Transcriptomic analysis of Synechocystis sp PCC6803 under low-temperature stress

In this study, cDNA microarrays were developed from 3569 mRNA reads to analyze the expression profiles of the transcriptomes of Synechocystis sp. PCC6803 under low temperature (LT) stress. Among the genes on the cDNA microarrays, 899 LT-affected genes exhibited a 1.5-fold (or greater) difference in expression compared with the genes from normal unstressed Synechocystis sp. PCC6803. Of the differentially expressed genes, 353 were up-regulated and 246 were down-regulated. The results showed that genes involved in photosynthesis were activated at LT (10A degrees C), including genes for photosystem I, photosystem II, photosynthetic electron transport, and cytochrome b6/f complex. Moreover, desB, one of four genes that encode the fatty acid desaturases, was also induced by LT. However, the LT conditions to some degree enhanced the transcription of some genes. In addition, LT (10A degrees C) may reduce cellular motility by regulating the transcription of spkA (sll1575), a serine/threonine protein kinase. The results reported in this study may contribute to a better understanding of the responses of the Synechocystis cell to LT, including pathways involved in photosynthesis and repair.In this study, cDNA microarrays were developed from 3569 mRNA reads to analyze the expression profiles of the transcriptomes of Synechocystis sp. PCC6803 under low temperature (LT) stress. Among the genes on the cDNA microarrays, 899 LT-affected genes exhibited a 1.5-fold (or greater) difference in expression compared with the genes from normal unstressed Synechocystis sp. PCC6803. Of the differentially expressed genes, 353 were up-regulated and 246 were down-regulated. The results showed that genes involved in photosynthesis were activated at LT (10A degrees C), including genes for photosystem I, photosystem II, photosynthetic electron transport, and cytochrome b6/f complex. Moreover, desB, one of four genes that encode the fatty acid desaturases, was also induced by LT. However, the LT conditions to some degree enhanced the transcription of some genes. In addition, LT (10A degrees C) may reduce cellular motility by regulating the transcription of spkA (sll1575), a serine/threonine protein kinase. The results reported in this study may contribute to a better understanding of the responses of the Synechocystis cell to LT, including pathways involved in photosynthesis and repair

Scientific Language Modeling: A Quantitative Review of Large Language Models in Molecular Science

Efficient molecular modeling and design are crucial for the discovery and exploration of novel molecules, and the incorporation of deep learning methods has revolutionized this field. In particular, large language models (LLMs) offer a fresh approach to tackle scientific problems from a natural language processing (NLP) perspective, introducing a research paradigm called scientific language modeling (SLM). However, two key issues remain: how to quantify the match between model and data modalities and how to identify the knowledge-learning preferences of models. To address these challenges, we propose a multi-modal benchmark, named ChEBI-20-MM, and perform 1263 experiments to assess the model's compatibility with data modalities and knowledge acquisition. Through the modal transition probability matrix, we provide insights into the most suitable modalities for tasks. Furthermore, we introduce a statistically interpretable approach to discover context-specific knowledge mapping by localized feature filtering. Our pioneering analysis offers an exploration of the learning mechanism and paves the way for advancing SLM in molecular science

Effects of Perioperative Psychological Intervention on Rehabilitation Process of Patients with Total Knee Arthroplasty

Background: This study focuses on evaluating the effects of perioperative psychological intervention on rehabilitation process of patients with total knee arthroplasty (TKA). Method: We selected 40 patients randomly which all need to receive total knee arthroplasty in Nanjing Drum Tower Hospital during the period from January 2022 to March 2022. The patients were randomly assigned to two Groups (20 in each group): an intervention group (Psychological intervention combined with routine nursing, drug and rehabilitation therapy) and a control group (routine nursing, drug rehabilitation therapy). During each patients’ perioperative TKA surgeries, three scales (including VAS, ROM and ADL) are used to assess two groups. Result: After one week of psychological intervention, the pain score of the intervention group was lower than that of control group, the knee motion was greater than that of control group, and the ADL score was higher than that of control group. There was a significant difference in the treatment recovery between the two groups (P<0.05) Conclusions: Perioperative psychological intervention can promote the rehabilitation process of TKA patients, It can significantly improve pain, joint activity limitation, disuse muscle atrophy and other problems in a short period of time after surgery. Besides, it will effectively help patients to overcome the fear of movement, anxiety and improve patients' confidence, rehabilitation cooperation and prevention of complications, make patients adapt to the later rehabilitation life

Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey

The precise prediction of molecular properties is essential for advancements in drug development, particularly in virtual screening and compound optimization. The recent introduction of numerous deep learning-based methods has shown remarkable potential in enhancing molecular property prediction (MPP), especially improving accuracy and insights into molecular structures. Yet, two critical questions arise: does the integration of domain knowledge augment the accuracy of molecular property prediction and does employing multi-modal data fusion yield more precise results than unique data source methods? To explore these matters, we comprehensively review and quantitatively analyze recent deep learning methods based on various benchmarks. We discover that integrating molecular information significantly improves molecular property prediction (MPP) for both regression and classification tasks. Specifically, regression improvements, measured by reductions in root mean square error (RMSE), are up to 4.0%, while classification enhancements, measured by the area under the receiver operating characteristic curve (ROC-AUC), are up to 1.7%. We also discover that enriching 2D graphs with 1D SMILES boosts multi-modal learning performance for regression tasks by up to 9.1%, and augmenting 2D graphs with 3D information increases performance for classification tasks by up to 13.2%, with both enhancements measured using ROC-AUC. The two consolidated insights offer crucial guidance for future advancements in drug discovery

GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text

Large language models have made significant strides in natural language processing, paving the way for innovative applications including molecular representation and generation. However, most existing single-modality approaches cannot capture the abundant and complex information in molecular data. Here, we introduce GIT-Mol, a multi-modal large language model that integrates the structure Graph, Image, and Text information, including the Simplified Molecular Input Line Entry System (SMILES) and molecular captions. To facilitate the integration of multi-modal molecular data, we propose GIT-Former, a novel architecture capable of mapping all modalities into a unified latent space. Our study develops an innovative any-to-language molecular translation strategy and achieves a 10%-15% improvement in molecular captioning, a 5%-10% accuracy increase in property prediction, and a 20% boost in molecule generation validity compared to baseline or single-modality models.Comment: 16 pages, 5 figure

Reliable and Safe Motion Control of Unmanned Vehicles

Unmanned vehicles (UVs) are playing an increasingly significant role in modern daily life. In the past decades, numerous commercial, scientific, and military communities across the world are developing fully autonomous UVs for a variety of applications, such as environmental monitoring and surveillance, post-disaster search and rescue, border patrol, natural resources exploration, and experimental platforms for new technologies verification. The excessive opportunities and threats that come along with these diverse applications have created a niche demand for UVs to extend their capabilities to perform more sophisticated and hazardous missions with greater autonomy, lower costs of development and operation, improved personnel safety and security, extended operational range (reliability) and precision, as well as increased flexibility in sophisticated environments including so-called dirty, dull, harsh, and dangerous missions. In order to successfully and effectively execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, greater autonomy together with more advanced reliable and safe motion control systems are required to offer the critical technologies for ensuring intelligent, safe, reliable, and efficient control of UVs in the presence of disturbances, actuator saturation, and even actuator faults, especially for practical applications. This thesis concentrates on the development of different reliable and safe motion control algorithms/strategies applicable to UVs, in particular, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A number of contributions pertaining to the fault detection and diagnosis (FDD), fault-tolerant control (FTC), disturbance estimation and compensation, and actuator saturation avoidance have been made in this thesis. In addition to the control problems, this thesis also presents several guidance-related contributions, including adaptive observer-based line-of-sight (LOS) guidance law, time-varying lookahead distance scheme, piecewise path switching criterion for guiding a single UV, as well as a proportional-integral (PI) type of leader-follower formation guidance strategy for a group of UVs