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Abstract With increasing coffee consumption worldwide, the efficient and sustainable management of spent coffee grounds (SCG) has become increasingly challenging. This study investigated the anaerobic co-digestion of small amounts of SCG with food waste (FW) at increasing co-feeding ratios of 1:100–1:10 (volatile solids basis) to assess the possibility of SCG treatment using the spare capacity of existing anaerobic digesters. Co-feeding SCG increased methane production compared to FW mono-digestion in the tested range of co-feeding ratios without compromising process stability. Methane yield did not further increase when the SCG/FW ratio increased above 4%, and process failure occurred at a 1:10 co-feeding ratio without trace element supplementation. The enhanced methanogenic performance was attributed to increased protein removal efficiency, which was potentially related to the promotion of peptide hydrolysis. The overall results suggest that co-feeding appropriate small amounts of SCG to FW digesters can be a realistic sustainable option for SCG management.

Abstract Background Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). Results First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen′s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. Conclusions Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.

The multikinase inhibitor sorafenib is the standard first-line treatment for advanced hepatocellular carcinoma (HCC), but many patients become sorafenib-resistant (SR). This study investigated the efficacy of another kinase inhibitor, regorafenib (Rego), as a second-line treatment. We produced SR HCC cells, wherein the PI3K-Akt, TNF, cAMP, and TGF-beta signaling pathways were affected. Acute Rego treatment of these cells reversed the expression of genes involved in TGF-beta signaling but further increased the expression of genes involved in PI3K-Akt signaling. Additionally, Rego reversed the expression of genes involved in nucleosome assembly and epigenetic gene expression. Weighted gene co-expression network analysis (WGCNA) revealed four differentially expressed long non-coding RNA (DElncRNA) modules that were associated with the effectiveness of Rego on SR cells. Eleven putative DElncRNAs with distinct expression patterns were identified. We associated each module with DEmRNAs of the same pattern, thus obtaining DElncRNA/DEmRNA co-expression modules. We discuss the potential significance of each module. These findings provide insights and resources for further investigation into the potential mechanisms underlying the response of SR HCC cells to Rego.

The genus Sinopoda Jäger, 1999 is a group of huntsman spiders (Araneae: Sparassidae: Heteropodinae), and currently seven species have been reported in Korea. In this study, three new species are described from Korea, Sinopoda bigibba sp. nov., Sinopoda bogil sp. nov., and Sinopoda pantherina sp. nov.; Sinopoda jirisanensis Kim & Chae, 2013 is revalidated with neotype designation, and had been formerly synonymized with Sinopoda forcipata (Karsch, 1881). Additionally, all previous records of Sinopoda stellatops Jäger & Ono, 2002 and S. forcipata from Korea are deemed misidentifications of S. jirisanensis and S. bogil sp. nov., respectively.

A simple steerable array antenna is designed and developed using a movable dielectric phase shifter. The change of effective dielectric constant at different dielectric slab positions on a coplanar waveguide is used as the phase shifter. The impedance matching and desired phase shift conditions are satisfied at two slab heights, and the reflection is designed to be minimized at these slab positions. The low-loss dielectric material is used as the dielectric slab and is placed close to a coplanar transmission line with airgap. The 4 × 4 steerable array antenna with the phase shifters is designed and fabricated at 20 GHz. The H-plane radiation patterns are measured at different phase shift positions and compared with the expected results. [ABSTRACT FROM AUTHOR]