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Abstract Globally, many parts of fire emissions are driven by deforestation. However, few studies have attempted to evaluate deforestation and vegetation degradation fires (DDF) and predict how they will change in the future. In this study, we expanded a fire model used in the Community Land Model to reflect the diverse causes of DDF. This enabled us to differentiate DDFs by cause (climate change, wood harvesting, and cropland, pastureland, and urban land‐use changes) and seasonality. We then predicted the state of fire regimes in the 2050s and 2090s under RCP 2.6 and RCP 6.0 scenarios. Our results indicate that the area affected by global total fires will decrease from the current 452 to 211–378 Mha yr−1 in the 2090s under RCP 6.0 and to 184–333 Mha yr−1 under RCP 2.6, mainly due to socioeconomic factors such as population and economic growth. We also predict that DDF will decrease from the current 73 million hectares per year (Mha yr−1) to 54–66 Mha yr−1 in the 2090s under RCP 6.0 and 46–55 Mha yr−1 under RCP 2.6. The main contributor to these decreases in DDF burned area was climate change, especially the increasing of precipitation. The impact of future land use change on future DDF was similar or slightly lower than present‐day. South America, Indonesia, and Australia were identified as high‐risk regions for future DDF, mainly due to the expansion of wood harvest and pastureland. Appropriate land and fire management policies will be needed to reduce future fire damage in these areas.

Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.

Laboratory tests have been carried out for investigation of the spectroscopic characteristics at visible wavelength of 12 common satellite materials used in satellite bus and payload. The obtained spectral data show that the materials can be classified and identified since their spectral features and albedos distinctly differ among them. It is suggested that the result of the laboratory tests for the satellite materials can be used for the predictions of material types, material composition ratios, sizes, and masses in comparison with the spectral data obtained from observations of new satellites or space debris.

Optical observations of Geostationary and Molynia orbit spin-stabilized satellites over the Korean peninsula have been carried out at the Kyung Hee University Observatory with a 30 inch telescope. We have observed 5 spin-stabilized satellites, and obtained spin periods, which can be used for deducing a design for each bus model. Verifications of spin periods of 3 known satellites from manufacturer, and observations of 2 unknown satellites were made. The difference between known spin periods and observed spin periods is 0.06sec on the average and the difference of those spin rates is 3.3rpm on the average. Those results indicate that spin periods and spin rates of observed geostationary spin-stabilized satellites are within operating limits. Spin rates of unknown satellites, Fengyun 2B and Molynia 1-87 are 89.3rpm, 78.4rpm each. It is suggested that the research of spin stabilized satellites can be used for the determinations of standard light sources for short period celestial objects and helpful for the constructions of satellite databases with photometric and/or spectroscopic satellite observations.

Low resolution spectroscopic observations of geo-stationary satellites over the Korean peninsula have been carried out at the KyungHee Optical Satellite Observing Facility (KOSOF) with a 40cm telescope. We have observed 9 telecommunication satellites and 1 weather satellite of 6 countries. The obtained spectral data showed that satellites could be classified and grouped with similar basic spectral feature. We divided the 10 satellites into 4 groups based on spectral slop and reflectance. It is suggested that the material types of the satellites can be determined through spectral comparisons with the ground laboratory data. We will continuously observe additional geo-stationary satellites for the accurate classification of spectral features.

Biomass from Conservation Reserve Program (CRP) land can be used as a lignocellulosic bioenergy feedstock. This experiment investigated the effect of harvest timing and N rate on changes in species composition, biomass, cell wall composition, energy content, and post-harvest soil nutrient content. The experiment was conducted from 2009 to 2012 in grass and alfalfa (Medicago sativa L.) mixed CRP land in central Montana in a split-plot design with three replications. The main-plot treatments were three N rates (0, 56, and 112 kg N ha-1), and subplot treatments were two harvest timings (July and October). Averaged across years, application of 112 kg N ha-1 significantly increased total (alfalfa plus grass) biomass from 3175 to 3685 kg ha-1. As the N application rates increased, the species composition shifted, with an increase in grass and a decrease in alfalfa contributions to biomass. The increased N rates also increased N uptake in the grass but not the alfalfa. Nitrogen fertilization did not affect cell wall composition for grass or alfalfa. July harvesting significantly increased total biomass and the contribution of alfalfa by 27 and 82%, respectively, compared with October harvesting. October harvested biomass had higher cellulose, hemicellulose, lignin, and energy content than July harvest per unit biomass, yet energy per hectare was higher for the July harvest due to the higher biomass yield. Because the October harvest provided higher energy content, it would contribute to reduced biomass storage and transportation costs. In addition, October harvest reduced biomass ash and minimized soil nutrient mining. [ABSTRACT FROM AUTHOR]

Background and study aim: Metal stents for malignant biliary obstruction are susceptible to occlusion by tumor ingrowth or overgrowth. Therefore, we previously reported our use of a metal stent covered with a paclitaxel-incorporated membrane giving an antitumor effect to prevent occlusion from tumor ingrowth.We have also developed a new generation of paclitaxel-eluting biliary stent using a membrane containing Pluronic F-127 for effective drug delivery. The aim of this study was to investigate the safety and efficacy of drug delivery for this newly developed stent in the biliary tract. Methods: Metal stents were coated with paclitaxel and various concentrations of Pluronic F-127 in phosphate-buffered saline solution. Stents containing varying concentrations were placed in the bile ducts of eight pigs divided as follows: group I, 0% Pluronic+0% paclitaxel; group II, 0% Pluronic+10% paclitaxel; group III, 10% Pluronic +10% paclitaxel; group IV, 20% Pluronic+10% paclitaxel. The histology of the porcine bile duct and the amount of paclitaxel in the porcine serum were examined. The amount of paclitaxel released was also measured in vitro. Results: Histologic changes in the porcine biliary epithelium were acceptable in terms of safety, based on inflammatory cell infiltration and fibrotic reaction. No significant differences in histology were observed between the groups. In the porcine serum analysis, released paclitaxel was detected for 28 days with the 10% Pluronic concentration (group III). However, released paclitaxel was observed for only 7 days in groups II and IV. In the in vitro experiments, long-lasting release of paclitaxel was also noted from the stent with 10% Pluronic. Conclusions: The new paclitaxel-eluting stent with 10% Pluronic F-127 is safe and provides enhanced local drug delivery. [ABSTRACT FROM AUTHOR]

We report a case of a girl with clinical features of Peters' Plus Syndrome (PPS) (association of anterior eye chamber defects; peculiar facies; cleft lip/palate; brachymelia; developmental delay; growth retardation) and documented growth hormone deficiency (height -3.5 SDS at chronological age 5 years 8 months; low growth factors; bone age delay; growth velocity 4.4 cm/year (<3rd centile); and peak growth hormone levels of 1.7 and 4.7 ng/ml by clonidine and insulin provocative testing, respectively). Treatment with recombinant human growth hormone (0.3 mg/kg/week) resulted in a dramatic increase in growth velocity, increasing the height from -3.5 to -1.5 SDS over 2.3 years of therapy, indicative of an excellent response. Growth retardation is a known association in PPS: a condition that includes other midline facial defects. This case supports a role for GHD in the pathogenesis of the short stature observed in these children; demonstrates the efficacy of GH treatment; and further reinforces the relationship of pituitary anomalies with common congenital defects. © 2003 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

This work presents an analysis of the dynamic behaviour of a HRSG (heat recovery steam generator) during start-up. A calculation program based on a quasi-steady method is constructed. A typical high-pressure HRSG is designed conceptually and analysis is performed to examine the influence of the gas inlet condition of the HRSG on its start-up behaviour. Effects of the gas turbine operation mode and the gas bypass are analysed. In addition, the water level control during start-up, which is one of the most important facts in the real plant operation, is simulated. Through a parametric calculation, the effect of the control parameters on the start-up behaviour is analysed and examples of optimum control are demonstrated. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Introduction Major Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses. Objectives This study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data. Methods The DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination. Results Demographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified. Conclusions Despite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder. Disclosure of Interest None Declared

The article presents a case study of a 61-year old woman with common bile duct cancer. It mentions that the woman was admitted to hospital for upper abdominal pain and fever. It notes that her pancreas had diffuse swelling with peripancreatic fluid and mild dilatation of the mid-pancreatic duct after an abdominal computed tomography (CT) scan. It notes that protruded mass of ampulla of Vater was confirmed when the patient underwent pylorus-preserving pancreaticoduodenectomy.

Exploiting the field response of magnetic tracers, magnetic particle imaging (MPI) allows direct, local quantification of the tracer concentration in bulk structures. Here, we investigated the use of characteristic field response functions to spatially resolve the absolute concentration of multiple nanoparticle species by simulation. In particular, using various drive and selection field strengths, we devised color MPI (i.e. cMPI) to quantify and disentangle MPI signals from the mixed Langevin particles of variable concentration and magnetic susceptibility. Specifically, the drive field strength was optimized to distinguish individual field responses from differently sized iron-oxide nanoparticles without compromising the image quality. The proposed cMPI technique is implementable on an existing MPI setup and can be used to quantify biophysical parameters including size-dependent bio-distribution and altered magnetic property of particles. The current study result, simultaneous visualization of the multiple magnetic tracers, theoretically validates the potential feasibility of cMPI as a versatile biosensor and contrast imaging method. [ABSTRACT FROM AUTHOR]

Prairie cordgrass (Spartina pectinata), a perennial C4 grass native to the North American prairie, has several distinctive characteristics that potentially make it a model crop for production in stressful environments. However, little is known about the transcriptome dynamics of prairie cordgrass despite its unique freezing stress tolerance. Therefore, the purpose of this work was to explore the transcriptome dynamics of prairie cordgrass in response to freezing stress at -5°C for 5 min and 30 min. We used a RNA-sequencing method to assemble the S. pectinata leaf transcriptome and performed gene-expression profiling of the transcripts under freezing treatment. Six differentially expressed gene (DEG) groups were categorized from the profiling. In addition, two major consecutive orders of gene expression were observed in response to freezing; the first being the acute up-regulation of genes involved in plasma membrane modification, calcium-mediated signaling, proteasome-related proteins, and transcription regulators (e.g., MYB and WRKY). The follow-up and second response was of genes involved in encoding the putative anti-freezing protein and the previously known DNA and cell-damage-repair proteins. Moreover, we identified the genes involved in epigenetic regulation and circadian-clock expression. Our results indicate that freezing response in S. pectinata reflects dynamic changes in rapid-time duration, as well as in metabolic, transcriptional, post-translational, and epigenetic regulation.