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The differential effects of so-called 'first- and second generation' antipsychotic medications, when given in the first episode, on the long-term outcome of schizophrenia remain to be elucidated. We compared the 9-year outcomes of individuals initially randomised to clozapine or chlorpromazine. One-hundred and sixty individuals with treatment-naive, first episode schizophrenia or schizophreniform disorder in a mental health centre in Beijing, China were randomised to clozapine or chlorpromazine treatment for up to 2 years,followed by up to an additional 7 years of naturalistic treatment. The primary outcome was remission status for individuals in each group. Individuals in both groups spent essentially equal amounts of time in each clinical state over the follow-up time period(remission, 78%; intermediate, 8%; relapse, 14%). There were no significant differences on other measures of illness severity. The clozapine group was more likely than the chlorpromazine group to remain on the medication to which they were originally assigned (26% v. 10%, P = 0.01). There were no significant differences between the two groups on other secondary efficacy outcomes. These findings support the comparability in effectiveness between antipsychotic medications but with slightly greater tolerability of clozapine in the treatment of first-episode psychosis.

Focusing on a pair of thin-web spur gears in an aero-engine turbofan’s accessory gearbox, this study investigates their high-speed traveling wave resonance characteristics. The simulation analysis method of traveling wave resonance of high-speed thin-web spur gears is formed, and the law of traveling wave resonance characteristics of thin-web spur gears under misalignment and high-speed conditions is revealed. Firstly, based on the static-modal coupling analysis, the natural frequency of the gear pair considering centrifugal force and contact stiffness is solved. Then, based on the explicit-dynamic algorithm and the theory of gear traveling wave resonance, the traveling wave resonance point prediction and dynamic calculation of high-speed thin-web spur gear are carried out. Finally, through the comparative analysis of modal prediction value, experimental value, and dynamic calculation value, the results show that the large axial force generated by the misalignment of the spur gear angle is the key factor to induce traveling wave resonance. There is a relative error of less than 8% between the modal prediction value, experimental value, and dynamic calculation value, which verifies the applicability and reliability of the established prediction method and explicit-dynamic model. It has certain theoretical support and engineering value for the study of vibration avoidance and the dynamic characteristics of the aviation gear system.

Complex video object segmentation serves as a fundamental task for a wide range of downstream applications such as video editing and automatic data annotation. Here we present the 2nd place solution in the MOSE track of PVUW 2024. To mitigate problems caused by tiny objects, similar objects and fast movements in MOSE. We use instance segmentation to generate extra pretraining data from the valid and test set of MOSE. The segmented instances are combined with objects extracted from COCO to augment the training data and enhance semantic representation of the baseline model. Besides, motion blur is added during training to increase robustness against image blur induced by motion. Finally, we apply test time augmentation (TTA) and memory strategy to the inference stage. Our method ranked 2nd in the MOSE track of PVUW 2024, with a \\(\\mathcal{J}\\) of 0.8007, a \\(\\mathcal{F}\\) of 0.8683 and a \\(\\mathcal{J}\\)\\&\\(\\mathcal{F}\\) of 0.8345.

Video panoptic segmentation is an advanced task that extends panoptic segmentation by applying its concept to video sequences. In the hope of addressing the challenge of video panoptic segmentation in diverse conditions, We utilize DVIS++ as our baseline model and enhance it by introducing a comprehensive approach centered on the query-wise ensemble, supplemented by additional techniques. Our proposed approach achieved a VPQ score of 57.01 on the VIPSeg test set, and ranked 3rd in the VPS track of the 3rd Pixel-level Video Understanding in the Wild Challenge.

Pixel-level Video Understanding requires effectively integrating three-dimensional data in both spatial and temporal dimensions to learn accurate and stable semantic information from continuous frames. However, existing advanced models on the VSPW dataset have not fully modeled spatiotemporal relationships. In this paper, we present our solution for the PVUW competition, where we introduce masked video consistency (MVC) based on existing models. MVC enforces the consistency between predictions of masked frames where random patches are withheld. The model needs to learn the segmentation results of the masked parts through the context of images and the relationship between preceding and succeeding frames of the video. Additionally, we employed test-time augmentation, model aggeregation and a multimodal model-based post-processing method. Our approach achieves 67.27% mIoU performance on the VSPW dataset, ranking 2nd place in the PVUW2024 challenge VSS track.

Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fusion. Despite their effectiveness in other modalities, their application in multimodal music generation faces challenges of data scarcity, weak cross-modal alignment, and limited controllability. This paper addresses these issues by using explicit bridges of text and music for multimodal alignment. We introduce a novel method named Visuals Music Bridge (VMB). Specifically, a Multimodal Music Description Model converts visual inputs into detailed textual descriptions to provide the text bridge; a Dual-track Music Retrieval module that combines broad and targeted retrieval strategies to provide the music bridge and enable user control. Finally, we design an Explicitly Conditioned Music Generation framework to generate music based on the two bridges. We conduct experiments on video-to-music, image-to-music, text-to-music, and controllable music generation tasks, along with experiments on controllability. The results demonstrate that VMB significantly enhances music quality, modality, and customization alignment compared to previous methods. VMB sets a new standard for interpretable and expressive multimodal music generation with applications in various multimedia fields. Demos and code are available at https://github.com/wbs2788/VMB.

Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentation Track based on MOSE dataset and Motion Expression guided Video Segmentation track based on MeViS dataset. In the two new tracks, we provide additional videos and annotations that feature challenging elements, such as the disappearance and reappearance of objects, inconspicuous small objects, heavy occlusions, and crowded environments in MOSE. Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments. These new videos, sentences, and annotations enable us to foster the development of a more comprehensive and robust pixel-level understanding of video scenes in complex environments and realistic scenarios. The MOSE challenge had 140 registered teams in total, 65 teams participated the validation phase and 12 teams made valid submissions in the final challenge phase. The MeViS challenge had 225 registered teams in total, 50 teams participated the validation phase and 5 teams made valid submissions in the final challenge phase.

In Saccharomyces cerevisiae, the Y-family DNA polymerase (Pol ) regulates genome stability in response to different forms of environmental stress by translesion DNA synthesis. To elucidate the role of Pol in oxidative stress-induced DNA damage, we deleted or overexpressed the corresponding gene RAD30, and used transcriptome analysis to screen the potential genes associated with RAD30 to respond to DNA damage. Under 2 mM H2O2, deletion of RAD30 resulted in a 2.2-fold decrease in survival and a 2.8-fold increase in DNA damage, whereas overexpression of RAD30 increased survival and decreased DNA damage by 1.2- and 1.4-fold, respectively, compared with that of the wild-type strain. Transcriptome and phenotypic analysis identified Lsm12 as a main factor involved in oxidative stress-induced DNA damage. Deleting LSM12 caused growth defects while its overexpression enhanced cell growth under 2 mM H2O2. This effect was due to the physical interaction of Lsm12 with the UBZ domain of Pol to enhance Pol deubiquitination through Ubp3, and consequently promote Pol recruitment. Overall, these findings demonstrate that Lsm12 is a novel regulator mediating Pol deubiquitination to promote its recruitment under oxidative stress. Furthermore, this study provides a potential strategy to maintain the genome stability of industrial strains during fermentation.

Delineating urban growth boundaries (UGBs) by combining the land-use/land-cover (LULC) change simulation method has become common in recent studies. However, few of the existing studies have integrated multi-source big data to analyze the driving factors of LULC dynamics in the simulation. Moreover, most of previous studies mainly focused on the UGBs delineation in macroscale areas rather than small-scale areas, such as the county area. In this study, taking Xinxing County of Guangdong Province as the study area, we coupled a system dynamics (SD) model and a patch-generating land-use simulation (PLUS) model to propose a framework for the LULC change simulation and UGBs delineation in the county area. Multi-source big data such as points of interest (POIs), night-time light (NTL) data and Tencent user density (TUD) were integrated to analyze the driving forces of LULC change. The validation results indicate that the coupled model received high accuracy both in the land-use demand projection and LULC distribution simulation. The combination of multi-source big data can effectively describe the influence of human socio-economic factors on the expansion of urban land and industrial land. The UGBs delineation results have similar spatial patterns with the LULC change simulation results, which indicates that the proposed UGBs delineation method can effectively transform the LULC simulation results into available UGBs for the county area. It has been proven that the proposed framework in this study is effective for the LULC change simulation and UGBs delineation in the county area, which can provide insight on territorial spatial planning in the county area.