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With the rapid development of 5G/6G communications, new energy vehicles, and lithium battery industries, the demand for lithium compounds (especially Li2CO3) has dramatically increased in recent years. Many countries have regarded lithium as a strategic mineral resource. Lithium is mainly found in liquid mineral resources around the world, and the extraction of lithium from the mother liquor of lithium precipitates has thus garnered significant attention. The main methods for recovering lithium from solutions include membrane separation, solvent extraction, electrochemistry, and adsorption. Among them, the adsorption method is one of the most promising. The key to successful adsorption technology is the construction of high-performance adsorbents with high adsorption capacity, high ionic selectivity, and high structural stability. The manganese-ion sieve named as LMO, is a promising adsorbent that has been widely studied because of its good chemical stability, excellent adsorption properties, and outstanding ion selectivity for lithium extraction. However, its inherent dissolution loss greatly restricts its practical application. To reduce the dissolution loss of manganese-ion sieves, several strategies, such as adjusting the synthesis process, composition optimization, ion doping, and surface modification, were adopted. This study introduces Co3+ doping to mitigate manganese loss in LMO, resulting in the preparation of cobalt-doped manganese-based ion sieves (LCMO). LCMOs prepared at various calcination temperatures and times were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy, and X-ray photoelectron spectroscopy (XPS). The XRD characterization results indicate that Co doping has no effect on the spinel structure of LMO. The SEM results confirmed the successful doping and uniform Co distribution in LCMO-5%. The XPS results show that a Co doping molar fraction of 5% can reduce the content of Mn3+ from 9.67% in the undoped precursor to 3.63%, which may be because the partial substitution of Mn3+ by Co3+ reduces the proportion of Mn3+. The lithium adsorption capacity increased from 39.299 to 41.708 mg·g−1, and the manganese dissolution significantly decreased from 1.288% to 0.84%. The performance improvement of the LCMO greatly promotes the practical application of manganese-based ion sieves. The prepared 5% molar fraction of the Co-doped ion sieve (LCMO-5%) exhibited excellent cycling performance, and the adsorption efficiency of Li+ remained above 81% after five cycles. In the simulated lithium precipitation mother liquor, the separation coefficients of Li/Na and Li/K were 74.655 and 64.547, respectively, indicating that LCMO-5% effectively adsorbed Li+ from solutions containing high concentrations of Na+ and K+. Therefore, the LCMO-5% ion sieve exhibits outstanding application prospects for Li+ extraction from liquid lithium resources.

Abstract This study investigates the microstructural characteristics and mechanical properties of a laser welded AZ80 magnesium alloy. The welding process led to the formation of coarse-grained fusion zone (FZ), where a secondary phase formed continuous network. Mg17Al12 precipitation and coarsening of grain boundaries occurred in the heat affected zone. The welded joint exhibited excellent mechanical properties with a yield strength of 202 MPa and a joint efficiency of 92%. The microstructure analysis via EPMA and EBSD in conjunction with synchrotron X-ray diffraction analysis reveals that precipitates and increased dislocation density in the fusion zone are primary strengthening mechanisms for the laser welded AZ80 Mg alloy.

The natural 15N abundance of ammonia (δ15N-NH3) emitted from livestock waste within a specific area (δ15N-NH3-LW) can be used to estimate its contribution to atmospheric NH3 through an isotope mixing model. However, the limited availability of reports on δ15N-NH3-LW hinders the accuracy of regional NH3 source partitioning. In this study, we conducted research in the suburbs of Shenyang, China, and combined our results (n = 71) with data from published literature (n = 56) to develop a δ15N-NH3 spectrum emitted from cattle, pig, sheep, and poultry waste. We also explored the national and global spatial distribution of δ15N-NH3-LW based on the proportion of NH3 emissions from these four types of livestock waste. Our results showed that the δ15N-NH3 values ranged from −59.9‰ to −7.7‰, with a mean value of −27.3‰. This value was significantly lower than that from non-agricultural sources, such as coal combustion and motor vehicle exhaust, but overlapped with values from fertilizer N volatilization. Specifically, the δ15N-NH3 values emitted from cattle, pig, sheep, and poultry waste were −32.1 ± 15.9‰, −20.7 ± 7‰, −24.3 ± 5.9‰, and −19.1 ± 4.9‰, respectively. Considering regional differences in livestock farming structures, δ15N-NH3-LW exhibited a gradual decreasing trend from Southeast to Northwest across China. For example, Fujian Province, located on the southeastern coast, exhibited the highest δ15N-NH3-LW value of −19.8‰, owing to its high proportion of poultry farming, while the Tibet Autonomous Region, in the western inland, displayed the lowest δ15N-NH3-LW value of −30.8‰, due to its high proportion of cattle farming. Globally, the δ15N-NH3-LW value was highest in Southeast Asia (−23.4‰) and lowest in East Africa (−30.4‰). Overall, our study revealed significant spatial variation in δ15N-NH3-LW at both national and global scales. To more accurately assess the regional contributions of NH3 emissions from livestock waste, it is essential to use region-specific δ15N-NH3-LW values for future NH3 source partitioning.

There is limited knowledge regarding the impact of nitrification inhibitors (NIs) and straw application on fertilizer N retention and in-season release. We conducted a trial to study the transformation of 15N-labeled urea in soils during the growing season of maize. To facilitate multiple destructive samplings throughout the season, we utilized a larger plot (25 m2) and a lower abundance 15N-fertilizer (1.193%) than usual. Soil extractable mineral N, mineral fixed ammonium, and organic N (ON) recovered 20 ± 21% (mean ± standard deviation), 6 ± 5%, and 25 ± 6% of the applied fertilizer N across three sampling stages of the growing season. On average, the bioavailability of fertilizer N in extractable mineral form was four times higher than that of mineral fixed ammonium. In contrast, fertilizer-derived ON represented a relatively stable N pool, maintaining high content throughout the growing period and becoming the major form (82%–93%) in the pool of total soil 15N at the physiological maturity stage of maize. Moreover, the co-application of nitrapyrin (a type of NI) significantly promoted fertilizer N storage in the ON form while the effect of straw was not significant. In conclusion, the NI-induced promotion of fertilizer-derived ON likely plays a critical role in storing fertilizer N for subsequent cultivations, rather than providing N nutrients for crop uptake during the current season.

Effects of the channel-land configuration (CLC) on the temperature-driven (TD) flow have been seldom examined. In this work, a 2-D, non-isothermal, two-phase model incorporating an agglomerate sub-model is established, considering the compression effect and gas diffusion layer (GDL) anisotropy. Results show that the TD flow flux of the through-plane (TP) direction, JTD,TP, is much higher than that of the in-plane direction (JTD,IP) by an order of magnitude. The TD flow is mainly one-dimensional along the TP direction. There are significant differences in the TD flow between the channel and land region. Besides, JTD,TP at the junction of the land region and channel region shows step changes due to the compression effect. After the current loading, the output voltage experiences an undershoot and then undergoes a slow rising and gradual falling process until remaining stable. It is found that the membrane water directly affects the dynamic behaviors of the output voltage. Increasing the inlet relative humidity helps to improve the dynamic response characteristic. JTD,TP also reveals an increasing trend with time after current step loading, contributing to the transition from membrane water to water vapor. This work aims to provide valuable references for better CLC design and satisfactory water management.

Abstract Due to the limitations imposed by urban power grid outages for maintenance, on‐line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real‐time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long‐term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect‐type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation‐maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture‐affected and normal cable states. The proposed on‐line detection and evaluation methods can effectively identify high‐risk cables with latent defects.

Abstract Spinal cord injury (SCI) leads to fibrotic scar formation at the lesion site, yet the heterogeneity of fibrotic scar remains elusive. Here we show the heterogeneity in distribution, origin, and function of fibroblasts within fibrotic scars after SCI in mice and female monkeys. Utilizing lineage tracing and single-cell RNA sequencing (scRNA-seq), we found that perivascular fibroblasts (PFs), and meningeal fibroblasts (MFs), rather than pericytes/vascular smooth cells (vSMCs), primarily contribute to fibrotic scar in both transection and crush SCI. Crabp2 + /Emb+ fibroblasts (CE-F) derived from meninges primarily localize in the central region of fibrotic scars, demonstrating enhanced cholesterol synthesis and secretion of type I collagen and fibronectin. In contrast, perivascular/pial Lama1 + /Lama2+ fibroblasts (LA-F) are predominantly found at the periphery of the lesion, expressing laminin and type IV collagen and functionally involved in angiogenesis and lipid transport. These findings may provide a comprehensive understanding for remodeling heterogeneous fibrotic scars after SCI.

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects an individual's behavior, speech, and social interaction. Early and accurate diagnosis of ASD is pivotal for successful intervention. The limited availability of large datasets for neuroimaging investigations, however, poses a significant challenge to the timely and precise identification of ASD. To address this problem, we propose a breakthrough approach, GARL, for ASD diagnosis using neuroimaging data. GARL innovatively integrates the power of GANs and Deep Q-Learning to augment limited datasets and enhance diagnostic precision. We utilized the Autistic Brain Imaging Data Exchange (ABIDE) I and II datasets and employed a GAN to expand these datasets, creating a more robust and diversified dataset for analysis. This approach not only captures the underlying sample distribution within ABIDE I and II but also employs deep reinforcement learning for continuous self-improvement, significantly enhancing the capability of the model to generalize and adapt. Our experimental results confirmed that GAN-based data augmentation effectively improved the performance of all prediction models on both datasets, with the combination of InfoGAN and DQN's GARL yielding the most notable improvement.

Reducing fertilizer-N rate, applying a nitrification inhibitor (NI), and incorporating straw are widely recommended to improve N use efficiency of crops and decrease N losses. A field 15N tracer study was conducted to compare their effectiveness on fertilizer-N fates during the maize growing season in Northeast China. The following six treatments were used: (1) no N fertilization (control); (2) 200 kg urea-N ha−1 (100%N); (3) 200 kg urea-N ha−1 and straw (100%N + S); (4) 160 kg urea-N ha−1 (80%N); (5) 160 kg urea-N ha−1 and NI (Nitrapyrin in this study) (80%N + NI); and (6) 160 kg urea-N ha−1, NI, and straw (80%N + NI + S). The results showed that the five N fertilization treatments yielded 16–25% more grain and 39–60% more crop N uptake than the control, but the differences among the five treatments were not statistically significant. Compared with the 100%N, 20% fertilizer-N reduction (80%N) decreased the 15N concentration in topsoil and plant pools but increased the proportion of plant 15N recovery at harvesting (NUE15N, 60% vs. 50%). Compared with the 80%N, NI co-application (80%N + NI) delayed soil nitrification and increased soil 15N retention at harvesting (52% vs. 36%), thereby decreasing NUE15N significantly. Straw incorporation decreased fertilizer-N retention in soil compared with NI co-application because it promoted NUE15N significantly. In conclusion, the results demonstrate that NI and straw additions are efficient strategies for stabilizing fertilizer-N in soils and potentially minimizing N loss; however, their effects on NUE15N vary and the related mechanism must be further clarified in long-term trials.

Gardenia jasminoides Ellis is widely used as healthy food and herbal medicine for its anti-inflammatory, analgesic, antihypertensive, and antiviral functions. The drying behavior and physicochemical quality of Gardenia jasminoides Ellis were studied to evaluate its adaptability under four drying techniques: hot air drying (HAD), medium-and short-wave infrared drying (MSWID), pulsed vacuum drying (PVD), and radio frequency-HAD (RF-HAD). Compared with HAD and MSWID, PVD and RF-HAD can form beneficial microporous channels for moisture migration inside Gardenia jasminoides Ellis, thus shortening drying time by 32.56–42.51 % and increasing geniposide content by 3.31–13.77 %, while better preserving the brightness and redness. In addition, Pearson correlation analysis confirmed the RF-HAD dried samples showed the best antioxidant activity with the highest content of active ingredients (chlorogenic acid, geniposide), and there was a significant positive correlation between sample color and yellow pigment content. After comprehensive comparison, RF-HAD is proposed to be the most suitable method for Gardenia jasminoides Ellis drying. This research could provide scientific basis and technical support for promoting the high quality development of industrial processing of Gardenia jasminoides Ellis.

Purpose: To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features. Materials and Methods: This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023. Models based on CT radiomics and clinical features were constructed for classification of AAH/AIS and MIA, MIA and IAC, as well as lepidic-predominant adenocarcinoma (LPA) and acinar-predominant adenocarcinoma (APA). Receiver operating characteristic (ROC) curve was used to evaluate the model performance. Finally, the nomograms based on the optimal models were established. Results: The nomogram based on the combined model (AAH/AIS versus MIA) consisting of lobulation, the GGN-vessel relationship, diameter, CT value, consolidation tumor ratio (CTR) and rad-score performed the best (AUC=0.841), while age, CT value, CTR and rad-score were the significant features for distinguishing MIA from IAC, the nomogram based on these features performed the best (AUC=0.878). There were no significant differences in clinical features between LPA and APA, while the radiomics model based on rad-score showed good performance for distinguishing LPA from APA (AUC=0.926). Conclusions: The nomograms based on radiomics and clinical features could predict the invasiveness of SSNs accurately. Moreover, radiomics models showed good performance in distinguishing LPA from APA.

In this study, nitrogen-sulfur codoped carbon quantum dots (N-S/CQDs) with various functions and properties were synthesized through a one-step method utilizing citric acid and cysteine as reaction substrates. The fluorescence of N-S/CQDs can be specifically quenched by permanganate ion (MnO4−), and the quenched fluorescence can be recovered by the presence of reduced glutathione (GSH). A fluorescence sensing system based on N-S/CQDs@MnO4− was developed and successfully applied for the determination of GSH in pharmaceutical preparations. Additionally, N-S/CQDs demonstrated broad-spectrum antibacterial activity, with minimum inhibitory concentrations of 32 μg/ml against Staphylococcus aureus (gram-positive bacterium) and 64 μg/ml against Escherichia coli (gram-negative bacterium). N-S/CQDs also proved effective for cell imaging, exhibiting excellent biocompatibility. These findings underscore the multifunctional characteristics and promising application potential of N-S/CQDs. Furthermore, this study provides a solid foundation for the development of multifunctional carbon quantum dots and the expansion of their applications in various fields.

Manufactured sand concrete (MSC) is a specific type of concrete successfully employed in many projects and structures made of reinforced concrete, and improving concrete structures. The important characteristic of mechanical sand is that the fine aggregate contains stone powder composed of particles which could pass 75 urn BS sieve. The influence of different stone powder contents on the properties of hardened concrete was studied through experiments. In addition, the experimental relations for estimating the mechanical parameters of MSC compared with CC is presented in this paper. Specimens made of MSC were prepared, on which the axial compression test, cube compression test, splitting test, flexural test and elastic modulus test were conducted. The experimental relations were obtained between the compressive strength, tensile strength and elastic modulus for MSC, and their validity were then investigated. Stone powder can fill the pores in the MSC, improve the overall grading of mechanism sand concrete, improve its working performance, and help to improve the mechanical strength of MSC. The optimum value of stone powder content is accepted as 7.5–10%. The conclusions obtained can provide a basis for the research and promotion application of mechanism sand concrete. The results indicated that the obtained relations could be used to estimate the mechanical properties of MSC with accuracy.

Newly emerged acoustic barriers provide effective solutions for noise reductions of varied kinds in which acoustic barriers made of Fano resonance-based space-coiling metamaterial are reported to have promising application prospects for their broadband noise reduction ability and good ventilation performance. However, current Fano resonance-based acoustic ventilation barriers are hard to practically apply since most of them are difficult to manufacture or install. To this end, this research proposes a novel acoustic ventilation barrier based on block-shaped space-coiling metamaterial, which is not only as functional as other acoustic ventilation barriers but also easy to manufacture and install. To obtain a more in-depth understanding of the noise reduction effect, the influence of the design parameters on transmission loss is numerically studied. Additionally, we conduct both numerical and experimental studies on the transmission loss and the ventilation performance of a full-scale meta-unit. Furthermore, through the corresponding optimization process, the proposed acoustic ventilation barrier can have transmission loss consistently above 10 dB across the frequency range of 495~1063 Hz. Lastly, a composite ventilation acoustic barrier obtained by stacking two layers of different proposed metamaterials is presented, which achieves multiband noise reduction performance across the ultra-broad frequency range of 479~1758 Hz.

Parkinson’s disease (PD) is a chronic neurodegenerative disease with unclear pathogenesis that involves neuroinflammation and intestinal microbial dysbiosis. Intercellular adhesion molecule-1 (ICAM-1), an inflammatory marker, participates in neuroinflammation during dopaminergic neuronal damage. However, the explicit mechanisms of action of ICAM-1 in PD have not been elucidated. We established a subacute PD mouse model by the intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and observed motor symptoms and gastrointestinal dysfunction in mice. Immunofluorescence was used to examine the survival of dopaminergic neurons, expression of microglial and astrocyte markers, and intestinal tight junction-associated proteins. Then, we use 16 S rRNA sequencing to identify alterations in the microbiota. Our findings revealed that ICAM-1-specific antibody (Ab) treatment relieved behavioural defects, gastrointestinal dysfunction, and dopaminergic neuronal death in MPTP-induced PD mice. Further mechanistic investigations indicated that ICAM-1Ab might suppress neuroinflammation by inhibiting the activation of astrocytes and microglia in the substantia nigra and relieving colon barrier impairment and intestinal inflammation. Furthermore, 16 S rRNA sequencing revealed that the relative abundances of bacterial Firmicutes, Clostridia, and Lachnospiraceae were elevated in the PD mice. However, ICAM-1Ab treatment ameliorated the MPTP-induced disorders in the intestinal microbiota. Collectively, we concluded that the suppressing ICAM-1 might lead to the a significant decrease of inflammation and restore the gut microbial community, thus ameliorating the damage of DA neurons.

Echocardiography is a key tool for the diagnosis of cardiac diseases, and accurate left ventricular (LV) segmentation in echocardiographic videos is crucial for the assessment of cardiac function. However, since semantic segmentation of video needs to take into account the temporal correlation between frames, this makes the task very challenging. This article introduces an innovative method that incorporates a modified mixed attention mechanism into the SegFormer architecture, enabling it to effectively grasp the temporal correlation present in video data. The proposed method processes each time series by encoding the image input into the encoder to obtain the current time feature map. This map, along with the historical time feature map, is then fed into a time-sensitive mixed attention mechanism type of convolution block attention module (TCBAM). Its output can serve as the historical time feature map for the subsequent sequence, and a combination of the current time feature map and historical time feature map for the current sequence. The processed feature map is then input into the Multilayer Perceptron (MLP) and subsequent networks to generate the final segmented image. Through extensive experiments conducted on two different datasets: Hamad Medical Corporation, Tampere University, and Qatar University (HMC-QU), Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) and Sunnybrook Cardiac Data (SCD), achieving a Dice coefficient of 97.92 % on the SCD dataset and an F1 score of 0.9263 on the CAMUS dataset, outperforming all other models. This research provides a promising solution to the temporal modeling challenge in video semantic segmentation tasks using transformer-based models and points out a promising direction for future research in this field.

Abstract Due to rapid homogenization in habitat types as a result of urbanization, some urban birds adapt their nesting strategies to changes in local habitat characteristics. Bird nesting decisions might have been mainly linked to resource constraints and ensuring reproductive success. In this study, we examined patterns of nesting behavior by spotted doves (Spilopelia chinensis) in a rapidly urbanizing area of Nanchang, China using ArcGIS 10.8, satellite tracking, camera traps, and field survey. To explore the mechanisms underlying nesting behavior in urban habitats, we assessed the correlations between nest reuse and reproductive success, and between nest reuse and nest predation. From December 2018 to December 2021, a total of 302 breeding nests were surveyed. The results revealed that the nest reuse rate was 38.08% (n = 115). Nests closer to trunk, with lower nest position and higher large‐scale urbanization score tended to have higher reuse rate. In addition, nests with the higher the nest height and percent of canopy cover, and the lower small‐scale urbanization score were more likely to reproduce successfully, and the reused nests also reproduce more successfully. The reproductive success associated with nest reuse was significantly higher than that associated with new nests (χ2 = 8.461, p = .004). High degree of urbanization promoted nest reuse of spotted doves (large‐scale urbanization score, z = 2.094, p = .036), which apparently enhanced their reproductive success (nest reuse, z = 2.737, p = .006). In conclusion, a nest structure with good permeability is the material basis for the nest reuse in spotted dove, while the relatively low risk of predation in urban habitat and the scarcity of nest site resources due to urbanization increase the tendency of birds to reuse old nests, which is associated with their reproductive success and evolutionary fitness.

The zinc smelting industry is a major source of atmospheric mercury emissions in China. It is crucial to develop a low-cost technology to reduce mercury pollution emissions for the green development of zinc smelting. Thus, based on the concept of “using waste to treat waste”, this study uses high-sulfur slag and secondary zinc oxide, which are produced in the zinc smelting process as raw materials, to prepare zinc sulfide adsorbent by high-temperature sulfurization in an inert atmosphere. The zinc sulfide adsorbents prepared under different roasting temperatures and time were characterized and analyzed by various analysis methods, including X-ray diffraction (XRD), Raman spectroscopy (Raman), scanning electron microscopy (SEM) and specific surface area analysis (BET). The effects of various reaction times and flue gas components on the mercury removal performance were studied. The experimental results reveal that the zinc sulfide adsorbent can be successfully prepared when the roasting temperature and holding time are more than 250 ℃ and 60 min, respectively. Under this condition, many mesoporous holes are formed. The prepared ZnS–250 ℃–90 min adsorbent has the best performance for Hg0 adsorption. Moreover, gaseous elemental mercury (Hg0) has an average adsorption efficiency above 98% at flue gas temperatures below 150 ℃, and it exhibits high resistance to O2, SO2, and H2O. When the Hg0 adsorption efficiency decreased to 50%, the Hg0 adsorption capacity of ZnS–250 ℃–90 min was 3.04 mg·g−1 in the simulated zinc smelting flue gas atmosphere, which is superior to conventional metal sulfides for Hg0 adsorption. Furthermore, the Hg0 adsorption on the surface of ZnS adsorbent is a chemical adsorption process, and the unsaturated short-chain sulfur on the surface of ZnS adsorbent is the critical active site, which can react with Hg0 to form stable HgS, thus realizing the high-efficiency purification of Hg0 from smelting flue gas. Furthermore, the spent ZnS adsorbent can be regenerated using thermal desorption and polysulfide impregnation. An excellent cycle performance for Hg0 capture is realized, and the Hg0 adsorption efficiency remains above 99% after five cycles. Simultaneously, the desorbed mercury can be recycled as elemental mercury by condensation during the regeneration process of ZnS adsorbent. Thus, this study not only provides a low-cost method for mercury pollution control and resource recovery from the zinc smelting industry but also supports the cyclic utilization of typical waste produced in the zinc smelting industry, promoting the green production of the zinc smelting industry.

The bond-slip behavior between cold-formed thin-walled steel (CTS) and foamed concrete (FC) is a critical issue in the mechanical performance of FC-filled CTS composite wall structures. Thus, this study provides experimental and theoretical research on the bond-slip behavior between CTS and FC. A total of eleven specimens were tested using push-out configurations, considering the number of web holes, foamed concrete (FC) strength, anchorage length, and CTS section splice form. A constitutive model for bond-slip was proposed, and the regression formulas for accurately predicting the characteristic bond strength between CTS and foamed concrete were established. A finite element model was developed to investigate the bond-slip mechanism at the interface between CTS and FC. The bond-slip constitutive model accurately fits the experimental and finite element results. The results indicate that the ultimate bond strength of the specimens increases with the number of web holes; when the number of web holes reaches two, the ultimate bond strength is 155.4% of that of the non-perforated specimens. As the concrete strength increases from 3.43 MPa to 11.26 MPa, the ultimate bond strength of specimens with two web holes improves by 23.1%, while non-perforated specimens have a 54.7% enhancement. When the anchorage length is extended from 200 mm to 400 mm, the ultimate bond strength decreases by 29.3%. Additionally, when steel sections are joined in a double-span I form, the bond strength increases by 91.6% and 95.8% compared to the single-span form and the double-span box form, respectively.

Abstract In various engineering projects such as mineral extraction, hydropower resource utilization, railway construction, and geological hazard mitigation, rock engineering is often encountered. Furthermore, dynamic loads and moisture content exert notable influence on the energy transformation processes within rocks. Yet, the specific interplay of dynamic loading and water's impact on the energy conversion mechanism within the sandstone remains unexplored. To address this gap, this study conducted impact loading experiments on sandstone, elucidating the rock’s mechanical response under these conditions and unraveling the underlying energy conversion mechanisms. It was observed that the strength of sandstone exhibits a direct correlation with impact velocity. Moreover, employing energy calculation principles, we established a connection between moisture content and the sandstone’s internal energy conversion properties. The study also delved into the microscopic fracture mechanisms within the sandstone, ultimately concluding that both water content and dynamic loading have a significant impact on these microscopic fracture mechanisms.

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm. The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm. Compared to other active learning methods resorting to experimental designs, the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification, which is applicable for most structural reliability estimation problems. Moreover, the validity, efficiency, and accuracy of the proposed method are demonstrated numerically. In addition, the optimal value of K that maximizes the computational efficiency is studied. Finally, the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements, which further validates its practicability.

Objective Colorectal cancer (CRC) is a common cancer worldwide. Although there are several treatments for cancer, the therapeutic effect on CRC remains unsatisfactory, and it is imperative to identify new therapeutic targets.Design Prefoldin (PFDN) is mainly used in the cytoskeleton assembly during the folding of actin and tubulin monomers. However, whether PFDN subunits are involved in regulating the development of CRC remains to be elucidated. In this study, molecular biology, cell culture, transcriptome sequencing and other experimental techniques, combined with bioinformatics, were used to verify the regulatory effects of PFDN6 on CRC.Results PFDN6 expression is elevated in patients with CRC and is closely associated with the development of CRC. Knockdown of PFDN6 reduced the tumour cell number, promoted apoptosis, and inhibited the migration and invasion of CRC cells in HCT-116 and RKO cell lines. Mechanistically, differentially expressed genes and related signalling pathways in RKO cells after PFDN6 knockdown were analysed by transcriptome sequencing.Conclusion PFDN6 was found to regulate the generation and development of CRC by targeting ZNF575. These results open new avenues for therapeutic interventions for patients with CRC.

Transplanting human neural progenitor cells is a promising method of replenishing the lost neurons after spinal cord injury (SCI), but differentiating neural progenitor cells into the diverse types of mature functional spinal cord neurons in vivo is challenging. In this study, engineered human embryonic spinal cord-like tissues with dorsal and ventral neuronal characters (DV-SC) were generated by inducing human neural progenitor cells (hscNPCs) to differentiate into various types of dorsal and ventral neuronal cells on collagen scaffold in vitro. Transplantation of DV-SC into complete SCI models in rats and monkeys showed better therapeutic effects than undifferentiated hscNPCs, including pronounced cell survival and maturation. DV-SC formed a targeted connection with the host's ascending and descending axons, partially restored interrupted neural circuits, and improved motor evoked potentials and the hindlimb function of animals with SCI. This suggests that the transplantation of pre-differentiated hscNPCs with spinal cord dorsal and ventral neuronal characteristics could be a promising strategy for SCI repair.

Abstract Spinal cord injury (SCI) leads to severe sensory and motor dysfunction below the lesion. However, the cellular dynamic responses and heterogeneity across different regions below the lesion remain to be elusive. Here, we used single-cell transcriptomics to investigate the region-related cellular responses in female rhesus monkeys with complete thoracic SCI from acute to chronic phases. We found that distal lumbar tissue cells were severely impacted, leading to degenerative microenvironments characterized by disease-associated microglia and oligodendrocytes activation alongside increased inhibitory interneurons proportion following SCI. By implanting scaffold into the injury sites, we could improve the injury microenvironment through glial cells and fibroblast regulation while remodeling spared lumbar tissues via reduced inhibitory neurons proportion and improved phagocytosis and myelination. Our findings offer crucial pathological insights into the spared distal tissues and proximal tissues after SCI, emphasizing the importance of scaffold-based treatment approaches targeting heterogeneous microenvironments.

Background: Stroke is a major cause of death globally and the leading cause in China. Excessive fluoride exposure has been linked to cardiovascular conditions related to stroke risk factors such as hypertension, atherosclerosis, dyslipidemia, and cardiomyopathy. However, evidence supporting the association between fluoride exposure and stroke risk is limited. Methods: We constructed an ecological study in Changwu Town, Heilongjiang Province, China, a typical endemic fluorosis area caused by excessive fluoride exposure from drinking water. We collected demographic data, stroke prevalence, and mortality information from 2017 to 2021. Fluoride exposure data were obtained from the national monitoring project on endemic fluorosis. Water fluoride concentrations were measured using the standardized methods. Trend changes in stroke rates were assessed using annual percentage change (APC). Differences in stroke rates among fluoride exposure groups were analyzed using chi-square tests. Results: From 2017 to 2021, the all-ages and age-standardized stroke prevalence rates of permanent residents in Changwu Town increased year by year, while the all-ages and age-standardized mortality rates did not change significantly. The prevalence rates of stroke were significantly higher in endemic fluorosis areas compared to non-endemic areas (p < 0.001). Stratifying the population into tertile groups based on the water fluoride cumulative exposure index (WFCEI) revealed statistically significant differences in stroke prevalence rates (p < 0.001), showing a dose–response relationship with the WFCEI. However, the all-ages and age-standardized mortality rates of stroke were not found to be related to fluoride exposure. Conclusions: Long-term excessive fluoride exposure from drinking water may increase the risk of stroke prevalence, indicating fluoride overexposure as a potential risk factor for stroke.

The shell explosion by hot air drying is a critical step in the processing of Camellia oleifera fruit (COF), which directly affects the degree of the shell explosion, and the separation effect of Camellia oleifera seed and Camellia oleifera shell after the shell explosion of COF. To reveal the characteristics of the COF shell explosion, a hot air drying device was designed based on mass conservation and drying principles. The physical characteristics of COF and the evolution of drying parameters were thoroughly analyzed with a combination method of drying analysis and experimental. Moreover, under the conditions of air temperature 50–70 °C, relative humidity 20–50%, and air velocity 1.3–1.9 m/s, the internal relationship between COF shell explosion formation through hot air drying and the hot air drying medium was systematically investigated by response surface methodology, and a prediction model for the shell explosion rate of COF by hot air drying was constructed using statistical methods. Results demonstrated that decreasing the relative humidity and increasing the temperature and air velocity of the drying medium could reduce the dehydration time of COF. The moisture content of Camellia oleifera shell was found to be 177.45% d.b. (dry basis) at the initial cracking stage of COF. Furthermore, at temperatures ranging from 50 to 70 °C Deff values of COF were estimated to be within the range of 0.915 × 10−9 to 1.782 × 10−9 m2/s. Similarly, at relative humidity levels of 20 to 50%, Deff values ranged from 1.226 × 10−9 to 1.501 × 10−9 m2/s. At an air velocity of 1.3 to 1.9 m/s, Deff values ranged from 0.956 × 10−9 to 1.501 × 10−9 m2/s. The measured values of the shell explosion rate were in close agreement with that calculated using the fitted model, with a correlation coefficient of 0.997 and a root mean square error of 0.9743. This study will provide a theoretical basis for optimizing the shell explosion process and improving shell explosion rate of COF by hot air drying.

This paper used machine learning to model the prediction of carbonation depth and the analysis of feature parameters for recycled aggregate concrete (RAC). Specifically, a database containing 579 sets of RAC carbonation test data was developed. Twelve parameters representing material characteristics and environmental conditions were input, along with one output parameter. On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. Different types of analysis, including statistical measures, shapley additive explanations (SHAP) sensitivity analysis, SHAP parametric study, and comparison study, were used to examine the performance of the developed models and the effects of the input parameters on predictions. The results show that the extreme gradient boosting (XGB) model exhibited the highest accuracy with an R² of 0.99 and a mean absolute percentage error (MAPE) of 6.632 on the training set, and an R² of 0.953 and a MAPE of 13.243 on the test set. The variable importance analysis shows that the carbonation depth for RAC was determined by both internal and external factors. The top five factors impacting RAC carbonation depth are exposure time, water-to-binder ratio, CO2 concentration, coarse aggregate density, and cement content. RAC carbonation depth correlates positively with the former three factors. In contrast, it exhibits a negative correlation with the remaining two variables. In addition, a graphical user interface (GUI) for RAC carbonation depth prediction was designed.

Neural stem progenitor cell (NSPC) transplantation has been regarded as a promising therapeutic method for spinal cord injury (SCI) repair. However, different NSPCs may have different therapeutic effects, and it is therefore important to identify the optimal NSPC type. In our study, we compared the transcriptomes of human fetal brain-derived NSPCs (BNSPCs), spinal cord-derived NSPCs (SCNSPCs) and H9 embryonic stem-cell derived NSPCs (H9–NSPCs) in vitro and subsequently we transplanted each NSPC type on a collagen scaffold into a T8-9 complete SCI rat model in vivo. In vitro data showed that SCNSPCs had more highly expressed genes involved in nerve-related functions than the other two cell types. In vivo, compared with BNSPCs and H9–NSPCs, SCNSPCs exhibited the best therapeutic effects; in fact, SCNSPCs facilitated electrophysiological and hindlimb functional recovery. This study demonstrates that SCNSPCs may be an appropriate candidate cell type for SCI repair, which is of great clinical significance.

Abstract Background Sepsis-associated AKI has been shown to be related to sepsis mortality. Macrophage activation and endothelial cell damage are involved in the progression of sepsis-associated AKI, but the specific mechanisms are still unclear. Methods In vitro experiments, exosomes extracted from lipopolysaccharide (LPS) -stimulated macrophages were co-incubated with rat glomerular endothelial cells (RGECs) and then detected the injury markers of RGECs. Acid sphingomyelinase (ASM) inhibitor amitriptyline were used to investigate the role of ASM. In vivo experiment, exosomes produced by LPS-stimulated macrophages were injected into mice through tail vein to further explore the role of macrophage-derived exosomes. Moreover, ASM knockout mice were used to verify the mechanism. Result In vitro, the secretion of macrophage exosomes increased upon the stimulation with LPS. Notably, macrophage-derived exosomes can cause glomerular endothelial cell dysfunction. In vivo, macrophage infiltration and exosome secretion in glomeruli of the LPS-induced AKI group increased. The exosomes produced by LPS-stimulated macrophages were injected into mice, which also led to the injury of renal endothelial cells. In addition, in the LPS-induced AKI mouse model, compared with wild-type mice, the secretion of exosomes in glomeruli of ASM gene knockout mice and the injury of endothelial cells were reduced. Conclusion Our study shows that ASM regulates the secretion of macrophage exosomes, leading to endothelial cell injury, which may be a therapeutic target in sepsis-associated AKI.

Biomedical Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) and can help mine knowledge from massive clinical and diagnostic records. However, the biomedical NER task often undergoes a low-resource training setting due to the high cost of human annotation, limiting the capability of traditional NER models. In this study, we propose a two-stage learning pipeline to tackle the oncological NER task in Chinese language, which is a typical task lacking training resources. In the first stage, two base models pre-trained by Word to Vector (Word2Vec) and Bidirectional Embeddings Representations from Transformer (BERT) are fine tuned to obtain domains-specific word embeddings that serve as the input for the downstream NER task. In the second stage, we feed the word embeddings into a neural network that consists of a Bidirectional Long and Short Time Memory Recurrent Neural Network (BiLSTM) and Linear-chain Conditional Random Field (CRF) for end task training. Meanwhile, we utilize a substitution-based generative model for data augmentation (DA), aiming to enhance the quantity and diversity of the training data. Experiments show that our proposed learning pipeline demonstrates superior performance compared to other model alternatives under a low-resource setting. Specifically, results show that the proposed fine-tuning strategy, when conducted on an augmented domain resource, can effectively incorporate rich domain knowledge into the final NER model, presenting a great potential in boosting a model’s predictive power with limited training data.

Recently, convolutional neural networks (CNNs) have been widely applied in the seismic facies classification. However, even state-of-the-art CNN architectures often encounter classification confusion distinguishing seismic facies at their boundaries. In addition, the annotation is a highly time-consuming task, especially when dealing with 3-D seismic data volumes. While traditional semisupervised methods reduce dependence on annotation, they are susceptible to interference from unreliable pseudolabels. To address these challenges, we propose a semisupervised seismic facies classification method called CONSS, which effectively mitigates classification confusion through contrastive learning. Our proposed method requires only 1% of labeled data, significantly reducing the demand for annotation. To minimize the influence of unreliable pseudolabels, we also introduce a confidence strategy to select positive and negative sample pairs from reliable regions for contrastive learning. Experimental results on the publicly available seismic datasets, the Netherlands F3 and SEAM AI challenge datasets, demonstrate that the proposed method outperforms classic semisupervised methods, including self-training and consistency regularization, achieving exceptional classification performance.

Cardiac magnetic resonance imaging analysis has been a useful tool in screening patients for heart disease. Early, timely and accurate diagnosis of diseases of the heart series is the key to effective treatment. MRI provides important material for the diagnosis of cardiac diseases. The rise of deep learning has transformed computer-aided diagnostic systems, especially in the field of medical imaging. Existing work on cardiac structure segmentation models based on MRI imaging mainly relies on convolutional neural networks (CNNs), which lack model diversity and limit the prediction performance. This paper introduces Visual Transformer with Feature Recombination and Feature Distillation(ViT-FRD), a novel learning pipeline that combines a visual transformer (ViT) and a CNN through knowledge refinement. The training procedure allows the student model, i.e., ViT, to learn from the teacher model, i.e., CNN, by optimizing distillation losses. Meanwhile, ViT-FRD provides two performance boosters to increase the efficacy and efficiency of training. The proposed method is validated on two cardiac MRI image datasets. The findings demonstrate that ViT-FRD achieves SOTA and outperforms the widely used baseline model.

ABSTRACTThe occurrence and development of Parkinson’s disease (PD) have been demonstrated to be related to gut dysbiosis, however, the impact of fecal microbiota transplantation (FMT) on microbiota engraftment in PD patients is uncertain. We performed a randomized, placebo-controlled trial at the Department of Neurology, Army Medical University Southwest Hospital in China (ChiCTR1900021405) from February 2019 to December 2019. Fifty-six participants with mild to moderate PD (Hoehn-Yahr stage 1–3) were randomly assigned to the FMT and placebo group, 27 patients in the FMT group and 27 in the placebo group completed the whole trial. During the follow-up, no severe adverse effect was observed, and patients with FMT treatment showed significant improvement in PD-related autonomic symptoms compared with the placebo group at the end of this trial (MDS-UPDRS total score, group×time effect, B = -6.56 [−12.98, −0.13], P

Parkinson's disease (PD) is the second most common neurodegenerative disease, and communication between the gut and brain (the gut-brain axis) has been found to be essential in behavior and cognitive function. However, the exact mechanisms underlying microbiota dysbiosis in PD progression have not yet been elucidated. Our study aimed to investigate the correlation between gut microbiota disturbances and feces metabolic disorders in PD. We used 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) to induce PD models and observed mice's motor symptoms, dopaminergic (DA) neuron death, and gastrointestinal dysfunction. To identify alterations in microbiota and metabolome, feces were collected from mice and analyzed using 16 S ribosomal RNA sequencing feces metabolomics. Pearson analysis was utilized to investigate correlations between the abundances of gut microbiota components and the levels of gut microbiota metabolites, displaying their interaction networks. Our findings revealed a significant increase in Desulfobacterota in the PD mouse model and 151 differentially expressed fecal metabolites between PD and vehicle mice. Moreover, Pearson correlation analysis suggested that the protective factor N-acetyl-L-leucine (NALL) may be associated with neuroinflammation in the striatum and substantia nigra, which also had a negative relationship with the concentration of Desulfobacterota. Additionally, we found that oral administration of NALL alleviated MPTP-induced Motor Impairments and DA neuronal deficits. All in all, we concluded that the decrease of NALL might lead to a significant increase of Desulfobacterota in the MPTP model mouse and subsequently result in the damage of DA neurons via the gut-brain aix pathway.

Abstract The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).

Endophytic bacterial microbiomes of plants contribute to the physiological health of the host and its adaptive evolution and stress tolerance. Wild rice possesses enriched endophytic bacteria diversity, which is a potential resource for sustainable agriculture. Oryza officinalis is a unique perennial wild rice species in China with rich genetic resources. However, endophytic bacterial communities of this species and their plant growth-promoting (PGP) traits remain largely unknown. In this study, endophytic bacteria in the root, stem, and leaf tissues of O. officinalis were characterized using 16S rRNA gene Illumina sequencing. Culturable bacterial endophytes were also isolated from O. officinalis tissues and characterized for their PGP traits. The microbiome analysis showed a more complex structure and powerful function of the endophytic bacterial community in roots compared with those in other tissue compartments. Each compartment had its specific endophytic bacterial biomarkers, including Desulfomonile and Ruminiclostridium for roots; Lactobacillus, Acinetobacter, Cutibacterium and Dechloromonas for stems; and Stenotrophomonas, Chryseobacterium, Achromobacter and Methylobacterium for leaves. A total of 96 endophytic bacterial strains with PGP traits of phosphate solubilization, potassium release, nitrogen fixation, 1-aminocyclopropane-1-carboxylate (ACC) deaminase secretion, and siderophore or indole-3-acetic acid (IAA) production were isolated from O. officinalis. Among them, 11 strains identified as Enterobacter mori, E. ludwigii, E. cloacae, Bacillus amyloliquefaciens, B. siamensis, Pseudomonas rhodesiae and Kosakonia oryzae were selected for inoculation of perennial rice based on their IAA production traits. These strains showed promising PGP effects on perennial rice seedlings. They promoted plants to form a strong root system, stimulate biomass accumulation, and increase chlorophyll content and nitrogen uptake, which could fulfil the ecologically sustainable cultivation model of perennial rice. These results provide insights into the bacterial endosphere of O. officinalis and its application potential in perennial rice. There is the prospect of mining beneficial endophytic bacteria from wild rice species, which could rewild the microbiome of cultivated rice varieties and promote their growth.

Abstract The efficient CO2 electroreduction into high‐value products largely relies on the CO2 adsorption/activation or electron‐transfer of electrocatalysts, thus site‐specific functionalization methods that enable boosted related interactions of electrocatalysts are much desired. Here, an oriented coordination strategy is reported to introduce N‐rich auxiliary (i.e., hexamethylenetetramine, HMTA) into metalloporphyrin metal organic frameworks (MOFs) to synthesize a series of site‐specific functionalized electrocatalysts (HMTA@MOF‐545‐M, M = Fe, Co, and Ni) and they are successfully applied in light‐assisted CO2 electroreduction. Noteworthy, thus‐obtained HMTA@MOF‐545‐Co presents approximately two times enhanced CO2 adsorption‐enthalpy and electrochemical active surface‐area with largely decreased impedance‐value after modification, resulting in almost twice higher CO2 electroreduction performance than its unmodified counterpart. Besides, its CO2 electroreduction performance can be further improved under light‐illumination and displays superior FECO (≈100%), high CO generation rate (≈5.11 mol m−2 h−1 at −1.1 V) and energy efficiency (≈70% at −0.7 V). Theoretical calculations verify that the oriented coordination of HMTA can increase the charge density of active sites, almost doubly enhance the CO2 adsorption energy, and largely reduce the energy barrier of rate determining step for the boosted performance improvement. This work might promote the development of modifiable porous crystalline electrocatalysts in high‐efficiency CO2 electroreduction.

Abstract Background Notch signaling is highly conserved and critically involved in cell differentiation, immunity, and survival. Activation of the Notch pathway modulates immune cell functions during the inflammatory response. However, it remains unknown whether and how the macrophage Notch1 may control the innate immune signaling TAK1, and RIPK3-mediated hepatocyte necroptosis in liver ischemia and reperfusion injury (IRI). This study investigated the molecular mechanisms of macrophage Notch1 in modulating TAK1-mediated innate immune responses and RIPK3 functions in liver IRI. Methods Myeloid-specific Notch1 knockout (Notch1M−KO) and floxed Notch1 (Notch1FL/FL) mice (n = 6/group) were subjected to 90 min partial liver warm ischemia followed by 6 h of reperfusion. In a parallel in vitro study, bone marrow-derived macrophages (BMMs) were isolated from these conditional knockout mice and transfected with CRISPR/Cas9-mediated β-catenin knockout (KO) vector followed by LPS (100 ng/ml) stimulation. Results IR stress-induced Notch1 activation evidenced by increased nuclear Notch intracellular domain (NICD) expression in liver macrophages. Myeloid Notch1 deficiency exacerbated IR-induced liver damage, with increased serum ALT levels, macrophage/neutrophil accumulation, and proinflammatory cytokines/chemokines production compared to the Notch1FL/FL controls. Unlike in the Notch1FL/FL controls, Notch1M−KO enhanced TRAF6, TAK1, NF-κB, RIPK3, and MLKL but reduced β-catenin activation in ischemic livers. However, adoptive transfer of lentivirus β-catenin-modified macrophages markedly improved liver function with reduced TRAF6, p-TAK1, RIPK3 and p-MLKL in IR-challenged livers. Moreover, disruption of RIPK3 in Notch1M−KO mice with an in vivo mannose-mediated RIPK3 siRNA delivery system diminished IR-triggered hepatocyte death. In vitro studies showed that macrophage NICD and β-catenin co-localized in the nucleus, whereby β-catenin interacted with NICD in response to LPS stimulation. Disruption of β-catenin with a CRISPR/Cas9-mediated β-catenin KO in Notch1FL/FL macrophage augmented TRAF6 activation leading to enhanced TAK1 function. While CRISPR/Cas9-mediated TRAF6 KO in Notch1M−KO macrophage inhibited RIPK3-mediated hepatocyte necroptosis after co-culture with primary hepatocytes. Conclusions Macrophage Notch1 controls TAK1-mediated innate immune responses and RIPK3-mediated hepatocyte necroptosis through activation of β-catenin. β-catenin is required for the macrophage Notch1-mediated immune regulation in liver IRI. Our findings demonstrate that the macrophage Notch1-β-catenin axis is a crucial regulatory mechanism in IR-triggered liver inflammation and provide novel therapeutic potential in organ IRI and transplant recipients. Video abstract

Drawing from the characteristics of centipedes, such as their low center of gravity, high stability in movement, adaptability to complex terrains, and ability to continue moving even after losing a limb, this paper designs a self-reconfigurable centipede-type rescue robot with relatively high stability while moving. The robot’s body can lift and traverse higher obstacles, and its multi-segmented structure enables self-disconnection and reconstruction for docking. Moreover, the proposed robot is adept at navigating diverse terrains and surmounting obstacles, equipped with a camera sensor facilitating life recognition, terrain surveying, scene understanding, and obstacle avoidance. Its capabilities prove advantageous for achieving challenging ground rescue missions. Motion stability tests, conducted across various terrains, showcase the robot’s ability to maintain a consistent movement path in rugged environments. Operating with a leg lift height of 0.02 m, the robot achieves a speed of 0.09 m per second. In simulated damaged conditions, the robot demonstrates the capacity to disconnect and reconnect its limbs swiftly, restoring movement capabilities within a single second. During environmental perception tasks, the robot processes and analyzes environmental data in real time at a rate of approximately 15 frames per second, with an 80% confidence level. With an F1 score exceeding 93% and an average precision rate surpassing 98%, the robot showcases its reliability and efficiency.

Idiopathic pulmonary fibrosis (IPF) is a serious and fatal pulmonary inflammatory disease with an increasing incidence worldwide. The drugs nintedanib and pirfenidone, are listed as conditionally recommended drugs in the “Evidence-Based Guidelines for the Diagnosis and Treatment of Idiopathic Pulmonary Fibrosis”. However, these two drugs have many adverse reactions in clinical application. Salvianolic acid B (Sal B), a water-soluble component of Salvia miltiorrhiza, could alleviate bleomycin-induced peroxidative stress damage, and prevent or delay the onset of IPF by regulating inflammatory factors and fibrotic cytokines during the disease's progression. However, Sal B is poorly absorbed orally, and patient compliance is poor when administered intravenously. Therefore, there is an urgent need to find a new non-injection route of drug delivery. In this study, Sal B was used as model drug and l-leucine (LL) as excipient to prepare Sal B dry powder inhaler (Sal B-DPI) by spray drying method. Modern preparation evaluation methods were used to assess the quality of Sal B-DPI. Sal B-DPI is promising for the treatment of IPF, according to studies on pulmonary irritation evaluation, in vivo and in vitro pharmacodynamics, metabolomics, pharmacokinetics, and lung tissue distribution.

This study investigated the role of N6-methyladenosine RNA methylation in liver regeneration following partial hepatectomy in mice. We created a liver-specific knockout mouse model by the deletion of Mettl3, a key component of the N6-methyladenosine methyltransferase complex, using the albumin-Cre system. Mettl3 liver-specific knockout mice and their wild-type littermates were subjected to 2/3 partial hepatectomy. Transcriptomic changes in liver tissue at 48 h after partial hepatectomy were detected by RNA-seq. Immunohistochemistry and immunofluorescence were used to determine protein expression levels of Ki67, hepatocyte nuclear factor 4 alpha, and cytokeratin 19. Terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling was also performed. Liver weight/body weight ratios after partial hepatectomy were significantly lower in Mettl3 liver-specific knockout mice than in wild-type mice at 48 h after 2/3 partial hepatectomy (3.1% ± 0.11% vs. 2.7% ± 0.03%). Compared with wild-type littermates, Mettl3 liver-specific knockout mice showed reduced bromodeoxyuridine staining and reduced Ki-67 expression at 48 h after 2/3 partial hepatectomy. RNA-seq analysis showed that Mettl3 liver-specific knockout delayed the cell cycle progression in murine liver by downregulating the expression levels of genes encoding cyclins D1, A2, B1, and B2. Loss of Mettl3-mediated N6-methyladenosine function led to attenuated liver regeneration by altering the mRNA decay of suppressor of cytokine signaling 6, thereby inhibiting the phosphorylation of signal transducer and activator of transcription 3 during early liver regeneration. These results demonstrated the importance of N6-methyladenosine mRNA modification in liver regeneration and suggest that Mettl3 targeting might facilitate liver regeneration.

Summary: Syringomyelia is a common clinical lesion associated with cerebrospinal fluid flow abnormalities. By a reversible model with chronic extradural compression to mimic human canalicular syringomyelia, we explored the spatiotemporal pathological alterations during syrinx development. The most dynamic alterations were observed in ependymal cells (EPCs), oligodendrocyte lineage, and microglia, as a response to neuroinflammation. Among different cell types, EPC subtypes experienced obvious dynamic alterations, which were accompanied by ultrastructural changes involving the ependymal cytoskeleton, cilia, and dynamic injury in parenchyma primarily around the central canal, corresponding to the single-cell transcripts. After effective decompression, the syrinx resolved with the recovery of pathological damage and overall neurological function, implying that for syringomyelia in the early stage, there was still endogenous repair potential coexisting with immune microenvironment imbalance. Ependymal remodeling and cilia restoration might be important for better resolution of syringomyelia and parenchymal injury recovery.

Pharmacological treatment of inflammatory bowel disease (IBD) is inefficient and difficult to discontinue appropriately, and enterobacterial interactions are expected to provide a new target for the treatment of IBD. We collected recent studies on the enterobacterial interactions among the host, enterobacteria, and their metabolite products and discuss potential therapeutic options. Intestinal flora interactions in IBD are affected in the reduced bacterial diversity, impact the immune system and are influenced by multiple factors such as host genetics and diet. Enterobacterial metabolites such as SCFAs, bile acids, and tryptophan also play important roles in enterobacterial interactions, especially in the progression of IBD. Therapeutically, a wide range of sources of probiotics and prebiotics exhibit potential therapeutic benefit in IBD through enterobacterial interactions, and some have gained wide recognition as adjuvant drugs. Different dietary patterns and foods, especially functional foods, are novel therapeutic modalities that distinguish pro-and prebiotics from traditional medications. Combined studies with food science may significantly improve the therapeutic experience of patients with IBD. In this review, we provide a brief overview of the role of enterobacteria and their metabolites in enterobacterial interactions, discuss the advantages and disadvantages of the potential therapeutic options derived from such metabolites, and postulate directions for further research.

Abstract Background Distant metastasis is a sign of poor prognosis for cancer patients. Extrahepatic liver cancer metastases commonly spread to the lung. Remodelling of the metastatic microenvironment is essential for tumour metastasis. Neutrophil‐associated metastatic microenvironment contributes to the early metastatic colonisation of cancer cells in the lung. Method The lung metastasis models were constructed via treated cancer cells by tail vein injection into mice. And samples of lung were harvested at the indicated time to analyze tumor growth and immune cells in the microenvironment. Tumors and lung metastasis specimens were obtained via surgical operations for research purposes. Neutrophils were obtained from peripheral blood of patients with liver cancer or healthy donors (HD). Results Hepatocellular carcinoma cells reduce the secretion of histidine‐rich glycoprotein (HRG), regulate the recruitment and activation of neutrophils in the metastatic microenvironment and promote the production of neutrophil extracellular traps (NETs), thereby promoting liver cancer lung metastasis. HRG binds to FCγR1 on the neutrophil membrane while inhibiting PI3K and NF‐κB activation, thereby reducing IL‐8 secretion to reduce neutrophil recruitment. Meanwhile, HRG inhibited IL8‐MAPK and NF‐κB pathway activation and ROS production, resulting in reduced NETs formation. Conclusions Our study reveals that liver cancer regulates neutrophil recruitment and NETs formation in the metastatic microenvironment by reducing HRG secretion, thereby promoting tumour lung metastasis. The results of this study will contribute to the development of possible strategies for treating metastases.

Liver cancer is an aggressive tumor originating in the liver with a dismal prognosis. Current evidence suggests that liver cancer is the fifth most prevalent cancer worldwide and the second most deadly type of malignancy. Tumor heterogeneity accounts for the differences in drug responses among patients, emphasizing the importance of precision medicine. Patient-derived models of cancer are widely used preclinical models to study precision medicine since they preserve tumor heterogeneity ex vivo in the study of many cancers. Patient-derived models preserving cell-cell and cell-matrix interactions better recapitulate in vivo conditions, including patient-derived xenografts (PDXs), induced pluripotent stem cells (iPSCs), precision-cut liver slices (PCLSs), patient-derived organoids (PDOs), and patient-derived tumor spheroids (PDTSs). In this review, we provide a comprehensive overview of the different modalities used to establish preclinical models for precision medicine in liver cancer.

Background: The ulcerative colitis (UC) Mayo endoscopy score is a useful tool for evaluating the severity of UC in patients in clinical practice. Objectives: We aimed to develop and validate a deep learning-based approach to automatically predict the Mayo endoscopic score using UC endoscopic images. Design: A multicenter, diagnostic retrospective study. Methods: We collected 15120 colonoscopy images of 768 UC patients from two hospitals in China and developed a deep model based on a vision transformer named the UC-former. The performance of the UC-former was compared with that of six endoscopists on the internal test set. Furthermore, multicenter validation from three hospitals was also carried out to evaluate UC-former’s generalization performance. Results: On the internal test set, the areas under the curve of Mayo 0, Mayo 1, Mayo 2, and Mayo 3 achieved by the UC-former were 0.998, 0.984, 0.973, and 0.990, respectively. The accuracy (ACC) achieved by the UC-former was 90.8%, which is higher than that achieved by the best senior endoscopist. For three multicenter external validations, the ACC was 82.4%, 85.0%, and 83.6%, respectively. Conclusions: The developed UC-former could achieve high ACC, fidelity, and stability to evaluate the severity of UC, which may provide potential application in clinical practice. Registration: This clinical trial was registered at the ClinicalTrials.gov (trial registration number: NCT05336773)

It is hard to use a single label to describe an image for the complexity of remote sensing scenes. Thus, it is a more general and practical choice to use multilabel image classification for high-resolution remote sensing (HRS) images. How to construct the relation between categories is a vital problem for multilabel classification. Some researchers use the recurrent neural network (RNN) or long short-term memory (LSTM) to exploit label relations over the last years. However, the RNN or LSTM could model such category dependence in a chain propagation manner. The performance of the RNN/LSTM might be questioned when a specific category is improperly inferred. To address this, we propose a novel HRS image multilabel classification network, transformer-driven semantic relation inference network. The network comprises two modules: semantic sensitive module (SSM) and semantic relation-building module (SRBM). The SSM locates the semantic attentional regions in the features extracted by a deep convolutional neural network and generates a discriminative content-aware category representation (CACR). The SRBM uses label relation inference from outputs of the SSM to predict final results. The characteristic of the proposed method is that it can extract semantic attentional regions relevant to the category and generate a discriminative CACR and natural and interpretable reasoning about label relations. Experiments were performed on the public UCM multilabel and MLRSNet datasets. Quantitative and qualitative analyses on state-of-the-art multilabel benchmarks proved that the proposed method could effectively locate semantic regions and build relationships between categories with better robustness.

Accurate instance segmentation is essential for the behavior and morphology analysis of neuronal cells. The main challenges of this segmentation task involve irregular and concave cell morphology, low contrast on cell boundaries, cell clustering and adhesion, and the background noise in the phase contrast microscopy (PCM) images. To address these challenges, we propose a learning pipeline with three performance boosters that have not been extensively explored in prior studies, including transferring knowledge from a model pre-trained on a larger but similar dataset, enhancing the contrast of cells in a PCM image, and a pointwise attentive path fusion module that allows the learning model to capture informative features from critical areas. Experiments have been conducted on the Sartorius Cell Instance Segmentation dataset with three neuronal cell lines. Results show that the final model, with three boosters enabled, brings an mAP gain of 10.3%. Compared to the top three places on the leaderboard, our method shows comparable performance without using any ensemble method, making our model the state-of-the-art solution among the single model-based methods.