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Listeria monocytogenes infection often leads to listeriosis in immunocompromised individuals, and screening for potential nutrients that improve immunity may be beneficial. Here, we found that pre-incubation with heptadecanoic acid (C17:0), an odd-chain saturated fatty acid, significantly inhibited L.monocytogenes replication in differentiated THP-1 and RAW264.7, as evidenced by comparison of listeriolysin expression and colony count. Mechanistically, the administration of C17:0 triggered the activation of the IFN and NF-κB signaling pathways. Furthermore, C17:0 upregulated ACOD1 expression and the generation of itaconate, an anti-bacterial metabolite in macrophages. Based on these results, our comparative analysis showed that sheep tail fat (STF), ruminant lipid rich in C17:0, had similar anti-L.monocytogenes effect. Collectively, our data highlighted the function of C17:0 in boosting the immune response, shedding light on the application of C17:0 and STF as potential candidates for strengthening anti-L.monocytogenes response.

In recent decades, ecological cyclic cultivation models have attracted increasing attention, primarily because the decomposition of crop residues and litter enhances soil organic matter content, thereby altering the soil microenvironment and regulating the diversity and functions of soil microbial communities. However, the effects of different coffee waste mulching on the diversity of soil microbial communities and their functions are still unclear. Therefore, this study set up four kinds of covering treatments: uncovered coffee waste (C), covered coffee litter (L), covered coffee pericarp (P), and both covered coffee litter and pericarp (PL). The results showed that compared to the control, coffee pericarp mulching significantly increased the soil available potassium (SAK) content by 18.45% and alkali hydrolyzed N (SAN) content by 17.29%. Furthermore, coffee pericarp mulching significantly increased bacterial richness and diversity by 7.75 and 2.79%, respectively, while litter mulching had little effect on bacterial abundance and diversity was smaller. The pericarp mulching significantly increased the abundance of Proteus by 22.35% and the abundance of Chlamydomonas by 80.04%, but significantly decreased the abundance of Cyanobacteria by 68.38%, while the coffee litter mulching significantly increased the abundance of Chlamydomonas by 48.28%, but significantly decreased the abundance of Cyanobacteria by 73.98%. The increase in soil SAK promoted bacterial Anoxygenic_photoautotrophy, Nitrogen_respiration, Nitrate_respiration, Nitrite_respiration, and Denitrification functions. The above results indicate that the increase in available soil potassium and alkali hydrolyzed N content under coffee pericarp cover is the main reason for promoting the diversity and richness of bacterial community and promoting the changes in bacterial community structure and function. The use of coffee pericarps in coffee plantations for ecological recycling helps to improve the diversity of the soil microbial community and maintain the relative stability of the microbial community structure and function, promoting soil health conservation and the sustainable development of related industries.

As intelligent systems continue to evolve, problems are becoming increasingly complex. The constant abundance of data puts a higher demand on the value of data utilization. Granular computing is a new computational paradigm for complex problem-solving. It takes structured thinking, structured problem-solving methods, and structured information processing patterns as its research objects and belongs to the scope of higher-level human cognitive mechanism research. The development and application of granular computing must be more standardized and unified. The granular computing standardization strategy is the most direct means to promote the regularization of granular computing. In this paper, we first sort out the main applications of granular computing in standards. According to the characteristics of granular computing, a framework of its standard system is proposed to provide a reference for the subsequent research of granular computing standards. The next direction of the granular computing standards strategy is discussed, and solutions are given.

This study used an analytical model to investigate the factors that affect the reconstruction accuracy composed of the baseline length, lens focal length, the angle between the optical axis and baseline, and the field of the view angle. Firstly, the theoretical expressions of the above factors and measurement errors are derived based on the binocular three-dimensional reconstruction model. Then, the structural parameters’ impact on the error propagation coefficient is analyzed and simulated using MATLAB software. The results show that structural parameters significantly impact the error propagation coefficient, and the reasonable range of structural parameters is pointed out. When the angle between the optical axis of the binocular camera and the baseline is between 30° and 55°, the ratio of the baseline length to the focal length can be reasonably reduced. In addition, using the field angle of the view that does not exceed 20° could reduce the error propagation coefficient. While the angle between the binocular optical axis and the baseline is between 40° and 50°, the reconstruction result has the highest accuracy, changing the angle out of this range will lead to an increase in the reconstruction error. The angle between the binocular optical axis and the baseline changes 30° through 60° leads to the error propagation coefficient being in a lower range. Finally, experimental verification and simulation results show that selecting reasonable structural parameters could significantly reduce measurement errors. This study proposes a model that constructs a binocular three-dimensional reconstruction system with high precision. A portable three-dimensional reconstruction system is built in the article.

Antibiotics have been detected in tiny environmental matrices all over the world, which caused a lot of concern. To solve this problem, biological treatment can be a low-cost and high-efficiency way. The use of biochar adsorbents made from the residual sludge of sewage for wastewater treatment can achieve pollutant removal while realizing pollutant reduction and reuse, which is of great significance for green development. In this study, a prepared biochar-based adsorbent (PBA) was modified and used for norfloxacin (NOR) removal. The composition of the adsorbent was characterized, and the influence of application factors on adsorption performance was investigated. After being modified and optimized, an overall removal efficiency of 84% was achieved for NOR in 4 h. The adsorption behavior was spontaneous and consistent with the Lagergren pseudo-second kinetic model and Langmuir model. The adsorption capacity of PBA reached 8.69 mg·L−1 for NOR. A total removal efficiency of 62% was obtained for five mixed quinolone antibiotics by PBA. The PBA could be well regenerated and reused five times. This study explored a new method of the bio-waste utilization of sewage sludge for antibiotic removal from wastewater.

Background: Bladder cancer (BCa), among the world’s most common malignant tumors in the urinary system, has a high morbidity and mortality. Though cuproptosis is a new type of cell death mediated by lipoylated tricarboxylic acid (TCA) cycle proteins, the role of cuproptosis-related long noncoding RNAs (crlncRNAs) in bladder tumors awaits further elucidation. In this paper, we tried to explore how important crlncRNAs are for BCa.Methods: The crlncRNAs were first obtained through Pearson correlation analysis of the RNA-seq data and corresponding clinical data downloaded from The Cancer Genome Atlas (TCGA). Then, three lncRNAs were acquired by Cox regression and Lasso regression to build a prognostic model of crlncRNAs for verification. In the meantime, clinicopathological correlation analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, principal component analysis (PCA), immunoassay, and half-maximal inhibitory concentration prediction (IC50) were carried out. Then, an entire tumor was classified into two clusters by crlncRNA expression to further discuss the differences in prognosis, immune status and drug susceptibility among different subgroups.Results: We obtained a total of 152 crlncRNAs and built a risk model for screened crlncRNAs. We validated the model and found that calibration charts feature a high consistency in verifying nomogram prediction. Receiver operating characteristic (ROC) curve and univariate and multivariate Cox regression suggested that this model can be applied as an independent prognostic factor of bladder cancer due to its high accuracy. According to KEGG analysis, high-risk groups were enriched in cancer and immune-related pathways. During tumor immunoassay, noticeable differences were observed in both immune infiltration and checkpoints between high- and low-risk patients. Of the two subgroups divided among patients by consensus clustering, cluster 2 had a better prognosis, whereas cluster 1 had higher immunoreactivity scores, more immune cell infiltrations and immune checkpoint expressions, and different sensitivities to drugs.Conclusion: The research findings demonstrate that crlncRNAs can be used to predict the prognosis and immune microenvironment of patients suffering from BCa, and differentiate between BCa subgroups to improve the individual therapy of BCa.

Pandanus amaryllifolius Roxb. is a special tropical spice crop resource with broad development prospects. It is widely cultivated under a Hevea brasiliensis (Willd. ex A. Juss.) Muell. Arg. canopy to improve the comprehensive benefits to Hevea brasiliensis plantations in Hainan Provence, China. However, the effects of intercropping with Hevea brasiliensis on the component number and relative contents of volatile substances in different categories in the Pandanus amaryllifolius leaves are still unknown. Therefore, a Hevea brasiliensis and Pandanus amaryllifolius intercropping experiment was set up to clarify the differences between several cultivated patterns on volatile substances in the Pandanus amaryllifolius leaves, and the key regulatory factors of volatile substances. The results showed that the soil pH was significantly decreased, while soil bulk density, alkali-hydrolyzable nitrogen and available phosphorus contents were significantly increased under the intercropping pattern. The component numbers of esters in volatile substances were increased by 6.20%, while the component numbers of ketones were decreased by 4.26% under the intercropping pattern. Compared with the Pandanus amaryllifolius monoculture, the relative contents of pyrroles, esters and furanones were significantly increased by 8.83%, 2.30% and 8.27%, respectively, while the relative contents of ketones, furans and hydrocarbons were decreased by 1.01%, 10.55% and 9.16% under the intercropping pattern, respectively. The relative contents of pyrroles, esters, furanones, ketones, furans and hydrocarbons were associated with changes in soil pH, soil available phosphorus content and air temperature. The results indicated that the reduction in soil pH and enhancement in soil-available phosphorus may be the main reasons for promoting the relative content of pyrroles and reducing the relative content of hydrocarbons under an intercropping pattern. Overall, Hevea brasiliensis intercropping with Pandanus amaryllifolius could not only improve soil properties, but also significantly increase the relative contents of the main volatile substances in Pandanus amaryllifolius leaves, which could provide a theoretical basis for the application and promotion of high-quality production patterns of Pandanus amaryllifolius.

Background: Antipsychotic treatment has improved the disrupted functional connectivity (FC) and neurometabolites levels of the default mode network (DMN) in schizophrenia patients, but a direct relationship between FC change, neurometabolic level alteration, and symptom improvement has not been built. This study examined the association between the alterations in DMN FC, the changes of neurometabolites levels in the medial prefrontal cortex (MPFC), and the improvements in psychopathology in a longitudinal study of drug-naïve first-episode psychosis (FEP) patients. Methods: Thirty-two drug-naïve FEP patients and 30 matched healthy controls underwent repeated assessments with the Positive and Negative Syndrome Scale (PANSS) and 3T proton magnetic resonance spectroscopy as well as resting-state functional magnetic resonance imaging. The levels of γ-aminobutyric acid, glutamate, N-acetyl-aspartate in MPFC, and the FC of DMN were measured. After 8-week antipsychotic treatment, 24 patients were re-examined. Results: After treatment, the changes in γ-aminobutyric acid were correlated with the alterations of FC between the MPFC and DMN, while the changes in N-acetyl-aspartate were associated with the alterations of FC between the posterior cingulate cortex/precuneus and DMN. The FC changes of both regions were correlated with patients PANSS positive score reductions. The structural equation modeling analyses revealed that the changes of DMN FC mediated the relationship between the changes of neurometabolites and the symptom improvements of the patients. Conclusions: The derived neurometabolic-functional changes underlying the clinical recovery provide insights into the prognosis of FEP patients. It is noteworthy that this is an exploratory study, and future work with larger sample size is needed to validate our findings.

The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer’s disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.

Motivated by the impressive success of deep learning in a wide range of scientific and industrial applications, we explore in this work the application of deep learning into a specific class of optimization problems lacking explicit formulas for both objective function and constraints. Such optimization problems exist in many design problems, e.g., rotor profile design, in which objective and constraint values are available only through experiment or simulation. They are especially challenging when design parameters are high-dimensional due to the curse of dimensionality. In this work, we propose a data-informed deep optimization (DiDo) approach emphasizing on the adaptive fitting of the the feasible region as follows. First, we propose a deep neural network (DNN) based adaptive fitting approach to learn an accurate DNN classifier of the feasible region. Second, we use the DNN classifier to efficiently sample feasible points and train a DNN surrogate of the objective function. Finally, we find optimal points of the DNN surrogate optimization problem by gradient descent. To demonstrate the effectiveness of our DiDo approach, we consider a practical design case in industry, in which our approach yields good solutions using limited size of training data. We further use a 100-dimension toy example to show the effectiveness of our approach for higher dimensional problems. Our results indicate that, by properly dealing with the difficulty in fitting the feasible region, a DNN-based method like our DiDo approach is flexible and promising for solving high-dimensional design problems with implicit objective and constraints.

Space technology is the core competitiveness of a powerful country. Space maintenance technology has just started in our country. The biggest innovation of this article is to design a set of interface system, which is suitable for on-orbit maintenance of space science instruments. It represents that this technology from scratch in our country. A new measuring method is proposed. According to the homogeneous coordinate transformation, the mathematical models of the relationships between fit clearances of interface system and the six-degree of freedom change of the maintenance module are derived. According to the index requirements, the corresponding fit clearance and repeat positioning accuracy are determined. Set up an experimental platform, and use autocollimator and laser tracker to measure the repeated positioning accuracy of the module. Based on the theory of small displacement, the least square method is used to deduce the rotation angle and translation amount of the module through the change of the characteristic point coordinates. The experiment proves that the interface meets the requirement of on-orbit maintainable operation, and the experimental data proves that the theoretical equation is correct. Providing engineering reference for other space maintenance scientific instruments.

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer’s disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

HCV core protein is the first structural protein synthesized during hepatitis C virus (HCV) infection and replication. It is released from virus infected liver cells and mediates multiple functions to affect host cell response. The innate immune response is the first line of defense against viral infection. After HCV infection, Kupffer cells (KCs) which are liver macrophages play an important role in host innate immune response. Kupffer cells act as phagocytes and release different cytokines and chemokines to counter viral infection and regulate inflammation and fibrosis in liver. Earlier, we have demonstrated that HCV core protein interacts with gC1qR and activates MAPK, NF-κB and PI3K/AKT pathways in macrophages. In this study, we explored the effect of HCV core protein on CCL2 and CXCL10 expression in macrophages and the signaling pathways involved. Upon silencing of gC1qR, we observed a significant decrease expression of CCL2 and CXCL10 in macrophages in the presence of HCV core protein. Inhibiting NF-κB pathway, but not P38, JNK, ERK and AKT pathways greatly reduced the expression of CCL2 and CXCL10. Therefore, our results indicate that interaction of HCV core protein with gC1qR could induce CCL2 and CXCL10 secretion in macrophages via NF-κB signaling pathway. These findings may shed light on the understanding of how leukocytes migrate into the liver and exaggerate host-derived immune responses and may provide novel therapeutic targets in HCV chronic inflammation.

How neurons are connected in the brain to perform computation is a key issue in neuroscience. Recently, the development of calcium imaging and multi-electrode array techniques have greatly enhanced our ability to measure the firing activities of neuronal populations at single cell level. Meanwhile, the intracellular recording technique is able to measure subthreshold voltage dynamics of a neuron. Our work addresses the issue of how to combine these measurements to reveal the underlying network structure. We propose the spike-triggered regression (STR) method, which employs both the voltage trace and firing activity of the neuronal population to reconstruct the underlying synaptic connectivity. Our numerical study of the conductance-based integrate-and-fire neuronal network shows that only short data of 20 ~ 100 s is required for an accurate recovery of network topology as well as the corresponding coupling strength. Our method can yield an accurate reconstruction of a large neuronal network even in the case of dense connectivity and nearly synchronous dynamics, which many other network reconstruction methods cannot successfully handle. In addition, we point out that, for sparse networks, the STR method can infer coupling strength between each pair of neurons with high accuracy in the absence of the global information of all other neurons.

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.

How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.