Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information

Benefiting from the advancements in deep learning, various genomic analytical techniques, such as survival analysis, classification of tumors and their subtypes, and exploration of specific pathways, have significantly enhanced our understanding of the biological mechanisms driving cancer. However, the overfitting issue, arising from the limited number of patient samples, poses a challenge in improving the accuracy of genome analysis by deepening the neural network. Furthermore, it remains uncertain whether novel approaches such as the sparsely gated mixture of expert (MOE) and self-attention mechanisms can improve the accuracy of genomic analysis. In this paper, we introduce a novel sparsely gated RNA-seq analysis framework called Gene-MOE. This framework exploits the potential of the MOE layers and the proposed mixture of attention expert (MOAE) layers to enhance the analysis accuracy. Additionally, it addresses overfitting challenges by integrating pan-cancer information from 33 distinct cancer types through pre-training.We pre-trained Gene-MOE on TCGA pan-cancer RNA-seq dataset with 33 cancer types. Subsequently, we conducted experiments involving cancer classification and survival analysis based on the pre-trained Gene-MOE. According to the survival analysis results on 14 cancer types, Gene-MOE outperformed state-of-the-art models on 12 cancer types. Through detailed feature analysis, we found that the Gene-MOE model could learn rich feature representations of high-dimensional genes. According to the classification results, the total accuracy of the classification model for 33 cancer classifications reached 95.8%, representing the best performance compared to state-of-the-art models. These results indicate that Gene-MOE holds strong potential for use in cancer classification and survival analysis

Improving the Predictability of Severe Convective Weather Processes by Using Wind Vectors and Potential Temperature Changes: A Case Study of a Severe Thunderstorm

Strong, local convective weather events are capable of causing extensive damage, but weather observation systems with limited resolution and radar monitoring can typically provide only a few minutes to hours of prior warning time. This paper presents a comprehensive case study of the cumulative evolution of several characteristic quantities during one extremely severe convective weather process. The research results indicate that the main feature of strong convective weather is the uneven distribution of thermal energy in the atmosphere, and the structure of this heat distribution determines the level of instability in the atmosphere. A vertical “clockwise rolling current” occurs in the wind field structure at the beginning of the process, and this is accompanied by a rapid drop in temperature at the top of the troposphere. When these signs occurred in the case study, radar technology was used to refine the precipitation region and spatial characteristics of the approaching storm. The height and vertical evolution of radar echoes were indicative of the characteristics of the system’s movement through space. Such findings may be useful for improving the forecasting times for strong convective weather

Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSP instances demonstrate that NAR4TSP outperforms four state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios.Comment: 14 pages, 5 figure

Observation study of using a small dose of rituximab treatment for thyroid-associated ophthalmopathy in seven Chinese patients: One pilot study

ObjectiveTo report the efficacy, long-term safety, and tolerability of using a small dose (125 mg/m2 weekly for 4 weeks) of rituximab to treat Chinese patients with thyroid-associated ophthalmopathy (TAO).MethodsSeven patients with active moderate-to-severe TAO were prospectively recruited in this study. A small dose of rituximab (125mg/m2 body surface area) was given weekly with a duration of four weeks. Thyroid function, thyrotropin receptor antibody (TRAb), B cell and T cell subsets, ophthalmological examination, magnetic resonance imaging derived parameters, and adverse reactions were recorded at each visit.ResultsSeven patients were followed for an average of 224 weeks. B-cell depletion was observed in all patients following rituximab infusion. The clinical activity score (CAS) decreased from 4.86 ± 0.69 to 3.00 ± 0.82 at 5 weeks after treatment (P = 0.033) and remained significantly lower than baseline values at the end of follow-up (P = 0.001). Compared to baseline values, significant decreases in exophthalmos of the right eye, the thickness of extraocular muscles with maximum signal intensity, and the highest signal intensity ratio (SIR) of extraocular muscle to ipsilateral temporal muscle values were observed at the last follow-up (all P < 0.05). Disease progressions or recurrences were not observed during follow-up. Only mild fatigue was observed after the first infusion as a side effect (n = 1).ConclusionSmall dose of rituximab may be a promising option with adequate safety, tolerability, and long-term efficacy for patients with active moderate-to-severe TAO

Fast–slow dynamic behaviors of a hydraulic generating system with multi-timescales

Hydraulic generating systems are widely modeled in the literature for investigating their stability properties by means of transfer functions representing the dynamic behavior of the reservoir, penstock, surge tank, hydro-turbine, and the generator. Traditionally, in these models the electrical load is assumed constant to simplify the modeling process. This assumption can hide interesting dynamic behaviors caused by fluctuation of the load as actually occurred. Hence, in this study, the electrical load characterized with periodic excitation is introduced into a hydraulic generating system and the responses of the system show a novel dynamic behavior called the fast–slow dynamic phenomenon. To reveal the nature of this phenomenon, the effects of the three parameters (i.e., differential adjustment coefficient, amplitude, and frequency) on the dynamic behaviors of the hydraulic generating system are investigated, and the corresponding change rules are presented. The results show that the intensity of the fast–slow dynamic behaviors varies with the change of each parameter, which provides reference for the quantification of the hydraulic generating system parameters. More importantly, these results not only present rich nonlinear phenomena induced by multi-timescales, but also provide some theoretical bases for maintaining the safe and stable operation of a hydropower station

Deciphering the microbial community structures and functions of wastewater treatment at high-altitude area

Introduction: The proper operation of wastewater treatment plants is a key factor in maintaining a stable river and lake environment. Low purification efficiency in winter is a common problem in high-altitude wastewater treatment plants (WWTPs), and analysis of the microbial community involved in the sewage treatment process at high-altitude can provide valuable references for improving this problem.Methods: In this study, the bacterial communities of high- and low-altitude WWTPs were investigated using Illumina high-throughput sequencing (HTS). The interaction between microbial community and environmental variables were explored by co-occurrence correlation network.Results: At genus level, Thauera (5.2%), unclassified_Rhodocyclaceae (3.0%), Dokdonella (2.5%), and Ferribacterium (2.5%) were the dominant genera in high-altitude group. The abundance of nitrogen and phosphorus removal bacteria were higher in high-altitude group (10.2% and 1.3%, respectively) than in low-altitude group (5.4% and 0.6%, respectively). Redundancy analysis (RDA) and co-occurrence network analysis showed that altitude, ultraviolet index (UVI), pH, dissolved oxygen (DO) and total nitrogen (TN) were the dominated environmental factors (p < 0.05) affecting microbial community assembly, and these five variables explained 21.4%, 20.3%, 16.9%, 11.5%, and 8.2% of the bacterial assembly of AS communities.Discussion: The community diversity of high-altitude group was lower than that of low-altitude group, and WWTPs of high-altitude aeras had a unique microbial community structure. Low temperature and strong UVI are pivotal factors contributing to the reduced diversity of activated sludge microbial communities at high-altitudes

Taxonomic and functional dynamics of nirS denitrifiers along a salinity gradient in the Pearl River Estuary

Understanding the factors that shape the diversity, distribution, and function of denitrifying microbes is vital for managing nitrogen cycling in these ecosystems. This study explores the diversity, biogeographic distribution, assembly processes, interaction, and denitrification potential of the nirS-encoding microbial community (nirS denitrifier) in the Pearl River Estuary based on high-throughput and metagenomics sequencing dataset. The results of this study show that salinity is a crucial regulatory environmental factor that determines the spatial distribution, phylogenetic turnover, and co-occurrence patterns of nirS denitrifiers. Additionally, the dissolved organic carbon (DOC), suspended sediment concentration (SSC), and dissolved oxygen (DO) in water also significantly impact the biodiversity and abundance of nirS denitrifiers. Furthermore, our findings demonstrate that, in comparison to environmental factors, the ecological and evolutionary characteristics of nirS denitrifiers play a more prominent role in regulating their denitrification potential, suggesting that alterations in the microbial community within dynamic changes in estuarine water can profoundly affect its denitrification function. Our results indicate the significant roles of denitrification microbial structure and phylogenetic characteristics in maintaining their ecological functions. Future studies should continue to explore the interactions between microbial communities and environmental factors to further elucidate the denitrification process in estuaries and its implications for ecosystem health and water quality

Microbiota characterization of the green mussel Perna viridis at the tissue scale and its relationship with the environment

Research on the microbiota associated with marine invertebrates is important for understanding host physiology and the relationship between the host and the environment. In this study, the microbiota of the green mussel Perna viridis was characterized at the tissue scale using 16S rRNA gene high-throughput sequencing and compared with the microbiota of the surrounding environment. Different mussel tissues were sampled, along with two environmental samples (the mussel's attachment substratum and seawater). The results showed that the phyla Proteobacteria, Bacteroidetes, and Spirochaetae were dominant in mussel tissues. The bacterial community composition at the family level varied among the tissues of P. viridis. Although the microbiota of P. viridis clearly differed from that of the surrounding seawater, the composition and diversity of the microbial community of the foot and outer shell surface were similar to those of the substratum, indicating their close relationship with the substratum. KEGG prediction analysis indicated that the bacteria harbored by P. viridis were enriched in the degradation of aromatic compounds, osmoregulation, and carbohydrate oxidation and fermentation, processes that may be important in P. viridis physiology. Our study provides new insights into the tissue-scale characteristics of mussel microbiomes and the intricate connection between mussels and their environment

Quantitative Proteomic and Transcriptomic Analyses of Metabolic Regulation of Adult Reproductive Diapause in Drosophila suzukii (Diptera: Drosophilidae) Females

Diapause is a form of dormancy used by many insects to survive adverse environmental conditions, which can occur in specific developmental stages in different species. Drosophila suzukii is a serious economic pest and we determined the conditions for adult reproductive diapause by the females in our previous studies. In this study, we combined RNA-Seq transcriptomic and quantitative proteomic analyses to identify adult reproductive diapause-related genes and proteins. According to the transcriptomic analysis, among 242 annotated differentially expressed genes in non-diapause and diapause females, 129 and 113 genes were up- and down-regulated, respectively. In addition, among the 2,375 proteins quantified, 39 and 23 proteins were up- and down-regulated, respectively. The gene expression patterns in diapause- and non-diapause were confirmed by qRT-PCR or western blot analysis. The overall analysis of robustly regulated genes at the protein and mRNA levels found four genes that overlapped in the up-regulated group and six genes in the down-regulated group, and thus these proteins/genes may regulate adult reproductive diapause. These differentially expressed proteins/genes act in the citrate cycle, insulin signaling pathway, PI3K-Akt signaling pathway, and amino acid biosynthesis pathways. These results provide the basis for further studies of the molecular regulation of reproductive diapause in this species