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Abstract Background The TetR family of transcriptional regulators (TFRs), serving as crucial regulators of diverse cellular processes, undergo conformational changes induced by small-molecule ligands, which either inhibit or activate them to modulate target gene expression. Some ligands of TFRs in actinomycetes and their regulatory effects have been identified and studied; however, regulatory mechanisms of the TetR family in the lincomycin-producing Streptomyces lincolnensis remain poorly understood. Results In this study, we found that AbrT (SLCG_1979), a TetR family regulator, plays a pivotal role in regulating lincomycin production and morphological development in S. lincolnensis. Deletion of abrT gene resulted in increased lincomycin A (Lin-A) production, but delayed mycelium formation and sporulation on solid media. AbrT directly or indirectly repressed the expression of lincomycin biosynthetic (lin) cluster genes and activated that of the morphological developmental genes amfC, whiB, and ftsZ. We demonstrated that AbrT bound to two motifs (5′-CGCGTACTCGTA-3′ and 5′-CGTACGATAGCT-3′) present in the bidirectional promoter between abrT and SLCG_1980 genes. This consequently repressed abrT itself and its adjacent gene SLCG_1980 that encodes an arabinose efflux permease. D-arabinose, not naturally occurring as L-arabinose, was identified as the effector molecule of AbrT, reducing its binding affinity to abrT-SLCG_1980 intergenic region. Furthermore, based on functional analysis of the AbrT homologue in Saccharopolyspora erythraea, we inferred that the TetR family regulator AbrT may play an important role in regulating secondary metabolism in actinomycetes. Conclusions AbrT functions as a regulator for governing lincomycin production and morphological development of S. lincolnensis. Our findings demonstrated that D-arabinose acts as a ligand of AbrT to mediate the regulation of lincomycin biosynthesis in S. lincolnensis. Our findings provide novel insights into ligand-mediated regulation in antibiotic biosynthesis.

Abstract Full-length p53 (p53α) plays a pivotal role in maintaining genomic integrity and preventing tumor development. Over the years, p53 was found to exist in various isoforms, which are generated through alternative splicing, alternative initiation of translation, and internal ribosome entry site. p53 isoforms, either C-terminally altered or N-terminally truncated, exhibit distinct biological roles compared to p53α, and have significant implications for tumor development and therapy resistance. Due to a lack of part and/or complete C- or N-terminal domains, ectopic expression of some p53 isoforms failed to induce expression of canonical transcriptional targets of p53α like CDKN1A or MDM2, even though they may bind their promoters. Yet, p53 isoforms like Δ40p53α still activate subsets of targets including MDM2 and BAX. Furthermore, certain p53 isoforms transactivate even novel targets compared to p53α. More recently, non-canonical functions of p53α in DNA repair and of different isoforms in DNA replication unrelated to transcriptional activities were discovered, amplifying the potential of p53 as a master regulator of physiological and tumor suppressor functions in human cells. Both regarding canonical and non-canonical functions, alternative p53 isoforms frequently exert dominant negative effects on p53α and its partners, which is modified by the relative isoform levels. Underlying mechanisms include hetero-oligomerization, changes in subcellular localization, and aggregation. These processes ultimately influence the net activities of p53α and give rise to diverse cellular outcomes. Biological roles of p53 isoforms have implications for tumor development and cancer therapy resistance. Dysregulated expression of isoforms has been observed in various cancer types and is associated with different clinical outcomes. In conclusion, p53 isoforms have expanded our understanding of the complex regulatory network involving p53 in tumors. Unraveling the mechanisms underlying the biological roles of p53 isoforms provides new avenues for studies aiming at a better understanding of tumor development and developing therapeutic interventions to overcome resistance.

Abstract Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient. Method In this study, we present a new algorithmic framework using meta-learning for learning personalized causal graphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causal graph learning. Results Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causal graph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline algorithms. Conclusion To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions.

Abstract The wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterwards, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.

Abstract Background Curcubita ficifolia Bouché (Cucurbitaceae) has high value as a food crop and medicinal plant, and also has horticultural value as rootstock for other melon species. China is home to many different cultivars, but the genetic diversity of these resources and the evolutionary relationships among them, as well as the differences between C. ficifolia and other Cucurbita species, remain unclear. Results We investigated the chloroplast (cp) genomes of 160 C. ficifolia individuals from 31 populations in Yunnan, a major C. ficifolia production area in China. We found that the cp genome of C. ficifolia is ~151 kb and contains 128 genes, of which 86 are protein coding genes, 34 encode tRNA, and eight encode rRNAs. We also identified 64 SSRs, mainly AT repeats. The cp genome was found to contain a total of 204 SNP and 57 indels, and a total of 21 haplotypes were found in the 160 study individuals. The reverse repeat (IR) region of C. ficifolia contained a few differences compared with this region in the six other Cucurbita species. Sequence difference analysis demonstrated that most of the variable regions were concentrated in the single copy (SC) region. Moreover, the sequences of the coding regions were found to be more similar among species than those of the non-coding regions. The phylogenies reconstructed from the cp genomes of 61 representative species of Cucurbitaceae reflected the currently accepted classification, in which C. ficifolia is sister to the other Cucurbita species, however, different interspecific relationships were found between Cucurbita species. Conclusions These results will be valuable in the classification of C. ficifolia genetic resources and will contribute to our understanding of evolutionary relationships within the genus Cucurbita.

The biosynthesis of bioactive secondary metabolites, specifically antibiotics, is of great scientific and economic importance. The control of antibiotic production typically involves different processes and molecular mechanism. Despite numerous efforts to improve antibiotic yields, joint engineering strategies for combining genetic manipulation with fermentation optimization remain finite. Lincomycin A (Lin-A), a lincosamide antibiotic, is industrially fermented by Streptomyces lincolnensis. Herein, the leucine-responsive regulatory protein (Lrp)-type regulator SLCG_4846 was confirmed to directly inhibit the lincomycin biosynthesis, whereas indirectly controlled the transcription of SLCG_2919, the first reported repressor in S. lincolnensis. Inactivation of SLCG_4846 in the high-yield S. lincolnensis LA219X (LA219XΔ4846) increases the Lin-A production and deletion of SLCG_2919 in LA219XΔ4846 exhibits superimposed yield increment. Given the effect of the double deletion on cellular primary metabolism of S. lincolnensis, Plackett-Burman design, steepest ascent and response surface methodologies were utilized and employed to optimize the seed medium of this double mutant in shake flask, and Lin-A yield using optimal seed medium was significantly increased over the control. Above strategies were performed in a 15-L fermenter. The maximal yield of Lin-A in LA219XΔ4846-2919 reached 6.56 g/L at 216 h, 55.1 % higher than that in LA219X at the parental cultivation (4.23 g/L). This study not only showcases the potential of this strategy to boost lincomycin production, but also could empower the development of high-performance actinomycetes for other antibiotics.

Semantic scene completion is a computer vision technique that combines semantic segmentation and shape completion. Its purpose is to infer a complete 3D scene with semantic information from single-view RGB-D images. In recent years, some methods have adopted the voxel-points-based approach, converting voxelized scenes into point clouds to reduce the computational cost associated with 3D convolutions. However, majority of such methods do not fully consider the geometric details of the objects in the scene. In this paper, we propose ASPNet (Attention-based Semantic Point Completion Network), a two-branch semantic scene completion algorithm that combines scene-level completion and object refinement. In the scene level completion branch, we design the SPT (Semantic-based Point Transformer) module, which introduces semantic information into the traditional Point Transformer layer to realize the feature aggregation of neighboring keypoints of the same category. Using the object detection module and the object refinement module, ASPNet refines the rough semantic complementation results obtained from direct coding and decoding of RGB-D inputs. The quantitative results show that ASPNet has much less computational overhead than the 3D convolution-based semantic scene completion algorithm, while the reconstruction results have more geometric details.

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery.

Abstract Background Spinal cord ischemia‒reperfusion injury (SCIRI) can lead to paraplegia, which leads to permanent motor function loss. It is a disastrous complication of surgery and causes tremendous socioeconomic burden. However, effective treatments for SCIRI are still lacking. PANoptosis consists of three kinds of programmed cell death, pyroptosis, apoptosis, and necroptosis, and may contribute to ischemia‒reperfusion-induced neuron death. Previous studies have demonstrated that hydrogen sulfide (H2S) exerts a neuroprotective effect in many neurodegenerative diseases. However, whether H2S is anti-PANoptosis and neuroprotective in the progression of acute SCIRI remains unclear. Thus, in this study we aimed to explore the role of H2S in SCIRI and its underlying mechanisms. Methods Measurements of lower limb function, neuronal activity, microglia/macrophage function histopathological examinations, and biochemical levels were performed to examine the efficacy of H2S and to further demonstrate the mechanism and treatment of SCIRI. Results The results showed that GYY4137 (a slow-releasing H2S donor) treatment attenuated the loss of Nissl bodies after SCIRI and improved the BBB score. Additionally, the number of TUNEL-positive and cleaved caspase-3-positive cells was decreased, and the upregulation of expression of cleaved caspase-8, cleaved caspase-3, Bax, and Bad and downregulation of Bcl-2 expression were reversed after GYY4137 administration. Meanwhile, both the expression and activation of p-MLKL, p-RIP1, and p-RIP3, along with the number of PI-positive and RIP3-positive neurons, were decreased in GYY4137-treated rats. Furthermore, GYY4137 administration reduced the expression of NLRP3, cleaved caspase-1 and cleaved GSDMD, decreased the colocalization NeuN/NLRP3 and Iba1/interleukin-1β-expressing cells, and inhibited proinflammatory factors and microglia/macrophage polarization. Conclusions H2S ameliorated spinal cord neuron loss, prevented motor dysfunction after SCIRI, and exerted a neuroprotective effect via the inhibition of PANoptosis and overactivated microglia-mediated neuroinflammation in SCIRI.

In this study, we investigated the effects of major royal jelly proteins (MRJPs) on the estrogen, gut microbiota, and immunological responses in mice. Mice given 250 or 500 mg/kg, not 125 mg/kg of MRJPs, enhanced the proliferation of splenocytes in response to mitogens. The splenocytes and mesenteric lymphocytes activated by T-cell mitogens (ConA and anti-CD3/CD28 antibodies) released high levels of IL-2 but low levels of IFN-γ and IL-17A. The release of IL-4 was unaffected by MRJPs. Additionally, splenocytes and mesenteric lymphocytes activated by LPS were prevented by MRJPs at the same dose as that required for producing IL-1β and IL-6, two pro-inflammatory cytokines. The production of IL-1β, IL-6, and IFN-γ was negatively associated with estrogen levels, which were higher in the MRJP-treated animals than in the control group. Analysis of the gut microbiota revealed that feeding mice 250 mg/kg of MRJPs maintained the stability of the natural intestinal microflora of mice. Additionally, the LEfSe analysis identified biomarkers in the MRJP-treated mice, including Prevotella, Bacillales, Enterobacteriales, Gammaproteobacteria, Candidatus_Arthromitus, and Shigella. Our results showed that MRJPs are important components of royal jelly that modulate host immunity and hormone levels and help maintain gut microbiota stability.

Despite standard treatment for non-small cell lung cancer (NSCLC) being surgical resection, cancer recurrence and complications, such as induction of malignant pleural effusion (MPE) and significant postoperative pain, usually result in treatment failure. In this study, an alginate-based hybrid hydrogel (SOG) is developed that can be injected into the resection surface of the lungs during surgery. Briefly, endoplasmic reticulum-modified liposomes (MSLs) pre-loaded with the signal transducer and activator of transcription 3 (STAT3) small interfering RNA and lidocaine hydrochloride are encapsulated in SOG. Once applied, MSLs strongly downregulated STAT3 expression in the tumor microenvironment, resulting in the apoptosis of lung cancer cells and polarization of tumor-associated macrophages towards the M1-like phenotype. Meanwhile, the release of lidocaine hydrochloride (LID) was beneficial for pain relief and natural killer cell activation. Our data demonstrated MSL@LID@SOG not only efficiently inhibited tumor growth but also potently improved the quality of life, including reduced MPE volume and pain relief in orthotopic NSCLC mouse models, even with a single administration. MSL@LID@SOG shows potential for comprehensive clinical management upon tumor resection in NSCLC, and may alter the treatment paradigms for other cancers.

This corrects the article published in European Journal of Histochemistry 2023;67:3535. doi: 10.4081/ejh.2023.3535

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as “DeepDOC”, and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Abstract Background Cucurbita ficifolia is one of the squash species most resistant to fungal pathogens, and has especially high resistance to melon Fusarium wilt. This species is therefore an important germplasm resource for the breeding of squash and melon cultivars. Results Whole-genome resequencing of 223 individuals from 32 populations in Yunnan Province, the main cucurbit production area in China, was performed and 3,855,120 single-nucleotide polymorphisms (SNPs) and 1,361,000 InDels were obtained. SNP analysis suggested that levels of genetic diversity in C. ficifolia were high, but that different populations showed no significant genetic differentiation or geographical structure, and that individual C. ficifolia plants with fruit rinds of a similar color did not form independent clusters. A Mantel test conducted in combination with geographical distance and environmental factors suggested that genetic distance was not correlated with geographical distance, but had a significant correlation with environmental distance. Further associations between the genetic data and five environmental factors were analyzed using whole-genome association analysis. SNPs associated with each environmental factor were investigated and genes 250 kb upstream and downstream from associated SNPs were annotated. Overall, 15 marker-trait-associated SNPs (MTAs) and 293 genes under environmental selection were identified. The identified genes were involved in cell membrane lipid metabolism, macromolecular complexes, catalytic activity and other related aspects. Ecological niche modeling was used to simulate the distribution of C. ficifolia across time, from the present and into the future. We found that the area suitable for C. ficifolia changed with the changing climate in different periods. Conclusions Resequencing of the C. ficifolia accessions has allowed identification of genetic markers, such as SNPs and InDels. The SNPs identified in this study suggest that environmental factors mediated the formation of the population structure of C. ficifolia in China. These SNPs and Indels might also contribute to the variation in important pathways of genes for important agronomic traits such as yield, disease resistance and stress tolerance. Moreover, the genome resequencing data and the genetic markers identified from 223 accessions provide insight into the genetic variation of the C. ficifolia germplasm and will facilitate a broad range of genetic studies.

Surgical gesture recognition is an important research direction in the field of computer-assisted intervention. Currently, research on surgical gesture recognition primarily focuses on robotic surgery, with a lack of studies in traditional surgery, particularly open surgery. Therefore, this study established a dataset simulating open surgery for research on surgical gesture recognition in the field of open surgery. With the assistance of professional surgeons, we defined a vocabulary of 10 surgical gestures based on suturing tasks in open procedures. In addition, this paper proposes a surgical gesture recognition method that integrates the R3D network with a multi-head attention mechanism (R3D-MHA). This method uses the R3D network to extract spatiotemporal features and combines it with the multi-head attention mechanism for relational learning of these features. The effectiveness of the R3D-MHA method in the field of open surgery gesture recognition was validated through two experiments: offline recognition and online recognition. The accuracy at the gesture instance level for offline recognition was 92.3%, and the frame accuracy for online recognition was 73.4%. Finally, its performance was further validated on the publicly available JIGSAWS dataset. Compared to other online recognition methods, the accuracy improved without using additional data. This work lays the foundation for research on surgical gesture recognition in open surgery and has significant applications in process monitoring, surgeon skill assessment and educational training for open surgeries.

The thermal performance of a lithium-ion battery pack for an electric vehicle by adding straight rib turbulators in battery cooling plate channels has been numerically investigated in this paper and the numerical model of the battery pack has been validated by experimental data, which exhibits a satisfactory prediction accuracy. The effects of rib shapes, rib angles, rib spacings, and irregular gradient rib arrangement configurations on the flow and heat transfer behaviors of battery pack cooling plates have been thoroughly explored and analyzed in this paper. In addition, the thermal performance of the ribbed battery cooling plates was examined at actual high-speed climbing and low-temperature heating operating conditions. The results indicate that compared to the original smooth cooling plate, the square-ribbed battery cooling plate with a 60° angle and 5 mm spacing reduced the maximum battery temperature by 0.3 °C, but increased the cross-sectional temperature difference by 0.357 °C. To address this issue, a gradient rib arrangement was proposed, which slightly reduced the maximum battery temperature and lowered the cross-sectional temperature difference by 0.445 °C, significantly improving temperature uniformity. The thermal performance of the battery thermal management system with this gradient rib configuration meets the requirements for typical electric vehicle operating conditions, such as high-speed climbing and low-temperature heating conditions.

Bodily self-consciousness (BSC), a subject of interdisciplinary interest, refers to the awareness of one’s bodily states. Previous studies have noted the existence of individual differences in BSC, while neglecting the underlying factors and neural basis of such individual differences. Considering that BSC relied on integration from both internal and external self-relevant information, we here review previous findings on individual differences in BSC through a three-level-self model, which includes interoceptive, exteroceptive, and mental self-processing. The data show that cross-level factors influenced individual differences in BSC, involving internal bodily signal perceptibility, multisensory processing principles, personal traits shaped by environment, and interaction modes that integrate multiple levels of self-processing. Furthermore, in interoceptive processing, regions like the anterior cingulate cortex and insula show correlations with different perceptions of internal sensations. For exteroception, the parietal lobe integrates sensory inputs, coordinating various BSC responses. Mental self-processing modulates differences in BSC through areas like the medial prefrontal cortex. For interactions between multiple levels of self-processing, regions like the intraparietal sulcus involve individual differences in BSC. We propose that diverse experiences of BSC can be attributed to different levels of self-processing, which moderates one’s perception of their body. Overall, considering individual differences in BSC is worth amalgamating diverse methodologies for the diagnosis and treatment of some diseases.

Carbon fiber reinforced plastic (CFRP) thin-walled structures have been widely acknowledged as superior energy absorbers due to comprehensive advantages on crashworthiness and light weight. However, few studies have been reported concerning CFRP thin-walled structures with nonlinearly variable sectional dimensions along the longitudinal direction. Hence, in the present study, a novel CFRP functionally-graded conical square thin-walled structure with variable circumscribed diameter (functionally-graded conical square tube, FGCST) is proposed and comprehensively investigated. Specifically, finite element (FE) models of FGCST under multi-angle crushing loading are first built and validated. Second, a comprehensive crashworthiness comparison among the FGCST, the conventional CFRP conical square tube (CST) and the CFRP straight square tube (SST) under multi-angle crushing loading is executed to explore the relative merits of FGCST over others. Third, a parameter study is implemented on FGCST to explore the action laws of its structural parameters on its overall crashworthiness under multi-angle crushing loading. Finally, the FGCST is multi-objective crashworthiness optimized for maximizing overall specific energy absorption (SEA θ ) and minimizing overall peak crushing force (PCF 0 ) simultaneously through Taguchi method coupled with grey relational analysis (GRA). The optimized FGCST obtains 6.06% higher of SEA θ and meanwhile 40.58% lower of PCF 0 compared with the baseline design, thus demonstrating good potential as a superior candidate for vehicle collision energy absorbers.

The mechanical-electrical-magnetic coupling effects caused by the air gap deformation in in-wheel motors exacerbate motor magnetic force oscillation, deteriorating the in-wheel motor operation and ride comfort. In order to enhance vehicle performances, it is imperative to mitigate these coupling effects during the design of hub-driven electric vehicle suspensions. In this paper, the analytical investigation is conducted on the coupling effects between electromagnetic excitation and transient dynamics in EVs based on the half-vehicle model. The dynamic responses of hub-driven EVs considering coupling effects are explored under both open-loop and closed-loop systems at various speeds and road conditions. Numerical simulations demonstrate that the closed-loop system with wheelbase preview robust control exhibits superior comprehensive performance in suppressing coupling effects of the rear wheel hub motor and enhancing ride comfort compared to the system without preview control. Furthermore, as vehicle speed increases, robust preview control effectively reduces fluctuations in pitch angle acceleration, pitch angle, and rear wheel rotor eccentricity. In addition, robust preview control yields better performance in reducing vehicle body centroid acceleration and rear wheel rotor eccentricity and enhancing motor and vehicle performance with increasing road roughness. This work provides a practical vibration model and control reference for studying the dynamic characteristics of hub-driven EVs.

Summary: Arousal and awareness are two components of consciousness whose neural mechanisms remain unclear. Spontaneous peaks of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal have been found to be sensitive to changes in arousal. By contrasting BOLD signals at different arousal levels, we find decreased activation of the ventral posterolateral nucleus (VPL) during transient peaks in the global signal in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) compared to wakefulness and in eyes-closed compared to eyes-open conditions in healthy awake individuals. Intriguingly, VPL-global co-activation remains high in patients with unresponsive wakefulness syndrome (UWS), who exhibit high arousal without awareness, while it reduces in rapid eye movement sleep, a state characterized by low arousal but high awareness. Furthermore, lower co-activation is found in individuals during N3 sleep compared to patients with UWS. These results demonstrate that co-activation of VPL and global activity is critical to arousal but not to awareness.

This study investigates the corrosion behavior of Ni-Cr binary alloys, including Ni-10Cr, Ni-15Cr, Ni-20Cr, Ni-25Cr, and Ni-30Cr, in a NaCl-KCl-MgCl2 molten salt mixture through gravimetric analysis. Corrosion tests were conducted at 700 °C, with the maximum immersion time reaching up to 100 h. The corrosion rate was determined by measuring the mass loss of the specimens at various time intervals. Verifying corrosion rates by combining mass loss results with the determination of element dissolution in molten salts using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Detailed examinations of the corrosion products and morphology were conducted using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Micro-area elemental analysis on the corroded surfaces was performed using an energy dispersive spectrometer (EDS), and the elemental distribution across the corrosion cross-sections was mapped. The results indicate that alloys with lower Cr content exhibit superior corrosion resistance in the NaCl-KCl-MgCl2 molten salt under an argon atmosphere compared to those with higher Cr content; no corrosion products were retained on the surfaces of the lower Cr alloys (Ni-10Cr, Ni-15Cr). For the higher Cr alloys (Ni-20Cr, Ni-25Cr, Ni-30Cr), after 20 h of corrosion, a protective layer was observed in certain areas. The formation of a stable Cr2O3 layer in the initial stages of corrosion for high-Cr content alloys, which reacts with MgO in the molten salt to form a stable MgCr2O4 spinel structure, provides additional protection for the alloys. However, over time, even under argon protection, the MgCr2O4 protective layer gradually degrades due to chloride ion infiltration and chemical reactions at high temperatures. Further analysis revealed that chloride ions play a pivotal role in the corrosion process, not only facilitating the destruction of the Cr2O3 layer on the alloy surfaces but also possibly accelerating the corrosion of the metallic matrix through electrochemical reactions. In conclusion, the corrosion behavior of Ni-Cr alloys in the NaCl-KCl-MgCl2 molten salt environment is influenced by a combination of factors, including Cr content, chloride ion activity, and the formation and degradation of protective layers. This study not only provides new insights into the corrosion resistance of Ni-Cr alloys in high-temperature molten salt environments but also offers significant theoretical support for the design and optimization of corrosion-resistant alloy materials.

In this study, a novel fabrication method was used to synthesize phenolic resin/phosphate hybrid coatings using aluminum dihydrogen phosphate (Al(H2PO4)3, hereafter denoted as Al), SC101 silica sol (Si) as the primary film-forming agent, and phenolic resin (PF) as the organic matrix. This approach culminated in the formation of Al+Si+PF organo–inorganic hybrid coatings. Fourier-transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS) results confirmed the successful integration of hybrid structures within these coatings. The crystalline structure of the coatings post-cured at various temperatures was elucidated using X-ray diffraction (XRD). Additionally, the surface and cross-sectional morphologies were meticulously analyzed using scanning electron microscopy (SEM), offering insights into the microstructural properties of the coatings. The coatings’ porosities under diverse thermal and temporal regimes were quantitatively evaluated using advanced image processing techniques, revealing a significant reduction in porosity to a minimum of 5.88% following a thermal oxidation process at 600 °C for 10 h. The antioxidant efficacy of the phosphate coatings was rigorously assessed through cyclic oxidation tests, which revealed their outstanding performance. Specifically, at 300 °C across 300 h of cyclic oxidation, the weight losses recorded for phosphate varnish and the phenolic resin-infused phosphate coatings were 0.15 mg·cm−2 and 0.09 mg·cm−2, respectively. Furthermore, at 600 °C and over an identical period, the weight reduction was noted as 0.21 mg·cm−2 for phosphate varnish and 0.085 mg·cm−2 for the hybrid coatings, thereby substantiating the superior antioxidation capabilities of the phenolic resin hybrid coatings in comparison to the pure phosphate varnish.

Low back pain (LBP) is a major public health problem worldwide and a significant health and economic burden. Intervertebral disc degeneration (IDD) is the reason for LBP. However, we have not identified effective therapeutic strategies to address this challenge. With accumulating knowledge on the role of circular RNAs in the pathogenesis of IDD, we realised that circular RNAs (circRNAs) may have tremendous therapeutic potential and clinical application prospects in this field. This review presents an overview of the current understanding of characteristics, classification, biogenesis, and function of circRNAs and summarises the protective and detrimental circRNAs involved in the intervertebral disc that have been studied thus far. This review is aimed to help researchers better understand the regulatory role of circRNAs in the progression of IDD, reveal their clinical therapeutic potential, and provide a theoretical basis for the prevention and targeted treatment of IDD.

Abstract Background Liuwei Dihuang Pill (LP) was verified to alleviate postmenopausal osteoporosis (PMOP) development. Nevertheless, the major constituent of LP and the related network pharmacology study remain unexplored. Methods Protein–protein interaction was established to identify the downstream target of LP in PMOP, and the related signaling pathway was investigated by bioinformatics analysis. MC3T3-E1 cells were added to ferric ammonium citrate (FAC) to mimic osteoporosis in vitro. The osteoblasts were identified by Alizarin red staining. Western blot was applied to evaluate protein levels. In addition, Cell Counting Kit-8 (CCK8) assay was applied to assess cell viability, and cell apoptosis was assessed by flow cytometry. Results Quercetin was the major constituent of LP. In addition, quercetin significantly reversed FAC-induced inhibition of osteogenic differentiation in MC3T3-E1 cells. In addition, quercetin notably abolished the FAC-induced upregulation of Bax, Caspase-3, FOS, JUN, TGFB1 and PPARD. In contrast, Bcl-2, p-mTOR/mTOR, p-AKT/AKT and p-PI3K/PI3K levels in MC3T3-E1 cells were reduced by FAC, which was restored by quercetin. Meanwhile, FAC notably inhibited the viability of MC3T3-E1 cells via inducing apoptosis, but this impact was abolished by quercetin. Furthermore, quercetin could reverse pcDNA3.1-FOS-mediated growth of FAC-treated osteoblasts by mediating PI3K/AKT/mTOR signaling. Conclusion Quercetin alleviated the progression of PMOP via activation of PI3K/AKT/mTOR signaling. Hence, this study would shed novel insights into discovering new methods against PMOP.

In this work, the effect of Ge content on the soft magnetic properties and microstructure of FeSiGe soft magnetic powder cores (SMPCs) was systematically investigated. The results show that the addition of Ge is beneficial to increase the electrical resistivity of Fe93.5-xSi6.5Gex SMPCs, and the coercivity (Hc) shows a trend of decreasing and then increasing with the increase of Ge content. When the content of Ge is 2 wt%, the SMPCs exhibit the best magnetic properties with stable effective permeability of 78.4 up to 1 MHz, low core loss of 589.5 mW/cm3 at a maximum magnetic induction of 0.1 T and frequency of 50 kHz. By doping with Ge, the micro-vortex dots in the magnetic domain structure are reduced and the domain walls move more easily with the applied field, and the magnetic properties are improved.

A considerable amount of research on link prediction has recently been driven by missing relationships between knowledge graph entities and the problem of the incompleteness of knowledge graphs. Some recent studies have shown that convolutional neural networks based on knowledge embeddings are highly expressive and have good performance in link prediction. However, we found that the convolutional neural network (CNN)-based models do not handle the link between relations and entities well. For this reason, this paper proposes a link prediction model (LPM) based on feature mapping and bi-directional convolution. For the modeling of the task, an encoding layer–mapping layer–decoding layer structure is used. Among these layers, the encoding layer adopts a graph attention network to encode multi-hop triad information and obtains richer encoding of entities and relationships. The mapping layer can realize the mapping transformation between entities and relations and project the entity encoding in the space of relation encoding to capture the subtle connection between entities and relations. The decoding layer adopts bidirectional convolution to merge and decode the triples in a sequential inverse order, which makes the decoding layer model more advantageous in prediction. In addition, the decoding layer also adopts the r-drop training method to effectively reduce the distribution error generated by training between models and enhance the robustness of the model. Our experiments demonstrated the effectiveness of mapping relations, bidirectional convolution, and r-drop, and the accuracy of the proposed model showed significant improvements for each evaluation metric on two datasets, WN18RR and FB15k-237.

Growing evidence suggests that the presence of cancer stem cells (CSCs) is a major challenge in current tumor treatments, especially the transition from non-CSCs to differentiation of CSCs for evading conventional therapies and driving metastasis. Here we propose a therapeutic strategy of synergistic differentiation therapy and phototherapy to induce differentiation of CSCs into mature tumor cells by differentiation inducers and synergistic elimination of them and normal cancer cells through phototherapy. In this work, we synthesized a biomimetic nanoplatform loaded with IR-780 and all-trans retinoic acid (ATRA) via biomineralization. This method can integrate aluminum ions into small-sized protein carriers to form nanoclusters, which undergo responsive degradation under acidic conditions and facilitate deep tumor penetration. With the help of CSC differentiation induced by ATRA, IR-780 inhibited the self-renewal of CSCs and cancer progression by generating hyperthermia and reactive oxygen species in a synergistic manner. Furthermore, ATRA can boost immunogenic cell death induced by phototherapy, thereby strongly causing a systemic anti-tumor immune response and efficiently eliminating CSCs and tumor cells. Taken together, this dual strategy represents a new paradigm of targeted eradication of CSCs and tumors by inducing CSC differentiation, improving photothermal therapy/photodynamic therapy and enhancing antitumor immunity.

In this paper, a novel thin-walled double-hexagonal crash box is first proposed and then multi-objective robust optimized for better overall crashworthiness under multi-angle impact loading, using a proposed hybrid method combining aluminum foam-filling and Taguchi-grey relational analysis (GRA). Specifically, the finite element (FE) models of the regularly-shaped double-hexagonal column (DHC) extracted from original irregularly-shaped crash box under multi-angle impact loading, including hollow (H-DHC) and foam-filled (F-DHC), are first built and validated by experiments. On this basis, a comprehensive crashworthiness comparison is conducted to explore relative merits of F-DHC over original H-DHC under multi-angle impact loading. After that, the F-DHC is multi-objective robust optimized for maximizing overall specific energy absorption (SEA θ ) and minimizing overall initial peak crushing force (IPCF 0 ) simultaneously under multi-angle impact loading, using a hybrid method of Taguchi-GRA. At last, a bumper-crash box integrated crashworthiness analysis under multi-angle impact loading is executed to further verify the optimization. The optimal F-DHC and the optimized crash box within the optimal F-DHC demonstrate evident improvement of crashworthiness compared to their respective initial designs, indicating aluminum foam-filling combined with Taguchi-GRA could be an effective approach for multi-objective robust optimization of the novel crash box and other similar vehicle structures.

Spinal cord injury (SCI) is a sudden onset of disruption to the spinal neural tissue, leading to loss of motor control and sensory function of the body. Oxidative stress is considered a hallmark in SCI followed by a series of events, including inflammation and cellular apoptosis. Melatonin was originally discovered as a hormone produced by the pineal gland. The subcellular localization of melatonin has been identified in mitochondria, exhibiting specific onsite protection to excess mitochondrial reactive oxygen species and working as an antioxidant in diseases. The recent discovery regarding the molecular basis of ligand selectivity for melatonin receptors and the constant efforts on finding synthetic melatonin alternatives have drawn researchers’ attention back to melatonin. This review outlines the application of melatonin in SCI, including 1) the relationship between the melatonin rhythm and SCI in clinic; 2) the neuroprotective role of melatonin in experimental traumatic and ischemia/reperfusion SCI, i.e., exhibiting anti-oxidative, anti-inflammatory, and anti-apoptosis effects, facilitating the integrity of the blood–spinal cord barrier, ameliorating edema, preventing neural death, reducing scar formation, and promoting axon regeneration and neuroplasticity; 3) protecting gut microbiota and peripheral organs; 4) synergizing with drugs, rehabilitation training, stem cell therapy, and biomedical material engineering; and 5) the potential side effects. This comprehensive review provides new insights on melatonin as a natural antioxidant therapy in facilitating rehabilitation in SCI.

Spinal cord injury (SCI) starts with a mechanical and/or bio-chemical insult, followed by a secondary phase, leading progressively to severe collapse of the nerve tissue. Compared to the peripheral nervous system, injured spinal cord is characterized by weak axonal regeneration, which leaves most patients impaired or paralyzed throughout lifetime. Therefore, confining, alleviating, or reducing the expansion of secondary injuries and promoting functional connections between rostral and caudal regions of lesion are the main goals of SCI therapy. Interleukin 10 (IL-10), as a pivotal anti-inflammatory and immunomodulatory cytokine, exerts a wide spectrum of positive effects in the treatment of SCI. The mechanisms underlying therapeutic effects mainly include anti-oxidative stress, limiting excessive inflammation, anti-apoptosis, antinociceptive effects, etc. Furthermore, IL-10 displays synergistic effects when combined with cell transplantation or neurotrophic factor, enhancing treatment outcomes. This review lists pleiotropic mechanisms underlying IL-10-mediated neuroprotection after SCI, which may offer fresh perspectives for clinical translation.

Abstract Switch/sucrose-nonfermenting (SWI/SNF) complexes play a key role in chromatin remodeling. Recent studies have found that SMARCC2, as the core subunit of the fundamental module of the complex, plays a key role in its early assembly. In this study, we found a unique function of SMARCC2 in inhibiting the progression of glioblastoma by targeting the DKK1 signaling axis. Low expression of SMARCC2 is found in malignant glioblastoma (GBM) compared with low-grade gliomas. SMARCC2 knockout promoted the proliferation of glioblastoma cells, while its overexpression showed the opposite effect. Mechanistically, SMARCC2 negatively regulates transcription by dynamically regulating the chromatin structure and closing the promoter region of the target gene DKK1, which can be bound by the transcription factor EGR1. DKK1 knockdown significantly reduced the proliferation of glioblastoma cell lines by inhibiting the PI3K–AKT pathway. We also studied the functions of the SWIRM and SANT domains of SMARCC2 and found that the SWIRM domain plays a more important role in the complete chromatin remodeling function of SMARCC2. In addition, in vivo studies confirmed that overexpression of SMARCC2 could significantly inhibit the size of intracranial gliomas in situ in nude mice. Overall, this study shows that SMARCC2, as a tumor suppressor, inhibits the proliferation of glioblastoma by targeting the transcription of the oncogene DKK1 through chromatin remodeling, indicating that SMARCC2 is a potentially attractive therapeutic target in glioblastoma.

Abstract The prediction of antidepressant response is critical for psychiatrists to select the initial antidepressant drug for patients with major depressive disorders (MDD). The implicated brain networks supporting emotion regulation (ER) are critical in the pathophysiology of MDD and the prediction of antidepressant response. Therefore, the primary aim of the current study was to identify the neuroimaging biomarkers for the prediction of remission in patients with MDD based on the resting-state functional connectivity (rsFC) of the ER networks. A total of 81 unmedicated adult MDD patients were investigated and they underwent resting-state functional magnetic resonance imagining (fMRI) scans. The patients were treated with escitalopram for 12 weeks. The 17-item Hamilton depression rating scale was used for assessing remission. The 36 seed regions from predefined ER networks were selected and the rsFC matrix was caculated for each participant. The support vector machine algorithm was employed to construct prediction model, which separated the patients with remission from those with non-remission. And leave-one-out cross-validation and the area under the curve (AUC) of the receiver operating characteristic were used for evaluating the performance of the model. The accuracy of the prediction model was 82.08% (sensitivity = 71.43%, specificity = 89.74%, AUC = 0.86). The rsFC between the left medial superior frontal gyrus and the right inferior frontal gyrus as well as the precuneus were the features with the highest discrimination ability in predicting remission from escitalopram among the MDD patients. Results from our study demonstrated that rsFC of the ER brain networks are potential predictors for the response of antidepressant drugs. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

The mortality and morbidity rates of ovarian cancer (OC) are high, but the underlying mechanisms of OC have not been characterized. In this study, we determined the role of Rho GTPase Activating Protein 30 (ARHGAP30) in OC progression. We measured ARHGAP30 abundance in OC tissue samples and cells using immunohistochemistry (IHC) and RT-qPCR. EdU, transwell, and annexin V/PI apoptosis assays were used to evaluate proliferation, invasiveness, and apoptosis of OC cells, respectively. The results showed that ARHGAP30 was overexpressed in OC tissue samples and cells. Inhibition of ARHGAP30 suppressed growth and metastasis of OC cells, and enhanced apoptosis. Knockdown of ARHGAP30 in OC cells significantly inhibited the PI3K/AKT/mTOR pathway. Treatment with the PI3K/AKT/mTOR pathway inhibitor buparlisib simulated the effects of ARHGAP30 knockdown on growth, invasiveness, and apoptosis of OC cells. Following buparlisib treatment, the expression levels of p-PI3K, p-AKT, and p-mTOR were significantly decreased. Furthermore, buparlisib inhibited the effects of ARHGAP30 upregulation on OC cell growth and invasiveness. In conclusion, ARHGAP30 regulated the PI3K/AKT/mTOR pathway to promote progression of OC.

AimAppraise the clinical features and influencing factors of the hospitalization times and length of stay in bipolar disorder (BD) patients.MethodsThis is a multicenter, observational, cohort study of patients diagnosed of type I or type II bipolar disorder. Five hundred twenty outpatients in seven hospitals from six cities in China were recruited from February 2013 to June 2014 and followed up using a continuous sampling pattern. The research included a retrospective period of 12 months and the prospective period of 9 months. The demographic and clinical features of the patients were collected. The influencing factors that could affect the length of stay (number of days spent in the hospital in the prospective period) were analyzed by poisson's regression and the hospitalization times (times of hospitalization in the prospective and retrospective period) was analyzed by general linear model. The selected variables included gender, age, years of education, occupational status, residence status, family history of mental disease, comorbid substance abuse, comorbid anxiety disorder, times of suicide (total suicide times that occurred in the retrospective and prospective period), polarity of the first mood episode, and BD type(I/II).ResultsPoisson's regression analysis showed that suicide times [Incidence Rate Ratio (IRR) = 1.20, p < 0.001], use of antipsychotic (IRR = 0.62, p = 0.011), and use of antidepressant (IRR = 0.56, p < 0.001) were correlated to more hospitalization times. Linear regression analysis showed that BD type II (β = 0.28, p = 0.005) and unemployment (β = 0.16, p = 0.039) which might mean longer duration of depression and poor function were correlated to longer length of stay. However, patients who experienced more suicide times (β = −0.21, p = 0.007) tended to have a shorter length of stay.ConclusionOverall, better management of the depressive episode and functional rehabilitation may help to reduce the length of stay. BD patients with more hospitalization times were characterized by higher risk of suicide and complex polypharmacy. Patients at high risk of suicide tended to have inadequate therapy and poor compliance, which should be assessed and treated adequately during hospitalization.Clinical trial registrationwww.ClinicalTrials.gov, Identifier: NCT01770704.

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

Abstract Background Inflammatory bowel disease (IBD) is a non‐specific chronic inflammatory disease of the intestine. In addition to genetic susceptibility, environmental factors and dysregulated host immunity, the gut microbiota is implicated in the pathogenesis of Crohn's disease (CD) or ulcerative colitis (UC), the two primary types of IBD. The P2X4 receptor has been demonstrated to have a crucial role in preventing infection, inflammation, and organ damage. However, it remains unclear whether the P2X4 receptor affects IBD and the underlying mechanisms. Methods Colitis was induced in mice administrated with dextran sodium sulphate (DSS). 16S rDNA sequencing was used to analyze the gut microbiota in knockout and wild‐type mice. Clinical and histopathological parameters were monitored throughout the disease progression. Results Gene Expression Omnibus analysis showed the downregulation of P2RX4 (P2rx4) expression in colonic tissues from patients or mice with IBD. However, its expression at the protein levels was upregulated on day 4 or 6 and then downregulated on day 7 in C57BL/6 mice treated with DSS. Gene ablation of P2rx4 aggravated DSS‐induced colitis accompanying gut microbiota dysbiosis in mice. Moreover, P2X4 receptor‐positive modulator ivermectin alleviated colitis and corrected dysregulated microbiota in wild‐type C57BL/6 mice. Further antibiotic‐treated gut microbiota depletion, cohousing experiment, and fecal microbiota transplantation proved that gut microbiota dysbiosis was associated with the aggravation of colitis in the mouse model initiated by P2rx4. Conclusions Our findings elaborate on an unrevealed etiopathophysiological mechanism by which microbiota dysbiosis induced by the P2X4 receptor influences the development of colitis, indicating that the P2X4 receptor represents a promising target for treating patients with CD and UC.

Relation extraction tasks aim to predict the type of relationship between two entities from a given text. However, many existing methods fail to fully utilize the semantic information and the probability distribution of the output of pre-trained language models, and existing data augmentation approaches for natural language processing (NLP) may introduce errors. To address this issue, we propose a method that introduces prompt information and Top-K prediction sets and utilizes ChatGPT for data augmentation to improve relational classification model performance. First, we add prompt information before each sample and encode the modified samples by pre-training the language model RoBERTa and using these feature vectors to obtain the Top-K prediction set. We add a multi-attention mechanism to link the Top-K prediction set with the prompt information. We then reduce the possibility of introducing noise by bootstrapping ChatGPT so that it can better perform the data augmentation task and reduce subsequent unnecessary operations. Finally, we investigate the predefined relationship categories in the SemEval 2010 Task 8 dataset and the prediction results of the model and propose an entity location prediction task designed to assist the model in accurately determining the relative locations between entities. Experimental results indicate that our model achieves high results on the SemEval 2010 Task 8 dataset.

Aflatoxins are liver carcinogens and are common contaminants in unpackaged peanut (UPP) oil. However, the health risks associated with consuming aflatoxins in UPP oil remain unclear. In this study, aflatoxin contamination in 143 UPP oil samples from Guangdong Province were assessed via liquid chromatography–tandem mass spectrometry (LC-MS). We also recruited 168 human subjects, who consumed this oil, to measure their liver functions and lipid metabolism status. Aflatoxin B1 (AFB1) was detected in 79.72% of the UPP oil samples, with levels ranging from 0.02 to 174.13 μg/kg. The average daily human intake of AFB1 from UPP oil was 3.14 ng/kg·bw/day; therefore, the incidence of liver cancer, caused by intake of 1 ng/kg·bw/day AFB1, was estimated to be 5.32 cases out of every 100,000 persons per year. Meanwhile, Hepatitis B virus (HBV) infection and AFB1 exposure exerted a synergistic effect to cause liver dysfunction. In addition, the triglycerides (TG) abnormal rate was statistically significant when using AFB1 to estimate daily intake (EDI) quartile spacing grouping (p = 0.011). In conclusion, high aflatoxin exposure may exacerbate the harmful effects of HBV infection on liver function. Contamination of UPP oil with aflatoxins in Guangdong urgently requires more attention, and public health management of the consumer population is urgently required.

Al1.5(CoxFe6-x)6Cr high-entropy alloy (HEA) soft magnetic powder cores (SMPCs) were designed and prepared via gas atomization and insulation, and the effects of the Co/Fe ratio on their phase formation, microstructure evolution, and soft magnetic properties were systematically investigated. An increase in Co content promoted the precipitation of the B2 phase from the body-centered cubic (BCC) matrix, and the content of the B2 phase was greater than that of the BCC phase at x = 4, thus replacing the BCC phase as the matrix, which contributed to the optimization of the soft magnetic properties of the AlCoFeCr HEA. The SMPC composed of the BCC single phase exhibits the lowest eddy current loss (Pe) of 55.1 mW/cm3 (@100 kHz, 50 mT), excellent direct current (DC) bias performance of 80.6 % at 100 Oe, and high stable effective permeability (μe) of 49.9 up to 1 MHz. The association of magnetic domains, grain size, and particle size with Hc variation was analyzed, providing guidelines for subsequent studies to obtain HEAs with good soft magnetic properties. By comprehensively and systematically comparing the microstructural differences of diverse matrix-phase HEAs and SMPCs, the specific application of this HEA system under high-temperature conditions can be expanded.

Abstract Exogenous antioxidant materials mimicking endogenous antioxidant systems are commonly used for the treatment of oxidative stress-induced injuries. Thus, artificial enzymes have emerged as promising candidates for balancing and treating the dysregulation of redox homeostasis in vivo. Herein, a one-pot hydrothermal strategy for the facile preparation of MoSe2-polyvinylpyrrolidone (PVP) nanoparticles (NPs) is reported. The synthesized NPs were biodegradable due to their exposure to oxygen and exhibited high stability. Moreover, they effectively mimicked various naturally occurring enzymes (including catalase, superoxide dismutase, peroxidase, and glutathione peroxidase) and scavenged free radicals, such as 3-ethylbenzothiazoline-6-sulfonic acid, ·OH, ·O2−, and 1,1-diphenyl-2-picrylhydrazyl radical. Further apoptosis detection studies revealed that MoSe2-PVP NPs significantly increased the cell survival probability in H2O2 in a concentration-dependent manner. The cytoprotective effect of MoSe2-PVP NPs was explored for an animal model of acute pancreatitis, which confirmed its remarkable therapeutic efficacy. Owing to the biodegradable and biocompatible nature of MoSe2-PVP NPs, the findings of this work can stimulate the development of other artificial nanoenzymes for antioxidant therapies. Graphical Abstract

Shelter identification is the fundamental issue for the shelter-transporting automated guided vehicle to detect and transport shelter effectively. Actively identifying shelter faces the challenge of high accuracy but slow speed using a complex model, and fast speed but low accuracy using a simple model. However, all kinds of target detection algorithms available has difficulty in achieving both high detection accuracy and speed. In this paper, the model YOLOv5n6* is developed based on the modified YOLOv5 model by selecting different model structures, introducing an attention mechanism, and improving loss function and non-maximum suppression. Then, the experiments for shelter recognition were carried out using the model YOLOv5n6*. The experimental results show that the box_loss is reduced by 1.2%, the mAP_0.5:0.95 is improved by 2%, and the detection accuracy is improved by 0.87% for the improved model YOLOv5n6* compared with the YOLOv5n6. However, the YOLOv5n6* size is only 7.2M, and the detection time is increased by 0.2ms. So it is proved that the modified model YOLOv5n6* not only has a significant improvement in the shelter detection ability but also has strong robustness, which meets both the requirements of the recognition accuracy and the detection speed.

The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function’s challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.

With the rapid development of the automotive industry, the application of sensors is of great importance in maintaining the reliability of electric vehicles and ensuring the safe operation of electric vehicles. Faced with the increasing data of thermal management system condition monitoring, sensor detection is widely used in the monitoring of electric vehicle thermal management system. In recent years, a large number of related studies and contributions to the literature have been published. Although a number of reviews have summarized this, these reviews lack an overview of the issues and methods raised in these studies. This paper reviews recent sensor applications for electric vehicle thermal management systems. Currently, battery internal sensors, battery external sensors and related multi-sensor fusion, traditional motor sensors, positionless motor sensors, and component-level sensors of air conditioning systems are the main application sensors in the field of thermal management systems. This article introduces the basic principles of each type of sensor, reviews the relevant applications of various thermal management modules, and summarizes the usage characteristics of each type of sensor. The main problems faced by the existing research on the application of thermal management system-based sensors, such as the detection accuracy of traditional sensors and the detection stability of advanced sensors, are summarized, and the solutions proposed by the existing research are also summarized. Finally, some future research directions, trends, and hotspots are outlined. It is hoped that this review can help readers to understand the problems and existing solutions for thermal-management-system-based sensor applications, and to conduct related research more effectively.

Implementing international strategy is essential to enhance corporate innovation and achieve sustainable development. Existing research has theoretically illustrated the concept and importance of carrying out both exploratory and exploitative internationalization, yet our understanding of the antecedents and consequences of both activities remains unclear. Based on the data of Chinese international manufacturing firms, this paper examines the differential effects of business, government and knowledge network capital on exploratory and exploitative internationalization, and probes into how organizational, strategic, and environmental factors moderate the innovation effectiveness of exploratory and exploitative internationalization. Results indicate that business network capital has the same positive impact on exploratory and exploitative internationalization; government network capital influences the implementation of exploratory internationalization, whereas knowledge network capital influences the execution of exploitative internationalization. Furthermore, our findings reveal that, to maximize innovation performance, pursuing exploratory internationalization is more beneficial for firms with older age, firms entering into countries with low host–home country similarity, and operating in a more dynamic international environment. By contrast, pursuing exploitative internationalization is more effective for younger firms, firms entering into countries with high host–home country similarity, and in more stable international environment. Theoretical and managerial implications are discussed.

Background The neuroimaging mechanism of major depressive episodes with mixed features (MMF) is not clear.Aims This study aimed to investigate the functional connectivity of the default mode network (DMN) subsystems among patients with MMF and patients with major depressive disorder without mixed features (MDDnoMF).Methods This study recruited 47 patients with MDDnoMF and 27 patients with MMF from Beijing Anding Hospital, Capital Medical University, between April 2021 and June 2022. Forty-five healthy controls (HCs) were recruited. All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments. Intranetwork and internetwork functional connectivity were computed in the DMN core subsystem, dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDDnoMF, MMF and HC groups.Results The functional connectivity within the DMN core (F=6.32, pFDR=0.008) and MTL subsystems (F=4.45, pFDR=0.021) showed significant differences among the MDDnoMF, MMF and HC groups. Compared with the HC group, the patients with MDDnoMF and MMF had increased functional connectivity within the DMN MTL subsystem, and the patients with MMF also showed increased functional connectivity within the DMN core subsystem. Meanwhile, compared with the MDDnoMF, the patients with MMF had increased functional connectivity within the DMN core subsystem (mean difference (MDDnoMF−MMF)=−0.08, SE=0.04, p=0.048). However, no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.Conclusions Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF, findings potentially beneficial to deepen our understanding of MMF’s neural basis.

ObjectiveThe role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction.MethodsRenal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR.ResultsThe prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on.ConclusionsTogether, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.

Introduction: Many acutely ill children in low- and middle-income settings have a high risk of mortality both during and after hospitalisation despite guideline-based care. Understanding the biological mechanisms underpinning mortality may suggest optimal pathways to target for interventions to further reduce mortality. The Childhood Acute Illness and Nutrition (CHAIN) Network (www.chainnnetwork.org) Nested Case-Cohort Study (CNCC) aims to investigate biological mechanisms leading to inpatient and post-discharge mortality through an integrated multi-omic approach. Methods and analysis; The CNCC comprises a subset of participants from the CHAIN cohort (1278/3101 hospitalised participants, including 350 children who died and 658 survivors, and 270/1140 well community children of similar age and household location) from nine sites in six countries across sub-Saharan Africa and South Asia. Systemic proteome, metabolome, lipidome, lipopolysaccharides, haemoglobin variants, toxins, pathogens, intestinal microbiome and biomarkers of enteropathy will be determined. Computational systems biology analysis will include machine learning and multivariate predictive modelling with stacked generalization approaches accounting for the different characteristics of each biological modality. This systems approach is anticipated to yield mechanistic insights, show interactions and behaviours of the components of biological entities, and help develop interventions to reduce mortality among acutely ill children. Ethics and dissemination. The CHAIN Network cohort and CNCC was approved by institutional review boards of all partner sites. Results will be published in open access, peer reviewed scientific journals and presented to academic and policy stakeholders. Data will be made publicly available, including uploading to recognised omics databases. Trial registration NCT03208725.

Water bodies in urban green spaces are important parts of urban landscapes, and their planar shapes are an important factor governing the creation of waterfront landscapes. To improve the aesthetics and functionality of water bodies, this paper takes Nanjing as an example for analysis to investigate whether it is possible to scientifically quantify the planar shape of urban green space water bodies. First, water bodies meeting the conditions within the municipal area were selected as the study objects for classification. Second, in view of the lack of theoretical and innovative problems in previous studies, the use of fractal theory was proposed to improve the scientificity. Finally, remote sensing data images were used to extract water body planes, and the fractal dimensions were calculated and quantitatively evaluated by coupling the box dimension method with fractal theory. The results show that the fractal dimension could be used as a quantitative parameter to determine the planar morphology of water bodies in urban green spaces, and the fractal dimension value is positively correlated with the complexity of the water body, which can be used for both quantitative assessment of the landscape aesthetics of existing water bodies in urban green spaces and theoretical support for the future design of water planar morphology.