Characterization of fine particle fluidization

The major challenge in Geldart group C fine particle fluidization is the cohesive nature of particle properties because of the strong interparticle forces. Nanoparticles as fluidization aids could improve fluidization behavior and reduce the phenomena of channeling and agglomeration. Fundamental studies on fine particle fluidization with nanoparticles were carried out with regard to pressure drop, bed expansion, and minimal fluidization velocity properties. These experiments provided a good base for multilevel analyses in particle size, particle density, nanoparticle concentration etc

Early-Life Intervention Using Exogenous Fecal Microbiota Alleviates Gut Injury and Reduce Inflammation Caused by Weaning Stress in Piglets

Fecal microbiota transplantation (FMT) could shape the structure of intestinal microbiota in animals. This study was conducted to explore the changes that happen in the structure and function of microbiota caused by weaning stress, and whether early-life FMT could alleviate weaning stress through modifying intestinal microbiota in weaned piglets. Diarrheal (D) and healthy (H) weaned piglets were observed, and in the same farm, a total of nine litters newborn piglets were randomly allocated to three groups: sucking normally (S), weaned at 21 d (W), and early-life FMT + weaned at 21 d (FW). The results demonstrated that differences of fecal microbiota existed in group D and H. Early-life FMT significantly decreased diarrhea incidence of weaned piglets. Intestinal morphology and integrity were improved in the FW group. Both ZO-1 and occludin (tight junction proteins) of jejunum were greatly enhanced, while the zonulin expression was significantly down-regulated through early-life FMT. The expression of IL-6 and TNF-α (intestinal mucosal inflammatory cytokines) were down-regulated, while IL-10 (anti-inflammatory cytokines) was up-regulated by early-life FMT. In addition, early-life FMT increased the variety of the intestinal microbial population and the relative amounts of some beneficial bacteria such as Spirochaetes, Akkermansia, and Alistipes. Functional alteration of the intestinal microbiota revealed that lipid biosynthesis and aminoacyl-tRNA biosynthesis were enriched in the FW group. These findings suggested that alteration of the microbiota network caused by weaning stress induced diarrhea, and early-life FMT alleviated weaning stress in piglets, which was characterized by decreased diarrhea incidence, improved intestinal morphology, reduced intestinal inflammation, and modified intestinal bacterial composition and function

Foodborne Pathogens of Enterobacteriaceae, Their Detection and Control

Foodborne pathogens of Enterobacteriaceae including Escherichia coli, Salmonella, Shigella, Yersinia, etc., causes a great number of diseases and has a significant impact on human health. Here, we reviewed the prevalence, virulence, and antimicrobial susceptibility of Enterobacteriaceae belonging to 4 genera: E. coli, Salmonella, Shigella, and Yersinia. The routes of the pathogens’ transmission in the food chain; the antimicrobial resistance, genetic diversity, and molecular epidemiology of the Enterobacteriaceae strains; novel technologies for detection of the bacterial communities (such as the molecular marker-based methods, Immunoaffinity based detection, etc.); and the controlling of the foodborne pathogens using chemical/natural compounds or physical methods (such as UV-C and pulsed-light treatment, etc.), is also summarized

Controllable sliding transfer of wafer‐size graphene

The innovative design of sliding transfer based on a liquid substrate can succinctly transfer high‐quality, wafer‐size, and contamination‐free graphene within a few seconds. Moreover, it can be extended to transfer other 2D materials. The efficient sliding transfer approach can obtain high‐quality and large‐area graphene for fundamental research and industrial applications

The Involvement of Renin-Angiotensin System in Lipopolysaccharide-Induced Behavioral Changes, Neuroinflammation, and Disturbed Insulin Signaling

Brain insulin signaling is accounted for the development of a variety of neuropsychiatric disorders, such as anxiety and depression, whereas both inflammation and the activated renin-angiotensin system (RAS) are two major contributors to insulin resistance. Intriguingly, inflammation and RAS can activate each other, forming a positive feedback loop that would result in exacerbated unwanted tissue damage. To further examine the interrelationship among insulin signaling, neuroinflammation and RAS in the brain, the effect of repeated lipopolysaccharide (LPS) exposure and co-treatment with the angiotensin II (Ang II) receptor type 1 (AT1) blocker, candesartan (Cand), on anxiety and depression-like behaviors, RAS, neuroinflammation and insulin signaling was explored. Our results demonstrated that prolonged LPS challenge successfully induced the rats into anxiety and depression-like state, accompanied with significant neural apoptosis and neuroinflammation. LPS also activated RAS as evidenced by the enhanced angiotensin converting enzyme (ACE) expression, Ang II generation and AT1 expression. However, blocking the activated RAS with Cand co-treatment conferred neurobehavioral protective properties. The AT1 blocker markedly ameliorated the microglial activation, the enhanced gene expression of the proinflammatory cytokines and the overactivated NF-κB signaling. In addition, Cand also mitigated the LPS-induced disturbance of insulin signaling with the normalized phosphorylation of serine 307 and tyrosine 896 of insulin receptor substrate-1 (IRS-1). Collectively, the present study, for the first time, provided the direct evidence indicating that the inflammatory condition may interact with RAS to impede brain insulin pathway, resulting in neurobehavioral damage, and inhibiting RAS seems to be a promising strategy to block the cross-talk and cut off the vicious cycle between RAS and immune system

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

Microbial Community Succession and Response to Environmental Variables During Cow Manure and Corn Straw Composting

In composting system, the composition of microbial communities is determined by the constant change in the physicochemical parameters. This study explored the dynamics of bacterial and fungal communities during cow manure and corn straw composting using high throughput sequencing technology. The relationships between physicochemical parameters and microbial community composition and abundance were also evaluated. The sequencing results revealed the major phyla included Proteobacteria, Bacteroidetes, Firmicutes, Chloroflexi and Actinobacteria, Ascomycota, and Basidiomycota. Linear discriminant analysis effect size (LEfSe) illustrated that Actinomycetales and Sordariomycetes were the indicators of bacteria and fungi in the maturation phase, respectively. Mantel test showed that NO3--N, NH4+-N, TN, C/N, temperature and moisture content significantly influenced bacterial community composition while only TN and moisture content had a significant effect on fungal community structure. Structural equation model (SEM) indicated that TN, NH4+-N, NO3--N and pH had a significant effect on fungal abundance while TN and temperature significantly affected bacterial abundance. Our finding increases the understanding of microbial community succession in cow manure and corn straw composting under natural conditions

AMP as a Low-Energy Charge Signal Autonomously Initiates Assembly of AXIN-AMPK-LKB1 Complex for AMPK Activation

The AMP-activated protein kinase (AMPK) is a master regulator of metabolic homeostasis by sensing cellular energy status. AMPK is mainly activated via phosphorylation by LKB1 when cellular AMP/ADP levels are increased. However, how AMP/ADP brings about AMPK phosphorylation remains unclear. Here, we show that it is AMP, but not ADP, that drives AXIN to directly tether LKB1 to phosphorylate AMPK. The complex formation of AXIN-AMPK-LKB1 is greatly enhanced in glucose-starved or AICAR-treated cells and in cell-free systems supplemented with exogenous AMP. Depletion of AXIN abrogated starvation-induced AMPK-LKB1 colocalization. Importantly, adenovirus-based knockdown of AXIN in the mouse liver impaired AMPK activation and caused exacerbated fatty liver after starvation, underscoring an essential role of AXIN in AMPK activation. These findings demonstrate an initiating role of AMP and demonstrate that AXIN directly transmits AMP binding of AMPK to its activation by LKB1, uncovering the mechanistic route for AMP to elicit AMPK activation by LKB1.http://news.xmu.edu.cn/s/13/t/542/22/a9/info139945.ht