Catheter-associated bacteremia by Mycobacterium senegalense in Korea

BACKGROUND: Rapidly growing mycobacteria is recognized as one of the causative agents of catheter-related infections, especially in immunocompromised hosts. To date, however, Mycobacterium senegalense, which was known as the principal pathogen of bovine farcy, has not been reported in human infection. CASE PRESENTATION: We describe the first case of human infection by M. senegalense, which has caused catheter-related bloodstream infection in a cancer patient in Korea. The microorganism was identified by the 16S rRNA gene, rpoB, and 16S-23S rRNA gene internal transcribed spacer (ITS) sequence analyses. CONCLUSION: Our first report of catheter-associated bacteremia caused by M. senegalense suggests the zoonotic nature of this species and indicates the expansion of mycobacterial species relating to human infection. M. senegalense should be considered as one of the causes of human infections in the clinical practice

Topological Cluster Analysis Reveals the Systemic Organization of the Caenorhabditis elegans Connectome

The modular organization of networks of individual neurons interwoven through synapses has not been fully explored due to the incredible complexity of the connectivity architecture. Here we use the modularity-based community detection method for directed, weighted networks to examine hierarchically organized modules in the complete wiring diagram (connectome) of Caenorhabditis elegans (C. elegans) and to investigate their topological properties. Incorporating bilateral symmetry of the network as an important cue for proper cluster assignment, we identified anatomical clusters in the C. elegans connectome, including a body-spanning cluster, which correspond to experimentally identified functional circuits. Moreover, the hierarchical organization of the five clusters explains the systemic cooperation (e.g., mechanosensation, chemosensation, and navigation) that occurs among the structurally segregated biological circuits to produce higher-order complex behaviors

Wiring

cost in the organization of a biological neuronal networ

Hydrogenation of monolayer molybdenum diselenide via hydrogen plasma treatment

Functionalization of transition metal dichalcogenides has been studied with the aim of tuning their electrical and optical properties, but structural information during functionalization and its reversibility have not been elucidated. We report a simple and effective method for hydrogenation of monolayer MoSe2 using hydrogen plasma treatment. The covalent bonding of hydrogen to MoSe2 was confirmed by X-ray photoelectron spectroscopy, and the degree of hydrogenation was modulated from 32% to 80% by increasing the plasma treatment time from 5 to 40 s. Transmission electron microscopy confirmed a 1.5% reduction in the lattice constant of hydrogenated MoSe2 without structural damages or defects; crystal structures of hydrogenated MoSe2 and as-prepared MoSe2 were identical. Photoluminescence (PL) investigation of hydrogenated MoSe2 showed charge transfer from hydrogen to MoSe2. Furthermore, reversible desorption of hydrogen from hydrogenated MoSe2 was achieved by heat treatment. The optical and electrical properties of as-prepared and hydrogenated MoSe2 samples were compared. The PL peak of hydrogenated MoSe2 returned to the as-prepared one after heat treatment at 500 degrees C. Furthermore, the electron mobility of MoSe2 decreased from 29 to 9 cm(2) V-1 s(-1) after hydrogenation and was restored to 27 cm(2) V-1 s(-1) upon heat treatment at 500 degrees C. This reversible hydrogen adsorption and desorption lends control over the optical and electrical properties of monolayer MoSe2 and contributes to the hydrogen functionalization of monolayer transition metal dichalcogenides and other two-dimensional materials

Lipid coating technology: a potential solution to address the problem of sticky containers and vanishing drugs

Pharmaceutical drugs and vaccines require the use of material containers for protection, storage, and transportation. Glass and plastic materials are widely used for packaging, and a longstanding challenge in the field is the nonspecific adsorption of pharmaceutical drugs to container walls – the so-called “sticky containers, vanishing drugs” problem – that effectively reduces the active drug concentration and can cause drug denaturation. This challenge has been frequently discussed in the case of the anticancer drug, paclitaxel, and the ongoing coronavirus disease 2019 (COVID-19) pandemic has brought renewed attention to this material science challenge in light of the need to scale up COVID-19 vaccine production and to secure sufficient quantities of packaging containers. To reduce nonspecific adsorption on inner container walls, various strategies based on siliconization and thin polymer films have been explored, while it would be advantageous to develop mass-manufacturable, natural material solutions, especially ones involving pharmaceutical grade excipients. Inspired by how lipid nanoparticles have revolutionized the vaccine field, in this perspective, we discuss the prospects for developing lipid bilayer coatings to prevent nonspecific adsorption of pharmaceutical drugs and vaccines and how recent advances in lipid bilayer coating fabrication technologies are poised to accelerate progress in the field. We critically discuss recent examples of how lipid bilayer coatings can prevent nonspecific sticking of proteins and vaccines to relevant material surfaces and examine future translational prospects.National Research Foundation (NRF)Published versionThis work was supported by the National Research Foundation of Singapore through a Proof-of-Concept grant (NRF2015NRF-POC0001-19) and by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2020R1C1C1004385 and2020R1C1C1005523). In addition, this work was supported by Brain Pool Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2019H1D3A1A01070318). This research was also supported by the International Research & Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(2020K1A3A1A39112724)

Massive multi-agent data-driven simulations of the github ecosystem

Simulating and predicting planetary-scale techno-social systems poses heavy computational and modeling challenges. The DARPA SocialSim program set the challenge to model the evolution of GitHub, a large collaborative software-development ecosystem, using massive multiagent simulations. We describe our best performing models and our agent-based simulation framework, which we are currently extending to allow simulating other planetary-scale techno-social systems. The challenge problem measured participant’s ability, given 30 months of metadata on user activity on GitHub, to predict the next months’ activity as measured by a broad range of metrics applied to ground truth, using agent-based simulation. The challenge required scaling to a simulation of roughly 3 million agents producing a combined 30 million actions, acting on 6 million repositories with commodity hardware. It was also important to use the data optimally to predict the agent’s next moves. We describe the agent framework and the data analysis employed by one of the winning teams in the challenge. Six different agent models were tested based on a variety of machine learning and statistical methods. While no single method proved the most accurate on every metric, the broadly most successful sampled from a stationary probability distribution of actions and repositories for each agent. Two reasons for the success of these agents were their use of a distinct characterization of each agent, and that GitHub users change their behavior relatively slowl