Influence of magmatic intrusions on organic nitrogen in coal: A case study from the Zhuji mine, the Huainan coalfield, China

Although the influence of magmatic intrusions on coal has been studied extensively at many locations, data on changes of organic nitrogen forms in coal in response to this kind of geological instantaneous heating is still scarce. To fill this information gap, a total of five coal samples, including four coal samples collected along a coal transect approaching a magmatic intrusion and one unaltered coal sample, were collected from the No. 3 coal seam of the Zhuji mine in the Huainan coalfield, China and were analyzed for organic nitrogen forms using X-ray photoelectron spectroscopy (XPS), together with the determination of coal quality parameters and elemental composition. Due to the effect of magmatic intrusion, ash yield and carbon content of the coals increase, whereas moisture, volatile matter, oxygen, nitrogen and total sulfur decrease. The N-5 peak is dominant in unaltered and moderately altered coals, but disappears entirely in the coals adjacent to the magmatic intrusion due to the strong thermal influence. The N-Q peak mainly represents "protonated" quaternary nitrogen in unaltered and moderately altered coals. The N-Q peak can be transformed to the N-6 peak through the deprotonation of "protonated" quaternary nitrogen resulting from the loss of oxygen groups under the thermal influence of the magmatic intrusion. Closer to the magmatic intrusion, the N-Q peak is assigned to "graphitic" quaternary nitrogen, which increases sharply and becomes the predominant form eventually. Magmatic intrusion is responsible for the conversion of less stable nitrogen forms to more stable forms in coal

Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-Computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human Vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively; the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.Comment: 8 pages, 9 figures, 2 tables, accepted by CVPR2023, see https://mind-vis.github.io/ for more informatio

Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities

Decoding visual stimuli from neural responses recorded by functional Magnetic Resonance Imaging (fMRI) presents an intriguing intersection between cognitive neuroscience and machine learning, promising advancements in understanding human visual perception and building non-invasive brain-machine interfaces. However, the task is challenging due to the noisy nature of fMRI signals and the intricate pattern of brain visual representations. To mitigate these challenges, we introduce a two-phase fMRI representation learning framework. The first phase pre-trains an fMRI feature learner with a proposed Double-contrastive Mask Auto-encoder to learn denoised representations. The second phase tunes the feature learner to attend to neural activation patterns most informative for visual reconstruction with guidance from an image auto-encoder. The optimized fMRI feature learner then conditions a latent diffusion model to reconstruct image stimuli from brain activities. Experimental results demonstrate our model's superiority in generating high-resolution and semantically accurate images, substantially exceeding previous state-of-the-art methods by 39.34% in the 50-way-top-1 semantic classification accuracy. Our research invites further exploration of the decoding task's potential and contributes to the development of non-invasive brain-machine interfaces.Comment: 17 pages, 6 figures, conferenc

Influence of magmatic intrusions on organic nitrogen in coal: A case studyfrom the Zhuji mine, the Huainan coalfield, China

Although the influence of magmatic intrusions on coal has been studied extensively at many locations, data on changes of organic nitrogen forms in coal in response to this kind of geological instantaneous heating is still scarce. To fill this information gap, a total of five coal samples, including four coal samples collected along a coal transect approaching a magmatic intrusion and one unaltered coal sample, were collected from the No. 3 coal seam of the Zhuji mine in the Huainan coalfield, China and were analyzed for organic nitrogen forms using X-ray photoelectron spectroscopy (XPS), together with the determination of coal quality parameters and elemental composition. Due to the effect of magmatic intrusion, ash yield and carbon content of the coals increase, whereas moisture, volatile matter, oxygen, nitrogen and total sulfur decrease. The N-5 peak is dominant in unaltered and moderately altered coals, but disappears entirely in the coals adjacent to the magmatic intrusion due to the strong thermal influence. The N-Q peak mainly represents “protonated” quaternary nitrogen in unaltered and moderately altered coals. The N-Q peak can be transformed to the N-6 peak through the deprotonation of “protonated” quaternary nitrogen resulting from the loss of oxygen groups under the thermal influence of the magmatic intrusion. Closer to the magmatic intrusion, the N-Q peak is assigned to “graphitic” quaternary nitrogen, which increases sharply and becomes the predominant form eventually. Magmatic intrusion is responsible for the conversion of less stable nitrogen forms to more stable forms in coal

Rapid Detection of A282S Mutation in the <i>RDL1</i> Gene of Rice Stem Borer via the Mutation-Specific LAMP Technique

Rice stem borer Chilo suppressalis (Walker) is one of the most serious pests on rice and is distributed worldwide. With the long-term and continuous usage of insecticides, C. suppressalis has developed high levels of resistance to various kinds of insecticides, including phenylpyrazole insecticides. As is well known, the resistance of C. suppressalis to phenylpyrazole insecticides is determined by the A282S mutation of the GABA receptor RDL subunit. In order to efficiently detect the resistance of C. suppressalis, a rapid and sensitive loop-mediated isothermal amplification (LAMP) technique was established and optimized in this study. The optimal concentration of components was Bst DNA polymerase (0.24 U/μL), dNTP (0.8 mM), Mg2+ (4 mM), betaine (0.6 M), forward inner primer and backward inner primer (1.6 μM), F3 and B3 (0.4 μM), and hydroxyl naphthol blue (150 mM), respectively, and the optimal reaction condition was 63 °C for 60 min, which could reduce the cost and time of detection. In addition, the accuracy of the optimized LAMP reaction system and parameters was verified in the field strains of C. suppressalis from different regions, including Jiangsu, Jiangxi, and Hu’nan provinces. The mutation (A2’S) was successfully detected in the field strains. As far as we know, this is the first report of the LAMP technique applied in the resistance monitoring of C. suppressalis to phenylpyrazole insecticides. According to our results, the optimized LAMP reaction system is feasible and easy to operate and to efficiently detect resistance-related mutation in a short time, as directly judged by the naked eye. Our results provide a new tool for detection of resistance of C. suppressalis, which is a very useful tool for comprehensive management of C. suppressalis

Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke

Abstract Background Atrial fibrillation (AF) is one of the most prevalent sustained arrhythmias, however, epidemiological data may understate its actual prevalence. Meanwhile, AF is considered to be a major cause of ischemic strokes due to irregular heart-rhythm, coexisting chronic vascular inflammation, and renal insufficiency, and blood stasis. We studied co-expressed genes to understand relationships between atrial fibrillation (AF) and stroke and reveal potential biomarkers and therapeutic targets of AF-related stroke. Methods AF-and stroke-related differentially expressed genes (DEGs) were identified via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE79768 and GSE58294, respectively. Subsequently, extensive target prediction and network analyses methods were used to assess protein–protein interaction (PPI) networks, Gene Ontology (GO) terms and pathway enrichment for DEGs, and co-expressed DEGs coupled with corresponding predicted miRNAs involved in AF and stroke were assessed as well. Results We identified 489, 265, 518, and 592 DEGs in left atrial specimens and cardioembolic stroke blood samples at < 3, 5, and 24 h, respectively. LRRK2, CALM1, CXCR4, TLR4, CTNNB1, and CXCR2 may be implicated in AF and the hub-genes of CD19, FGF9, SOX9, GNGT1, and NOG may be associated with stroke. Finally, co-expressed DEGs of ZNF566, PDZK1IP1, ZFHX3, and PITX2 coupled with corresponding predicted miRNAs, especially miR-27a-3p, miR-27b-3p, and miR-494-3p may be significantly associated with AF-related stroke. Conclusion AF and stroke are related and ZNF566, PDZK1IP1, ZFHX3, and PITX2 genes are significantly associated with novel biomarkers involved in AF-related stroke