Morphological, anatomical, and transcriptomics analysis reveals the regulatory mechanisms of cassava plant height development.

BACKGROUND: Cassava is one of three major potato crops and the sixth most important food crop globally. Improving yield remains a primary aim in cassava breeding. Notably, plant height significantly impacts the yield and quality of crops; however, the mechanisms underlying cassava plant height development are yet to be elucidated. RESULTS: In this study, we investigated the mechanisms responsible for cassava plant height development using phenotypic, anatomical, and transcriptomic analyses. Phenotypic and anatomical analysis revealed that compared to the high-stem cassava cultivar, the dwarf-stem cassava cultivar exhibited a significant reduction in plant height and a notable increase in internode tissue xylem area. Meanwhile, physiological analysis demonstrated that the lignin content of dwarf cassava was significantly higher than that of high cassava. Notably, transcriptome analysis of internode tissues identified several differentially expressed genes involved in cell wall synthesis and expansion, plant hormone signal transduction, phenylpropanoid biosynthesis, and flavonoid biosynthesis between the two cassava cultivars. CONCLUSIONS: Our findings suggest that internode tissue cell division, secondary wall lignification, and hormone-related gene expression play important roles in cassava plant height development. Ultimately, this study provides new insights into the mechanisms of plant height morphogenesis in cassava and identifies candidate regulatory genes associated with plant height that can serve as valuable genetic resources for future crop dwarfing breeding.

Gastroesophageal reflux disease increases the risk of essential hypertension: results from the Nationwide Readmission Database and Mendelian randomization analysis.

BACKGROUND: The link between gastroesophageal reflux disease (GERD) and essential hypertension (EH) and its causal nature remains controversial. Our study examined the connection between GERD and the risk of hypertension and assessed further whether this correlation has a causal relationship. METHODS: First, we utilized the National Readmission Database including 14 422 183 participants to conduct an observational study. Dividing the population into GERD and non-GERD groups, we investigated the correlation between GERD and EH using multivariate logistic regression. Next, bidirectional two-sample Mendelian randomization was adopted. The summary statistics for GERD were obtained from a published genome-wide association study including 78 707 cases and 288 734 controls. We collected summary statistics for hypertension containing 70 651 cases and 223 663 controls from the FinnGen consortium. We assessed causality primarily by the inverse-variance weighted method with validation by four other Mendelian randomization approaches as well as an array of sensitivity analyses. RESULTS: In the unadjusted model, GERD patients had a higher risk of EH than the non-GERD group, regardless of gender (odds ratio, 1.43; 95% confidence interval: 1.42-1.43; P < .001). Further adjusting for critical confounders did not change this association. For Mendelian randomization, we found that genetically predicted GERD was causally linked to an enhanced risk of EH in inverse-variance weighted technique (odds ratio, 1.52; 95% confidence interval: 1.39-1.67; P = 3.51 × 10-18); conversely, EH did not raise the risk of GERD causally. CONCLUSIONS: GERD is a causal risk factor for EH. Further research is required to probe the mechanism underlying this causal connection.

Analysis of intestinal flora and cognitive function in maintenance hemodialysis patients using combined 16S ribosome DNA and shotgun metagenome sequencing.

BACKGROUND: Cognitive impairment is widely prevalent in maintenance hemodialysis (MHD) patients, and seriously affects their quality of life. The intestinal flora likely regulates cognitive function, but studies on cognitive impairment and intestinal flora in MHD patients are lacking. METHODS: MHD patients (36) and healthy volunteers (18) were evaluated using the Montreal Cognitive Function Scale, basic clinical data, and 16S ribosome DNA (rDNA) sequencing. Twenty MHD patients and ten healthy volunteers were randomly selected for shotgun metagenomic analysis to explore potential metabolic pathways of intestinal flora. Both16S rDNA sequencing and shotgun metagenomic sequencing were conducted on fecal samples. RESULTS: Roseburia were significantly reduced in the MHD group based on both 16S rDNA and shotgun metagenomic sequencing analyses. Faecalibacterium, Megamonas, Bifidobacterium, Parabacteroides, Collinsella, Tyzzerella, and Phascolarctobacterium were positively correlated with cognitive function or cognitive domains. Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included oxidative phosphorylation, photosynthesis, retrograde endocannabinoid signaling, flagellar assembly, and riboflavin metabolism. CONCLUSION: Among the microbiota, Roseburia may be important in MHD patients. We demonstrated a correlation between bacterial genera and cognitive function, and propose possible mechanisms.

Fuzzy State-Driven Cross-Time Spatial Dependence Learning for Multivariate Time-Series Anomaly Detection.

Cross-time spatial dependence (i.e., the interaction between different variables at different time points) is indispensable for detecting anomalies in multivariate time series, as certain anomalies may have time delays in their propagation from one variable to another. However, accurately capturing cross-time spatial dependence remains a challenge. Specifically, real-world time series usually exhibits complex and incomprehensible evolutions that may be compounded by multiple temporal states (i.e., temporal patterns, such as rising, fluctuating, and peak). These temporal states mix and overlap with each other and exhibit dynamic and heterogeneous evolution laws in different time series, making the cross-time spatial dependence extremely intricate and mutable. Therefore, a cross-time spatial graph network with fuzzy embedding is proposed to disentangle latent and mixing temporal states and exploit it to meticulously learn cross-time spatial dependence. First, considering that temporal states are diversiform and their mixing modes are unknown, we introduce a fuzzy state set to uniformly characterize potential temporal states and adaptively generate corresponding membership degrees to depict how these states mix. Further, we propose a cross-time spatial graph, quantifying similarities among fuzzy states and sensing their dynamic evolutions, to flexibly learn mutable cross-time spatial dependence. Finally, we design state diversity and temporal proximity constraints to ensure the differences among fuzzy states and the evolution continuity of fuzzy states. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art models.

Towards consensual representation: Model-agnostic knowledge extraction for dual heterogeneous federated fault diagnosis.

Federated fault diagnosis has attracted increasing attention in industrial cloud-edge collaboration scenarios, where a ubiquitous assumption is that client models have the same architecture. Practically, this assumption cannot always be fulfilled due to requirements for personalized models, thereby resulting in the problem of model heterogeneity. Many approaches dealing with heterogeneous models tend to neglect the issue of representation bias, particularly in the context of non-identically and independently distributed data. In this article, to address the representation bias problem, Federated Model-Agnostic Knowledge Extraction (FedMAKE) is proposed. To bridge the information gap among clients, different from methods with public datasets, we initially develop two novel architecture-independent knowledge carriers. These carriers are derived based on the importance of process variables, without the need for additional datasets. Subsequently, we introduce a bi-directional distillation algorithm utilizing the two knowledge carriers. This algorithm facilitates the mutual transfer of knowledge embedded in carriers between a generative network and client models, thereby enabling the generation of fault data that is unbiased and well-balanced across categories. Furthermore, to mitigate the impact of statistical heterogeneity, we formulate a local objective for each client using two global knowledge carriers to guide local knowledge extraction and constrain client drift. Extensive experiments conducted on two prevalent industry datasets (TE and CWRU) illustrate that our proposed FedMAKE outperforms baseline methods. Specifically, FedMAKE enhances fault diagnosis accuracy by up to 11.7% on the TE dataset and up to 3.31% on the CWRU dataset compared to the sub-optimal method.

A retrospective study on iRoot BP Plus full pulpotomy for primary molars with partial irreversible pulpitis.

OBJECTIVES: This study aimed to observe the outcomes of iRoot BP Plus full pulpotomy in primary molars with partial irreversible pulpitis retrospectively. METHODS: Collect 102 cases of primary molars with partial irreversible pulpitis undergoing iRoot BP Plus full pulpotomy from January 2019 to August 2023, with a follow-up period of 24-47 months. Based on the presence of irreversible pulpitis symptoms before surgery, the included cases will be divided into asymptomatic group (n=53) and symptomatic group (n=49). Observe the clinical and imaging success rates of both groups. RESULTS: Clinical success rates were 96.2% and 97.9% in asymptomatic and symptomatic groups, and radiographic success rates were 96.2% and 93.9% respectively. CONCLUSIONS: iRoot BP Plus full pulpotomy can be used for the treatment of primary molars with partial irreversible pulpitis under an enhanced pulpotomy protocol.

Copper-Catalyzed Remote Regio- and Enantioselective Yne-Allylic Substitution of Coumarins.

Chiral coumarins and their derivatives are prominent bioactive structural units present in a wide range of natural products and pharmaceutical candidates. Therefore, the development of straightforward and efficient methodologies for the synthesis of readily functionalized chiral coumarins is of significant interest. Herein we report an enantioselective copper-catalyzed yne-allylic substitution of coumarins, resulting in a highly regioselective synthesis of diverse new classes of chiral coumarin derivatives with high efficiency and excellent functional group tolerance. Subsequent versatile transformations further demonstrate the substantial synthetic potential of this strategy in the field of biochemical research.

iHPPPVis: Interactive Visual Analytics Approach for Production Performance Monitoring of Heavy-Plate Production Process.

Efficient monitoring of production performance is crucial for ensuring safe operations and enhancing the economic benefits of the Iron and Steel Corporation. Although basic modeling algorithms and visualization diagrams are available in many scientific platforms and industrial applications, there is still a lack of customized research in production performance monitoring. Therefore, this article proposes an interactive visual analytics approach for monitoring the heavy-plate production process (iHPPPVis). Specifically, a multicategory aggregated monitoring framework is proposed to facilitate production performance monitoring under varying working conditions. In addition, A set of visualizations and interactions are designed to enhance analysts' analysis, identification, and perception of the abnormal production performance in heavy-plate production data. Ultimately, the efficacy and practicality of iHPPPVis are demonstrated through multiple evaluations.

The consumption of lard oil during pregnancy and postpartum periods has negative effects on cognitive function by altering the fatty acid profile and activating neuroinflammation via calcium signaling pathway in the maternal mice brain.

It has been suggested that dietary intake of lipids and fatty acids may influence cognitive function, however, the effect of lard intake during pregnancy and postpartum periods on cognitive function of mother remains to be elucidated. We investigated the effect and mechanism of consuming soybean oil (SO), the mixed oil of lard and soybean oil at the ratio of 1:1 (LS) and lard oil (LO) during the pregnancy and postpartum periods on cognitive function of the maternal mice. All pregnant C57BL/6JNifdc mice were fed with soybean oil diet during day 0-10 (the day when vaginal plugs appeared in female mice was recorded as day 0), and then randomly assigned to SO, LS and LO groups (n = 10) from day 11 to day 44. The time in center zone and the number of times to enter in center zone were significantly higher in the SO group than in the LO group detected by the open-field test. The levels of neuroglial cells, NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex and pyroptosis related proteins in brain of the LO group were significantly higher than those in the SO group. RNA-sequencing results showed that the calcium signaling pathway related genes in brain, including Adcy8, Ntsr1, Trhr, Oxtr, Htr5b and Camk2d levels significantly higher in the LO group than in the SO group. Lipidomic analysis indicated that PG 18:2_18:2, PG 20:5_22:6, and CL 12:0_16:0_22:3_22:5 of glycerophospholipid metabolism in brain significantly connected with Htr5b of calcium signaling pathway. In conclusion, the intake of lard during the pregnancy and postpartum periods is detrimental to the cognitive function of maternal mice, which probably due to changes in the composition of fatty acid in the brain, thereby activating neuroinflammation via calcium signaling pathway in brain.

Intelligent condition monitoring with CNN and signal enhancement for undersampled signals.

High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.

Exploration on pharmacological mechanisms of YZP against neuropathic pain via inhibiting spinal inflammation and the rationality of its compatibility.

ETHNOPHARMACOLOGICAL RELEVANCE: Yuanhu Zhitong Prescription (YZP) is a well-known traditional Chinese medicine (TCM) formula for neuropathic pain (NP) therapy with a satisfying clinical efficacy. However, the underlying pharmacological mechanism and its compatibility principle remain unclear. AIM OF THE STUDY: This study aims to investigate the analgesic and compatibility mechanisms of YZP on neuropathic pain (NP) at the gene and biological process levels. MATERIALS AND METHODS: The chronic constriction injury (CCI) rats were intragastrically administrated with extracts of YZP, YH and BZ separately, and then mechanical hypersensitivity were measured to evaluate the analgesic effects between YH and BZ before and after compatibility. Then, RNA-seq and bioinformatics analyses were performed to elucidate the potential mechanisms underlying YZP's analgesia and compatibility. Finally, the expression levels and significant differences of key genes were analyzed. RESULTS: Behaviorally, both YZP and YH effectively alleviated mechanical allodynia in CCI rats, with YZP being superior to YH. In contrast, we did not observe an analgesic effect of BZ. Genetically, YZP, YH, and BZ reversed the expression levels of 52, 34, and 42 aberrant genes in the spinal cord of CCI rats, respectively. Mechanically, YZP was revealed to alleviate NP mainly by modulating the inflammatory response and neuropeptide signaling pathway, which are the dominant effective processes of YH. Interestingly, the effective targets of YZP were especially enriched in leukocyte activation and cytokine-mediated signaling pathways. Moreover, BZ was found to exert an adjunctive effect in enhancing the analgesic effect of YH by promoting skeletal muscle tissue regeneration and modulating calcium ion transport. CONCLUSIONS: YH, as the monarch drug, plays a dominant role in the analgesic effect of YZP that effectively relieves NP by inhibiting the spinal inflammation and neuropeptide signaling pathway. BZ, as the minister drug, not only synergistically enhances analgesic processes of YH but also helps to alleviate the accompanying symptoms of NP. Consequently, YZP exerted a more potent analgesic effect than YH and BZ alone. In conclusion, our findings offer new insights into understanding the pharmacological mechanism and compatibility principle of YZP, which may support its clinical application in NP therapy.