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Abstract Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). Imputation method was applied to deal with the missing data. A generative ML model Vanilla Gan was utilized to create synthetic data to further augment the size of available dataset and the SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results indicated that data imputation allowed for the full utilization of the limited dataset, leading to good machine learning prediction performance and preventing common overfitting problems with small-sized data. Additionally, augmenting experimental data with synthetic data significantly improved prediction accuracy and considerably reduced overfitting issues. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws.

Cancer-associated fibroblasts (CAFs) can interact with macrophages in the tumor microenvironment by secreting extracellular vesicles (EVs), thereby affecting tumor progression. However, the mechanisms of CAF-secreted EVs in gastric cancer (GC) remain not well understood. Here, we investigated the effect of CAF-EVs on macrophage polarization in GC and the underlying mechanisms. Macrophage polarization was evaluated using flow cytometry and quantitative real-time polymerase chain reaction. GC cell proliferation was determined using cell counting kit-8, EdU, and colony formation assays. The molecular mechanism was explored using microarray analysis, dual-luciferase reporter assay, and RNA pull-down analysis. The results showed that CAFs secreted EVs that inhibit macrophage M1 polarization and promote M2 polarization. Moreover, miR-4253 expression was increased in CAF-EVs, and inhibition of miR-4253 reversed the macrophage polarization induced by EVs. IL6R was identified as the target of miR-4253. Additionally, macrophages treated with EVs that encapsulated miR-4253 promote GC cell proliferation. In conclusion, CAF-secreted EVs packaging miR-4253 facilitate macrophage polarization from M1 to M2 phenotype by targeting IL6R, thereby accelerating GC cell proliferation. The findings suggest that EV-encapsulated miR-4253 may be a promising therapeutic target of GC.

Abstract In cold regions, the environment dynamics lead to variations of soil temperature, water content, and deformation, which are characterized by highly coupled physical interplay. The hydraulic and thermal properties of unsaturated soils are highly nonlinear, which is further complicated when subjected to freezing. This paper presents a comprehensive multiphysics coupling model to evaluate these complex processes. The model considers the behaviors of unsaturated frozen soils. It accounts for the influences of meteorological, geothermal, and hydrological factors. The model is validated through two pavement case studies using Long-Term Pavement Performance (LTPP) road section data. The first case analysis is performed for a pavement section in Vermont, and the simulation lasted for 30 days during a non-freezing season on an hourly basis. The results validated the performance of the model considering unsaturated soil behaviors. The second case study is based on a daily analysis of a pavement section in South Dakota over a freezing–thawing cycle over 194 days. The results validated the model in considering the frozen unsaturated soil behaviors. Both case studies demonstrate the performance of this comprehensive model in quantifying the spatial and temporal variations of soil temperature and water content in response to environmental stressors. The capability of the model in accurately predicting the responses of pavement to the meteorological factors unleashes the potential of this model to assess the effects of climate and climate change on cold region pavement, as well as other types of geo-structures.

Abstract There are over 2.2 million miles of underground water pipes serving the cities in the United States. Many are in poor conditions and deteriorate rapidly. Failures of these pipes could cause enormous financial losses to the customers and communities. Inspection provides crucial information for pipe condition assessment and maintenance plan; it, however, is very expensive for underground pipes due to accessibility issues. Therefore, water agencies commonly face the challenge to 1) decide whether it is worthwhile to schedule expensive water pipe inspections under financial constraints, and 2) if so, how to optimize the inspection schedule to maximize its value. This study leverages the physical model and data-based ML (ML) models for underground water pipe failure prediction to shed light on these two important questions for decision making. Analyses are firstly conducted to assess the value of water pipe inspection. Results by use of a physical-based failure model and Monte Carlo simulations indicate that by inspecting pipe’s condition, i.e., assessment of pipe’s erosion depth, the uncertainty of water pipe failure prediction can be narrowed down by 51%. For optimal inspection schedule, an artificial neural network (ANN) model, trained with historical inspection data, is evaluated for its performance in forecasting the future pipe failure probability. The results showed that a biased pipe failure prediction can occur under limited rounds of inspection. However, incorporating more rounds of inspection allows to predict the pipe failure conditions over its life cycle. From this, an optimal inspection plan can be proposed to achieve the maximum benefits of inspection in uncertainty reduction. A few salient results from the analyses include 1) the optimal schedule for inspection is not necessarily equal in the time interval, 2) by setting the goal of uncertainty reduction, an optimal inspection schedule can be obtained, where ML (ML) model augmented by continuously training with inspection data allows to reliably predict water pipe failure conditions over its life cycle. While this study focuses on underground pipe inspection, the general observations and methodology are applicable to optimize the inspection of other types of infrastructure as well.

Abstract Climate change is projected to have profound impacts on the resilience and sustainability of built infrastructure. This study aims to understand the impacts of climate change on water supply systems and to facilitate adaptive actions. A premium database maintained by the Cleveland Water Division, Cleveland, Ohio, USA is analyzed. It contains 29,621 pipe failure records of 51,832 pipes over the past 30 years, representing one of the largest dataset in current literature. From the database, pipe failure rate models have been developed for water pipes made of different types of materials at different ages. The influence of climate (temperature and precipitation) on fragility of water pipes are obtained. Based on the developed climate-fragility failure rate models, the impacts of climate change on the water systems located in different geographic regions are evaluated by predicting the failure rate and number of failures in the water systems in the next 80 years (2020 to 2100). Climate models are used to predict weather under different climate change scenarios. The results demonstrate that the impacts of climate change on water supply system are likely complicated and are dependent upon factors such as the geographic location, pipe material, pipe age, and maintenance strategies. Water pipes in the cold regions may experience fewer number breaks due to the warmer weather and less severe winter, whereas those located in the hot regions may experience more failures associated with more corrosion. Different pipe replacement strategies are compared, which demonstrate the importance of considering the aging of water supply system in future maintenance decisions. This study enriches current understandings on the impacts of climate change on the water systems. The results will help water utilities to design climate change adaptation strategies.

In this paper, the characteristics of absolute value memristors are verified through the circuit implementation and construction of a chaotic system with a conditional symmetric fractional-order memristor. The dynamic behavior of fractional-order memristor systems is explored using fractional-order calculus theory and the Adomian Decomposition Method (ADM). Concurrently, the investigation probes into the existence of coexisting symmetric attractors, multiple coexisting bifurcation diagrams, and Lyapunov exponent spectra (LEs) utilizing system parameters as variables. Additionally, the system demonstrates an intriguing phenomenon known as offset boosting, where the embedding of an offset can adjust the position and size of the system’s attractors. To ensure the practical applicability of these findings, a fractional-order sliding mode synchronization control scheme, inspired by integer-order sliding mode theory, is designed. The rationality and feasibility of this scheme are validated through a theoretical analysis and numerical simulation.

Abstract As the backbone and the ‘blood vessel’ of modern cities, road networks provide critical support for community activities and economic growth, with their roles even more crucial due to the dramatic progress in urbanization. The service of road networks is subjected to the increasing frequency of high-consequence natural hazards such as earthquakes, floods, hurricanes, etc. Identifying resilient restoration sequences is essential to mitigate the disruption of such important infrastructure networks. This paper investigates a novel decision-support model to optimize post-disaster road network repair sequence. The model, named as GCN-DRL model, integrates the advantages of deep reinforced learning (DRL) with graph convolutional neural network (GCN), two emerging artificial intelligence (AI) techniques to achieve efficient recovery of road network service. The model is applied to analyze two cases of community road networks in the US that are subjected to different types of hazards, i.e., earthquakes and flooding. The performance of repair sequence by the GCN-DRL model is compared with two commonly used methods, i.e., repair sequence by the genetic algorithm and by prioritization based on graph importance with betweenness centrality. The results showed the decision sequence by GCN-DRL model consistently achieved superior performance in road network restoration than the conventional methods. The AI-based decision model also features high computational efficiency since the GCN-DRL model can be trained before the hazard. With a pre-trained GCN-DRL model, a close to optimal decision-making process can be made available rapidly for different types of new hazards, which is advantageous in efficiently responding to hazards when they happen. This study demonstrates the promise of a new AI-based decision support model to improve the resilience of road networks by enabling efficient post-hazards recovery.

In order to improve the reinforcement effect of microbially induced calcium carbonate precipitation (MICP) technology on sand, the test of MICP-treated sand reinforced by pozzolan was designed and carried out considering the porous structure and activity characteristics of pozzolan. The enhancement effect and enhancement mechanism of pozzolan on the MICP-treated sand were systematically analyzed by integrating macroscopic physical and mechanical tests and microscopic tests. The results showed that: 1) Pozzolan could significantly improve the bacteria fixation rate and cementation material production in the process of sand microbial reinforcement, and the optimal amount of pozzolan content was around 10%, which increased the bacteria fixation rate by 118.28% and the cementation substance production by 29.55% compared with the conventional MICP. 2) The addition of pozzolan greatly improved the compressive strength and resistance to deformation of bio-cemented soil. Under different confining pressures, the compressive strength of the bio-cemented sand increased by 52.26%−62.96%, and the strain at failure increased by 100.00%−112.58%. 3) After the addition of pozzolan, the pore size and void ratio of the bio-cemented sand were decreased obviously, and the overall compactness and impermeability performance were further improved, with the void ratio decreased from 20.12% to 14.17% and the permeability coefficient reduced by an order of magnitude. 4) The enhancement mechanism of pozzolan on MICP-treated sand mainly included three aspects: for one thing, the pozzolan played a good filling effect between the sand particles, which significantly reduced the large pores between the particles and increased the compactness of the sample; for another, the good adsorption of pozzolan effectively increased the content of bacteria in the sample, which increased the production of calcium carbonate and enhanced the uniformity of distribution; on the third hand, the active substances in pozzolan participate in the reaction to generate gelling substances and calcium carbonate crystals to form a composite gel, which could further enhanced the cementation properties and compactness of the bio-cemented sand.

Waning of neutralizing titres along with decline of protection efficacy after the second dose of COVID-19 vaccines was observed, including China-made inactivated vaccines. Efficacy of a heterologous boosting using one dose of a recombinant SARS-CoV-2 fusion protein vaccine (V-01) in inactivated vaccine-primed population was studied, aimed to restore the immunity. A randomized, double-blind and placebo-controlled phase III trial was conducted in healthy people aged 18 years or older in Pakistan and Malaysia. Each eligible participant received one dose of the V-01 vaccine developed by Livzon Mabpharm Inc. or placebo within the 3-6 months after the two-dose primary regimen, and was monitored for safety and efficacy. The primary endpoint was protection against confirmed symptomatic SARS-CoV-2 infection. A total of 10,218 participants were randomly assigned to receive a vaccine or placebo. Virus-neutralizing antibodies were assessed in 419 participants. A dramatic increase (11.3-fold; 128.3–1452.8) of neutralizing titres was measured in the V-01 group at 14 days after the booster. Over two months of surveillance, vaccine efficacy was 47.8% (95%CI: 22.6–64.7) according to the intention-to-treat principle. The most common adverse events were transient, mild-to-moderate pain at the injection site, fever, headache, and fatigue. Serious adverse events occurred almost equally in V-01 (0.12%) and placebo (0.16%) groups. The heterologous boosting with the V-01 vaccine was safe and efficacious, which could elicit robust humoral immunity under the epidemic of the Omicron variant.Trial registration: ClinicalTrials.gov identifier: NCT05096832.

Abstract This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions.

Geotextile offers numerous benefits in improving pavement performance, including drainage, barrier functionality, filtration, and reinforcement. Wicking geotextile, a novel variant in this category, possesses the intrinsic ability to drain water autonomously from soils. This paper details the development and application of a comprehensive multiphysics model that simulates the performance of wicking geotextile within a pavement system under freezing climates. The model considers the inputs of various environmental dynamics, including the impact of meteorological factors, groundwater levels, ground heat, and drainage on the pavement system. The model was firstly validated using field data from a long-term pavement performance (LTPP) road section in the cold region. It was subsequently applied to assess the impacts of wicking geotextile if it was installed on the road section. The model simulated the coupled temporal and spatial variations in soil moisture content and temperature. The simulation results demonstrated that wicking geotextile would create a suction zone around its installation location to draw water from surrounding soils, therefore reducing the overall unfrozen water content in the pavement. The results also showed that the installation of wicking geotextile would delay the initiation of frost heave and reduce its magnitude in cold region pavement.

Abstract This study conducted a comprehensive characterization and analyses on the fire-resistant behaviors of novel fungal fibers grown with substrate containing Silica (Si) source at multiple scales. At micro-scale, the results of SEM showed that silica affected the physiological activities of fungi, with the extent of effects depending upon its concentration. Fourier-transform infrared (FTIR) spectra displayed the existence of Si–O–C chemical bonds in fungal fibers grown with Si source, indicating that Si source becomes a part of the structure of fungal fibers. Thermogravimetric analysis (TGA) and Microscale combustion calorimetry (MCC) of fungal fibers exhibit an early thermal decomposition of non-combustible components, which will potentially help release the thermal stress and mitigation of spalling when used in concrete. Compared with polypropylene (PP) fibers, fungal fibers have a lower thermal degradation rate, a higher residual weight, a lower heat release peak temperature, and less total heat of combustion; all of these indicate improved thermal stability and fire resistance, and a lower rate of function loss in case of a fire. Additionally, the thermal stability and fire resistance of fungal fibers were improved with the increase of Si source concentration in the nutrition medium. For example, addition of 2% Si source in the feeding substrate leads to a 23.21% increase in residual weight in TGA, and a 23.66 W/g decrease in peak heat release rate as well as a 2.44 kJ/g reduction in total heat of combustion in MCC. At laboratory scale, compared with PP fibers, fungal fibers grown with 2% Si source have a higher residual weight of 40.40%, a higher ignition temperature of 200.50 °C, and a declined flame height of 11.64 mm in real fire scenarios. Furthermore, only in the fungal fibers grown with Si source, partial burning occurred. In post-fire conditions, the microstructure of residual char from fungal fibers grown with higher content of Si source became denser, which would lead to a reduction of the fuel vapor release and heat transfer. FTIR spectra of residual char demonstrated that fungal fibers grown with Si source formed more stable chemical bonds with higher heat of chemical bond formation, contributing to improved thermal stability and fire resistance. Therefore, compared with traditional fibers used for fiber reinforced concrete, incorporating the new natural grown fibers will potentially further improve the fire resistance of concrete and mitigate the concrete spalling.

This paper aims to provide insight on factors affecting the bulk shear strength parameters of clay, i.e., the cohesion and internal friction as well as the effects of memory of preconsolidation pressure. A unique Discrete Element Method (DEM) model is built on platy particles with customized particle interaction force model. The particle interaction force model considers non-contact forces (such as the long-range electrostatic repulsion, short-range van der Waals attraction) as well as the direct contact force. The long-range electrostatic forces are calibrated by measurement with Atomic Force Microscope (AFM). Parameters for direct particle contacts of the regular DEM contact model, such as the contact stiffness and the friction coefficient, are calibrated by use of experimental consolidation and direct shear testing data. Computational simulations are conducted on digital clay specimen subjected to virtual direct shear tests. The predicted experimental responses of the digital specimens are consistent with typically observed in experiments including the shear stress-shear strain relationship, volumetric contraction and dilation, shear band formation, etc. From the stress–strain curves, the soil strength parameters are obtained with common experimental criteria. The DEM simulation results show that higher preconsolidation pressure drives more particles to overcome non-contact force into direct contacts and consequently bonding by van der Waals force. Consequently, the bulk cohesion of clay increases with increasing preconsolidation pressure. The results show that bulk cohesion strength is mainly attributed to the attractive interparticle force as well as the interlocking of platy particle, while the bulk internal friction angle is affected by the particle friction coefficient and particle fabric. Overall, the simulation results indicate the experimentally observed macroscopic shear strength parameters c and φ are linked to the microscope characteristics of soil particles and their mutual interactions. The results offer insight on the microscopic properties of particles on the bulk macroscopic strength behaviors. Besides, this work demonstrates a new strategy for simulation-based prediction of bulk soil strength parameters, by incorporating microscale characterization of the interparticle interactions and particle fabric into the particle-based DEM model.

Composite coatings were prepared by using polisilizane as matric resin and h-BN as functional filler.Raw BN was modified by chemical method to improve its distribution in composite coatings.FT-IR and TEM characterizations were applied to analyse the chemical structure of the raw BN and modified BN samples.SEM was used to observe the microstructure of the as-prepared coatings.The optimal resin content was set as 50% (mass fraction) by characterizing the microstructure of composite coatings with varied resin contents. In addition, basic properties of composite coatings were also tested and analysed.The results show that raw BN is successfully modified using chemical method.The modified BN displays better distribution in composite coatings as expected.The as-prepared coatings have good impermeability and thermal-oxidation resistance.

Road closure is an effective measure to reduce mobility and prevent the spread of an epidemic in severe public health crises. For instance, during the peak waves of the global COVID-19 pandemic, many countries implemented road closure policies, such as the traffic-calming strategy in the UK. However, it is still not clear how such road closures, if used as a response to different modes of epidemic spreading, affect the resilient performance of large-scale road networks in terms of their efficiency and overall accessibility. In this paper, we propose a simulation-based approach to theoretically investigate two types of spreading mechanisms and evaluate the effectiveness of both static and dynamic response scenarios, including the sporadic epidemic spreading based on network topologies and trajectory-based spreading caused by superspreaders in megacities. The results showed that (1) the road network demonstrates comparatively worse resilient behavior under the trajectory-based spreading mode; (2) the road density and centrality order, as well as the network's regional geographical characteristics, can substantially alter the level of impacts and introduce heterogeneity into the recovery processes; and (3) the resilience lost under static recovery and dynamic recovery scenarios is 8.6 and 6.9%, respectively, which demonstrates the necessity of a dynamic response and the importance of making a systematic and strategic recovery plan. Policy and managerial implications are also discussed. This paper provides new insights for better managing the resilience of urban road networks against public health crises in the post-COVID era.

Objective To explore the effects of TLK2 in breast cancer (BC) cells and its possible mechanism. Methods Immunohistochemistry and Western blot were used to detect the expression of TLK2 in BC tissues and cell lines. RT-qPCR was employed to detect the expression of TLK2 mRNA and miR-622 in BC tissues and cell lines. CCK-8, BrdU and Transwell assays were used to detect the effects of TLK2 over-expression plasmid and miR-622 inhibitors on the proliferation, migration and invasion of HCC70 cells. The targeted regulatory relationship between miR-622 and TLK2 was predicted and verified by Target scanning online analysis database and dual-luciferase reporter gene, respectively. Results The expression of TLK2 in BC tissues or cell lines was significantly up-regulated (P＜0.05), and TLK2 significantly promoted the proliferation, migration and invasion of HCC70 cells (P＜0.01). The possible mechanism suggested that miR-622 can bind to the 3′UTR of TLK2. Conclusions TLK2 is involved in promoting the proliferation, migration and invasion of BC cells, and is negatively regulated by miR-622.

Objective To analyze the effect of total etching and self-etching adhesive systems on resin-dentin interfaces and stress distribution under static tensile loading in order to understand the function of 2 interfaces under static loading. Methods Two idealized micro-scaled three-dimensional finite element models were constructed according to the micromorphological characteristics of the total etching and self-etching adhesive system bonded dentin interfaces. The stress distribution of the 2 interfaces was analyzed under a static tensile load of 20 MPa, as well as stress concentration factors of each structural components were calculated. Results The structural components of both bonded interfaces were subjected to tensile stress under static tensile loading, as the stress was concentrated predominantly at the junctions of different structural components. The highest stress concentration factor was found in the partially demineralized dentin in the total etching bonded interface (2.91), and the factor was 2.52 in the intact dentin in the self-etching bonded interface. The stress was lower in the self-etching bonded interface than the total etching bonded interface (50.32 vs 58.27 MPa). Conclusion Based on the 2 micro-scaled interfacial models constructed in this study, the mechanical stability of self-etching bonded interface is much better than that of the total etching bonded interface.

The objective of this study was to examine the effects of dietary inclusion of microalgae (Schizochytrium spp.) on milk yield, milk composition, and docosahexaenoic acid (DHA) transfer efficiency in dairy cows. Thirty-six lactating Chinese-Holstein dairy cows were randomly allocated to three treatment groups (n = 12; 0, 170, and 255 g microalgae supplement per day) in a 60-day experimental period. No significant treatment effect was observed on DMI and milk performance. Similarly, there was no significant microalgae supplement effect on blood haematological and biochemical parameters, except for platelets (P < 0.01) and thrombocytosis (P < 0.01), suggesting that the inclusion of microalgae in dairy cow diets would not affect production performance and animal health. Compared to the control group, adding 170 and 255 g microalgae to diets significantly increased the proportion of linoleic acid, DHA, n-3 and n-3/n-6 ratio in the blood (P < 0.05). Consequently, DHA concentration and n-3/n-6 ratio were increased in milk, indicating that the milk fatty acid composition could be affected by nutritional manipulation. The overall DHA transfer efficiency was 10.1% and 11.3% for 170 and 255 g microalgae supplement, suggesting that the addition of microalgae to dairy cow diets is a feasible strategy to produce DHA enriched milk in practice.

A pavement’s roughness seriously affects its service life and driving comfort. Considering the complexity and low accuracy of the current recognition algorithms for the roughness grade of pavements, this paper proposes a real-time pavement roughness recognition method with a lightweight residual convolutional network and time-series acceleration. Firstly, a random input pavement model is established by the white noise method, and the pavement roughness of a 1/4 vehicle vibration model is simulated to obtain the vehicle vibration response data. Then, the residual convolutional network is used to learn the deep-level information of the sample signal. The residual convolutional neural network recognizes the pavement roughness grade quickly and accurately. The experimental results show that the residual convolutional neural network has a robust feature-capturing ability for vehicle vibration signals, and the classification features can be obtained quickly. The accuracy of pavement roughness classification is as high as 98.7%, which significantly improves the accuracy and reduces the computational effort of the recognition algorithm, and is suitable for pavement roughness grade classification.

The nutrition loss of silage is partly due to the heat production of silage. In this study, the amount of nutrition loss of silage was estimated by measuring the heat production of silage, and the concept of the accumulated temperature of fermentation was put forward. The laboratory measured the fermentation-accumulated temperature of whole-plant corn silage with different compaction densities. The samples were analyzed to establish a multiple linear regression model with nutrition loss. The results demonstrated a significant non-linear relationship between the whole-plant corn silage compaction density and fermentation-accumulated temperature. The multiple linear regression model between the accumulated-fermentation temperature and nutrition loss was significant under different densities. The amount of silage nutrition loss can be predicted by the fermentation-accumulated temperature.

In ruminants, the bacterial community in the gastrointestinal tract (GIT) has an essential role in healthy growth. Examining the bacterial composition in the GIT between growth-retarded and normal yaks could improve our understanding of the role of microorganisms in yaks with growth retardation. In this study, eight male yaks with growth retardation were used as the growth-retarded yak (GRY) group, and another eight male growth normal yaks (GNYs) with the same breed and age were used as the GNY group. We compared the bacterial community in the rumen, duodenum, jejunum, ileum, cecum, and colon between GRY and GNY groups based on the 16S ribosomal RNA gene sequencing. Alpha-diversity revealed that the Shannon index in the duodenum and ileum of the GNY group was higher (P < 0.05) than that of the GRY group. However, the opposite trend was found in the jejunum and cecum. The principal coordinates analysis (PCoA) showed that the bacterial structure in all segments of GIT differed from each other between two groups. In the rumen, the relative abundances of Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-014, and Treponema 2 were higher (P < 0.05) in the GNY group as compared with the GRY group. However, the Christensenellaceae R-7 group exhibited an opposite trend. In the jejunum, compared with the GNY group, the unclassified Chitinophagaceae was enriched significantly (P < 0.05) in the GRY group. However, the unclassified Peptostreptococcaceae, Christensenellaceae R-7 group, and Lachnospiraceae NK3A20 group were enriched (P < 0.05) in the GNY group. In the ileum, the relative abundances of the Rikenellaceae RC9 gut group and Prevotellaceae UCG-004 were higher (P < 0.05) in the GNY group than those in the GRY group. In the cecum, the GNY group showed a higher (P < 0.05) relative abundance of Prevotellaceae UCG-003 as compared with the GRY group. In the colon, the relative abundances of Treponema 2 and unclassified Lachnospiraceae were slightly higher (0.05 < P < 0.10) in the GNY group than those in the GRY group. Overall, these results improve our knowledge about the bacterial composition in the GIT of growth-retarded and normal yaks, and regulating the bacterial community may be an effective solution to promote the compensatory growth of GRYs.

The current study was designed to investigate the chemical composition, amino acid content, and rumen degradation characteristics (nylon bag method) of six organic feeds to illustrate their feeding values. The feeds analyzed were: corn grain (CG), soybean cake (SC), wheat bran (WB), corn silage (CS), oat hay (OT), and alfalfa hay (AF). Our results showed that the contents of crude protein (CP) (47.46%) and ether extract (EE) (8.23%) in SC were highest. The contents of neutral detergent fiber (NDF) (65.00%) and acid detergent fiber (ADF) (39.16%) in OT were highest. The contents of total amino acid (TAA) (42.95%) and essential amino acid (EAA) (19.73%) in SC were highest. Among SC, WB, and CG, the effective degradation rate (ED) of dry matter (DM) is SC (87.89%) > WB (73.32%) > CG (64.25%); the ED value of CP is CG (82.37%) > WB (82.40%) > SC (60.47%). Among CS, OT, and AF, the effective degradation rate (ED) of DM is CS (72.68%) > OT (59.97%) > AF (58.89%); the ED value of CP is AF (76.46%) > CS (72.03%) > OT (71.99%). In conclusion, the chemical composition, amino acid content, and rumen degradation rate of SC and AF were better than those of the other four feeds.

Nitrogen-doped TiO2 has a great potential as a photocatalyst under visible light irradiation with applications in the removal of air and water pollutants, and the treatment of bacterial contaminations. In this study, nitrogen-doped TiO2 nanoparticles were synthesized via the sol–gel method and a post-annealing heat treatment approach. The effects of annealing treatment on the photocatalyst crystalline size and degree of crystallinity were analyzed. Methylene blue dye was used as the model water contaminant for the evaluation of the photoactivity of the synthesized nitrogen-doped TiO2 nanoparticles. The degradation of methylene blue was attributed to three mechanisms, i.e., adsorption, photocatalysis, and direct light photolysis. A kinetic model was developed to distinguish the impact of these three different mechanisms on the removal of contaminants. Adsorption and photocatalysis are heterogeneous processes for removing water organic contaminants. The characterization analysis demonstrates that they are relevant to the microstructures and surface chemical compositions of nitrogen-doped TiO2 photocatalysts. The processing–structure–performance relationship helped to determine the optimal processing parameters for nitrogen-doped TiO2 photocatalyst to achieve the best performance. While we used methylene blue as the model contaminant, the generalized quantitative model framework developed in this study can be extended to other types of contaminants after proper calibration.

Type IV pili are present on a wide range of bacterial pathogens and mediate diverse functions. Here the authors report a high resolution crystal structure of the pilin subunit PilE, and a cryoEM reconstruction of the Type IV pilus filament from N. meningitidisthat offer insight into pilus assembly and functions.

β-casein is a primary protein in milk, and its variants have been associated with changes in the protein content of bovine milk. However, there has been little research focused on the effects of β-casein variants on milk metabolites. In the present study, dairy cows producing milk with β-casein variant A1/A1 (A1), A2/A2 (A2), and their heterozygote A1/A2 (A12) were screened by a high-resolution melting method. Individual milk samples were then collected from each of the cows, and the milk metabolites were separated and analyzed using nuclear magnetic resonance spectroscopy- and liquid-chromatography mass spectrometry-based metabolomics techniques. Differences in metabolites among the variant groups were evaluated by multivariate statistical analysis. The relative abundances of methionine, proline, and α-lactose were the highest in β-casein variant A2 milk, whereas choline, glycine, citric acid, and cyclic adenosine monophosphate (cAMP) showed the highest abundances in variant A1 milk. Metabolic pathways analysis indicated that the differential metabolites between variants A1 and A2 were involved in pantothenate and coenzyme A biosynthesis, butanoate metabolism, and valine, leucine, and isoleucine biosynthesis. Our results reveal the differences in milk metabolites among the β-casein variants A1, A2, and the heterozygote. These findings, thus, provide novel insights into the effects of β-casein variants on milk metabolites, facilitating further research into the mechanism of the biosynthesis of milk components in the mammary gland and the potential physiological function of milk associated with β-casein variants.

Daughter mineral species, chemical compositions and distribution of daughter minerals in fluid inclusions can provide important constraints on the composition of hydrothermal fluids, physicochemical conditions of mineralization, the regularity of fluid evolution and ore-forming mechanisms. The traditional methods however, have many limitations such as finding target minerals and identifying opaque minerals. Described in this paper, fluid inclusions in minerals are frozen and then opened by Frozen Transmission Equipment (Cryotransfer). The compositions of daughter minerals and liquid phases are determined by Scanning Electron Microscope (SEM) and Energy Disperse Spectroscope. This method solved the difficulty in finding target minerals by SEM. Daughter minerals cannot be lost after opening liquid inclusions, and cannot be mixed with debris. This method can effectively determine the compositions of small-sized (1-2 μm) opaque daughter minerals and the solid-liquid phases of fluid inclusions in opaque minerals. Mass fractions of Na+, K+, Cl- in liquid inclusions of Wangjiazhuang copper deposit in Zouping of Shandong province are 5.85%, 3.60% and 16.18%, respectively, and the calculated salinity is 35.35%. The opaque daughter minerals are identified to be chalcopyrite, which proves that chalcopyrite has crystallized from hydrothermal fluids with high salinity. This study provides a new determination method for fluid inclusion, and has great significance in analyzing the compositions of solid and liquid phases in fluid inclusions.

The flotation process is a particle-hydrophobic surface-based separation technique. To improve the essential flotation steps of collision and attachment probabilities, and reduce the step of detachment probabilities between air bubbles and hydrophobic particles, a selectively designed cavitation venturi tube combined with a static mixer can be used to generate very high numbers of pico and nano bubbles in a flotation column. Fully embraced by those high numbers of tiny bubbles, hydrophobic particles readily attract the tiny bubbles to their surfaces. The results of column flotation of Pittsburgh No. 8 seam coal are obtained in a 5.08 cm ID and 162 cm height flotation column equipped with a static mixer and cavitation venturi tube, using kerosene as collector and MIBC as frother. Design of the experimental procedure is combined with a statistical two-stepwise analysis to determine the optimal operating conditions for maximum recovery at a specified grade. The effect of independent variables on the responses has been explained. Combustible material recovery of 85–90% at clean coal product of 10–11% ash is obtained from feed of 29.6% ash, with a much-reduced amount of frother and collector than that used in conventional column flotation. The column flotation process utilizing pico and nano bubbles can also be extended to the lower limit and upper limit of particle size ranges, minus 75 μm and 300–600 μm, respectively, for better recovery. Keywords: Pico–nano bubble generation, Cavitation venturi tube, Fine coal flotation, Statistical analysis method