Effects of thinning on the understory light environment of different stands and the photosynthetic performance and growth of the reforestation species Phoebe bournei

Light levels determine regeneration in stands and a key concern is how to regulate the light environment of different stand types to the requirements of the understory.In this study,we selected three stands typical in south China(a Cryptomeria japonica plantation,a Quercus acutissima plantation,and a mixed stand of both)and three thinning intensities to determine the best understory light environ-ment for 3-year-old Phoebe bournei seedlings.The canopy structure,understory light environment,and photosynthe-sis and growth indicators were assessed following thin-ning.Thinning improved canopy structure and understory light availability of each stand;species composition was the reason for differences in the understory light environ-ment.Under the same thinning intensity,the mixed stand had the greatest light radiation and most balanced spectral composition.P.bournei photosynthesis and growth were closely related to the light environment;all three stands required heavy thinning to create an effective and sustained understory light environment.In a suitable understory light environment,the efficiency of light interception,absorption,and use by seedlings was enhanced,resulting in a higher carbon assimilation the main limiting factor was stomatal conductance.As a shade-avoidance signal,red/far-red radia-tion is a critical factor driving changes in photosynthesis and growth of P.bournei seedlings,and a reduction increased light absorption and use capacity and height:diameter ratios.The growth advantage transformed from diameter to height,enabling seedlings to access more light.Our findings suggest that the regeneration of shade-tolerant species such as P.bournei could be enhanced if a targeted approach to thinning based on stand type was adopted.

Analytical Models of Concrete Fatigue:A State-of-the-Art Review

Fatigue failure phenomena of the concrete structures under long-term low amplitude loading have attractedmore attention.Some structures,such as wind power towers,offshore platforms,and high-speed railways,may resist millions of cycles loading during their intended lives.Over the past century,analytical methods for concrete fatigue are emerging.It is concluded that models for the concrete fatigue calculation can fall into four categories:the empirical model relying on fatigue tests,fatigue crack growth model in fracture mechanics,fatigue damage evolution model based on damage mechanics and advanced machine learning model.In this paper,a detailed review of fatigue computing methodology for concrete is presented,and the characteristics of different types of fatigue models have been stated and discussed.

Fra-1 protooncogene regulates IL-6 expression in macrophages and promotes the generation of M2d macrophages

The tumor microenvironment （TME） plays a prominent role in the growth of tumor cells. As the major inflammatory component of the TME, M2d macrophages are educated by the TME such that they adopt an immunosnppressive role that promotes tumor metastasis and progression. Fra-1 forms activator protein-1 heterodimers with Jun partners and drives gene transcription. Fra-1 is thought to drastically induce tumorigenesis and progression. However, the functional role of Fra-1 in the generation of M2d macrophages is poorly understood to date. Here, we demonstrate that 4T1 mammary carcinoma cells, when co-cultured with RAW264.7 macrophage cells, skew the RAW264.7 macrophage cell differentiation into M2d macrophages. The 4T1 cells stimulate de novo overexpression of Fra-1 in RAW264.7 cells, and then Fra-1 binds to the interleukin 6 （IL-6） promoter to increase the production of the cytokine IL-6 in RAW264.7 cells. IL-6 acts in an autocrine fashion to skew RAW264.7 macrophage cell differentiation into M2d macrophages. These findings open new insights into how to reverse M2d macrophage-induced immune tolerance to improve the efficacy of immunotherapeutic approaches.

Protein corona mediated liposomal drug delivery for bacterial infection management

Liposomes have been widely investigated as a class of promising antibiotic delivery systems for the treatment of life-threatening bacterial infections.However,the inevitable formation of protein corona on the liposomal surface can heavily impact in vivo performance.A better understanding of the effects of protein corona on liposomal behavior can significantly improve antibacterial liposomal drug development.Here,the critical role of protein corona in mediating liposome-bacteria interactions was elucidated.Adsorption of negatively charged protein on cationic liposome weakened electrostatic attractionenhanced liposomal binding to the bacteria.Cumulative complement deposition on anionic liposome composed of phosphatidylglycerol(DSPG s Lip)contributed to a superior binding affinity of DSPG s Lip to planktonic bacteria and biofilms,which was exploited to enhance bacteria-targeted drug delivery.In both S.aureus-related osteomyelitis and pneumonia mice models,DSPG s Lip was demonstrated as a promising antibiotic nanocarrier for managing MRSA infection,indicating the benefits of lipid composition-based protein corona modulation in liposomal antibiotic delivery for bacterial infection treatment.

Grain production space reconstruction and land system function tradeoffs in China

Grain production space reconstruction referred to the changes in the quantity,quality and pattern of grain produc-tion space caused by functional tradeoffs and conflicts between grain production space,urban-rural development space,and ecological service space.Exploring tradeoffs between land system functions caused by grain produc-tion space reconstruction was particularly important for ensuring food security,promoting the construction of ecological civilization,and achieving sustainable development.Therefore,this study identified four relationships of land system functions during the process of grain production space reconstruction(1980-2018)in China by using Set Pair Analysis.Research results showed that the reconstruction of grain production space was achieved mainly through three pathways:Grain for Green,deforestation and reclamation,and urban expansion.Generally,ecological service had spatial negative correlation with grain production,economic development and population carrying capacity(P<0.01),but grain production,economic development and population carrying capacity were positively correlated with each other(P<0.01).In the process of grain production space reconstruction,eco-logical services and economic development,ecological services and population carrying capacity had all shown inverse synergies;there was a tradeoffbetween grain production and ecological services,a codirectional tradeoffbetween grain production and economic development,but a strong synergy between economic development and population carrying capacity.However,the functions of land systems only appeared as synergies or tradeoffs,and there were no inverse synergies and codirectional tradeoffs in the separate processes of Grain for Green,deforestation and reclamation,and urban expansion.It can be concluded that the relationships between land system functions were relatively simple in a single process,but it became complex and diverse when multiple processes were integrated for system analysis.

Glycnsisitin A:A promising bicyclic peptide against heart failure that facilitates TFRCmediated uptake of iron in cardiomyocytes

Zhigancao decoction is a traditional prescription for treating irregular pulse and palpitations in China.As the monarch drug of Zhigancao decoction,the bioactive molecules of licorice against heart diseases remain elusive.We established the HRESIMS-guided method leading to the isolation of three novel bicyclic peptides,glycnsisitins A-C(1-3),with distinctive C-C and C-O-C side-chain-toside-chain linkages from the roots of Glycyrrhiza uralensis(Licorice).Glycnsisitin A demonstrated stronger cardioprotective activity than glycnsisitins B and C in an in vitro model of doxorubicin(DOX)-induced cardiomyocyte injury.Glycnsisitin A treatment not only reduced the mortality of heart failure(HF)mice in a dose-dependent manner but also significantly attenuated DOX-induced cardiac dysfunction and myocardial fibrosis.Gene set enrichment analysis(GSEA)of the differentially expressed genes indicated that the cardioprotective effect of glycnsisitin A was mainly attributed to its ability to maintain iron homeostasis in the myocardium.Mechanistically,glycnsisitin A interacted with transferrin and facilitated its binding to the transferrin receptor(TFRC),which caused increased uptake of iron in cardiomyocytes.These findings highlight the key role of bicyclic peptides as bioactive molecules of Zhigancao decoction for the treatment of HF,and glycnsisitin A constitutes a promising therapeutic agent for the treatment of HF.

Neutrophil extracellular traps induced by interleukin 8 via CXCR1/2 promote the progression of gastric carcinoma through transcription factor IIB-related factor 1 and cyclin

Neutrophils constitute a significant portion of the immune cells present within the tumor microenvironment.Evidence generated by our group has confirmed neutrophils to be an adverse independent prognostic factor affecting the disease-free survival of gastric carcinoma(GC)patients.1 Neutrophil extracellular traps(NETs),protein-covered DNA webs that interact with tumor cells in the tumor microenvironment,were detected in GC tissues and found to clinically associated with disease progression in our previous study.2 NETosis could be mediated by a series of agonists,such as interleukin 8(IL-8).3 However,the mechanism of IL-8 in the progression of GC occurs via NETs remains unclear.In this study,we identified that IL-8-mediated NETosis can promote the proliferation,migration,and invasion of GC cells in vitro,which can be abrogated by NET degradation through deoxyribonucleaseI(DNase I)or IL-8 suppression through neutralizing antibodies.Disruption of NETs by DNase I or blockade of the IL-8-CXC chemokine receptor 2(CXCR2)axis could result in growth retardation in mouse models.We also confirmed that NETs may influence the biological behavior of GC cells by up-regulating the expression of transcription factor IB-related factor 1(BRF1)and cyclin p21/p27.

A genetically engineered neuronal membrane-based nanotoxoid elicits protective immunity against neurotoxins

Given their dangerous effects on the nervous system,neurotoxins represent a significant threat to public health.Various therapeutic approaches,including chelating agents,receptor decoys,and toxin-neutralizing antibodies,have been explored.While prophylactic vaccines are desirable,it is oftentimes difficult to effectively balance their safety and efficacy given the highly dangerous nature of neurotoxins.To address this,we report here on a nanovaccine against neurotoxins that leverages the detoxifying properties of cell membrane-coated nanoparticles.A genetically modified cell line with constitutive overexpression of theα7 nicotinic acetylcholine receptor is developed as a membrane source to generate biomimetic nanoparticles that can effectively and irreversibly bind toα-bungarotoxin,a model neurotoxin.This abrogates the biological activity of the toxin,enabling the resulting nanotoxoid to be safely delivered into the body and processed by the immune system.When co-administered with an immunological adjuvant,a strong humoral response againstα-bungarotoxin is generated that protects vaccinated mice against a lethal dose of the toxin.Overall,this work highlights the potential of using genetic modification strategies to develop nanotoxoid formulations against various biological threats.

G protein-coupled receptor 35 attenuates nonalcoholic steatohepatitis by reprogramming cholesterol homeostasis in hepatocytes

Nonalcoholic fatty liver disease(NAFLD)is the most common chronic liver disease worldwide.Fat accumulation“sensitizes”the liver to insult and leads to nonalcoholic steatohepatitis(NASH).G protein-coupled receptor 35(GPR35)is involved in metabolic stresses,but its role in NAFLD is unknown.We report that hepatocyte GPR35 mitigates NASH by regulating hepatic cholesterol homeostasis.Specifically,we found that GPR35 overexpression in hepatocytes protected against high-fat/cholesterol/fructose(HFCF)diet-induced steatohepatitis,whereas loss of GPR35 had the opposite effect.Administration of the GPR35 agonist kynurenic acid(Kyna)suppressed HFCF diet-induced steatohepatitis in mice.Kyna/GPR35 induced expression of StAR-related lipid transfer protein 4(STARD4)through the ERK1/2 signaling pathway,ultimately resulting in hepatic cholesterol esterification and bile acid synthesis(BAS).The overexpression of STARD4 increased the expression of the BAS rate-limiting enzymes cytochrome P450 family 7 subfamily A member 1(CYP7A1)and CYP8B1,promoting the conversion of cholesterol to bile acid.The protective effect induced by GPR35 overexpression in hepatocytes disappeared in hepatocyte STARD4-knockdown mice.STARD4 overexpression in hepatocytes reversed the aggravation of HFCF diet-induced steatohepatitis caused by the loss of GPR35 expression in hepatocytes in mice.Our findings indicate that the GPR35–STARD4 axis is a promising therapeutic target for NAFLD.

Vision-based measuring method for individual cow feed intake using depth images and a Siamese network

Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images.An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data.The experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing works.This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.

A Transgenic Study on Affecting Potato Tuber Yield by Expressing the Rice Sucrose Transporter Genes OsSUT5Z and OsSUT2M

In many plants, sucrose transporters are essential for both sucrose exports from sources and imports into sinks, indicating a function in assimilate partitioning. To investigate whether sucrose transporters can improve the yield of starch plant, potato plants （Solanum tuberosum L. cv. D^siree） were transformed with cDNAs of the rice sucrose transporter genes OsSUT5Z and OsSUT2M under the control of a tuber- specific, class-I patatin promoter. Compared to the controls, the average fructose content of OsSUTSZ transgenic tubers significantly increased. However, the content of the sugars and starch in the OsSUT2M transgenic potato tubers showed no obvious difference. Correspondingly, the average tuber yield, average number of tubers per plant and average weight of single tuber showed no significant difference in OsSUT2M transgenic tubers with controls. In the OsSUTSZ transgenic lines, the average tuber yield per plant was 1.9-fold higher than the controls, and the average number of tubers per plant increased by more than 10 tubers on average, whereas the average weight of a single tuber did not increase significantly. These results suggested that the average number of tubers per plant showed more contribution than the average weight of a single tuber to the tuber yield per plant.

Brain tumor-targeted drug delivery strategies

Despite the application of aggressive surgery,radiotherapy and chemotherapy in clinics,brain tumors are still a difficult health challenge due to their fast development and poor prognosis.Brain tumor-targeted drug delivery systems,which increase drug accumulation in the tumor region and reduce toxicity in normal brain and peripheral tissue,are a promising new approach to brain tumor treatments.Since brain tumors exhibit many distinctive characteristics relative to tumors growing in peripheral tissues,potential targets based on continuously changing vascular characteristics and the microenvironment can be utilized to facilitate effective brain tumor-targeted drug delivery.In this review,we briefly describe the physiological characteristics of brain tumors,including blood–brain/brain tumor barriers,the tumor microenvironment,and tumor stem cells.We also review targeted delivery strategies and introduce a systematic targeted drug delivery strategy to overcome the challenges.

Tyrosine phosphatase SHP2 in ovarian granulosa cells balances follicular development by inhibiting PI3K/AKT signaling

In mammals,the growth and maturation of oocytes within growing follicles largely depends on ovarian granulosa cells(GCs)in response to gonadotropin stimulation.Many signals have been shown to regulate GC proliferation and apoptosis.However,whether the tyrosine phosphatase SHP2 is involved remains unclear.In this study,we identified the crucial roles of SHP2 in modulating GC proliferation and apoptosis.The production of both mature oocytes and pups was increased in mice with Shp2 specifically deleted in ovarian GCs via Fshr-Cre.Shp2 deletion simultaneously promoted GC proliferation and inhibited GC apoptosis.Furthermore,Shp2 deficiency promoted,while Shp2 overexpression inhibited,the proliferation of cultured primary mouse ovarian GCs and the human ovarian granulosa-like tumor cell line KGN in vitro.Shp2 deficiency promoted follicule-stimulating hormone(FSH)-activated phosphorylation of AKT in vivo.SHP2 deficiency reversed the inhibitory effect of hydrogen peroxide(H_(2)O_(2))on AKT activation in KGN cells.H_(2)O_(2) treatment promoted the interaction between SHP2 and the p85 subunit of PI3K in KGN cells.Therefore,SHP2 in GCs may act as a negative modulator to balance follicular development by suppressing PI3K/AKT signaling.The novel function of SHP2 in modulating proliferation and apoptosis of GCs provides a potential therapeutic target for the clinical treatment of follicle developmental dysfunction.

Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration

Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy cows.The purpose of this study is to develop a method based on triaxial acceleration to automatically recognize feeding and ruminating of dairy cows.During the experiment,five diary cows raised in a barn were used as experimental subjects.A triaxial acceleration sensor was used as the device to collect jawmovement data of dairy cows,and the behaviors of dairy cows were classified into three categories:feeding,ruminating and other behavior.The features of time-domain and frequency-domain were extracted from the raw acceleration data.Three machine learning algorithms including k-nearest neighbor,support vector machine and probabilistic neural network were used for the classification and the results based on four different data segment lengths were compared.The results show that the three algorithms can be used for recognition of feeding and ruminating with high accuracy.Under the condition that the sampling frequency of the sensor is 5 Hz,the combination of data segment length of 256 and k-nearest neighbor algorithm is the best scheme for recognition of feeding and ruminating in this study.The precision and recall of recognition for feeding were 92.8%and 95.6%respectively,and those of recognition for ruminating were 93.7%and 94.3%respectively.The specificity and AUC of recognition for feeding were 96.1%and 0.959 respectively,and those of recognition for ruminating were 97.5%and 0.959 respectively.This will provide an effective method for real-time monitoring of ingestive-related behaviors of dairy cows and lay a foundation for prediction of dairy cows’health status and welfare to further achieve the purpose of disease prediction and adjusting feeding and management methods.

Assessment of dairy cow feed intake based on BP neural network with polynomial decay learning rate

To overcome the shortcomings of traditional dairy cow feed intake assessment model andBP neural network, this paper proposes a method of optimizing BP neural network usingpolynomial decay learning rate, taking the cow’s body weight, lying duration, lying times,walking steps, foraging duration and concentrate-roughage ratio as input variables andtaking the actual feed intake is the output variable to establish a dairy cow feed intakeassessment model, and the model is trained and verified by experimental data collectedon site. For the sake of comparative study, feed intake is simultaneously assessed by SVRmodel, KNN logistic regression model, traditional BP neural network model, and multilayerBP neural network model. The results show that the established BP model using the polynomial decay learning rate has the highest assessment accuracy, the MSPE, RMSE, MAE,MAPE and R2 are 0.043 kg2/d and 0.208 kg/d, 0.173 kg/d, 1.37% and 0.94 respectively. Compared with SVR model and KNN mode, the RMSE value reduced by 43.9% and 26.5%, it isalso found that the model designed in this paper has many advantages in comparison withthe BP model and multilayer BP model in terms of precision and generalization. Therefore,this method is ready to be applied for accurately evaluating the dairy cow feed intake, andit can provide theoretical guidance and technical support for the precise-feeding and canalso be of high significance in the improvement of dairy precise-breeding.

Rumination recognition method of dairy cows based on the change of noseband pressure

Rumination is closely related to ruminant’s health and welfare,so it is very important to monitor rumination.Rumination monitoring equipment based on chewing and swallowing audio is easy to be affected by ambient noise,equipment based on acceleration sensor is not easy to monitor number of cuds and other information,equipment based on video monitoring has high cost and is easy to be affected by weather and other cows,while equipment based on chewing pressure can accurately record the chewing pressure of dairy cows,and use related processing technology to obtain the number of cuds and other information.In the related research,the peak value and interval of masticatory pressure peak in time domain were used to identify rumination.In view of the problem that the standard of pressure peak was not well defined due to the different head size and masticatory strength of different age and different breed cows,this paper proposed a method to identify the number of rumination,duration of rumination and number of cuds of cows by using standard deviation and noseband pressure signal frequency spectrum.The accurate and stable frequency spectrum range of rumination noseband pressure signal was obtained by frequency spectrum analysis,and the interference of feeding noseband pressure signal to rumination noseband pressure signal was eliminated by using standard deviation.Compared with the direct observation,the results showed that the recognition accuracy of the method proposed in this paper were 100%,94.2%and 94.45%for the number of rumination,duration of rumination and number of cuds respectively,and the average absolute errors were 0,2.0029%and 2.5623%respectively,which showed that the method can accurately identify the rumination information of dairy cows.

Predicting the excretion of feces,urine and nitrogen using support vector regression:A case study with Holstein dry cows

Predicting the excretion of feces,urine and nitrogen(N)from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled farming.The traditional prediction methods such as pollutant generation coefficient(PGC)and mathematical model based on linear regression(LR)may be limited by prediction range and regression function assumption,and sometimes may deviate from the actual condition.In order to solve these problems,the support vector regression(SVR)was applied for predicting the cows'feces,urine and N excretions,taking Holstein dry cows as a case study.SVR is a typical non-parametric machine learning model that does not require any specific assumptions about the regression function in advance and only by learning the training sample data,and also it can fit the function closest to the actual in most cases.To evaluate prediction accuracy effectively,the SVR technique was compared with the LR and radial basis function artificial neural network(RBF-ANN)methods,using the required sample data obtained from actual feeding experiments.The prediction results indicate that the proposed technique is superior to the other two conventional(especially LR)methods in predicting the main indicators of feces,urine,and N excretions of Holstein dry cows.

Computational investigation of hydrodynamics and cracking reaction in a heavy oil riser reactor

This paper presents a computational investigation of hydrodynamics, heat transfer and cracking reaction in a heavy oil riser operated in a novel operating mode of low temperature contact and high catalyst-to-oil ratio. Through incorporating feedstock vaporization and a 12-lump cracking kinetics model, a validated gas-solid flow model has been extended to the analysis of the hydrodynamic and reaction behavior in an industrial riser. The results indicate that the hydrodynamics, temperature and species concentration exhibit significantly nonuniform behavior inside the riser, especially in the atomization nozzle region. The lump concentration profiles along the riser height provide useful information for riser optimization. Compared to conventional fluid catalytic cracking （FCC） process, feedstock conversion and gasoline yield are respectively increased by 1.9 units and 1.0 unit in the new FCC process, the yield of liquefied petroleum gas is increased by about 1.0 unit while dry gas yield is reduced by about 0.3 unit.

Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression

In order to effectively evaluate the diet nutritional value of dairy cows,it is essential to accurately predict the diet nutrients digestibility(DND).Conventional predicting DND methods are usually based on the least squares linear regression analysis(LS-LRA),which often relies on a large amount of training samples to accomplish reliable predictions.However,in real-world applications,it is often extremely difficult,costly and time-consuming to obtain a large number of measured samples,especially for the DND prediction of dairy cows.This paper applies a Gaussian process regression(GPR)technique to predict the DND indicators of dairy cows in small samples.To evaluate prediction accuracy effectively,we compared the GPR technique with the LS-LRA,radial basis function artificial neural network(RBF-ANN),support vector regression(SVR)and least squares support vector regression(LS-SVR)methods,using the required sample data obtained from actual digestion experiments.The prediction results indicate that the GPR technique is superior to other conventional methods(especially the LS-LRA method)in predicting the main DND indicators of dairy cows such as dry matter digestibility(DMD),organic matter digestibility(OMD),neutral detergent fiber(NDFD),acid detergent fiber(ADFD),and crude protein digestibility(CPD).It is worth mentioning that the developed GPR-based prediction technique is more suitable for the prediction problems with small samples,which is often the case in the prediction of DND indicators of dairy cows,and then more coincide with actual needs.