA-binding activity of Czf1p in the morphological yeast-to-hyphal transition triggered by matrix-embedded growth