The co-occurrence of mPFC-STN theta phase synchronization and STN theta-gamma PAC reflects a neural substrate for fronto-subthalamic communication during conflict processing. More broadly, it may be a general mechanism for neuronal interactions in the cortico-basal ganglia circuits via a combination of long-range, within-frequency phase synchronization and local cross-frequency PAC.The market-expanding lutein is currently mainly supplied by plant extraction, with microbial fermentation using engineered cell factory emerging as a promising substitution. During construction of lutein-producing yeast, α-carotene formation through asymmetric ε- and β-cyclization of lycopene was found as the main limiting step, attributed to intra-pathway competition of the cyclases for lycopene, forming β-carotene instead. To solve this problem, temperature-responsive expression of β-cyclase was coupled to constitutive expression of ε-cyclase for flux redirection to α-carotene by allowing ε-cyclization to occur first. Meanwhile, the ε-cyclase was engineered and re-localized to the plasma membrane for further flux reinforcement towards α-carotene. Finally, pathway extension with proper combination of carotenoid hydroxylases enabled lutein (438 μg/g dry cells) biosynthesis in S. cerevisiae. The success of heterologous lutein biosynthesis in yeast suggested temporospatial pathway control as a potential strategy in solving intra-pathway competitions, and may also be applicable for promoting the biosynthesis of other natural products. This study proposed and investigated the performance of a deep learning based three-dimensional (3D) convolutional neural network (CNN) model for automatic segmentation of the pharyngeal airway space (PAS). A dataset of 103 computed tomography (CT) and cone-beam CT (CBCT) scans was acquired from an orthognathic surgery patients database. The acquisition devices consisted of 1 CT (128-slice multi-slice spiral CT, Siemens Somatom Definition Flash, Siemens AG, Erlangen, Germany) and 2 CBCT devices (Promax 3D Max, Planmeca, Helsinki, Finland and Newtom VGi evo, Cefla, Imola, Italy) with different scanning parameters. A 3D CNN-based model (3D U-Net) was built for automatic segmentation of the PAS. The complete CT/CBCT dataset was split into three sets, training set (n=48) for training the model based on the ground-truth observer-based manual segmentation, test set (n=25) for getting the final performance of the model and validation set (n=30) for evaluating the model's performance versus observer-based segmenty diagnose, plan treatment and follow-up patients with dento-skeletal deformities and obstructive sleep apnea which might influence the upper airway space, thereby further improving patient care. To investigate the effect of rapid high-intensity light-curing on the marginal integrity of four bulk-fill composites, including two materials specifically designed for high-intensity curing. Class V cavities were prepared on buccal surfaces of intact human molars with simulated pulpal pressure, filled in a single increment and light-cured using a conventional (10s @ 1,340mW/cm ) or high-intensity (3s @ 3,440mW/cm ) protocol. The restorations were subjected to thermo-mechanical loading (TML) comprising 1,200,000 mechanical loading cycles and 3,000 thermocycles. Quantitative margin analysis was performed before and after TML using a scanning electron microscope, and the marginal integrity was expressed as percentage of continuous margin (PCM). All PCM values measured before TML were statistically similar regardless of the material and curing protocol (p>0.05). A statistically significant effect of the curing protocol (p=0.021) was identified only after TML for one material. PCM was significantly diminished by TML (p<0.001) for most combinations of material and curing protocol. The PCM values of the sculptable composites after TML were statistically similar regardless of the curing protocol (p>0.05). Compared to these values, significantly lower PCM after TML was identified for the flowable composites cured with the high-intensity protocol (p=0.001-0.045). In most cases, high-intensity and conventional curing generally led to similar marginal integrity. SAG agonist chemical structure Although all of the investigated composites initially performed similarly well, the flowable composites light-cured using the high-intensity protocol showed a significantly inferior marginal integrity compared to the sculptable composites after loading. Rapid high-intensity light-curing cannot be recommended for flowable bulk-fill composites since it may compromise the tooth-restoration interface.Rapid high-intensity light-curing cannot be recommended for flowable bulk-fill composites since it may compromise the tooth-restoration interface. This study evaluated the importance of defining the reference and the test object during 3D surface comparisons to assess the trueness of an intraoral scanner. A maxillary complete-arch cast with interdental spaces was digitized with a high-resolution scanner to obtain the ground truth dataset [GT]. Fifteen intraoral scanning datasets [IOS] were obtained with an intraoral scanner. The trueness of the [IOS] datasets were evaluated by two different comparison procedures using a 3D analysis software In the first comparison [REF-GT], the [GT] dataset was set as reference object and the [IOS] dataset was defined as test object. In the second comparison [REF-IOS], the [IOS] dataset were set as reference object and the [GT] dataset was defined as test object. The mean trueness of both comparisons was calculated with absolute mean deviation, (90-10)/2 percentile, and root-mean-squared (RMS) error method. Statistical significance was analyzed using the t-test (α=0.05). The mean trueness values of [REF-GT] were 31.4(±6.1) µm for (90-10)/2 percentile, 77.0(±5.3) µm for absolute mean deviation, and 203.1(±4.8) µm for RMS error method. [REF-IOS] revealed 23.9(±4.8) µm, 28.3(±6.3) µm, and 39.6(±9.5) µm, respectively. The results differed significantly. The datasets obtained from the intraoral scanner captured more adequately interproximal spaces in comparison to the [GT] dataset. Therefore, the [GT] dataset defined as reference object in the analysis software for 3D comparisons revealed misleading results. The selection of the reference object and of the areas to be compared have to be defined carefully regarding complete arch scanning accuracy analysis.The selection of the reference object and of the areas to be compared have to be defined carefully regarding complete arch scanning accuracy analysis.