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Abstract Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and the dimensionality-reduced representation, leading to better interpretability. In this research, we present a tied-weight autoencoder as a dimensionality reduction model with the merit of both linear and nonlinear methods. Although the tied-weight autoencoder is a nonlinear dimensionality reduction model, we approximate it to function as a linear model. This is achieved by removing the hidden layer units that are largely inactivated by the input data, while preserving the model’s effectiveness. We evaluate the proposed model by comparing its performance with other linear and nonlinear models using benchmark datasets. Our results show that the proposed model performs comparably to the nonlinear model of a similar autoencoder structure to the proposed model. More importantly, we show that the proposed model outperforms the linear models in various metrics, including the mean square error, data reconstruction, and the classification of low-dimensional projections of the input data. Thus, our study provides general recommendations for best practices in dimensionality reduction.

Multiparental mapping populations hold great potential for dissecting quantitative traits and rapidly identifying genetic determinants. We developed a japonica nested association mapping population, KNU_NAM, comprising 880 lines derived from ten recombinant inbred lines (RILs) families of prominent varieties and the elite Korean variety Shindongjin. Genetic characterization of KNU_NAM revealed 48,159 polymorphic SNPs, with family counts ranging from 18,787 to 42,578 and an average of 30,019 SNPs per family. Further molecular diversity analysis of KNU_NAM indicated reduced population structure and broad genetic diversity. Genome-wide association studies (GWAS) on five morphological traits identified 47 significant marker-trait associations (MTAs), with a set of 18 MTAs located on chromosome 9. Linkage disequilibrium (LD) block analysis of this region revealed 15 haplotypes and identified five key genes associated with panicle architecture: OsDEP1, OsEATB, OsLGD1, and OsSPL18. Additionally, two non-synonymous MTAs on chromosome 7 were located on the exon of OsPRR37/Ghd7.1, a gene associated with plant height, heading date, and grain number per panicle. Further phenotypic performance analysis of haplotypes from these hotspot regions revealed significant differences in the targeted traits. The study validates the potential of KNU_NAM and GWAS for high-resolution genetic mapping in rice breeding programs, highlighting the utility of these populations for enhancing genetic diversity and improving trait selection in rice.

As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection.

Early season flooding is a major constraint in direct-seeded rice, as rice genotypes vary in their coleoptile length during anoxia. Trehalose-6-phosphate phosphatase 7 (OsTPP7, Os09g0369400) has been identified as the genetic determinant for anaerobic germination (AG) and coleoptile elongation during flooding. We evaluated the coleoptile length of a diverse rice panel under normal and flooded conditions and investigated the Korean rice collection of 475 accessions to understand its genetic variation, population genetics, evolutionary relationships, and haplotypes in the OsTPP7 gene. Most accessions displayed enhanced flooded coleoptile lengths, with the temperate japonica ecotype exhibiting the highest average values for normal and flooded conditions. Positive Tajima’s D values in indica, admixture, and tropical japonica ecotypes suggested balancing selection or population expansion. Haplotype analysis revealed 18 haplotypes, with three in cultivated accessions, 13 in the wild type, and two in both. Hap_1 was found mostly in japonica, while Hap-2 and Hap_3 were more prevalent in indica accessions. Further phenotypic performance of major haplotypes showed significant differences in flooded coleoptile length, flooding tolerance index, and shoot length between Hap_1 and Hap_2/3. These findings could be valuable for future selective rice breeding and the development of efficient haplotype-based breeding strategies for improving flood tolerance.

Gamma-tocopherol methyltransferase (γ-TMT), a key gene in the vitamin E biosynthesis pathway, significantly influences the accumulation of tocochromanols, thereby determining rice nutritional quality. In our study, we analyzed the γ-TMT gene in 475 Korean rice accessions, uncovering 177 genetic variants, including 138 SNPs and 39 InDels. Notably, two functional SNPs, tmt-E2-28,895,665-G/A and tmt-E4-28,896,689-A/G, were identified, causing substitutions from valine to isoleucine and arginine to glycine, respectively, across 93 accessions. A positive Tajima’s D value in the indica group suggests a signature of balancing selection. Haplotype analysis revealed 27 haplotypes, with two shared between cultivated and wild accessions, seven specific to cultivated accessions, and 18 unique to wild types. Further, profiling of vitamin E isomers in 240 accessions and their association with haplotypes revealed that Hap_2, distinguished by an SNP in the 3′ UTR (tmt-3UTR-28,897,360-T/A) exhibited significantly lower α-tocopherol (AT), α-tocotrienol (AT3), total tocopherol, and total tocotrienol, but higher γ-tocopherol (GT) in the japonica group. Additionally, in the indica group, Hap_2 showed significantly higher AT, AT3, and total tocopherol, along with lower GT and γ-tocotrienol, compared to Hap_19, Hap_20, and Hap_21. Overall, this study highlights the genetic landscape of γ-TMT and provides a valuable genetic resource for haplotype-based breeding programs aimed at enhancing nutritional profiles.

As an extension of the previous work, this paper is concerned with the modeling of composite sandwich shells in a dynamic framework. The variational-asymptotic method is first used to construct an equivalent shell model taking into account inertial and transverse deformation effects, but still providing the zeroth-order approximation. At this time, each layer of the sandwich structure is individually considered as a shell in which all material constitutive constants have the same order. Next, the sandwich shell is regarded as the assembly of three curved layers with mismatched material and geometric properties, whereby an order transition in the transverse deformations occurs. By imposing continuity conditions on the interfaces between layers, a universal model with a three-dimensional recovery relation is systematically derived, allowing the mechanical behaviors of composite sandwich shells to be predicted. Finally, preliminary validations are implemented to demonstrate the capabilities and accuracy of the present approach.

Abstract Signal transducer and activator of transcription 3 (STAT3) modulates a variety of genes involved in the regulation of critical functions, including cell proliferation, differentiation, apoptosis, angiogenesis, metastasis, and immunity. For many cancers, elevated levels of STAT3 signaling have been associated with a poor prognosis and the development of chemotherapy resistance. In this study, we investigated the inhibitory effects of a novel small-molecule inhibitor of STAT3, STX-0119, on the cell viability and survival of human lung cancer cells. STX-0119 inhibited activated STAT3 and the expression of STAT3-regulated oncoproteins such as c-Myc, cyclin D1, and survivin in lung cancer cells. STX-0119 also decreased the amount of STAT3 in the nuclear fraction as well as induced apoptosis of these lung cancer cell lines as evidenced by increases in apoptotic cells (Annexin V positive) and poly (ADP-ribose) polymerase (PARP) cleavage. The efficacy of STX-0119 in a mouse xenograft model was confirmed. However, a hematological side effect, which had not been previously reported, was observed. The level of white blood cells was significantly lowered when treated at the dose at which STX-0119 alone showed a significant tumor-suppressive effect. In conclusion, we suggest that STX-0119 may be a potent therapeutic agent against lung cancer. Consideration of the side effect suggests, it is necessary to study whether low-dose STX-0119 is effective for lung treatment with a combination of classic lung cancer therapeutics.

In this study, we suggested a hypothesis test method that was robust to different genotype encodings in a genome-wide association analysis of continuous traits. When the population stratification is corrected for using a method based on principal component analysis, ordinally (or categorically) encoded genotypes are adjusted and turn into continuous values. Due to the adjustment of the encoded genotype, the association test result using conventional methods, such as the test of Pearson's correlation coefficient, was shown to be dependent on how genotypes were encoded. To overcome this shortcoming, we proposed a non-parametric test based on Kendall's tau. Because Kendall's tau deals with rank, rather than value, associations between adjusted genotype and phenotype values, Kendall's test can be more robust than Pearson's test under different genotype encodings. We assessed the robustness of Kendall's test and compared with that of Pearson's test in terms of the difference in p-values obtained by using different genotype encodings. With simulated as well as real data set, we demonstrated that Kendall's test was more robust than Pearson's test under different genotype encodings. The proposed method can be applicable to the broad topics of interest in population genetics and comparative genomics, in which novel genetic variants are associated with traits. This study may also bring about a cautious approach to the genotype encoding in the numerical analysis.

Abstract Osteoarthritis (OA) is a major degenerative joint condition that causes articular cartilage destruction. It was recently found that enhancement of chondroclasts and suppression in Treg cell differentiation are involved in the pathogenesis of OA. Kartogenin (KGN) is a small drug-like molecule that induces chondrogenesis in mesenchymal stem cells (MSCs). This study aimed to identify whether KGN can enhance severe pain behavior and improve cartilage repair in OA rat model. Induction of OA model was loaded by IA-injection of MIA. In the OA rat model, treatment an intra-articular injection of KGN. Pain levels were evaluated by analyzing PWL and PWT response in animals. Histological analysis and micro-CT images of femurs were used to analyze cartilage destruction. Gene expression was measured by real-time PCR. Immunohistochemistry was analyzed to detect protein expression. KGN injection significantly decreased pain severity and joint destruction in the MIA-induced OA model. KGN also increased mRNA levels of the anti-inflammatory cytokine IL-10 in OA patients’ chondrocytes stimulated by IL-1β. Decreased chondroclast expression, and increased Treg cell expression. KGN revealed therapeutic activity with the potential to reduce pain and improve cartilage destruction. Thus, KGN could be a therapeutic molecule for OA that inhibits cartilage damage.

We propose an efficient method of finding an optimal solution for a multi-item continuous review inventory model in which a bivariate Gaussian probability distribution represents a correlation between the demands of different items. By utilizing appropriate normalizations of the demands, we show that the normalized demands are uncorrelated. Furthermore, the set of equations coupled with different items can be decoupled in such a way that the order quantity and reorder point for each item can be evaluated independently from those of the other. As a result, in contrast to conventional methods, the solution procedure for the proposed method can be much simpler and more accurate without any approximation. To demonstrate the advantage of the proposed method, we present a solution scheme for a multi-item continuous review inventory model in which the demand of optional components depend on that of a “vanilla box,” representing the customer’s stochastic demand, under stochastic payment and budget constraints. We also perform a sensitivity analysis to investigate the dependence of order quantities and reorder points on the correlation coefficient. Keywords: Continuous review inventory system, Optimal solution, Normalization, Modularization and postponement

Purpose: To investigate the form and level of daily caffeine intake recommended above 400 mg in medical students expected to consume caffeinated beverages to enhance their performance. Methods: From May to June 2023, freshman through senior medical students at a medical school in Korea were administered a seven-item questionnaire designed to measure the amount of caffeine-containing foods consumed, the weekly interval between consumption, the reason for consumption, and the level of caffeine-induced symptoms experienced. Results: Out of 443 students, 361 responded (81.5%). The most commonly consumed caffeine beverages were coffee (79.2%), followed by soda (33.2%), tea (27.4%), chocolate (25.2%), and energy drinks (20.5%). The estimated (average±standard deviation) daily intake was estimated to 274.6±276.5 mg, and they consumed caffeine on an average of 4.25±2.26 days per week. Students who consumed 400 mg or more of caffeine daily consumed 19.9%. The primary motivation for caffeine intake was “to improve academic performance” (60.9%) and “preferred food” (51.8%). Among the responders, 98% of them replied they had symptoms that could be caused by caffeine, in order of palpitations (47.4%), frequent urination (42.9%), anxiety (27.1%), indigestion (17.5%), and excitement (17.5%). A total of 45.7% reported two symptoms, and 24.7% reported three or more. Conclusion: Caffeinated beverage consumption was routine among current medical students, with 20% consuming more than the recommended daily amount. Most students experienced at least one caffeine-induced symptom, with two symptoms in half, suggesting the need for policy measures and warnings about caffeine-containing foods. [ABSTRACT FROM AUTHOR]

When SO2 and NO2 are simultaneously absorbed into limestone slurry, the SO32- (including HSO3-) ions formed by the hydrolysis of SO2 enhance the absorption rate of NO2. However, NO2 absorption decreases due to the oxidation of SO32- in the presence of O2. To address this issue, simultaneous absorption was performed by adding organic additives: formic, acetic, propionic acid, monoethanolamine (MEA), diethanolamine (DEA), and triethanolamine (TEA). These limestone slurries were denoted FS, AS, PS, MS, DS, and TS, respectively. As the acidity of the organic acid increases, SO32- oxidation inhibition also increases. Accordingly, the denitrification rate of the slurries with added organic acid was in the order of PS < AS < FS. SO32- oxidation inhibition by the ethanolamine additives increased with the number of 2-hydroxyethyl. The denitrification rate within 50 min of reaction was in the order of MS < TS < DS, which is attributed to the basicity and SO32- oxidation inhibition by ethanolamine. The denitrification rate after 50 min of the reaction was in the order of MS 32- oxidation inhibition and degradation due to the CO2 absorption rate. [ABSTRACT FROM AUTHOR]