Synthesis and optimization of picolinamide derivatives as a novel class of 11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1) inhibitors.

The synthesis and structure-activity relationship of a series of 6-substituted picolinamide inhibitors of 11ß-hydroxysteroid dehydrogenase type 1 are described. The optimization of the left-hand side of lead compound 1 resulted in the discovery of the highly potent, selective, and orally available inhibitor 24, which demonstrated an excellent activity in a mouse ex vivo pharmacodynamic model. Moreover, compound 24 reduced the blood glucose and improved the lipid profiles in ob/ob mice after oral administration.

Charge scheduling of an energy storage system under time-of-use pricing and a demand charge.

A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS and by 8% compared to a scheduling algorithm based on net power.

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection's pivotal role in future sarcopenia research.

Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study.

We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea's regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity. Additionally, we present a deep neural network-based surrogate model, greatly enhancing efficiency compared to simulation-based optimization. Addressing inefficiencies in mobilizing locations that store oil skimmers, further optimization aimed to minimize mobilized locations and was validated through scenario-based simulations resembling actual situations. Based on major oil spills in South Korea, this strategy significantly reduced work time and required locations. These findings demonstrate the effectiveness of the proposed genetic algorithm and mobilized location minimization strategy in enhancing oil spill response operations.

Longitudinal standards for height and height velocity in Korean children and adolescents: the Kangwha study. [corrected].

Longitudinal standards for height and height velocity are essential to monitor for appropriate linear growth. We aimed to construct standards in Korean children and adolescents through the population-based longitudinal Kangwha study. Our study was a part of a community-based prospective cohort study from 1986 to 1999 with 800 school children. Height and height velocity were recorded annually from age 6 until final height. Results were compared with cross-sectional data from the 2007 Korean National Growth Charts. Final height was 173.5 cm in boys and 160.5 cm in girls. Although final height was similar between longitudinal and cross-sectional standards, the mean height for age was higher in the longitudinal standard by 1-4 cm from age 6 until the completion of puberty. Using the longitudinal standard, age at peak height velocity (PHV) was 12 in boys and 10 in girls; height velocity at PHV was 8.62 cm/yr in boys and 7.07 cm/yr in girls. The mean height velocity was less than 1 cm/yr at age 17 in boys and 15 in girls. Thus, we have presented the first report of longitudinal standards for height and height velocity in Korean children and adolescents by analyzing longitudinal data from the Kangwha cohort.

Comparing Left Ventricular Diastolic Function between Peritoneal Dialysis and Non-Dialysis Patients with Stage 5 Chronic Kidney Disease: A Propensity Score-Matched Analysis.

Patients with chronic kidney disease (CKD) have a high incidence of left ventricular diastolic dysfunction (LVDD), which increases the risk of heart failure and mortality. We assessed fluid overload as an independent risk factor for LVDD in patients with decreased kidney function and compared its impact on the E/e' ratio as a parameter for assessing left ventricular diastolic functions between patients undergoing continuous ambulatory peritoneal dialysis (CAPD) and those with non-dialysis CKD stage 5 (CKD5) using propensity score matching (PSM). After PSM, 222 patients (CAPD, n = 111; CKD5, n = 111) were included. Fluid balance was assessed using bio-impedance spectroscopy and LVDD was determined by echocardiography based on an E/e' ratio of >15. The CKD5 group had a significantly higher E/e' ratio (p = 0.002), while fluid overload (OH/ECW) did not differ significantly between the groups. In the CAPD group, there were no significant differences in OH/ECW between patients with and without LVDD (p = 0.517). However, in the CKD5 group, patients with LVDD showed a significantly higher OH/ECW (p = 0.001). In a regression analysis investigating factors associated with the E/e' ratio, OH/ECW was not significantly associated with the E/e' ratio in the CAPD group (p = 0.087), but in the CKD5 group, it was independently correlated (p = 0.047). The factors closely associated with LVDD varied depending on dialysis dependence. While fluid overload independently influenced LVDD in non-dialysis patients, it was not statistically significant in patients with CAPD. Early assessment and management of volume status are crucial in addressing LVDD in patients with advanced-stage CKD.

Adolescent build plotting on body composition chart and the type of diabetes mellitus.

Although the prevalence of type 2 diabetes is increasing, there are cases difficult to categorize into certain type in pediatric diabetic patients. The aims of this study were to detect and choose a proper treatment modality for atypical cases of diabetes mellitus, using the body composition chart. We conducted a retrospective study from August 2005 to 2012 with patients who visited Konkuk University Medical Center, and were diagnosed with diabetes mellitus. The medical records were reviewed for the anthropometric data and indices of body composition. The subjects were grouped by the type of diabetes and gender. We constructed a body composition chart plotting fat free mass index and fat mass index (FMI). Body mass index and all body composition indices were higher in type 2 diabetes, in each gender in analysis with Mann-Whitney test. Significant determinant of diabetes type was revealed as FMI and contributing factors on FMI were analyzed with regression analysis. Six atypical cases were identified by a body composition chart including non-obese type 2 diabetes showing suboptimal growth with lower BMI related to relatively lower insulin secretion and type 1 diabetes with insulin resistance resulted from obesity. Body composition chart analysis might be useful in characterization of diabetes type and detection of atypical cases and early adjustment of diabetes management strategy.

Maximizing the Coverage of Sensor Deployments Using a Memetic Algorithm and Fast Coverage Estimation.

We derive the upper and lower bounds on the coverage of a 2-D deployment of static sensors. We use these bounds in constructing a method of estimating the coverage of deployment by assuming that there are only pairwise intersections between the disks representing the range of each sensor. The speed of this approximation allows it to be built into a local search technique, as part of a memetic algorithm (MA) that tries to deploy a given set of sensors with maximum coverage. We show that this MA outperforms the previous techniques in terms of both speed and coverage achieved.

Comparative Study of Classification Algorithms for Various DNA Microarray Data.

Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN), and the resulting accuracies were compared. k-fold cross-validation was used in evaluating the performance and the result was analyzed by comparing the performances of the five machine learning methods. Through the experiments, it was observed that the two tree-based methods, DT and RF, showed similar trends in results and the remaining three methods, MLP, SVM, and DT, showed similar trends. DT and RF generally showed worse performance than other methods except for one dataset. This suggests that, for the effective classification of microarray data, selecting a classification algorithm that is suitable for data traits is crucial to ensure optimum performance.

Identification of a novel mutation in the ATP7A gene in a Korean patient with Menkes disease.

Menkes disease is an infantile-onset X-linked recessive neurodegenerative disorder caused by diverse mutations in a copper-transport gene, ATP7A. Affected patients are characterized by progressive hypotonia, seizures, failure to thrive and death in early childhood. Here, we report a case of Menkes disease presented by intractable seizures and infantile spasms. A 3-month-old male infant had visited our pediatric clinic for lethargy, floppy muscle tone, poor oral intake and partial seizures. His hair was kinky, brown colored and fragile. Partial seizures became more frequent, generalized and intractable to antiseizure medications. An EEG showed frequent posteriorly dominant generalized spikes that were consistent with a generalized seizure. From a genetic analysis, a c.2743C>T (p.Gln915X) mutation was detected and diagnosed as Menkes disease. The mutation is a novel one that has not been previously reported as a cause of Menkes disease.

Optimizing taxon addition order and branch lengths in the construction of phylogenetic trees using maximum likelihood.

Taxon addition order and branch lengths are optimized by genetic algorithms (GAS) within the fastDNAml algorithm for constructing phylogenetic trees of high likelihood. Results suggest that optimizing the order in which taxa are added improves the likelihood of the resulting trees.

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning.

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection.

The KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class. We define the U2R, R2L, and Probe classes, which are each less than 1% of the total dataset, as rare classes. In this study, we attempt to mitigate the class imbalance of the dataset. Using the synthetic minority oversampling technique (SMOTE), we attempted to optimize the SMOTE ratios for the rare classes (U2R, R2L, and Probe). After randomly generating a number of tuples of SMOTE ratios, these tuples were used to create a numerical model for optimizing the SMOTE ratios of the rare classes. The support vector regression was used to create the model. We assigned each instance in the test dataset to the model and chose the best SMOTE ratios. The experiments using machine-learning techniques were conducted using the best ratios. The results using the proposed method were significantly better than those of previous approach and other related work.

Two Cases of Successful Type 2 Diabetes Control with Lifestyle Modification in Children and Adolescents.

Obesity and obesity-related disease are becoming serious global issues. The incidence of obesity and type 2 diabetes has increased in children and adolescents. Type 2 diabetes is a chronic disease that is difficult to treat, and the accurate assessment of obesity in type 2 diabetes is becoming increasingly important. Obesity is the excessive accumulation of fat that causes insulin resistance, and body composition analyses can help physicians evaluate fat levels. Although previous studies have shown the achievement of complete remission of type 2 diabetes after focused improvement in lifestyle habits, there are few cases of complete remission of type 2 diabetes. Here we report on obese patients with type 2 diabetes who were able to achieve considerable fat loss and partial or complete remission of diabetes through lifestyle changes. This case report emphasizes once again that focused lifestyle intervention effectively treats childhood diabetes.

Reassessing the significance of the PAH c.158G>A (p.Arg53His) variant in patients with hyperphenylalaninemia.

BACKGROUND: The accurate interpretation of sequence variation is critical for successful molecular diagnoses. It is also fundamental to the accurate diagnosis and treatment of phenylketonuria (PKU). This study aims to evaluate the significance of the c.158G>A (p.Arg53His) variant in the PAH gene, which was previously reported to be a pathogenic mutation that results in decreased phenylalanine hydroxylase enzyme activity in hyperphenylalaninemia (HPA) patients. METHODS: Seven unrelated Korean patients with HPA genotyped with the c.158G>A variant were included in this study. The variant c.158G>A was classified by the standards and guidelines for the interpretation of sequence variants by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. RESULTS: By both directly collecting genetic data and comprehensively reviewing the existing literature, we found that this variant is more appropriately classified as "Likely benign" rather than pathogenic. The allele's frequency is 2.57% in the general Korean population, which was greater than expected for phenylketonuria. This variant was observed to be homozygous in healthy subjects and was also observed in cis with other pathogenic variants. It is common in East Asian populations (especially in Koreans) compared to Western populations. There is a possibility that it causes decreased enzyme activity without leading to the full pathology of phenylketonuria. CONCLUSIONS: This study expands our understanding of the consequences of variation in PAH and its relationship to HPA.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning.

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km(2), from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.

Discovery of 2-((R)-4-(2-Fluoro-4-(methylsulfonyl)phenyl)-2-methylpiperazin-1-yl)-N-((1R,2s,3S,5S,7S)-5-hydroxyadamantan-2-yl)pyrimidine-4-carboxamide (SKI2852): A Highly Potent, Selective, and Orally Bioavailable Inhibitor of 11ß-Hydroxysteroid Dehydrogenase Type 1 (11ß-HSD1).

A series of picolinamide- and pyrimidine-4-carboxamide-based inhibitors of 11ß-hydroxysteroid dehydrogenase type 1 was synthesized and evaluated to optimize the lead compound 9. The combination of the replacement of a pyridine ring of 9 with a pyrimidine ring and the introduction of an additional fluorine substituent at the 2-position of the phenyl ring resulted in the discovery of a potent, selective, and orally bioavailable inhibitor, 18a (SKI2852), which demonstrated no CYP and PXR liabilities, excellent PK profiles across species, and highly potent and sustainable PD activity. Repeated oral administration of 18a significantly reduced blood glucose and HbA1c levels and improved the lipid profiles in ob/ob mice. Moreover, the HbA1c-lowering effect of metformin was synergistically enhanced in combination with 18a.

A genetic filter for cancer classification on gene expression data.

We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-correlation among selected genes. The proposed genetic filter was tested on well-known leukemia datasets, and significant improvement over previous work was obtained.

Role of Kupffer cells in the vasoregulatory gene expression during hepatic ischemia/reperfusion.

Hepatic microcirculatory failure is a major component of reperfusion injury in the liver. Recent data provided some evidence that endothelium-derived vasoconstrictors and vasodilators may be functionally important to the control of the total hepatic blood flow under these conditions of circulatory failure. Since Kupffer cells provide signals that regulate the hepatic response in ischemia/reperfusion (I/R), the aim of this study was to investigate the role of Kupffer cells in the I/R-induced imbalance of vasoregulatory gene expression. Rats were subjected to 60 min hepatic ischemia, followed by 5 h of reperfusion. The Kupffer cells were inactivated by gadolinium chloride (GdCl3, 7.5 mg/kg body weight, intravenously) 1 day prior to ischemia. Liver samples were obtained 5 hrs after reperfusion for RT-PCR analysis of the mRNA for genes of interest: endothelin-1 (ET-1), its receptors ETA and ETB, endothelial nitric oxide synthase (eNOS), inducible nitric oxide synthase (iNOS) and heme oxygenase-1 (HO-1). ET-1 mRNA expression was increased by I/R. mRNA levels for ETA receptors showed no change, whereas ETB receptor transcripts increased in the I/R group. The increases in ET-1 and ETB mRNA were not prevented by the GdCl3 pretreatment. The mRNA levels for iNOS and eNOS significantly increased within the I/R group with no significant difference between the I/R group and the GdCl3-treated I/R group. HO-1 mRNA expression significantly increased in the I/R group and this increase was attenuated by GdCl3. In conclusion, we have demonstrated that an imbalance in hepatic vasoregulatory gene expression occurs during I/R. Our findings suggest that the activation of Kupffer cells is not required for I/R-induced hepatic microvascular dysfunction.

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.