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This study investigated the feature importance of near-infrared spectra from random forest regression models constructed to predict the carbonization characteristics of hydrochars produced by hydrothermal carbonization of kraft lignin. The model achieved high coefficients of determination of 0.989, 0.988, and 0.985 with root mean square errors of 0.254, 0.003, and 0.008 when predicting the carbon content, atomic O/C ratio, and H/C ratio, respectively. The random forest models outperformed the multilayer perceptron models for all predictions. In the feature importance analysis, the spectral regions at 1600–1800 nm, the first overtone of C–H stretching vibrations, and 2000–2300 nm, the combination bands, were highly important for predicting the carbon content and O/C predictions, whereas the region at 1250–1711 nm contributed to predicting H/C. The random forest models trained with the high-importance regions achieved better prediction performances than those trained with the entire spectral range, demonstrating the usefulness of the feature importance yielded by the random forest and the feasibility of selective application of the spectral data.

Aminoacyl-tRNA synthetases (ARSs) have evolved to acquire various additional domains. These domains allow ARSs to communicate with other cellular proteins in order to promote non-translational functions. Vertebrate cytoplasmic isoleucyl-tRNA synthetases (IARS1s) have an uncharacterized unique domain, UNE-I. Here, we present the crystal structure of the chicken IARS1 UNE-I complexed with glutamyl-tRNA synthetase 1 (EARS1). UNE-I consists of tandem ubiquitin regulatory X (UBX) domains that interact with a distinct hairpin loop on EARS1 and protect its neighboring proteins in the multi-synthetase complex from degradation. Phosphomimetic mutation of the two serine residues in the hairpin loop releases IARS1 from the complex. IARS1 interacts with BRCA1 in the nucleus, regulates its stability by inhibiting ubiquitylation via the UBX domains, and controls DNA repair function.

This paper investigates the moral dimension of English language teaching by analyzing the collaborative reflections of two professional university teachers in Korea. Data were collected from the teacher participants who wrote, shared, and responded to each other’s weekly teaching reflections throughout a 15-week semester. The data were compiled and selectively coded to isolate morality-related content and then analyzed to identify the categories and themes that emerged. Overall, 28.3% of the codes were identified as being associated with the moral dimension of teaching and were divided into three distinct but closely related categories. The category labels are reflective of contradictory discourses in education including (1) Humanization vs. Dehumanization of Students, (2) Alternative vs. Traditional Assessment, and (3) Teacher as Learner vs. Teacher as Knower. The results illustrate that the teachers were frequently conflicted by competing ideologies and draw attention to the need for language teachers to engage in dialogue regarding the value-based decisions which shape their teaching pedagogies.