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The chemical diversity of peptides from ribosomal origin is a growing field of research. Here, the authors report the discovery, genomic and biosynthetic investigations of kintamdin, a ribosomally synthesized and post-translationally modified peptides featuring a beta-enamino acid and a bis-thioether macrocyclic motif.

Abstract Exchange bias has increasingly practical significance in magnetoresistive and spintronic devices. However, the underlying mechanism of exchange bias in bulk compounds with the structural single‐phase and inhomogeneous magnetic phases is still elusive. Herein, based on experimental and simulation results, two important parameters are studied, i.e., the antiferromagnetic (AFM) volume fraction and the ferromagnetic (FM)/AFM interface area, which essentially determine the (spontaneous) exchange bias of Mn‐rich Ni44Co6Mn44‐xSn6+x (x = 0 ∼ 6) magnetic shape memory alloys. The substitution Sn for Mn changes magnetic ground state following the sequence of superparamagnetic/AFM → dilute spin glass/AFM → transition zone → cluster spin glass/AFM → FM, accompanying the growth of FM cluster and the weakening of AFM interactions. The results reveal that the magnetic ground state for exchange bias is optimized at transition zone between dilute spin glass/strong AFM and cluster spin glass/weak AFM, in which the optimal AFM volume fraction and FM/AFM interface area are achieved by tuning magnetic fields. A giant exchange bias field of 702.7 mT and a spontaneous exchange bias field of 318.7 mT are demonstrated. The work contributes to in‐depth understanding of (spontaneous) exchange bias in magnetically inhomogeneous compounds.

Non-Ribosomal Peptide Synthetases (NRPSs) are responsible for the construction of many types of natural products. Here the authors characterize a key type II thioesterase domain that sheds light on the chain translocation processes of legonmycin NRPSs.

ObjectiveThe pathogenesis of schizophrenia is associated with neuropeptide Y (NPY) gene polymorphism to explore the relationship between rs16141, rs16145, and rs5573 polymorphisms in the NPY gene and antipsychotics response in the Chinese population.MethodsThe unrelated 228 Chinese Han patients with schizophrenia were enrolled in the present study. Genotypisation within NPY gene was performed using the KASP genotyping assays. Before treatment and on the weekends of the 2nd, 4th, and 8th weeks after treatment, the medication status of the patients was recorded and the positive and negative syndrome scale (PANSS) was used to evaluate the clinical effect. A reduction in total PANSS scores ≥50% were classified as good responders, while others were poor responders. We evaluated the association between NPY gene and antipsychotic efficacy by comparing allele and genotype distribution, correlation analysis, linkage imbalance, and five genetic models between the two groups.ResultsNo significant associations were found in the rs16141, rs16145, and rs5573 of NPY and antipsychotic treatment response (all p > 0.05). There was no significant relationship between the three SNPs polymorphisms in the NPY gene and the changes of positive, negative and general psychopathology subscales scores at each stage (all p > 0.05). The distribution of genotype and allele frequencies of locus rs16141 was not statistically difference between good responders and poor responders (genotype: χ2 =4.088, p=0.043, p-correction = 0.129; allele: χ2 = 4.088, p = 0.027, p-correction = 0.081). The allele distribution of rs5573 was significantly different between groups, yet the difference was disappeared after correcting (χ2 = 4.136, p = 0.042, p-correction =0.126). The distribution frequencies of TA/TG and GG haplotypes constituted by rs16141 and rs5573 showed no statistical difference between the two groups (p > 0.05). In recessive inheritance mode, NPYrs5573 was found to be associated with antipsychotic drug response (G/G vs. A/A +A/G: p = 0.028, AIC = 197.2, BIC = 210.9).ConclusionsThis study didn't found association between polymorphisms in the NPY gene locus (rs16141, rs16145, and rs5573) and the response to antipsychotics after Bonferroni correction. The polymorphism of NPY gene and the efficacy of antipsychotic drugs in patients with schizophrenia need further study.

The ferroelectric field effect transistor (Fe-FET) is considered to be one of the most important low-power and high-performance devices. It is promising to combine a ferroelectric field effect with a photodetector to improve the photodetection performance. This study proposes a strategy for ZnO ultraviolet (UV) photodetectors regulated by a ferroelectric gate. The ZnO nanowire (NW) UV photodetector was tuned by a 2D CuInP2S6 (CIPS) ferroelectric gate, which decreased the dark current and enhanced the responsivity and detectivity to 2.40 × 104 A/W and 7.17 × 1011 Jones, respectively. This strategy was also applied to a ZnO film UV photodetector that was tuned by a P(VDF-TrFE) ferroelectric gate. Lower power consumption and higher performance can be enabled by ferroelectric tuning of ZnO ultraviolet photodetectors, providing new inspiration for the fabrication of high-performance photodetectors.

Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence’s current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round’s loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model.

A decision list is an ordered list of rules. Each rule is specified by a term, which is a conjunction of literals, and a value. Given an input, the output of a decision list is the value corresponding to the first rule whose term is satisfied by the input. Decision lists generalize both CNFs and DNFs and have been studied both in complexity theory and in learning theory. The size of a decision list is the number of rules, and its width is the maximal number of variables in a term. We prove that decision lists of small width can always be approximated by decision lists of small size, where we obtain sharp bounds for such approximation. This also resolves a conjecture of Gopalan, Meka, and Reingold (Computational Complexity, 2013) on DNF sparsification. An ingredient in our proof is a new random restriction lemma, which allows to analyze how DNFs (and more generally, decision lists) simplify if a small fraction of the variables are fixed. This is in contrast to the more commonly used switching lemma, which requires most of the variables to be fixed. [ABSTRACT FROM AUTHOR]