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Abstract Anesthetics are widely used in electrophysiological tests to assess retinal and visual system functions to avoid experimental errors caused by movement and stress in experimental animals. To determine the most suitable anesthetic for visual electrophysiological tests, excluding ketamine and chloral hydrate due to regulatory and side effect concerns, this study investigated the effects of ethyl carbamate (EC), avertin (AR), and pentobarbital sodium (PS) on visual signal conduction in the retina and primary visual cortex. Assessments included flash electroretinogram (FERG), pattern electroretinogram (PERG), pattern visual evoked potentials (PVEP), and flash visual evoked potentials (FVEP), FERG and FVEP were used to evaluate the responses of the retina and visual cortex to flash stimuli, respectively, while PERG and PVEP assessed responses to pattern stimuli. The research showed that AR demonstrates the least disruption to the visual signal pathway, as evidenced by consistently high characteristic peaks in the AR group across various tests. In contrast, mice given EC exhibited the lowest peak values in both FERG and FVEP, while subjects anesthetized with PS showed suppressed oscillatory potentials and PERG responses. Notably, substantial PVEP characteristic peaks were observed only in mice anesthetized with AR. Consequently, among the three anesthetics tested, AR is the most suitable for visual electrophysiological studies.

ABSTRACT Purpose To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). Methods A total of 59 AD patients and 59 healthy subjects were selected. The Mini‐Mental State Examination (MMSE) was used for neuropsychological assessment. Blood glucose and serum lipid levels were detected by biochemical analyzer. Polymerase chain reaction (PCR) was used to detect apolipoprotein E (Apo E) ε3/ε4 genotypes in the plasma. Hippocampal volume was calculated using Slicer software. Independent‐sample t test or Mann–Whitney U test were used to compare the levels of various indicators between the two groups. Spearman's correlation analysis was used to analyze the correlation between each level. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to compare the diagnostic efficacy of individual and combined detection of serum Apo B100 levels and hippocampal volume in AD. Results Compared with the healthy control group, the levels of serum total cholesterol (TC), low‐density lipoprotein (LDL), Apo B100, and plasma Apo E ε3/ε4 were higher in the AD group, and serum high‐density lipoprotein (HDL) level was lower in the AD group (both p

The scarcity of fresh water resources has severely limited agricultural production in arid areas. Although brackish water irrigation or fresh water deficit irrigation can alleviate the water resources crisis, both may cause water and salt stress to crop. Therefore, this study is based on the functional advantages of Bacillus subtilis in soil improvement and crop growth promotion to alleviate water and salt stress and build safe and efficient water-saving irrigation patterns. In this study, cotton (No. 50 Chuangmian) was selected as the research crop, and five application rates of Bacillus subtilis (0, 22.5, 45, 67.5 and 90 kg·ha−1) were combined with three irrigation patterns (brackish water, fresh water and fresh water deficit irrigations) to study the effects of Bacillus subtilis on soil moisture and salinity, soil microbial community, cotton physiology and growth under water and salt stress. The results showed that Bacillus subtilis could enhance soil water retention capacity, promote soil desalination, improve cotton growth indices (plant height, stem diameter, leaf area index, dry matter accumulation), and then increase yield and water use efficiency (WUE). Compared with the control treatment, the yield and WUE of Bacillus subtilis application treatments increased by 3.32–54.67 % and 1.68–41.07 %, respectively. In the cotton physiology characteristics, Bacillus subtilis increased proline content and the activity of superoxide dismutase, peroxidase and catalase while decreased malondialdehyde content in cotton leaves. Bacillus subtilis could enhance the relative abundance of bacteria with the functions of nitrogen fixation, stress resistance and biocontrol. A structural equation model proved that Bacillus subtilis could improve yield and WUE indirectly by directly improving soil microbial diversity, alleviating water and salt stress, and then improving cotton physiology and growth. According to a comprehensive evaluation of cotton physiology and growth, it was determined that the optimal improvement effect was achieved when the application rate of Bacillus subtilis was 45 kg ha−1; the synergistic effect of brackish water irrigation and Bacillus subtilis (45 kg·ha−1) was superior to that of fresh water deficit irrigation combining with Bacillus subtilis (45 kg·ha−1), which could be considered a priority strategy for alleviating the fresh water crisis in arid areas and promoting the efficient increase in cotton yield.

Abstract Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn .

Background: Major depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation. Methods: We systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls. Results: Differential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power. Conclusions: The biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.

This work aims to collect cutting-edge developments in the area of hydrodynamics and flow control, including both fundamental and engineering research [...]

IntroductionSchizophrenia is a complex psychiatric disorder, of which molecular pathogenesis remains largely unknown. Accumulating evidence suggest that gut microbiota may affect brain function via the complex gut-brain axis, which may be a potential contributor to schizophrenia. However, the alteration of gut microbiota showed high heterogeneity across different studies. Therefore, this study aims to identify the consistently altered gut microbial taxa associated with schizophrenia.MethodsWe conducted a systematic search and synthesis of the up-to-date human gut microbiome studies on schizophrenia, and performed vote counting analyses to identify consistently changed microbiota. Further, we investigated the effects of potential confounders on the alteration of gut microbiota.ResultsWe obtained 30 available clinical studies, and found that there was no strong evidence to support significant differences in α-diversity and β-diversity between schizophrenic patients and healthy controls. Among 428 differential gut microbial taxa collected from original studies, we found that 8 gut microbial taxa were consistently up-regulated in schizophrenic patients, including Proteobacteria, Gammaproteobacteria, Lactobacillaceae, Enterobacteriaceae, Lactobacillus, Succinivibrio, Prevotella and Acidaminococcus. While 5 taxa were consistently down-regulated in schizophrenia, including Fusicatenibacter, Faecalibacterium, Roseburia, Coprococcus and Anaerostipes.DiscussionThese findings suggested that gut microbial changes in patients with schizophrenia were characterized by the depletion of anti-inflammatory butyrate-producing genera, and the enrichment of certain opportunistic bacteria genera and probiotics. This study contributes to further understanding the role of gut microbiota in schizophrenia, and developing microbiota-based diagnosis and therapy for schizophrenia.

The rapid evolution of gene editing technology has markedly improved the outlook for treating genetic diseases. Base editing, recognized as an exceptionally precise genetic modification tool, is emerging as a focus in the realm of genetic disease therapy. We provide a comprehensive overview of the fundamental principles and delivery methods of cytosine base editors (CBE), adenine base editors (ABE), and RNA base editors, with a particular focus on their applications and recent research advances in the treatment of genetic diseases. We have also explored the potential challenges faced by base editing technology in treatment, including aspects such as targeting specificity, safety, and efficacy, and have enumerated a series of possible solutions to propel the clinical translation of base editing technology. In conclusion, this article not only underscores the present state of base editing technology but also envisions its tremendous potential in the future, providing a novel perspective on the treatment of genetic diseases. It underscores the vast potential of base editing technology in the realm of genetic medicine, providing support for the progression of gene medicine and the development of innovative approaches to genetic disease therapy.

ObjectivePancreatic carcinoma is characterised by high aggressiveness and a bleak prognosis; optimising related treatment decisions depends on the availability of reliable prognostic markers. This study was designed to compare various blood biomarkers, such as neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), platelet/lymphocyte ratio (PLR), C-reactive protein (CRP), albumin (Alb), plasma fibrinogen (PF), and CRP/Alb in patients with pancreatic carcinoma.MethodsOur study retrospectively reviewed 250 patients with pancreatic carcinoma diagnosed between July 2007 and December 2018. The Cutoff Finder application was used to calculate the optimal values of CRP/Alb and PF. The Chi-square test or Fisher’s exact test was used to analyse the correlation of CRP/Alb and PF with other clinicopathological factors. Conducting univariate and multivariate analyses allowed further survival analysis of these prognostic factors.ResultsMultivariate analysis revealed that, in a cohort of 232 patients with pancreatic ductal adenocarcinoma (PDAC), the PF level exhibited statistical significance for overall survival (hazard ratio (HR) = 0.464; p = 0.023); however, this correlation was not found in the entire group of 250 patients with pancreatic carcinoma. Contrastingly, the CRP/Alb ratio was demonstrated statistical significance in both the entire pancreatic carcinoma cohort (HR = 0.471; p = 0.026) and the PDAC subgroup (HR = 0.484; p = 0.034). CRP/Alb and PF demonstrated a positive association (r=0.489, p

Extracting polyphenolic bioactive compounds from Pinus elliottii needles, a forestry residue, promises economic and environmental benefits, however, relevant experimental data are lacking. Herein, a comprehensive investigation of the polyphenolic composition of pine needles (PNs) was carried out. Ultrasound-Assisted Extraction (UAE) was applied to extract the polyphenolic compounds of pine needles. The optimal conditions of extracts were determined by Response Surface Methodology (RSM). The maximum total phenolic content (TPC) of 40.37 mg GAE/g PNs was achieved with solid–liquid ratio of 1:20, 60 % ethanol, and 350 W for 25 min at 45 °C. Polyphenolic extracts showed antioxidant activity in scavenging free radicals and reducing power (DPPH, IC50 41.05 μg/mL; FRAP 1.09 mM Fe2+/g PNs; ABTS, IC50 214.07 μg/mL). Furthermore, the second-order kinetic model was also constructed to describe the mechanism of the UAE process, with the extraction activation energy estimated at 12.26 kJ/mol. In addition, 37 compounds in PNs were first identified by UHPLC-Q-Exactive Orbitrap MS/MS, including flavonoids and phenolic acids. The results suggest that Ultrasound-Assisted is an effective method for the extraction of natural polyphenolic compounds from pine needles and this study could serve as a foundation for utilizing phenolics derived from PNs in the food and pharmaceutical industries.

Severe cadmium contamination poses a serious threat to food security and human health. Plant–microbial combined remediation represents a potential technique for reducing heavy metals in soil. The main objective of this study is to explore the remediation mechanism of cadmium-contaminated soil using a combined approach of lawn plants and microbes. The target bacterium Bacillus cereus was selected from cadmium-contaminated soil in mining areas, and two lawn plants (Festuca arundinacea A‘rid III’ and Poa pratensis M‘idnight II’) were chosen as the target plants. We investigated the remediation effect of different concentrations of bacterial solution on cadmium-contaminated soil using two lawn plants through pot experiments, as well as the impact on the soil microbial community structure. The results demonstrate that Bacillus cereus promotes plant growth, and the combined action of lawn plants and Bacillus cereus improves soil quality, enhancing the bioavailability of cadmium in the soil. At a bacterial suspension concentration of 105 CFU/mL, the optimal remediation treatment was observed. The removal efficiency of cadmium in the soil under Festuca arundinacea and Poa pratensis treatments reached 33.69% and 33.33%, respectively. Additionally, the content of bioavailable cadmium in the rhizosphere soil increased by up to 13.43% and 26.54%, respectively. Bacillus cereus increased the bacterial diversity in the non-rhizosphere soil of both lawn plants but reduced it in the rhizosphere soil. Additionally, the relative abundance of Actinobacteriota and Firmicutes, which have potential for heavy metal remediation, increased after the application of the bacterial solution. This study demonstrates that Bacillus cereus can enhance the potential of lawn plants to remediate cadmium-contaminated soil and reshape the microbial communities in both rhizosphere and non-rhizosphere soils.

The design of porous structure that mimic trabecular bone is an effective method for optimizing the elastic modulus and osseointegration properties of titanium alloy implants. However, there is no consensus on which structure is best. In this study, we fabricated 24 different types of Ti6Al4V trabecular bone scaffolds with varying porosity and average pore size using Voronoi algorithm and selective laser melting technology. The biomechanics and osseointegration properties were studied by mechanical tests, computational fluid dynamics, cell and animal experiments. Our results showed that with an increase of porosity and average pore size, the scaffold's yield strength, ultimate strength, elastic modulus and shear stress exhibited an overall decreasing trend while the permeability and nutrient transport improved. Cell experiments showed that reducing the average pore size enhanced the adhesion and proliferation of MC3T3-E1 cells when the porosity was constant. Animal experiments showed that the scaffold with 65% porosity and 550 μm pore size had the most significant new bone formation effect. Based on our research results, we concluded that a porous structure design with 65% porosity and an average pore size of 550 μm, resembling bone trabecular, had the most significant effect on enhancing the comprehensive performance of titanium alloy implants.

The K-means algorithm, widely used in cluster analysis, is a centroid-based clustering method known for its high efficiency and scalability. However, in realistic situations, the operating environment is susceptible to contamination issues caused by outliers and distribution departures, which may lead to clustering results from K-means that are distorted or rendered invalid. In this paper, we introduce three other alternative algorithms, including K-weighted-medians, K-weighted-L2-medians, and K-weighted-HLs, to address these issues under the consideration of data with weights. The impact of contamination is investigated by examining the estimation effects on optimal cluster centroids. We explore the robustness of the clustering algorithms from the perspective of the breakdown point, and then conduct experiments on simulated and real datasets to evaluate their performance using two new numerical metrics: relative efficiencies based on generalized variance and average Euclidean distance. The results demonstrate the effectiveness of the proposed K-weighted-HLs algorithm, surpassing other algorithms in scenarios involving both contamination issues.

Abstract The low deposition time efficiency and small thickness limit the expansion of polydopamine (PDA) application to fiber-reinforced high-temperature ceramic composites. In this work, the electric field-assisted polymerization (EFAP) route was developed to improve the deposition time efficiency of PDA coating and overcome the thickness limitation. Carbonized polydopamine (C-PDA) coating was used as the interphase of carbon fiber-reinforced ZrB2-based composites (Cf/ZrB2-based composite) to bond rigid fibers and brittle ceramics, where C-PDA coating was prepared by the carbonization of PDA coating. Firstly, uniform and dense PDA coatings were deposited on carbon fibers (Cf) by EFAP. The thickness of PDA coating reached the micron level (over 1800 nm) for the first time. Benefiting from the EFAP route promoting the oxidation process of dopamine (DA) and accelerating the aggregation and in-situ polymerization of DA and its derivatives on the surface of Cf, the deposition rate of PDA coating reached 5589 nm/h, which was 3 orders of magnitude higher than that of the traditional self-polymerization process. By adjusting the EFAP parameters (e.g. DA-concentration, current, and deposition time), the thickness of PDA coating could be conveniently designed from nano-scale to micro-scale. Then, PDA coating was pyrolyzed to obtain C-PDA coating. C-PDA coating was well bonded on Cf without visible cross-sticking among neighboring fibers. C-PDA coating presented a layered structure and the thickness of C-PDA coating could be designed by controlling the thickness of PDA. C-PDA coating was used as the interfacial phase of the Cf/ZrB2-based composite, which ensured that the composite possessed good load-bearing capacity and thermal stability. Moreover, extraordinary damage resistance of the composite was achieved, with work of fracture up to 9936 ± 548 J/m2 at room temperature and 19,082 ± 3458 J/m2 at 1800 °C. The current work provides a high time efficiency processing route for depositing PDA coating on carbon fibers and demonstrates the attractive potential of PDA coating in fiber-reinforced high-temperature ceramic composites.

Scented tea, as an integral part of tea culture for several centuries, is important to prevent chronic metabolic diseases. Coreopsis tinctoria Nutt. (CT) tea is often used to prevent and treat diabetes, but the mechanism of action needs further study. Liquid chromatography-mass spectrometry was used to analyze 23 chemical components of CT extracts, and a “compounds-target-healthy function” network was constructed while the pathway enrichment analysis was carried out. Eleven CT compounds, including flavanomarein, luteolin, isookanin, marein, 7,3′,5′-trihydroxy-flavanone, okanin, caffeic acid, cynaroside, acetylcoreopsin, quercetin-7-O-β-D-glucoside, protocatechuic acid, were obtained through network pharmacology screening. The corresponding targets SRC, TP53, HSP90AA1, STAT3, MAPK1, PIK3R1, AKT1 and PIK3CA were associated with anti-glycemic effects. The rat model of type 2 diabetic mellitus (T2DM) was induced by a high-fat diet combined with low-dose streptozotocin injection and biochemical indexes (blood glucose, serum total cholesterol, and triglyceride) were determined. The low-dose water extract of CT can significantly regulate the total cholesterol level of diabetic rats (p

The fracture network generated by hydraulic fracturing in unconventional shale reservoirs contains numerous microfractures that are connected to macroscopic fractures. These microfractures serve as crucial pathways for shale gas to flow out from micro- and nano-scale pores, playing a critical role in enhancing shale gas recovery. Currently, more attention is being given by academia and industry to the evolution of macroscopic fracture networks, while the understanding of the microfracture mechanisms and evolution is relatively limited. A significant number of microfractures are generated during the hydraulic fracturing process of shale. These microfractures subsequently propagate, merge, and interconnect to form macroscopic fractures. Therefore, studying the fracture process of rock masses from a microscale perspective holds important theoretical significance and engineering value. Based on the authors’ research experience and literature review, this paper provides a brief overview of current progress in shale microfracture research from five aspects: in situ observation experiments of microfractures in shale, formation and evolution processes of discontinuous microfractures, the impact of inhomogeneity on microfracture propagation, measurement methods for microscale mechanical parameters and deformation quantities in shale, and numerical simulation of shale microfractures. This paper also summarizes the main challenges and future research prospects in shale microfracture studies, including: (1) quantitative characterization of in situ observation experimental data on shale microfractures; (2) formation and evolution laws of macroscopic, mesoscopic, and microscopic multi-scale discontinuous fractures; (3) more in-depth and microscale characterization of shale heterogeneity and its deformation and fracture mechanisms; (4) acquisition of shale micro-mechanical parameters; (5) refinement and accuracy improvement of the numerical simulation of microfractures in shale. Addressing these research questions will not only contribute to the further development of microfracture theory in rocks but also provide insights for hydraulic fracturing in shale gas extraction.

ObjectiveThis paper aims to explore the hydrodynamic characteristics of underwater vehicles under the action of internal solitary waves.MethodsBased on the internal solitary wave mKdV theory under the strong stratification assumption, the incompressible Navier-Stokes （N-S） equation is discretized by the finite volume method. Combining the velocity entrance wave-making and overset grid methods, a numerical model of an underwater vehicle coupled with internal solitary waves in stratified flow is established. Through this model, the coupling process of internal solitary waves and fixed and suspended vehicles in different internal wave environments is simulated, and the variation law of the hydrodynamic load is obtained by numerical solution. ResultsThe amplitude of the force and moment of the underwater vehicle increases with the increase in wave amplitude. When the position of the fixed vehicle is close to the interface of two layers of fluid, it is more severely affected. When the suspended vehicle is close to the wave trough, the impact is more significant. The increase in the initial pitch angle will lead to a sharp increase in the horizontal force amplitude of the vehicle. In addition, the "falling depth" phenomenon occurs when a vehicle with initial speed is affected by the main wave and coda wave of an internal solitary wave.ConclusionThe results obtained in this paper have reference value for safe operation of underwater vehicles.

Abstract Numerous studies have investigated metabolite alterations resulting from pharmacological treatment in depression models although few quantitative studies explored metabolites exhibiting constant alterations. This study aimed to identify consistently dysregulated metabolites across such studies using a knowledgebase-driven approach. This study was based on 157 studies that identified an assembly of 2757 differential metabolites in the brain, blood, urine, liver, and feces samples of depression models with pharmacological medication. The use of a vote-counting approach to identify consistently upregulated and downregulated metabolites showed that serotonin, dopamine, norepinephrine, gamma-aminobutyric acid, anandamide, tryptophan, hypoxanthine, and 3-methoxytyramine were upregulated in the brain, while quinolinic acid, glutamic acid, 5-hydroxyindoleacetic acid, myo-inositol, lactic acid, and the kynurenine/tryptophan ratio were downregulated. Circulating levels of trimethylamine N-oxide, isoleucine, leucine, tryptophan, creatine, serotonin, valine, betaine, and low-density lipoprotein were elevated. In contrast, levels of alpha-d-glucose, lactic acid, N-acetyl glycoprotein, glutamine, beta-d-glucose, corticosterone, alanine, phenylacetylglycine, glycine, high-density lipoprotein, arachidonic acid, myo-inositol, allantoin, and taurine were decreased. Moreover, 12 metabolites in urine and nine metabolites in the liver were dysregulated after treatment. Pharmacological treatment also increased fecal levels of butyric acid, acetic acid, propionic acid, and isovaleric acid. Collectively, metabolite disturbances induced by depression were reversed by pharmacological treatment. Pharmacological medication reversed the reduction of brain neurotransmitters caused by depression, modulated disturbance of the tryptophan-kynurenine pathway and inflammatory activation, and alleviated abnormalities of amino acid metabolism, energy metabolism, lipid metabolism, and gut microbiota-derived metabolites.

Electrical conductivity (EC) is an important index of soil salinity level and an essential factor in judging whether improvement can be made and assessing the improvement, if any. However, saline soils of different types show different spectral characteristic bands, which affects the retrieval precision to a certain extent. To tackle this issue, we present a novel way of modeling. Specifically, we compare the correlation of sensitive bands, multiplication of sensitive bands, and spectral indexes with EC data to determine the optimal input parameters, and take the EC data after Box-Cox transformation as the output. We then try out several machine-learning algorithms to establish retrieval models for soil EC. Validation results show that the model precision increases by 3.23–85.71% after EC data transformation and machine learning models produce 29.41–86.67% higher precision than the linear regression model. Finally, using the optimal model, we retrieve pixel-level (10 m × 10 m) EC for the soil in our study areas. This provides necessary data support for working out soil desalinization initiatives and evaluating the effectiveness of such initiatives.

Aging is a systemic physiological degenerative process, with alterations in gut microbiota and host metabolism. However, due to the interference of multiple confounding factors, aging-associated molecular characteristics have not been elucidated completely. Therefore, based on 16S ribosomal RNA (rRNA) gene sequencing and non-targeted metabolomic detection, our study systematically analyzed the composition and function of the gut microbiome, serum, and fecal metabolome of 36 male rhesus monkeys spanning from 3 to 26 years old, which completely covers juvenile, adult, and old stages. We observed significant correlations between 41 gut genera and age. Moreover, 86 fecal and 49 serum metabolites exhibited significant age-related correlations, primarily categorized into lipids and lipid-like molecules, organic oxygen compounds, organic acids and derivatives, and organoheterocyclic compounds. Further results suggested that aging is associated with significant downregulation of various amino acids constituting proteins, elevation of lipids, particularly saturated fatty acids, and steroids. Additionally, age-dependent changes were observed in multiple immune-regulatory molecules, antioxidant stress metabolites, and neurotransmitters. Notably, multiple age-dependent genera showed strong correlations in these changes. Together, our results provided new evidence for changing characteristics of gut microbes and host metabolism during aging. However, more research is needed in the future to verify our findings.

Phytoremediation is an environment-friendly method for toxic elements remediation. The aim of this study was to improve the phytoremediation efficiency of Brassica juncea and the rhizosphere soil micro-ecology in cadmium (Cd) and arsenic (As) contaminated soil. A field experiment was conducted with six treatments, including a control treatment (CK), two treatments with two contents of Trichoderma harzianum (T1: 4.5 g m−2; T2: 9 g m−2), one biochar treatment (B: 750 g m−2), and two combined treatments of T1B and T2B. The results showed Trichoderma harzianum promoted the total chlorophyll and translocation factor of Brassica juncea, while biochar promoted plant biomass compared to CK. T2B treatment showed the best results, which significantly increased Cd accumulation by 187.49–308.92%, and As accumulation by 125.74–221.43%. As a result, the soil’s total Cd content was reduced by 19.04% to 49.64% and total As contents by 38.76% to 53.77%. The combined amendment increased the contents of soil available potassium, phosphorus, nitrogen, and organic matter. Meanwhile, both the activity of glutathione and peroxidase enzymes in plants, together with urease and sucrase enzymes in soil, were increased. Firmicutes (dominant bacterial phylum) and Ascomycota (dominant fungal phylum) showed positive and close correlation with soil nutrients and plant potentially toxic elements contents. This study demonstrated that phytoremediation assisted by biochar and Trichoderma harzianum is an effective method of soil remediation and provides a new strategy for enhancing plant remediation efficiency.

Xuanfei Baidu granule (XFBD) is a recommended patented drug for the prevention and treatment of Corona Virus Disease 2019 (COVID-19), which is approved by the National Medical Products Administration. XFBD suppresses the over-activated immune response caused by inflammatory factor storms in COVID-19 infection. The intestine plays a crucial role in the immune system. The mass spectrometry based fecal metabolomics with 16S rDNA sequencing were combined to evaluate the effects of XFBD on host metabolism and gut microbiome. Short-chain fatty acids (SCFAs) contents in fecal matter were quantified by gas chromatography-mass spectrometry (GC-MS). Plasma samples were used to detect immune and inflammatory levels. The results were verified with a rat model of intestinal disorder. Results indicated that XFBD could increase the immune level of Immunoglobulin A (IgA), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) (p < 0.05). The OPLS-DA analysis results showed that a total of 271 differential metabolites (178 up-regulated and 93 down-regulated) were identified based on the VIP ≥1, p < 0.05, FC ≥ 2 and FC ≤ 0.5. The metabolic pathways mainly involved D-Glutamine and D-glutamate metabolism, Arginine biosynthesis, Biotin metabolism, et al. XFBD modified the gut bacteria structure according to the principal component analysis (PCA), that is, 2 phyla, 3 classes, 5 orders, 11 families and 14 genera were significantly different based on taxonomic assignment. In addition, it could partially callback the relative abundance of intestinal microflora in bacterial disorder rats caused by antibiotics. It is suggested that the intervention mechanism of XFBD might be related to the regulation of intestinal flora composition. The evidence obtained in the study provides a useful reference for understanding the mechanism of XFBD.

Cryptotanshinone (CTS), a diterpenoid quinone, is found mostly in Salvia miltiorrhiza Bunge (S. miltiorrhiza) and plays a crucial role in many cellular processes, such as cell proliferation/self-renewal, differentiation and apoptosis. In particular, CTS’s profound physiological impact on various stem cell populations and their maintenance and fate determination could improve the efficiency and accuracy of stem cell therapy for high-incidence disease. However, as much promise CTS holds, these CTS-mediated processes are complex and multifactorial and many of the underlying mechanisms as well as their clinical significance for high-incidence diseases are not yet fully understood. This review aims to shed light on the impact and mechanisms of CTS on the actions of diverse stem cells and the involvement of CTS in the many processes of stem cell behavior and provide new insights for the application of CTS and stem cell therapy in treating high-incidence diseases.

Alzheimer's disease and epilepsy are common nervous system diseases in older adults, and their incidence rates tend to increase with age. Patients with mild cognitive impairment and Alzheimer's disease are more prone to have seizures. In patients older than 65 years, neurodegenerative conditions accounted for ~10% of all late-onset epilepsy cases, most of which are Alzheimer's disease. Epilepsy and seizure can occur in the early and late stages of Alzheimer's disease, leading to functional deterioration and behavioral alterations. Seizures promote amyloid-β and tau deposits, leading to neurodegenerative processes. Thus, there is a bi-directional association between Alzheimer's disease and epilepsy. Epilepsy is a risk factor for Alzheimer's disease and, in turn, Alzheimer's disease is an independent risk factor for developing epilepsy in old age. Many studies have evaluated the shared pathogenesis and clinical relevance of Alzheimer's disease and epilepsy. In this review, we discuss the clinical associations between Alzheimer's disease and epilepsy, including their incidence, clinical features, and electroencephalogram abnormalities. Clinical studies of the two disorders in recent years are summarized, and new antiepileptic drugs used for treating Alzheimer's disease are reviewed.

Abstract Heparanase (HPSE) is a kind of multifunctional extracellular hydrolase, and related to metastasis of hepatocellular carcinoma (HCC). Endothelial necroptosis promotes the metastasis of cancer cells. It is not clear whether HPSE could mediate necroptosis of microvascular endothelial cells (MVECs) to promote HCC metastasis. Here we found HPSE expression was up-regulated in HCC tissues and its over-expression was correlated with multiple tumor foci, microvascular invasion, and poor outcome of HCC patients. Non-contact co-culture experiments showed high-expressed HPSE in HCC cells mediated the necroptosis of human umbilical vein endothelial cells (HUVECs) and elevated the expression levels of syndecan-1 (SDC-1) and tumor necrosis factor-α (TNF-α) in vitro. As a result of necroptosis, trans-endothelial migration (TEM) of HCC cells was increased. Conversely, both HPSE and SDC-1 knockdowns reversed necroptosis and decreased TNF-α expression level, while HPSE over-expression increased SDC-1 and TNF-α expression and aggravated necroptosis. Animal experiments found that the nude mice, intraperitoneally injected with HPSE high expressing HCC cells, had obvious necroptosis of MVECs and high intrahepatic metastasis rate, which could be relieved by inhibitor of necroptosis. Morever, HPSE elevated the expression levels of p38 mitogen-activated protein kinase (p38 MAPK) rather than nuclear factor kappa B in vitro. Our data suggest that HPSE induces necroptosis of MVECs to promote the metastasis of HCC by activating HPSE/SDC-1/TNF-α axis and p38 MAPK pathway.

The process of cavitation involves generation, growth, coalescence, and collapse of small bubbles and is tremendously influenced by bubble–bubble interactions. To understand these interactions, a new cavitation model based on the transport equation is proposed herein. The modified Rayleigh–Plesset equation is analyzed to determine the bubble growth rate by assuming equal-sized spherical bubble clouds. The source term in the transport equation is then derived according to the bubble growth rate with the bubble-bubble interaction. The proposed model is validated by various test simulations, including microscopic bubble cloud evolution as well as macroscopical two- and three-dimensional cavitating flows. Compared with previous models, namely the Kunz and Zwart cavitation models, the newly proposed model does not require adjustable parameters and generally results in better predictions both microscopic and macroscopical cases. This model is more physical.

Context Tangwang Mingmu granule (TWMM), a traditional Chinese medicine, has been widely used in the treatment of diabetic retinopathy (DR), the most common microvascular complication in diabetes mellitus. Objective To establish a method to select target compounds from herbs for a pharmacokinetic study using network pharmacology, which could be applied in clinical settings. Materials and methods First, UPLC/Q Exactive Q-Orbitrap and GCMS 2010 were used to determine the non-volatile and volatile ingredients of TWMM. Based on the identified compounds, network pharmacology was used to screen the key compounds and targets of TWMM in the treatment of DR. Based on the compound-target-pathway network and identification of components emigrant into blood, the potential compound markers in vivo were chosen. Then, Sprague-Dawley (SD) rats were administrated of TWMM at a 9.6 g/kg dose to investigating pharmacokinetic parameters using the UPLC-QQQ-MS. Results Ninety and forty-five compounds were identified by UPLC-MS and GC-MS, respectively. Based on the network pharmacology, nine compounds with a degree value above 15 were screened and implied that these compounds are the most active in DR treatment. Moreover, criteria of degree value greater than 7 were applied, and PTGS2, NOS2, AKT1, ESR1, TNF, and MAPK14 were inferred as the core targets in treating DR. After identification of components absorbed into blood, luteolin and formononetin were selected and used to investigate the pharmacokinetic parameters of TWMM after its oral administration. Conclusions The reported strategy provides a method that combines ingredient profiling, network pharmacology, and pharmacokinetics to determine luteolin and formononetin as the pharmacokinetic markers of TWMM. This strategy provides a clinically relevant methodology that allows for the screening of pharmacokinetic markers in Chinese medicines.

Lamiophlomis rotata (Benth.) Kudo (L. rotata) belongs to Lamiaceae family, which is an important medicinal plant endemic to Qinghai Tibet Plateau. Traditionally, the whole herb of L. rotata is used for medicine, especially for the treatment of rheumatoid arthritis (RA) in clinical practise. As a result of absolute digging, the plant has a long regeneration cycle after excavation and the damage to plateau grassland ecological environment is difficult to recover. It has been encouraged to use aerial part of the plant with the purpose of protecting environment and maintaning biological diversity. At present, researchers have compared the primary metabolites and iridoids between aerial parts and roots, but there are few reports on the chemical differences and activity comparison of secondary metabolites. In order to characterize the secondary metabolites of different parts, UPLC/Q-Orbitrap-MS was employed to collect data from the extracts of aerial parts and roots, in combination with plant metabolomics technology to screen and quantify differential metabolites. At the same time, network pharmacological analysis with rheumatoid arthritis and immunity as the key words was carried out according to the identification results to clarify the active ingredients of L. rotata in the treatment of RA, so as to speculate the pharmacological effects of aerial parts and roots based on the distribution of active components. A total of 16 potential markers were selected and identified to differentiate two parts. Among them, 8 characteristic flavonoids with similar skeletons were unique in aerial parts, while the other 8 components, including 2 iridoid glycosides and 6 phenylethanoid glycosides, were detected in both aerial parts and roots, but with differentiate contents. Among the predicted 6 active components, there were 5 flavonoids, of which 3 (namely luteolin, apigenin and 2″-acetylastragalin) were still differential metabolites and mainly distributed in the aerial parts. The results revealed that certain flavonoids as potential markers made a distinction between aerial part and root of L. rotata, and were the main active components against RA, which provided a theoretical basis for the aerial parts to replace the whole herbs, and laid a material foundation for further pharmacological research.

To solve the problems of low image contrast and low feature representation in infrared and visible image fusion, an image fusion algorithm based on latent low-rank representation (LatLRR) and non-subsampled shearlet transform (NSST) methods is proposed. First, infrared and visible images are decomposed into base subbands, saliency subbands and sparse noise subbands by the LatLRR model. Then, the base subbands are decomposed into low-frequency and high-frequency coefficients by NSST, and a feature extraction algorithm based on VGGNet and a logical weighting algorithm based on filtering are proposed to merge the coefficients. An adaptive threshold algorithm based on the regional energy ratio is proposed to fuse the saliency subbands. Finally, the fused base subbands are reconstructed, the sparse noise subbands are discarded, and a fused image is obtained by combining the subband information after fusion. Experimental results show that for the fused image produced, the algorithm performs well in both subjective and objective evaluation.

A high molecular polymer solution with viscoelasticity has the effect of reducing frictional drag, which is quite practical for energy saving. Effective simulations of viscoelastic flows in a pipeline with a high Reynolds number is realized by incorporating the constitutive equation of viscoelasticity into the k−ε−v′2¯−f turbulence model. The Finitely Extensive Nonlinear Elastic Peterlin (FENE-P) model is employed for characterizing the viscoelasticity. The drag reduction of fully developed viscoelastic pipe flow with a fixed mass flow rate is studied. Different from increasing the center velocity and without changing the velocity near the wall at a fixed pressure drop rate, the addition of a polymer reduces the velocity near the wall and increases the velocity at the center of the pipe and makes the flow tend to be a laminar flow. Decreasing the solvent viscosity ratio or increasing the maximum extensibility or the Weissenberg number can effectively reduce the turbulence intensity and the wall friction. Under the premise of ensuring calculation accuracy, this Reynolds-averaged simulation method for viscoelastic flow has significant advantages in both computational cost and accuracy, which is promising for drag reduction simulation and practical engineering applications.

Ocean flows and their interactions with marine structures, vehicles, etc [...]

The high spatial and temporal resolution of water body data offers valuable guidance for disaster monitoring and assessment. These data can be employed to quickly identify water bodies, especially small water bodies, and to accurately locate affected areas, which is significant for protecting people’s lives and property. However, the application of optical remote sensing is often limited by clouds and fog during actual floods. In this paper, water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM) are proposed, in which the MDFM fuses optical and synthetic aperture radar (SAR) images, and all-weather water extraction is achieved by using spectral information of optical images, texture information and the good penetration performance of SAR images. The SWEM further improves the accuracy of the water boundary with superpixel decomposition for extracted water boundaries using the fully constrained least squares (FCLS) method. The results show that the correlation coefficient (r) and area accuracy (Parea) of the MDFM and SWEM are improved by 2.22% and 9.20% (without clouds), respectively, and 3.61% and 18.99% (with clouds), respectively, compared with the MDFM, and 41.54% and 85.09% (without clouds), respectively, and 32.31% and 84.31% (with clouds), respectively, compared with the global surface water product of the European Commission Joint Research Centre’s Global Surface Water Explorer (JRC-GSWE). The MDFM and SWEM can extract water bodies with all weather and superpixel and improve the temporal and spatial resolution of water extraction, which has obvious advantages.

The mechanism of reaction in isobutane/2-butene alkylation systems is extremely complicated, accompanied by numerous side reactions. Therefore, a comprehensive understanding of the reaction pathways in this system is essential for an in-depth discussion of the reaction mechanism and for improving the selectivity of the major products (clean fuel blend components). The alkylation of isobutane/2-butene was studied using a self-made intermittent reaction device with a metering, cooling, reaction, vacuum and analysis system. The alkylates were qualitatively and quantitatively analyzed using a capillary gas chromatography-mass spectrometry-data system (CGC-MS-DS) and capillary gas chromatography with flame ionization detection (CCGC-FID), respectively, and the precision and recovery of the quantitative analytical methods were verified. The results showed that the relative standard deviation (RSD) of the standard sample was below 0.78%, and the recoveries were from 98.53% to 102.85%. Under the specified reaction conditions, 79 volatile substances were identified from the alkylates, and the selectivity of C8 and trimethylpentanes (TMPs) reached 63.63% and 53.81%, respectively. The changes of the main chemical components in the alkylation reaction with time were tracked and analyzed, based on which reaction pathways were determined, and a complex reaction network containing the main products’ and the by-products’ generation pathway was constructed.

Background: Schizophrenia is a serious mental disorder with complicated biological mechanisms. Few studies explore the transcriptional features that are shared in brain tissue and peripheral blood. In the present study, we aimed to explore the biological pathways with similar expression patterns in both peripheral blood mononuclear cells (PBMCs) and brain tissues.Methods: The present study used transcriptomics technology to detect mRNA expression of PBMCs of 10 drug-naïve patients with schizophrenia and 20 healthy controls. Transcriptome data sets of brain tissue of patients with schizophrenia downloaded from public databases were also analyzed in our study. The biological pathways with similar expression patterns in the PBMCs and brain tissues were uncovered by differential expression analysis, weighted gene co-expression network analysis (WGCNA), and pathway analysis. Finally, the expression levels of differential expressed genes (DEGs) were validated by real-time fluorescence quantitative polymerase chain reaction (qPCR) in another 12 drug-naïve patients with schizophrenia and 12 healthy controls.Results: We identified 542 DEGs, 51 DEGs, 732 DEGs, and 104 DEGs in PBMCs, dorsolateral prefrontal cortex, anterior cingulate gyrus, and nucleus accumbent, respectively. Five DEG clusters were recognized as having similar gene expression patterns in PBMCs and brain tissues by WGCNA. The pathway analysis illustrates that these DEG clusters are mainly enriched in several biological pathways that are related to phospholipid metabolism, ribosome signal transduction, and mitochondrial oxidative phosphorylation. The differential significance of PLAAT3, PLAAT4, PLD2, RPS29, RPL30, COX7C, COX7A2, NDUFAF2, and ATP5ME were confirmed by qPCR.Conclusions: This study finds that the pathways associated with phospholipid metabolism, ribosome signal transduction, and energy metabolism have similar expression patterns in PBMCs and brain tissues of patients with schizophrenia. Our results supply a novel insight for revealing the pathogenesis of schizophrenia and might offer a new approach to explore potential biological markers of peripheral blood in schizophrenia.

In this paper, we propose a model-free volumetric Next Best View (NBV) algorithm for accurate 3D reconstruction using a Markov Chain Monte Carlo method for high-mix-low-volume objects in manufacturing. The volumetric information gain based Next Best View algorithm can in real-time select the next optimal view that reveals the maximum uncertainty of the scanning environment with respect to a partially reconstructed 3D Occupancy map, without any priori knowledge of the target. Traditional Occupancy grid maps make two independence assumptions for computational tractability but suffer from the overconfident estimation of the occupancy probability for each voxel leading to less precise surface reconstructions. This paper proposes a special case of the Markov Chain Monte Carlo (MCMC) method, the Gibbs sampler, to accurately estimate the posterior occupancy probability of a voxel by randomly sampling from its high-dimensional full posterior occupancy probability given the entire volumetric map with respect to the forward sensor model with a Gaussian distribution. Numerical experiments validate the performance of the MCMC Gibbs sampler algorithm under the ROS-Industry framework to prove the accuracy of the reconstructed Occupancy map and the completeness of the registered point cloud. The proposed MCMC Occupancy mapping could be used to optimise the tuning parameters of the online NBV algorithms via the inverse sensor model to realise industry automation.

Every metal and metallurgical industry is associated with the generation of wastewater, influencing the living and non-living environment, which is alarming to environmentalists. The strict regulations about the dismissal of acid and metal into the environment and the increasing emphasis on the recycling/reuse of these effluents after proper remedy have focused the research community’s curiosity in developing distinctive approaches for the recovery of acid and metals from industrial wastewaters. This study reports the synthesis of UiO-66-(COOH)2 using dual ligand in water as a green solvent. Then, the prepared MOF nanoparticles were introduced into the DMAM quaternized QPPO matrix through a straightforward blending approach. Four defect-free UiO-66-(COOH)2/QPPO MMMs were prepared with four different MOF structures. The BET characterization of UiO-66-(COOH)2 nanoparticles with a highly crystalline structure and sub-nanometer pore size (~7 Å) was confirmed by XRD. Because of the introduction of MOF nanoparticles with an electrostatic interaction and pore size screening effect, a separation coefficient (SHCl/FeCl2) of 565 and UHCl of 0.0089 m·h−1 for U-C(60)/QPPO were perceived when the loading dosage of the MOF content was 10 wt%. The obtained results showed that the prepared defect-free MOF membrane has broad prospects in acid recovery applications.

Thermal decomposition technology is one of the main ways to treat biomass wastes. By utilizing chicken manure as raw material, thermogravimetric and derivative thermogravimetry (TG and DTG) are carried out on chicken manure at various heating rates (5, 10, 15, and 20 °C/min) under nitrogen and air atmosphere to explore the thermal decomposition characteristics and kinetics. The obtained results indicate that there are great discrepancies between them in these two atmospheres. Chicken manure is pyrolyzed under nitrogen; however, it is first thermally decomposed and then developed for combustion under air. The temperature range of the main peak of thermal decomposition in the air atmosphere is significantly lower than that in the nitrogen atmosphere by about 30 °C. Furthermore, the fluctuation of the DTG curve pertinent to the air atmosphere is commonly greater than that obtained in the nitrogen atmosphere, and the main combustion section usually presents an obvious W-shape peak. The thermal decomposition kinetics of the chicken manure is also determined as random nucleation model F1 by employing the Malek method, and the mechanism function is derived by f(α)=1−α. Under nitrogen atmosphere, the activation energy is between 24.18~31.55 kJ·mol−1. Under air atmospheric conditions, the minimum activation energy of the second temperature section is 8.85 kJ·mol−1, and the activation energy of the first section is less than that of the third section. In the presence of both nitrogen and air, the best result for the thermal decomposition of the sample is attained under the action of 10 °C/min heating rate. The corresponding activation energies are 31.55 kJ·mol−1 and 95.91 kJ·mol−1, respectively.

The solubility of dehydroabietic acid in (−)-α-pinene, p-cymene, (−)-β-caryophyllene, (−)-α-pinene + p-cymene, (−)-β-caryophyllene + p-cymene and (−)-α-pinene + (−)-β-caryophyllene were determined using the laser monitoring method at atmospheric pressure. The solubility of dehydroabietic acid was positively correlated with temperature from 295.15 to 339.46 K. (−)-α-pinene, p-cymene, and (−)-β-caryophyllene were found to be suitable for the solubilization of dehydroabietic acid. In addition, the non-random two liquid (NRTL), universal quasi-chemical (UNIQUAC), modified Apelblat, modified Wilson, modified Wilson–van’t Hoff, and λh models were applied to correlate the determined solubility data. The modified Apelblat model gave the minor deviation for dehydroabietic acid in monosolvents, while the λh equation showed the best result in the binary solvents. A comparative analysis of compatibility between solutes and solvents was carried out using Hansen solubility parameters. The thermodynamic functions of ΔsolH0, ΔsolS0, ΔsolG0 were calculated according to the van’t Hoff equation, indicating that the dissolution was an entropy-driven heat absorption process. The Conductor-like Screening Model for Real Solvents (COSMO-RS) combined with an experimental value was applied to predict the reasonable solubility data of dehydroabietic acid in the selected solvents systems. The interaction energy of the dehydroabietic acid with the solvent was analyzed by COSMO-RS.

Abstract Background Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. Methods To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. Results The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. Conclusions The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

The continuous-space single- and multi-facility location problem has attracted much attention in previous studies. This study focuses on determining the globally optimal facility locations for two- and higher-dimensional continuous-space facility location problems when the Manhattan distance is considered. Before we propose the exact method, we start with the continuous-space single-facility location problem and obtain the global minimizer for the problem using a statistical approach. Then, an exact method is developed to determine the globally optimal solution for the two- and higher-dimensional continuous-space facility location problem, which is different from the previous clustering algorithms. Based on the newly investigated properties of the minimizer, we extend it to multi-facility problems and transfer the continuous-space facility location problem to the discrete-space location problem. To illustrate the effectiveness and efficiency of the proposed method, several instances from a benchmark are provided to compare the performances of different methods, which illustrates the superiority of the proposed exact method in the decision-making of the continuous-space facility location problems.

The poor water retention capacity of sandy soils commonly aggregate soil erosion and ecological environment on the Chinese Loess Plateau. Due to its strong capacity for absorption and large specific surface area, the use of nanocarbon made of coconut shell as a soil amendment that could improve water retention was investigated. Soil column experiments were conducted in which a layer of nanocarbon mixed well with the soil was formed at a depth of 20 cm below the soil surface. Four different nanocarbon contents by weight (0%, 0.1%, 0.5%, and 1%) and five thicknesses of the nanocarbon- soil mixture layer ranging from 1 to 5 cm were considered. Cumulative infiltration and soil water content distributions were determined when water was added to soil columns. Soil Water Characteristic Curves (SWCC) were obtained using the centrifuge method. The principal results showed that the infiltration rate and cumulative infiltration increased with the increases of nanocarbon contents, to the thicknesses of the nano carbon-soil mixture layer. Soil water contents that below the soil-nano carbon layer decreased sharply. Both the Brooks-Corey and van Genuchten models could describe well the SWCC of the disturbed sandy soil with various nano carbon contents. Both the saturated water content (θs), residual water content (θr) and empirical parameter (α) increased with increasing nano carbon content, while the pore-size distribution parameter (n) decreased. The available soil water contents were efficiently increased with the increase in nanocarbon contents.

The Qinghai-Tibet Plateau (QTP) is experiencing severe permafrost degradation, which can affect the hydrological and biogeochemical processes. Yet how the permafrost change affects riverine carbon export remains uncertain. Here, we investigated the seasonal variations of dissolved inorganic and organic carbon (DIC and DOC) during flow seasons in a watershed located in the central QTP permafrost region. The results showed that riverine DIC concentrations (27.81 ± 9.75 mg L−1) were much higher than DOC concentrations (6.57 ± 2.24 mg L−1). DIC and DOC fluxes were 3.95 and 0.94 g C m−2 year−1, respectively. DIC concentrations increased from initial thaw (May) to freeze period (October), while DOC concentrations remained relatively steady. Daily dissolved carbon concentrations were more closely correlated with baseflow than that with total runoff. Spatially, average DIC and DOC concentrations were positively correlated with vegetation coverage but negatively correlated with bare land coverage. DIC concentrations increased with the thawed and frozen depths due to increased soil interflow, more thaw-released carbon, more groundwater contribution, and possibly more carbonate weathering by soil CO2 formed carbonic acid. The DIC and DOC fluxes increased with thawed depth and decreased with frozen layer thickness. The seasonality of riverine dissolved carbon export was highly dependent on active layer thawing and freezing processes, which highlights the importance of changing permafrost for riverine carbon export. Future warming in the QTP permafrost region may alter the quantity and mechanisms of riverine carbon export.

Three-dimensional (3D) triangulation based on active binocular vision has increasing amounts of applications in computer vision and robotics. An active binocular vision system with non-fixed cameras needs to calibrate the stereo extrinsic parameters online to perform 3D triangulation. However, the accuracy of stereo extrinsic parameters and disparity have a significant impact on 3D triangulation precision. We propose a novel eye gaze based 3D triangulation method that does not use stereo extrinsic parameters directly in order to reduce the impact. Instead, we drive both cameras to gaze at a 3D spatial point P at the optical center through visual servoing. Subsequently, we can obtain the 3D coordinates of P through the intersection of the two optical axes of both cameras. We have performed experiments to compare with previous disparity based work, named the integrated two-pose calibration (ITPC) method, using our robotic bionic eyes. The experiments show that our method achieves comparable results with ITPC.

The xylitol ester of hydrogenated rosin (XEHR) was obtained for the first time from biomass-based hydrogenated rosin and xylitol using an environmentally friendly, high-pressure CO2 catalytic synthesis. This compound is intended for use as an emulsifier for food. Analyses by ICP-AES showed the absence of heavy metal residues in the product, such that it met food standards. Fourier transform infrared and nuclear magnetic resonance spectroscopies together with gel permeation chromatography confirmed the successful esterification and the formation of a monoester and diester with molar masses of 427 and 772 g/mol. The emulsification of water/soybean oil mixtures by adding the XEHR was assessed at pH values of 4, 6.86, and 10 and in the presence of NaCl, KCl, MgCl2, and CaCl2. The XEHR was found to act as an emulsifier by reducing the interfacial tension of such mixtures to less than 2 mN/m under all conditions. The highest emulsifying activity index (9.52 m2/g) and emulsifying stability index (94.53%) were obtained after adding MgCl2 (100 mM). Particle size and confocal microscopy showed that the presence of salts gave a more uniform droplet size and a finer emulsion structure. The high viscosities of the emulsions containing salts also suggested a more cohesive oil droplet network.

A heterogeneous catalyst (ZnO/SFCCR) composed of ZnO supported on spent fluid cracking catalyst by wet impregnation was synthesized and applied to the esterification of colophony acids with methanol under subcritical CO2 conditions. The catalyst was characterized by SEM-EDS, BET, ICP, FTIR, XRD and Py-IR. An experimental set-up involving a new injection technique was designed to promote the heterogeneous methyl esterification, and the subcritical CO2 played a role in auxiliary acid catalysis (a pH range of 3.54–3.91), increasing the lifespan of ZnO/SFCCR, reducing the viscosity of the system to promote gas–liquid mass transfer. A maximum conversion rate of 97.01% was obtained in a relatively short time of 5 h. Kinetic experiments were performed from 190 to 220°C using a special high-temperature sampling device and analysing aliquots with high-performance liquid chromatography. A new reaction pathway, involving methyl abietate, methyl dehydroabietate, methyl neoabietate and methyl palustrate along with other kinds of colophony acids, was developed. The kinetic parameters were obtained using the Levenberg–Marquardt nonlinear least-squares method, and the activation energies for the isomerizations of neoabietic and palustric acids and for the methyl esterification of neoabietic, abietic, palustric and dehydroabietic acids were found to be 107.09, 113.95, 68.99, 49.85, 75.43 and 59.20 kJ mol−1, respectively. The results from the kinetic model were in good agreement with experimental values.

Rosin esters are widely applied as masticatory substances and beverage stabilizers, while classical acid-catalysed processes will lead to metal residue or environmental issues. Super/subcritical CO2–enriched high temperature liquid water (HTLW) as a green acid catalyst in the esterification reaction of rosin with glycerol was investigated. The pH of CO2–H2O binary system, as calculated based on gas–liquid equilibrium, charge balance and chemical equilibrium equations, ranged from 3.49 to 3.70 depending on the reaction conditions, indicating effective acid catalysis. Response surface methodology experiments showed the optimum conditions were 3.5 h, 3.9 MPa CO2 pressure, a rosin-to-glycerol molar ratio of 1.32 and 269°C, and an enhanced esterification yield of 94.74% was achieved, which was superior to that obtained using a ZnO catalyst. It was found that the esterification kinetics was a pseudo first-order reaction, and the enthalpy and entropy of activation were calculated using the Arrhenius–Polanyi equation. The presence of super/subcritical CO2-enriched HTLW catalyst can decrease the activation energy and significantly accelerate the reaction rate.