Grazing on Microcystis (Cyanophyceae) by testate amoebae with special reference to cyanobacterial abundance and physiological state

We examined the growth of testate amoebae preying on Microcystis whose physiological states were different in laboratory experiments and a hypertrophic pond. We prepared three experimental systems using water samples dominated by Microcystis aeruginosa: light incubation (control), dark incubation (dark), and light incubation with addition of nitrogen and phosphorus (+NP). In all the systems, the colony density of M. aeruginosa decreased slightly during incubation. Physiological activity of phytoplankton as determined by chlorophyll fluorescence was high and almost constant in the control and +NP systems, whereas it decreased in the dark system. Cell densities of testate amoebae increased in the control and +NP systems, whereas in the dark system they remained low. Thus, growth of the amoebae was low in the systems where physiological activity of Microcystis was low. In a hypertrophic pond, cell density of testate amoebae increased and remained high when M. aeruginosa predominated. Cell density of testate amoebae increased remarkably, simultaneously with the increases in M. aeruginosa colony density and phytoplankton physiological activity. We also found a significant correlation between densities of M. aeruginosa colonies and testate amoebae. We suggested that the physiological activity of Microcystis is one important factor affecting the growth of testate amoebae grazing on Microcystis

Analysis of triterpenoids, carotenoids, and phenylpropanoids in the flowers, leaves, roots, and stems of white bitter melon (Cucurbitaceae, Momordica charantia)

Purpose: To evaluate the contents of carotenoids, triterpenoids, and phenylpropanoids in different parts of white bitter melon.Methods: We evaluated the accumulation of 2 triterpenoids, 10 carotenoids, and 11 phenylpropanoids in different parts of white bitter melon, including fruits at four different developmental stages using HPLC.Results: Charantin, lutein, and rutin were the main triterpenoids, carotenoids, and phenylpropanoids, respectively. The accumulation of triterpenoids (momordicine and charantin), carotenoids (antheraxanthin, lutein, violaxanthin, α-carotene, and β-carotene), and phenylpropanoids (caffeic acid, chlorogenic acid, epicatechin, gallic acid, p-coumaric acid, rutin, and trans-cinnamic acid) was high inthe leaves and/or flowers, which are exposed to direct sunlight, but low in the roots.Conclusion: Most of the analyzed components were accumulated at high levels in the leaves and/or flowers. These results will help exploit the compounds in various parts of white bitter melon that are beneficial for human health. Keywords: Momordica charantia, Bitter melon, Triterpenoid, Carotenoid, Phenylpropanoi

Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Genetic parameters of calving ease using sire-maternal grandsire model in Korean Holsteins

Objective Calving ease (CE) is a complex reproductive trait of economic importance in dairy cattle. This study was aimed to investigate the genetic merits of CE for Holsteins in Korea. Methods A total of 297,614 field records of CE, from 2000 to 2015, from first parity Holstein heifers were recorded initially. After necessary data pruning such as age at first calving (18 to 42 mo), gestation length, and presence of sire information, final datasets for CE consisted of 147,526 and 132,080 records for service sire calving ease (SCE) and daughter calving ease (DCE) evaluations, respectively. The CE categories were ordered and scores ranged from CE1 to CE5 (CE1, easy; CE2, slight assistance; CE3, moderate assistance; CE4, difficult calving; CE5, extreme difficulty calving). A linear transformation of CE score was obtained on each category using Snell procedure, and a scaling factor was applied to attain the spread between 0 (CE5) and 100% (CE1). A sire-maternal grandsire model analysis was performed using ASREML 3.0 software package. Results The estimated direct heritability (h2) from SCE and DCE evaluations were 0.11±0.01 and 0.08±0.01, respectively. Maternal h2 estimates were 0.05±0.02 and 0.04±0.01 from SCE and DCE approaches, respectively. Estimates of genetic correlations between direct and maternal genetic components were −0.68±0.09 (SCE) and −0.71±0.09 (DCE). The average direct genetic effect increased over time, whereas average maternal effect was low and consistent. The estimated direct predicted transmitting ability (PTA) was desirable and increasing over time, but the maternal PTA was undesirable and decreasing. Conclusion The evidence on sufficient genetic variances in this study could reflect a possible selection improvement over time regarding ease of calving. It is expected that the estimated genetic parameters could be a valuable resource to formulate sire selection and breeding plans which would be directed towards the reduction of calving difficulty in Korean Holsteins

Lowest threshold lasing modes localized on marginally unstable periodic orbits in a semiconductor microcavity laser

The lowest threshold lasing mode in a rounded D-shape microcavity is theoretically analyzed and experimentally demonstrated. To identify the lowest threshold lasing mode, we investigate threshold conditions of different periodic orbits by considering the linear gain condition due to the effective pumping region and total loss consisting of internal and scattering losses in ray dynamics. We compare the ray dynamical result with resonance mode analysis, including gain and loss. We find that the resonance modes localized on the pentagonal marginally unstable periodic orbit have the lowest threshold in our fabrication configuration. Our findings are verified by obtaining the path lengths and far-field patterns of lasing modes. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.1

2018 Korean society of hypertension guidelines for the management of hypertension: part III-hypertension in special situations

Treatment of hypertension improves cardiovascular, renal, and cerebrovascular outcomes. However, the benefit of treatment may be different according to the patients characteristics. Additionally, the target blood pressure or initial drug choice should be customized according to the special conditions of the hypertensive patients. In this part III, we reviewed previous data and presented recommendations for some special populations such as diabetes mellitus, chronic kidney disease, elderly people, and cardio-cerebrovascular disease