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Previous literature suggests that a balance between Pavlovian and instrumental decision-making systems is critical for optimal decision-making. Pavlovian bias (i.e., approach toward reward-predictive stimuli and avoid punishment-predictive stimuli) often contrasts with the instrumental response. Although recent neuroimaging studies have identified brain regions that may be related to Pavlovian bias, including the dorsolateral prefrontal cortex (dlPFC), it is unclear whether a causal relationship exists. Therefore, we investigated whether upregulation of the dlPFC using transcranial current direct stimulation (tDCS) would reduce Pavlovian bias. In this double-blind study, participants were assigned to the anodal or the sham group; they received stimulation over the right dlPFC for 3 successive days. On the last day, participants performed a reinforcement learning task known as the orthogonalized go/no-go task; this was used to assess each participant's degree of Pavlovian bias in reward and punishment domains. We used computational modeling and hierarchical Bayesian analysis to estimate model parameters reflecting latent cognitive processes, including Pavlovian bias, go bias, and choice randomness. Several computational models were compared; the model with separate Pavlovian bias parameters for reward and punishment domains demonstrated the best model fit. When using a behavioral index of Pavlovian bias, the anodal group showed significantly lower Pavlovian bias in the punishment domain, but not in the reward domain, compared with the sham group. In addition, computational modeling showed that Pavlovian bias parameter in the punishment domain was lower in the anodal group than in the sham group, which is consistent with the behavioral findings. The anodal group also showed a lower go bias and choice randomness, compared with the sham group. These findings suggest that anodal tDCS may lead to behavioral suppression or change in Pavlovian bias in the punishment domain, which will help to improve comprehension of the causal neural mechanism. [ABSTRACT FROM AUTHOR]

Recent advances in three‐dimensional (3D) bioprinting technologies have enabled the fabrication of sophisticated live 3D tissue analogs. Despite the existing hydrogel‐based bioinks, the development of advanced bioink materials that can accurately reproduce the composition of a native extracellular matrix and mimic the intrinsic properties of laden cells remains challenging. In this study, 3D printed skin equivalents incorporating hair follicle structures and epidermal/papillary dermal layers were fabricated using gelatin methacryloyl/hyaluronic acid methacryloyl (GelMA/HAMA) bioink. The composition of collagen and glycosaminoglycan in native skin was recapitulated by adjusting the combination of GelMA and HAMA. The GelMA/HAMA bioink exhibited excellent viscoelastic and physicochemical properties, 3D printability, cytocompatibility, and functionality to maintain hair‐inductive potency while facilitating spontaneous hair pore development. The results indicate that GelMA/HAMA hydrogels are promising candidates as bioinks for the 3D printing of skin equivalents. Furthermore, they may serve as useful models for skin tissue engineering and regeneration. [ABSTRACT FROM AUTHOR]

Bacillus lichenformis is an industrially promising generally recognized as safe (GRAS) strain that can be used for the production of a valuable chemical, 2,3‐butanediol (BDO). Conventional gene deletion vectors and/or methods are time‐consuming and have poor efficiency. Therefore, clustered regularly interspaced short palindromic repeat (CRISPR)‐Cas9 mediated homologous recombination was used to engineer a newly isolated and UV‐mutagenized B. licheniformis 4071–15 strain. With the help of a CRISPR‐Cas9 system, this one‐step process could be used for the deletion of ldh gene within 4 days with high‐efficiency exceeding 60%. In addition, the sequential deletion of target genes for engineering studies was evaluated, and it was confirmed that a triple mutant strain (ldh, dgp, and acoR) could be obtained by repeated one‐step cycles. Furthermore, a practical metabolic engineering study was carried out using a CRISPR‐Cas9 system for the stereospecific production of (2R,3S)‐BDO. The predicted (2R,3R)‐butanediol dehydrogenase encoded by the gdh gene was selected as a target for the production of (2R,3S)‐BDO, and the mutant was successfully obtained. The results show that the stereospecific production of (2R,3S)‐BDO was possible with the gdh deletion mutant, while the 4071–15 host strain still generated 26% of (2R,3R)‐BDO. It was also shown that the 4071–15 Δgdh mutant could produce 115 g/L of (2R,3S)‐BDO in 64 hr by two‐stage fed‐batch fermentation. This study has shown the efficient development of a (2R,3S)‐BDO producing B. licheniformis strain based on CRISPR‐Cas9 and fermentation technologies. [ABSTRACT FROM AUTHOR]

The first author, Hyeonjin Kim, is incorrectly noted as the corresponding author. The correct corresponding author is Woo-Young Ahn. Woo-Young Ahn's email address is: wahn55@snu.ac.kr.By Hyeonjin Kim; Jihyun K. Hur; Mina Kwon; Soyeon Kim; Yoonseo Zoh and Woo-Young AhnReported by Author; Author; Author; Author; Author; Author [Extracted from the article]

Background: An imbalance between model-based and model-free decision-making systems is a common feature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive features associated with model-based and model-free systems in IGD and alcohol use disorder (AUD)., Method: Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group., Results: The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE., Discussion and Conclusions: The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.