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Abstract Background Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization. Method We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization. Results The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making. Conclusion This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.

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.

The present study examined the effect of two dietary regimens with elevated salt concentrations (4% and 8% salt) on hemorheological functions of SD rats, and explored the underlying mechanisms mainly through microbiome-metabolome analysis. An 8% HSD substantially altered the hemorheological parameters, and compromised intestinal barrier integrity and reduced the short-chain fatty acid levels. The microbiome-metabolome analysis revealed that 49 genus-specific microorganisms and 156 metabolites showed a consistent trend after exposure to both 4% and 8% HSDs. Pathway analysis identified significant alterations in key metabolites within bile acid and arachidonic acid metabolism pathways. A two-sample Mendelian randomization (MR) analysis verified the link between high dietary salt intake and hemorheology. It also suggested that some key microbes and metabolites (such as Ruminococcaceae_UCG-005, Lachnospiraceae_NK4A136, Ruminiclostridium_6, and Ruminococcaceae_UCG-010, TXB-2, 11,12-diHETrE, glycochenodeoxycholate) may involve in abnormalities in blood rheology caused by high salt intake. Collectively, our findings underscored the adverse effects of high dietary salt on hemorheological functions and provide new insight into the underlying mechanism based on microbiome-metabolome analysis.

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.

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.

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.

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods.

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.

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.

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.

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.

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.

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.

Peracetic acid (PAA) is emerging as a versatile agent for generating long-lived and selectively oxidative organic radicals (R-O•). Currently, the conventional transition metal-based activation strategies still suffer from metal ion leaching, undesirable by-products formation, and uncontrolled reactive species production. To address these challenges, we present a method employing BiOI with a unique electron structure as a PAA activator, thereby predominantly generating CH3C(O)O• radicals. The specificity of CH3C(O) O• generation ensured the superior performance of the BiOI/PAA system across a wide pH range (2.0 to 11.0), even in the presence of complex interfering substances such as humic acids, chloride ions, bicarbonate ions, and real-world water matrices. Unlike conventional catalytic oxidative methods, the BiOI/PAA system degrades sulfonamides without producing any toxic by-products. Our findings demonstrate the advantages of CH3C(O)O• in water decontamination and pave the way for the development of eco-friendly water decontaminations based on organic peroxides. [ABSTRACT FROM AUTHOR]

Rhizobacteria are capable of inducing defense responses via the expression of pathogenesis-related proteins (PR-proteins) such as chitinases, and many studies have validated the functions of plant chitinases in defense responses. Soybean (Glycine max) is an economically important crop worldwide, but the functional validation of soybean chitinase in defense responses remains limited. In this study, genome-wide characterization of soybean chitinases was conducted, and the defense contribution of three chitinases (GmChi01, GmChi02, or GmChi16) was validated in Arabidopsis transgenic lines against the soil-borne pathogen Fusarium oxysporum. Compared to the Arabidopsis Col-0 and empty vector controls, the transgenic lines with GmChi02 or GmChi16 exhibited fewer chlorosis symptoms and wilting. While GmChi02 and GmChi16 enhanced defense to F. oxysporum, GmChi02 was the only one significantly induced by Burkholderia ambifaria. The observation indicated that plant chitinases may be induced by different rhizobacteria for defense responses. The survey of 37 soybean chitinase gene expressions in response to six rhizobacteria observed diverse inducibility, where only 10 genes were significantly upregulated by at least one rhizobacterium and 9 genes did not respond to any of the rhizobacteria. Motif analysis on soybean promoters further identified not only consensus but also rhizobacterium-specific transcription factor-binding sites for the inducible chitinase genes. Collectively, these results confirmed the involvement of GmChi02 and GmChi16 in defense enhancement and highlighted the diverse inducibility of 37 soybean chitinases encountering F. oxysporum and six rhizobacteria. [ABSTRACT FROM AUTHOR]

Abstract Biological organisms can perform flexible and controllable multimodal motion under external stimuli owing to the hierarchical assembly of anisotropic structures across multiple length scales. However, artificial soft actuators exhibit the limited response speed, deformation programmability and movement capability especially in harsh environments because of insufficient anisotropic hierarchy and precision in structural design. Here, we report a programmed assembly directed confinement polymerization method for the fabrication of environmentally tolerant and fast responsive hydrogels with lamellar assembly-confined cellular structure interpenetrated with highly aligned nanopillars by the directional freezing-assisted polymerization in the predesigned anisotropic laminar scaffold. The obtained hydrogel exhibits ultrafast responsiveness and anisotropic deformation exposed to temperature/light/solvent stimulation, maintaining highly consistent responsive deformation capability in all-polarity solvents over 100 days of soaking. Moreover, the hydrogels implement photoactive programmable multi-gait locomotion whose amplitude and directionality are precisely regulated by the intrinsic structure, including controlled crawling and rotation in water and non-polar solvents, and 3D self-propulsion floating and swimming in polar solvents. Thus, this hydrogel with hierarchically ordered structure and dexterous locomotion may be suitable for flexible intelligent actuators serving in harsh solvent environments.

Recently, image inpainting has been proposed as a solution for restoring the polluted image in the field of computer vision. Further, face inpainting is a subfield of image inpainting, which refers to a set of image editing algorithms re-conducting the missing regions in face smoothly. Actually, face inpainting is more challenging than general image inpainting because it needs more face structure information. Although a number of past studies were proposed for face inpainting by using face segmentation, face edge and face topology, there is some important information ignored, such as geometric and symmetric properties. Based on such concepts, in this paper, we propose a two-stage face inpainting method called CGAN (Conditional Generative Adversarial Network) which integrates face landmarks and Generative Adversarial Network (called GAN). In the first stage, the face landmark is predicted as the condition, providing GAN with important information of geometry and symmetry. The main idea in this stage is to dynamically adjust the loss by the proposed view degree. Accordingly, the masked face image and the corresponding face landmark are used as conditions input to the GAN in the second stage. Finally, the missing-regions are inpainted by the proposed CGAN. To reveal the effectiveness of proposed method, a number of evaluations were conducted on real datasets. The experimental results show that, the proposed method predicts a better face landmark by information of geometric structures and symmetric outlooks, and thereupon the proposed CGAN reconstructs the missing regions superior to the compared methods.

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

Objective: To compare the efficacy of several local antibiotic regimens in preventing surgical site infection (SSI) in clean surgical wounds., Data Sources: The authors searched CNKI (China National Knowledge Infrastructure), the VIP (VIP information resource integration service platform), Wanfang Data knowledge service platform (WANFANG), SinoMed, Cochrane Library, EMBASE, and PubMed., Study Selection: A total of 20 randomized controlled trials published between January 1, 2000 and April 1, 2021 were included in this meta-analysis., Data Extraction: Authors extracted the name of the first author, publication date, country, type of surgery, follow-up time, mean age of participants, sample size of each group, interventions, outcome indicators, and study type from each article., Data Synthesis: The overall effectiveness of eight local managements in reducing the incidence of the SSI effect were compared through the SUCRA (surface under the cumulative ranking curve) probabilities. The results of a network meta-analysis demonstrated that gentamicin ointment (odds ratio [OR], 0.16; 95% CI, 0.04-0.60), mupirocin ointment (OR, 0.44; 95% CI, 0.21-0.94), and gentamicin soaking of the graft (OR, 0.63; 95% CI, 0.44-0.91) significantly reduced the incidence of SSI compared with control. Further, vancomycin soaking of the graft (86.7%) ranked first, followed by gentamicin ointment (81.1%), gentamicin irrigation (79.9%), mupirocin ointment (56.8%), triple antibiotic ointment (47.8%), gentamicin soaking of the graft (42.3%), and vancomycin powder (22.1%); ampicillin powder (17.8%) was the least effective drug., Conclusions: The findings indicate that local antibiotics combined with conventional antibiotics in the wound before wound closure are effective in reducing the incidence of SSI in clean surgical wounds. Vancomycin inoculation of the graft exhibited the best effect., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)