High-throughput screening of spike variants uncovers the key residues that alter the affinity and antigenicity of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has elicited a worldwide pandemic since late 2019. There has been ~675 million confirmed coronavirus disease 2019 (COVID-19) cases, leading to more than 6.8 million deaths as of March 1, 2023. Five SARS-CoV-2 variants of concern (VOCs) were tracked as they emerged and were subsequently characterized. However, it is still difficult to predict the next dominant variant due to the rapid evolution of its spike (S) glycoprotein, which affects the binding activity between cellular receptor angiotensin-converting enzyme 2 (ACE2) and blocks the presenting epitope from humoral monoclonal antibody (mAb) recognition. Here, we established a robust mammalian cell-surface-display platform to study the interactions of S-ACE2 and S-mAb on a large scale. A lentivirus library of S variants was generated via in silico chip synthesis followed by site-directed saturation mutagenesis, after which the enriched candidates were acquired through single-cell fluorescence sorting and analyzed by third-generation DNA sequencing technologies. The mutational landscape provides a blueprint for understanding the key residues of the S protein binding affinity to ACE2 and mAb evasion. It was found that S205F, Y453F, Q493A, Q493M, Q498H, Q498Y, N501F, and N501T showed a 3-12-fold increase in infectivity, of which Y453F, Q493A, and Q498Y exhibited at least a 10-fold resistance to mAbs REGN10933, LY-CoV555, and REGN10987, respectively. These methods for mammalian cells may assist in the precise control of SARS-CoV-2 in the future.

Biometric recognition via texture features of eye movement trajectories in a visual searching task.

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers' temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.

Mirror-normal difference in the late phase of mental rotation: An ERP study.

Mirror-normal letter discriminations are thought to require mental rotation in order to transform the rotated alphanumeric character into its canonical orientation. Moreover, out-of-plane rotation is likely to occur after in-plane rotation to fully normalize the mirror version before the final mirror-normal judgment. The so-called rotation-related negativity, which varies with orientation, is found in both ERPonset (averaged with respect to stimulus onset) and ERPRT (averaged with respect to response time), representing the involvement of mental rotation in both time windows. Additionally, the mean amplitude of ERPRT correlates with individual performance. We performed a comprehensive analysis of the mirror-normal differences in the early and late phases of mental rotation and deduced that out-of-plane rotation is more likely to occur in the late phase and interacts with both in-plane rotation and the decision-making process, as indicated by both behavioral and electrophysiological findings.

Uncovering the cognitive processes underlying mental rotation: an eye-movement study.

Mental rotation is an important paradigm for spatial ability. Mental-rotation tasks are assumed to involve five or three sequential cognitive-processing states, though this has not been demonstrated experimentally. Here, we investigated how processing states alternate during mental-rotation tasks. Inference was carried out using an advanced statistical modelling and data-driven approach - a discriminative hidden Markov model (dHMM) trained using eye-movement data obtained from an experiment consisting of two different strategies: (I) mentally rotate the right-side figure to be aligned with the left-side figure and (II) mentally rotate the left-side figure to be aligned with the right-side figure. Eye movements were found to contain the necessary information for determining the processing strategy, and the dHMM that best fit our data segmented the mental-rotation process into three hidden states, which we termed encoding and searching, comparison, and searching on one-side pair. Additionally, we applied three classification methods, logistic regression, support vector model and dHMM, of which dHMM predicted the strategies with the highest accuracy (76.8%). Our study did confirm that there are differences in processing states between these two of mental-rotation strategies, and were consistent with the previous suggestion that mental rotation is discrete process that is accomplished in a piecemeal fashion.

A crucial temporal accuracy test of combining EEG and Tobii eye tracker.

Eye tracking and event-related potentials have been widely used in the field of cognitive psychology and neuroscience. Both techniques have the ability to refine cognitive models through a precise timeline description; nevertheless, they also have severe limitations. Combining measures of event-related potentials and eye movements can contribute to cognitive process capture, which provides the possibility to determine precisely when and in which order different cognitive operations occur. Combining of event-related potentials and eye movements has been recently conducted by synchronizing measures from an infrared eye tracker with an electroencephalograph to allow simultaneous data recording. Here, we describe in detail 4 types of co-registration methods for event-related potentials and eye movements on the Tobii platform. Moreover, the present investigation was designed to evaluate the temporal accuracy of data obtained using the 4 methods. We found that the method based on the Tobii Pro Analytics software development kit had a higher degree of temporal accuracy than the other co-registration methods. Furthermore, the reasons for the different temporal accuracies were assessed, and potential measures to correct clock drift were taken. General suggestions are made regarding timing in the co-registration of the electroencephalograph and eye tracker.

Defining the multivalent functions of CTCF from chromatin state and three-dimensional chromatin interactions.

CCCTC-binding factor (CTCF) is a multi-functional protein that is assigned various, even contradictory roles in the genome. High-throughput sequencing-based technologies such as ChIP-seq and Hi-C provided us the opportunity to assess the multivalent functions of CTCF in the human genome. The location of CTCF-binding sites with respect to genomic features provides insights into the possible roles of this protein. Here we present the first genome-wide survey and characterization of three important functions of CTCF: enhancer insulator, chromatin barrier and enhancer linker. We developed a novel computational framework to discover the multivalent functions of CTCF based on chromatin state and three-dimensional chromatin architecture. We applied our method to five human cell lines and identified ∼46 000 non-redundant CTCF sites related to the three functions. Disparate effects of these functions on gene expression were found and distinct genomic features of these CTCF sites were characterized in GM12878 cells. Finally, we investigated the cell-type specificities of CTCF sites related to these functions across five cell types. Our study provides new insights into the multivalent functions of CTCF in the human genome.