Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data

The ocean is closely related to global warming and on-going climate change by regulating amounts of carbon dioxide through its interaction with the atmosphere. The monitoring of ocean carbon dioxide is important for a better understanding of the role of the ocean as a carbon sink, and regional and global carbon cycles. This study estimated the fugacity of carbon dioxide (fCO(2)) over the East Sea located between Korea and Japan. In situ measurements, satellite data and products from the Geostationary Ocean Color Imager (GOCI) and the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data were used through stepwise multi-variate nonlinear regression (MNR) and two machine learning approaches (i.e., support vector regression (SVR) and random forest (RF)). We used five ocean parameters-colored dissolved organic matter (CDOM; <0.3 m(-1)), chlorophyll-a concentration (Chl-a; <21 mg/m(3)), mixed layer depth (MLD; <160 m), sea surface salinity (SSS; 32-35), and sea surface temperature (SST; 8-28 degrees C)-and four band reflectance (Rrs) data (400 nm-565 nm) and their ratios as input parameters to estimate surface seawater fCO(2) (270-430 mu atm). Results show that RF generally performed better than stepwise MNR and SVR. The root mean square error (RMSE) of validation results by RF was 5.49 mu atm (1.7%), while those of stepwise MNR and SVR were 10.59 mu atm (3.2%) and 6.82 mu atm (2.1%), respectively. Ocean parameters (i.e., sea surface salinity (SSS), sea surface temperature (SST), and mixed layer depth (MLD)) appeared to contribute more than the individual bands or band ratios from the satellite data. Spatial and seasonal distributions of monthly fCO(2) produced from the RF model and sea-air CO2 flux were also examined

Symbiotic relationship between filamentous algae (Halomicronema sp.) and extracellular polymeric substance-producing algae (Chlamydomonas sp.) through biomimetic simulation of natural algal mats

To lower the cost of biomass harvesting, the growth of natural biofilm is considered to be an optimal alternative to microalgae aggregation. This study investigated algal mats that naturally agglomerate into a lump and float on water surfaces. Halomicronema sp., a filamentous cyanobacterium with high cell aggregation and adhesion to substrates, and Chlamydomonas sp., which grows rapidly and produces high extracellular polymeric substances (EPS) in certain environments, are the main microalgae that make up selected mats through next-generation sequencing analysis. These two species play a major role in the formation of solid mats, and showed a symbiotic relationship as the medium and nutritional source, particularly owing to the large amount of EPS formed by the reaction between EPS and calcium ions through zeta potential and Fourier-transform infrared spectroscopy analysis. This led to the formation of an ecological biomimetic algal mat (BAM) that mimics the natural algal mat system, and this is a way to reduce costs in the biomass production process as there is no separate treatment process for harvesting

Distinct firing activities of the hypothalamic arcuate nucleus neurons to appetite hormones

The hypothalamic arcuate nucleus (Arc) is a central unit that controls the appetite through the integration of metabolic, hormonal, and neuronal afferent inputs. Agouti-related protein (AgRP), proopiomelanocortin (POMC), and dopaminergic neurons in the Arc differentially regulate feeding behaviors in response to hunger, satiety, and appetite, respectively. At the time of writing, the anatomical and electrophysiological characterization of these three neurons has not yet been intensively explored. Here, we interrogated the overall characterization of AgRP, POMC, and dopaminergic neurons using genetic mouse models, immunohistochemistry, and whole-cell patch recordings. We identified the distinct geographical location and intrinsic properties of each neuron in the Arc with the transgenic lines labelled with cell-specific reporter proteins. Moreover, AgRP, POMC, and dopaminergic neurons had different firing activities to ghrelin and leptin treatments. Ghrelin led to the increased firing rate of dopaminergic and AgRP neurons, and the decreased firing rate of POMC. In sharp contrast, leptin resulted in the decreased firing rate of AgRP neurons and the increased firing rate of POMC neurons, while it did not change the firing rate of dopaminergic neurons in Arc. These findings demonstrate the anatomical and physiological uniqueness of three hypothalamic Arc neurons to appetite control

Modular thermal Hall effect measurement setup for fast-turnaround screening of materials over wide temperature range using capacitive thermometry

We demonstrate a simple and easy-to-build probe designed to be loaded into a widely available Quantum Design Physical Properties Measurement System (PPMS) cryostat, with a detachable shielded sample puck section and robust heat sinking of three pairs of coaxial cables. It can be in principle used with any low-temperature cryostat. Our modular puck design has a radiation shield for thermal isolation and protection of the delicate sample space while handling and allows any variety of experimental setup benefiting from shielded coaxial wiring to be constructed on a selection of sample pucks. Pucks can be quickly and easily switched, and the system makes use of the simple yet extremely stable temperature and magnetic field control of the easy-to-use PPMS system. We focus on a setup designed for measurements of the thermal Hall effect and show that this system can yield unprecedented resolution over a wide temperature range and with rapid sample mounting or changing—allowing a large collection of potential samples to be screened for this novel physics. Our design aims to make these sensitive but challenging measurements quick, reliable, cheap, and accessible, through the use of a standard, widespread base cryostat and a system of modular removable sample stage pucks to allow quick turnaround and screening of a large number of candidate samples for potential new thermal Hall physics

Novel next generation sequencing panel method for the multiple detection and identification of foodborne pathogens in agricultural wastewater

Detecting and identifying the origins of foodborne pathogen outbreaks is a challenging. The Next-Generation Sequencing (NGS) panel method offers a potential solution by enabling efficient screening and identification of various bacteria in one reaction. In this study, new NGS panel primer sets that target 18 specific virulence factor genes from six target pathogens (Bacillus cereus, Yersinia enterocolitica, Staphylococcus aureus, Vibrio cholerae, Vibrio parahaemolyticus, and Vibrio vulnificus) were developed and optimized. The primer sets were validated for specificity and selectivity through singleplex PCR, confirming the expected amplicon size. Crosscheck and multiplex PCR showed no interference in the primer set or pathogenic DNA mixture. The NGS panel analysis of spiked water samples detected all 18 target genes in a single reaction, with pathogen concentrations ranging from 108 to 105 colony-forming units (CFUs) per target pathogen. Notably, the total sequence read counts from the virulence factor genes showed a positive association with the CFUs per target pathogen. However, the method exhibited relatively low sensitivity and occasional false positive results at low pathogen concentrations of 105 CFUs. To validate the detection and identification results, two sets of quantitative real-time PCR (qPCR) analyses were independently performed on the same spiked water samples, yielding almost the same efficiency and specificity compared to the NGS panel analysis. Comparative statistical analysis and Spearman correlation analysis further supported the similarity of the results by showing a negative association between the NGS panel sequence read counts and qPCR cycle threshold (Ct) values. To enhance NGS panel analysis for better detection, optimization of primer sets and real-time NGS sequencing technology are essential. Nonetheless, this study provides valuable insights into applying NGS panel analysis for multiple foodborne pathogen detection, emphasizing its potential in ensuring food safety

Experiments and failure analysis of SHCC and reinforced concrete composite slabs

For all types of concrete structures, controlling of cracking, as well as the enhancement of serviceability and ultimate flexural capacity are important issues for deck slabs. This study presents an experimental campaign and accompanying nonlinear analysis of a series of Strain Hardening Cementitious Composite (SHCC) and reinforced concrete slab systems, simply-supported and subjected to four-point loading. In order to improve flexural performance both at the service and ultimate limit states, an SHCC layer with thickness of 150–400 mm was placed on the soffit of the composite slab; the SHCC was manufactured using two different processes, namely cast-in-situ SHCCs and extruded precast SHCC panel. Nonlinear analysis of SHCC and reinforced concrete slabs was also carried out to predict moment and curvature as well as deflections of the slab systems. The developed slab systems were found to have enhanced performance with regard to both at serviceability and flexural capacity, compared to the conventional reinforced concrete slab