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Gastrin plays important role in stimulating the initiation and development of many gastrointestinal diseases through interacting with the cholecystokinin 2 receptor (CCK2R). The smallest bioactive unit of gastrin activating CCK2R is the C-terminal tetrapeptide capped with an indispensable amide end. Understanding the mechanism of this smallest bioactive unit interacting with CCK2R on a molecular basis could provide significant insights for designing CCK2R antagonists, which can be used to treat gastrin-related diseases. To this end, we performed extensive Gaussian accelerated molecular dynamics simulations to investigate the interaction between gastrin C-terminal pentapeptide capped with/without amide end and CCK2R. The amide cap influences the binding modes of the pentapeptide with CCK2R by weakening the electrostatic attractions between the C-terminus of the pentapeptide and basic residues near the extracellular domain in CCK2R. The C-terminus with the amide cap penetrates into the transmembrane domain of CCK2R while floating at the extracellular domain without the amide cap. Different binding modes induced different conformational dynamics of CCK2R. Residue pairs in CCK2R had stronger correlated motions when binding with the amidated pentapeptide. Key residues and interactions important for CCK2R binding with the amidated pentagastrin were also identified. Our results provide molecular insights into the determinants of the bioactive unit of gastrin activating CCK2R, which would be of great help for the design of CCK2R antagonists.

Mura is a phenomenon in which the displays have various uneven display defects and has the characteristics of irregular shape and different sizes. For Mura detection, traditional detection methods have the following two problems: One is the problem of data imbalance. The second is that new shapes and sizes of Mura may appear at any time during the inspection process. In response to the above problems, this paper proposes the Res-unetGAN network, which is an unsupervised anomaly detection method based on generative adversarial network. The generative network is an autoencoder structure composed of ResNet50 and UNet to learn the normal distribution of normal samples. The discriminator is a convolutional neural network based on deep separable convolution and forms a game process with the generator. The network only needs normal samples during the training process, and the network is optimized by the error loss between the original samples and the reconstructed samples. In the test, a reconstruction error score will be designed according to the reconstruction quality, and the defect in the sample will be judged by the reconstruction error score, so as to achieve the goal of anomaly detection. After repeated experiments on the Mura data set, the detection accuracy of Mura defect is better than that of several models compared. The proposed model has a unique application prospect in other industrial anomaly detection since it only requires normal samples for training.

Mura is defined as visual unevenness on the display panel. It can cause unpleasant feelings, so it is necessary to perform Mura inspection during the display quality test. However, Mura is quite difficult to be detected because of its irregular shape and size as well as its low contrast. To solve this practical problem, we proposed a GAN-based model named UADD-GAN to detect Mura in this work. Consisting of a proposed UADD generator and a discriminator, the model is trained using only normal samples, after which the generator is able to simulate the distribution of normal samples. During training, the generator takes normal images as inputs and outputs their reconstructions, while the discriminator receives images and determines whether they are original or reconstructed ones, defiantly helping the generator to perform reconstructions better. The symmetric structure and the skip-adding operation make it easy for the UADD generator to reconstruct the normal samples well. In the detecting procedure, the generator performs worse in the reconstruction of samples with Mura so that we can distinguish them from the normal ones. To make full use of discriminator, we use multiple feature layers of the discriminator for supervision instead of using only the classification layer, helping the generator to reconstruct normal samples better. Meanwhile, a two-side detecting method was used to detect Mura since all the samples are not in a square shape and it greatly improved the detecting accuracy. We have conducted experiments of Mura data sets with different proportion and our research indicates that our proposed method surpasses other state of the art methods.

The measurement of the volume scattering function (VSF) of suspended particles is an ongoing research topic since it has the potential to provide a rapid, nondestructive means of analyzing hydrosols. Despite the success of scattering theory, the measurement of multidirectional scattering intensity distributions over a wide, continuous range of scattering angles and multiple scattering planes, which is described by a three-dimensional VSF (3D VSF), has proven to be difficult. To measure the 3D VSF of submicron particles suspended in water, a measurement approach is proposed, built and tested with several standard submicron particles with diameters ranging from 60 nm to 1 μm. The experimental results indicate that the proposed setup can obtain an accurate 3D VSF for scattering angles from 21° to 160° and azimuthal angles from 1° to 179°. Comparisons between the experiment and theory show a high level of consistency for particles with diameters less than 200 nm. The variation in the VSF in response to the polarization direction of the incident light is also discussed. Both the experiment and theory show the potential of the device to detect the 3D VSF of marine submicron biological particles.

In order to research the effect of turbulence on underwater imaging system and image restoration, the underwater turbulence model is simulated by computer fluid dynamics. This model is obtained in different underwater turbulence intensity, which contains the pressure data that influences refractive index distribution. When the pressure value is conversed to refractive index, the refractive index distribution can be received with the refraction formula. In the condition of same turbulent intensity, the distribution of refractive index presents gradient in the whole region, with disorder and mutations in the local region. With the turbulence intensity increase, the holistic variation of the refractive index in the image is larger, and the refractive index change more tempestuously in the local region. All the above are illustrated by the simulation results with he ray tracing method and turbulent refractive index model. According to different turbulence intensity analysis, it is proved that turbulence causes image distortion and increases noise.

Microfluidic impedance sensors play a significant role in point-of-care applications, clinical tests and laboratory studies. Instead of the traditional signal process method with the envelope detector, filter banks and multistage amplifier, we have reported a new implement using synchronous sampling and orthogonal detecting. By studying the intensity and phase modulation in the cell impedance sensing progress, we have calculated the SNR of the new method compare with the raw data. Base on the physical model and the calculation, we have demonstrated a simulation result which shows a capability to detect the signal of 1um cell in a noisy environment.

Sampling enrichment toward a target state, an analogue of the improvement of sampling efficiency (SE), is critical in both the refinement of protein structures and the generation of near-native structure ensembles for the exploration of structure-function relationships. We developed a hybrid molecular dynamics (MD)-Monte Carlo (MC) approach to enrich the sampling toward the target structures. In this approach, the higher SE is achieved by perturbing the conventional MD simulations with a MC structure-acceptance judgment, which is based on the coincidence degree of small angle x-ray scattering (SAXS) intensity profiles between the simulation structures and the target structure. We found that the hybrid simulations could significantly improve SE by making the top-ranked models much closer to the target structures both in the secondary and tertiary structures. Specifically, for the 20 mono-residue peptides, when the initial structures had the root-mean-squared deviation (RMSD) from the target structure smaller than 7 Å, the hybrid MD-MC simulations afforded, on average, 0.83 Å and 1.73 Å in RMSD closer to the target than the parallel MD simulations at 310K and 370K, respectively. Meanwhile, the average SE values are also increased by 13.2% and 15.7%. The enrichment of sampling becomes more significant when the target states are gradually detectable in the MD-MC simulations in comparison with the parallel MD simulations, and provide >200% improvement in SE. We also performed a test of the hybrid MD-MC approach in the real protein system, the results showed that the SE for 3 out of 5 real proteins are improved. Overall, this work presents an efficient way of utilizing solution SAXS to improve protein structure prediction and refinement, as well as the generation of near native structures for function annotation.

To explore the inhibitory mechanism of catechins for digestive enzymes, we investigated the binding mode of catechins to a typical digestive enzyme-trypsin and analyzed the structure-activity relationship of catechins, using an integration of molecular docking, molecular dynamics simulation and binding free energy calculation. We found that catechins with different structures bound to a conservative pocket S1 of trypsin, which is comprised of residues 189-195, 214-220 and 225-228. In the trypsin-catechin complexes, Asp189 by forming strong hydrogen bonding, and Gln192, Trp215 and Gly216 through hydrophobic interactions, all significantly contribute to the binding of catechins. The number and the position of hydroxyl and aromatic groups, the structure of stereoisomers, and the orientation of catechins in the binding pocket S1 of trypsin all affect the binding affinity. The binding affinity is in the order of Epigallocatechin gallate (EGCG) > Epicatechin gallate (ECG) > Epicatechin (EC) > Epigallocatechin (EGC), and 2R-3R EGCG shows the strongest binding affinity out of other stereoisomers. Meanwhile, the synergic conformational changes of residues and catechins were also analyzed. These findings will be helpful in understanding the knowledge of interactions between catechins and trypsin and referable for the design of novel polyphenol based functional food and nutriceutical formulas.