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Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain's shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status. [ABSTRACT FROM AUTHOR]

Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence‐based antibody generation and the often overlooked non‐animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high‐quality antigen formulations. This journey has traversed various stages, from methods involving serum‐based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in‐depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum‐based acquisition, lengthy and difficult hybridoma‐derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products. [ABSTRACT FROM AUTHOR]

Adeno-associated virus (AAV) serotypes from primates are being developed and clinically used as vectors for human gene therapy. However, the evolutionary mechanism of AAV variants is far from being understood, except that genetic recombination plays an important role. Furthermore, little is known about the interaction between AAV and its natural hosts, human and nonhuman primates. In this study, natural AAV capsid genes were subjected to systemic evolutionary analysis with a focus on selection drives during the diversification of AAV lineages. A number of positively selected sites were identified from these AAV lineages with functional relevance implied by their localization on the AAV structures. The selection drives of the two AAV2 capsid sites were further investigated in a series of biological experiments. These observations did not support the evolution of the site 410 of the AAV2 capsid driven by selection pressure from the human CD4+ T-cell response. However, positive selection on site 548 of the AAV2 capsid was directly related to host humoral immunity because of the profound effects of mutations at this site on the immune evasion of AAV variants from human neutralizing antibodies at both the individual and population levels. Overall, this work provides a novel interpretation of the genetic diversity and evolution of AAV lineages in their natural hosts, which may contribute to their further engineering and application in human gene therapy. Author summary: Adeno-associated virus (AAV) is a viral vector that is universally applied in human gene therapy. Despite their versatile applications in gene therapy, the origin and evolution of AAV serotypes have not been extensively studied. In this study, positive selection upon diversification of AAV capsid genes was carefully investigated by evolutionary analysis. Numerous diversifying selected sites were dispersed among the AAV lineages, and evolutionary pathways were inferred for two selected sites of the AAV2 capsid gene. Furthermore, the putative immunological drives underlying the selection of AAV2 capsid genes were experimentally dissected. This work provides a novel interpretation of the evolution and diversity of AAV serotypes and might be helpful for their engineering and application in gene therapy. [ABSTRACT FROM AUTHOR]

The White Paper on Peak Carbon and Carbon Neutral Action 2022 states that China is to achieve peak carbon by 2030 and carbon neutrality by 2060. Based on the "3060 dual-carbon" goal, how to improve the efficiency of energy performance is an important prerequisite for building a low-carbon, energy-saving, green, and beautiful China. The office performance building studied in this paper is located in the urban area of Turpan, where the climate is characterized by an extremely hot summer environment and a cold winter environment. At the same time, the building is oriented east–west, with the main façade facing west, and the main façade consists of a large area of single-layer glass curtain wall, which is affected by western sunlight. As a result, there are serious problems with the building's energy consumption, which in turn leads to excessive carbon emissions and high life cycle costs for the building. To address the above problems, this paper analyzes and optimizes the following four dimensions. First, the article creates a Convolutional Neural Network (CNN) prediction model with Total Energy Use in Buildings (TEUI), Global Warming Potential (GWP), and Life Cycle Costs (LCC) as the performance objectives. After optimization, the R2 of the three are 0.9908, 0.9869, and 0.9969, respectively, thus solving the problem of low accuracy of traditional prediction models. Next, the NSGA-II algorithm is used to optimize the three performance objectives, which are reduced by 41.94%, 40.61%, and 31.29%, respectively. Then, in the program decision stage, this paper uses two empowered Topsis methods to optimize this building performance problem. Finally, the article analyzes the variables using two sensitivity analysis methods. Through the above research, this paper provides a framework of optimization ideas for office buildings in extremely hot and cold regions while focusing on the four major aspects of machine learning, multi-objective optimization, decision analysis, and sensitivity analysis systematically and completely. For the development of office buildings in the region, whether in the early program design or in the later stages, energy-saving measures to optimize the design have laid the foundation of important guidelines. [ABSTRACT FROM AUTHOR]

Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24 mm(2)) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20 hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility.

Based on Global Coupled Ionosphere‐Thermosphere‐Electrodynamics Model, the solution of the 3‐dimensional current in the ionospheric region, the equivalent sheet current and filed‐aligned current are examined. The simulation study enables a comprehensive analysis of the effect of the geomagnetic field configuration, especially the non‐dipole component and tilt angle, on the ionospheric electrodynamics phenomena. Different geomagnetic field configurations are specified in the present work, including realistic geomagnetic field (RGF), tilted dipole geomagnetic field (TDGF) and zero‐declination dipole geomagnetic field (ZDGF). Our simulation focuses on the seasonal variation of Sq current, primarily governed by annual and semi‐annual variation. The modulation of the tilt angle of the geomagnetic field is globally distributed, whereas the modulation of the geomagnetic anomaly is localized. At mid‐latitudes, the annual mean and semi‐annual amplitudes of the Sq current are negatively correlated with the magnetic field strength, especially shown in geomagnetic anomaly area, while there is the opposite effect in the geomagnetic conjugate regions of the opposite hemispheres. The annual variation of Sq current system is more affected by the offset of the geomagnetic latitudes and geographical latitudes. The seasonal variation of the total Sq current is also modulated by the geomagnetic field. The annual mean, the annual and semi‐annual components of the total Sq current are negatively correlated with the magnetic field strength, while the annual variation is also controlled by the tilt angle of the geomagnetic field. The solar radiation affects the semi‐annual variation of the current more strongly than the annual variation. Plain Language Summary: Ionospheric currents and magnetic fields exert significant influences on energy and material transport within the ionosphere, playing a pivotal role in Earth's electromagnetic environment, which is the primary focus of ionospheric research. To investigate the impact of the geomagnetic field on the ionospheric current system, we employ a theoretical model (GCITEM) for simulation work. In this simulation, we consider both the realistic geomagnetic field configuration and two hypothetical idealized configurations of the geomagnetic field: one excluding non‐dipole terms and the other further aligning the geomagnetic axis with Earth's spin axis. The analysis of the longitude and the seasonal variation of the simulated currents under realistic magnetic field conditions has been validated through previous observation results. In comparison to simulation results obtained under hypothetical configurations, we discern and analyze the modulation and mechanism of non‐dipole terms and the tilt angle of geomagnetic field on the variability of the ionospheric current system, which are discussed in detail within this paper. This study facilitates the analysis and prediction of long‐term changes in the geomagnetic field's effects on ionospheric current generation and the impact on space's electromagnetic environment. Key Points: The geomagnetic field intensity exerts a significant influence on the Sq currents and exhibits a negative correlation with Sq currentThe annual mean and annual variation amplitude of Sq current are positively correlated with the geomagnetic anomaly at conjugate pointsThe tilt angle of the geomagnetic field affects the annual variation of Sq current globally, but hardly effects the semiannual variation [ABSTRACT FROM AUTHOR]