Seasonal Variations in Temperature–Suicide Associations across South Korea

South Korea has among the highest rates of suicide in the world, and previous research suggests that suicide frequency increases with anomalously high temperatures, possibly as a result of increased sunshine. However, it is unclear whether this temperature–suicide association exists throughout the entire year. Using distributed lag nonlinear modeling, which effectively controls for nonlinear and delayed effects, we examine temperature–suicide associations for both a warm season (April–September) and a cool season (October–March) for three cities across South Korea: Seoul, Daegu, and Busan. We find consistent, statistically significant, mostly linear relationships between relative risk of suicide and daily temperature in the cool season but few associations in the warm season. This seasonal signal of statistically significant temperature–suicide associations only in the cool season exists among all age segments, but especially for those 35 and older, along with both males and females. We further use distributed lag nonlinear modeling to examine cloud cover–suicide associations and find few significant relationships. This result suggests that that high daily temperatures in the cool season, and not exposure to sun, are responsible for the strong temperature–suicide associations found in South Korea

BioCAS: Biometeorological Climate impact Assessment System for building-scale impact assessment of heat-stress related mortality

An urban climate analysis system for Seoul was combined with biometeorological models for the spatially distributed assessment of heat stress risks. The Biometeorological Climate impact Assessment System (BioCAS) is based on the Climate Analysis Seoul (CAS) workbench which provides urban planners with gridded data relevant for local climate assessment at 25 m and 5 m spatial resolutions. The influence of building morphology and vegetation on mean radiant temperature Tmrt was simulated by the SOLWEIG model. Gridded hourly perceived temperature PT was computed using the Klima-Michel Model for a hot day in 2012. Daily maximum perceived temperature PTmax was then derived from these data and applied to an empirical-statistical model that explains the relationship between PTmax and excess mortality rate rEM in Seoul. The resultant rEM map quantifies the detrimental impact of hot weather at the building scale. Mean (maximum) values of rEM in old and new town areas in an urban re-development site in Seoul were estimated at 2.3 % (50.7 %) and 0 % (8.6 %), respectively, indicating that urban re-development in the new town area has generally resulted in a strong reduction of heat-stress related mortality. The study illustrates that BioCAS can generally be applied for the quantification of the impacts of hot weather on human health for different urban development scenarios. Further improvements are required, particularly to consider indoor climate conditions causing heat stress, as well as socio-economic status and population structure of local residents

Selection of internal reference genes for SYBR green qRT-PCR studies of rhesus monkey (Macaca mulatta) tissues

<p>Abstract</p> <p>Background</p> <p>The rhesus monkey (<it>Macaca mulatta</it>) is a valuable and widely used model animal for biomedical research. However, quantitative analyses of rhesus gene expression profiles under diverse experimental conditions are limited by a shortage of suitable internal controls for the normalization of mRNA levels. In this study, we used a systematic approach for the selection of potential reference genes in the rhesus monkey and compared their suitability to that of the corresponding genes in humans.</p> <p>Results</p> <p>Eight housekeeping genes (HKGs) (<it>GAPDH, SDHA, ACTB, RPL13A, RPL32, UBA52, PGK1Y</it>, and <it>YWHAZ</it>) from rhesus monkeys and humans were selected to test for normalization of expression levels in six different tissue types (brain, colon, kidney, liver, lung, and stomach). Their stability and suitability as reference genes were validated by <it>geNorm</it>, <it>NormFinder </it>and <it>BestKeeper </it>programs. Intriguingly, <it>RPL13A </it>and <it>RPL32 </it>were selected as ideal reference genes only in rhesus monkeys.</p> <p>Conclusion</p> <p>The results clearly indicated the necessity of using different reference genes for normalization of expression levels between rhesus monkeys and humans in various tissues.</p

HOXB13 promotes androgen independent growth of LNCaP prostate cancer cells by the activation of E2F signaling

<p>Abstract</p> <p>Background</p> <p>Androgen signaling plays a critical role in the development of prostate cancer and its progression. However, androgen-independent prostate cancer cells emerge after hormone ablation therapy, resulting in significant clinical problems. We have previously demonstrated that the HOXB13 homeodomain protein functions as a prostate cancer cell growth suppressor by inhibiting androgen-mediated signals. However, the role of the HOXB13 in androgen-independent growth of prostate cancer cells remains unexplained.</p> <p>Results</p> <p>In this report, we first demonstrated that HOXB13 was highly overexpressed in hormone-refractory tumors compared to tumors without prostate-specific antigen after initial treatment. Functionally, in an androgen-free environment minimal induction of HOXB13 in LNCaP prostate cancer cells, to the level of the normal prostate, markedly promoted cell proliferation while suppression inhibited cell proliferation. The HOXB13-mediated cell growth promotion in the absence of androgen, appears to be mainly accomplished through the activation of RB-E2F signaling by inhibiting the expression of the p21^waftumor suppressor. Indeed, forced expression of HOXB13 dramatically decreased expression of p21^waf; this inhibition largely affected HOXB13-mediated promotion of E2F signaling.</p> <p>Conclusions</p> <p>Taken together, the results of this study demonstrated the presence of a novel pathway that helps understand androgen-independent survival of prostate cancer cells. These findings suggest that upregulation of HOXB13 is associated with an additive growth advantage of prostate cancer cells in the absence of or low androgen concentrations, by the regulation of p21-mediated E2F signaling.</p

Hepatic Cellular Distribution of Silica Nanoparticles by Surface Energy Modification

The cellular distribution of silica nanoparticles (NPs) in the liver is not well understood. Targeting specific cells is one of the most important issues in NP-based drug delivery to improve delivery efficacy. In this context, the present study analyzed the relative cellular distribution pattern of silica NPs in the liver, and the effect of surface energy modification on NPs. Hydrophobic NP surface modification enhanced NP delivery to the liver and liver sinusoid fFendothelial cells (LSECs). Conversely, hydrophilic NP surface modification was commensurate with targeting hepatic stellate cells (HSCs) rather than other cell types. There was no notable difference in NP delivery to Kupffer cells or hepatocytes, regardless of hydrophilic or hydrophobic NP surface modification, suggesting that both the targeting of hepatocytes and evasion of phagocytosis by Kupffer cells are not associated with surface energy modification of silica NPs. This study provides useful information to target specific cell types using silica NPs, as well as to understand the relationship between NP surface energy and the NP distribution pattern in the liver, thereby helping to establish strategies for cell targeting using various NPs. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.1

Changes in Sensitization Rate to Weed Allergens in Children with Increased Weeds Pollen Counts in Seoul Metropolitan Area

The prevalence of allergic diseases in children has increased for several decades. We evaluated the correlation between pollen count of weeds and their sensitization rate in Seoul, 1997-2009. Airborne particles carrying allergens were collected daily from 3 stations around Seoul. Skin prick tests to pollen were performed on children with allergic diseases. Ragweed pollen gradually increased between 1999 and 2005, decreased after 2005 and plateaued until 2009 (peak counts, 67 in 2003, 145 in 2005 and 83 grains/m3/day in 2007). Japanese hop pollen increased between 2002 and 2009 (peak counts, 212 in 2006 and 492 grains/m3/day in 2009). Sensitization rates to weed pollen, especially ragweed and Japanese hop in children with allergic diseases, increased annually (ragweed, 2.2% in 2000 and 2.8% in 2002; Japanese hop, 1.4% in 2000 and 1.9% in 2002). The age for sensitization to pollen gradually became younger since 2000 (4 to 6 yr of age, 3.5% in 1997 and 6.2% in 2009; 7 to 9 yr of age, 4.2% in 1997 and 6.4% in 2009). In conclusion, sensitization rates for weed pollens increase in Korean children given increasing pollen counts of ragweed and Japanese hop

The Revised Edition of Korean Calendar for Allergenic Pollens

The old calendar of pollens did not reflect current pollen distribution and concentrations that can be influenced by changes of weather and environment of each region in South Korea. A new pollen calendar of allergenic pollens was made based on the data on pollen concentrations obtained in eight regions nationwide between 1997 and 2009. The distribution of pollen was assessed every day at 8 areas (Seoul, Guri, Busan, Daegu, Jeonju, Kwangju, Kangneung, and Jeju) for 12 years between July 1, 1997 and June 30, 2009. Pollens were collected by using Burkard 7-day sampler (Burkard Manufacturing Co Ltd, UK). Pollens which were stained with Calberla's fuchsin staining solution were identified and counted. Pine became the highest pollen in May, and the pollen concentrations of oak and birch also became high. Ragweed appeared in the middle of August and showed the highest pollen concentration in the middles of September. Japanese hop showed a high concentration between the middle of August and the end of September, and mugwort appeared in the middles of August and its concentration increased up until early September. In Kangneung, birch appeared earlier, pine showed a higher pollen concentration than in the other areas. In Daegu, Oriental thuja and alder produced a large concentration of pollens. Pine produced a large concentration of pollens between the middle of April and the end of May. Weeds showed higher concentrations in September and mugwort appeared earlier than ragweed. In Busan the time of flowering is relatively early, and alder and Oriental thuja appeared earliest among all areas. In Kwangju, Oriental thuja and hazelnut appeared in early February. Japanese cedar showed the highest pollen concentration in March in Jeju. In conclusion, update information on pollen calendar in South Korea should be provided for allergic patients through the website to manage and prevent the pollinosis

Effects of Interrupted Wetness Periods on Conidial Germination, Germ Tube Elongation and Infection Periods of Botryosphaeria dothidea Causing Apple White Rot

Responses of Botryosphaeria dothidea to interrupted wetness periods were investigated under in vivo and in vitro conditions. Conidia of B. dothidea were allowed to germinate on apple fruits under wetting condition at 25ºC for 5 hr. They were air-dried for 0, 1, 2 or 4 hr, and then rewetted at 25ºC for 5 hr. Following an initial wetness period of 5 hr, 83% of the conidia germinated. The percent conidial germination increased to 96% when wetting was extended continuously another 5 hr. However, no further conidial germination was observed when wetting was interrupted by dry periods of 1, 2 and 4 hr, resulting in 83, 81 and 82%, respectively. The mean length of the germ tubes was 37 μm after 5 hr of wetting and elongated to 157 μm after 10 hr of continuous wetting. On the other hand, interruption of wetting by a dry period of 1 hr or longer after the 5 hr of initial wetting arrested the germ tube elongation at approximately 42 μm long. Prolonged rewetting up to 40 hr did not restore germ tube elongation on slide glasses under substrate treatments. Model simulation using weather data sets revealed that ending infection periods by a dry period of at least 1 hr decreased the daily infection periods, avoiding the overestimation of infection warning. This information can be incorpo- rated into infection models for scheduling fungicide sprays to control apple white rot with fewer fungicide applications

Emulators of a Physical Model for Estimating Leaf Wetness Duration

Leaf wetness duration (LWD) has rarely been measured due to lack of standard protocol. Thus, empirical and physical models have been proposed to resolve this gap. Although the physical model provides robust performance in diverse conditions, it requires many variables. The empirical model requires fewer variables; nevertheless, its performance is specific to a given condition. A universal LWD estimation model using fewer variables is thus needed to improve LWD estimation. The objective of this study was to develop emulators of the LWD estimation physical model for use as universal empirical models. It is assumed that the Penman–Monteith (PM) model determines LWD and can be employed as a physical model. In this study, a simulation was designed and conducted to investigate the characteristics of the PM model and to build the emulators. The performances of the built emulators were evaluated based on a case study of LWD data obtained in South Korea. It was determined that a machine learning algorithm can properly emulate the PM model in LWD estimations based on the simulation. Moreover, the poor performances of some emulators that use wind speed may have been due to the limitation of wind speed measurement. The accuracy of the anemometer is thus critical to estimating LWD using physical models. A deep neural network using relative humidity and air temperature was found to be the most appropriate emulator of those tested for LWD estimation

Leaf Wetness Duration Models Using Advanced Machine Learning Algorithms: Application to Farms in Gyeonggi Province, South Korea

Leaf wetness duration (LWD) models have been proposed as an alternative to in situ LWD measurement, as they can predict leaf wetness using physical mechanism and empirical relationship with meteorological conditions. Applications of advanced machine learning (ML) algorithms in the development of empirical LWD model can lead to improvements in the LWD prediction. The current study developed LWD model using extreme learning machine, random forest method, and a deep neural network. Additionally, performances of these ML-based LWD models are evaluated and compared with existing models. Observed LWD and meteorological variable data are obtained from nine farms in South Korea. Temporal and geographical information were also used. Additionally, the priorities of the employed variables in the development of the ML-based LWD models were analyzed. As a result, the ML-based LWD models outperformed the existing models; the random forest led to the best performance for LWD prediction among the tested LWD models. Strengths of associations between input variables and leaf wetness were relative humidity, short wave radiation, air temperature, hour, latitude, longitude, and wind speed in descending order. Uses of the geographical and time information in development of LWD model can improve the performance of LWD model