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Effective management of corn straw and stubble is critical in conservation tillage, as it impacts soil health and productivity. However, accurate classification of different types of straw cover has been hindered by their similar spectral and spatial characteristics and the low spatial resolution of hyperspectral satellite imagery. Moreover, traditional convolution neural network (CNN)-based methods, which rely on first-order statistics for feature extraction, often struggle to extract distinguishable features of highly similar objects effectively, thereby reducing classification accuracy. In this study, a second-order statistical-feature extraction algorithm based on CNN that uses fused multispectral and hyperspectral data was tested for its ability to classify types of straw cover. In the first step, coupled non-negative matrix factorization (CNMF) was used to fuse hyperspectral and multispectral images effectively, thereby enhancing the spatial resolution of the hyperspectral data. In this study, we integrated pointwise convolution (PWC), depthwise convolution (DWC), and a distance covariance matrix (DCM) to form a mixed convolution and DCM (MCDCM) network; we used this to extract and integrate deep spectral–spatial features of the hyperspectral images. Our experimental results show that the MCDCM network significantly improved classification accuracy compared to traditional methods, with accuracy rates for the different straw-cover types exceeding 90% and overall accuracy reaching 98.26%. The fused image also exhibited better preservation of feature edges and contours. The accurate identification of corn-straw-cover types achieved with the proposed MCDCM method is a major step in optimizing conservation-farming practices, improving soil fertility and farm productivity, and supporting sustainable ecological development.

ObjectiveChildhood hearing impairment has potential repercussions on the mental well-being of both children and their parents. As a vulnerable population in accessing health care services, they may face specific challenges, especially during the COVID-19 pandemic. This cross-sectional study aims to investigate the association between childhood hearing impairment and the mental health of children and their parents, and to assess health care utilization of hearing-impaired children and its impact on mental outcomes for both during the COVID-19 pandemic.MethodsUsing the National Health Interview Survey (NHIS) database, we analyzed data for 15,989 children aged 5–17 and their corresponding parents. The correlations between childhood hearing impairment and mental outcomes were examined using logistic regression models. The 2020 (quarter 3 and quarter 4)-2021 NHIS data was singled out and re-analyzed, focusing on the utilization of medical care during the COVID-19 pandemic.ResultsAfter accounting for covariates, hearing-impaired children exhibited a higher frequency of anxiety (OR 2.33, 95% CI 1.79–3.02) or depression (OR 2.14, 95% CI 1.59–2.88). Parents of hearing-impaired children had significantly higher odds of a higher frequency of anxiety (OR 1.55, 95% CI 1.20–2.01) or depression (OR 1.73, 95% CI 1.30–2.29). Interaction effect of hearing impairment with survey year on parents’ mental health outcomes was observed (p for interaction

The thermal failure mechanism of sliding composite leaf springs (SCLSs) is examined in this research. Bench fatigue testing is used in the experimental investigation of the thermal failure temperature and mode. The findings demonstrate that after 20 min, when the temperature reaches the glass transition temperature, cracks start to form around the bolt hole of the composite body. The findings from bench experiments indicate that thermal fatigue failure of SCLS can be divided into three main forms: delamination failure of the resin-rich layer, the resin-fiber interface, and fibre pull-out failure. The utilization of finite element simulation has demonstrated that when subjected to thermal load, the leaf spring’s internal defects can lead to localized stress concentration. The structure of the spring end is optimized using the insulating panel scheme and the metal joint scheme. The thermal fatigue issue with SCLS is best addressed by the heat dissipation approach, according to bench tests.

Neonatal genital prolapse is a rare situation. This report presents a unique case involving a pair of premature female twins who both developed vaginal wall prolapse without any neurological deficits. Multiple factors such as selective intrauterine growth restriction, feeding intolerance, extrauterine growth retardation, and elevated intra-abdominal pressure after birth may have contributed to the development of this phenomenon. Notably, the severity of prolapse was more pronounced in the twin with lower birth weight and smaller for gestational age. After a five-month follow-up period, the twins’ prolapsed vaginal wall fully retracted due to a combination of conservative treatment and enhanced nutritional support.

The microstructure evolution and strengthening mechanism of materials after shot peening have always been the focus of research. In this paper, a novel coupled constitutive model was proposed to predict the depth of fine-grain of the 2060-T8 aluminum-lithium (Al–Li) alloy after shot peening. Besides, combining the numerical and experimental methods, the influences of shot size on yield stress, dislocation density, and crystal orientation of Al–Li alloy were systematically studied to reveal the strengthening mechanism. The results show that larger shot sizes can significantly improve the depth of the dislocation density layer and the fine-grain layer of 2060 Al–Li alloy. The depth of the refinement layer increases from 50 μm to 80 μm when the shot size grows from 0.3 mm to 0.6 mm. It is found the depth of the grain refinement layer in the experiment is consistent with the depth where is a sudden change of grain size in the simulation. Additionally, electron backscatter diffraction (EBSD) shows that many sub-grains are produced in the material due to high dislocation density, and the proportion of low-angle grain boundaries (LAGB) increases significantly, from 48.3% to more than 98.4%. The crystal orientation in the grain has changed obviously and Goss texture {100} obtained in the deformation band. In contrast, with the increase in shot size, the proportion of the Schmid factor with a high-level trend increases. However, the hardening effect of the material is much greater than the softening effect which results in higher yield stress and microhardness of the top surface.

Abstract Tetracycline (TET) is a broad-spectrum antibiotic commonly used in the treatment of animals. TET residues in food inevitably threaten human health. High-performance analytical techniques for TET detection are required in food quality assessment. The objective of this study was to establish a label-free fluorescent biosensor for TET detection using specific aptamer-templated silver nanoclusters (AgNCs). An aptamer with a high specific binding ability to TET was used to synthesize a novel DNA-templated AgNCs (DNA-AgNCs). When TET is present, the aptamer’s conformation switched from an antiparallel G-quadruplex to a hairpin structure, altering the connection between AgNCs and the aptamer. Following the transformation of AgNCs into large sized silver nanoparticles (AgNPs), a fluorescence decrease was detected. When used to detect TET in milk, the proposed biosensor displayed high sensitivity and selectivity, with a limit of detection of 11.46 ng/mL, a linear range of 20 ng/mL−10 g/mL, and good recoveries of 97.7–114.6% under optimized conditions. These results demonstrate that the proposed biosensor was successfully used to determine TET quantitatively in food samples, suggesting that our method provides an efficient and novel reference for detecting antibiotics in food while expanding the application of DNA-AgNCs in related fields.

Manufacturability analysis is a critical step before manufacturing to reduce costs and risks. It is used widely in conventional manufacturing (CM) processes. However, to the best of our knowledge, there is no natural method to evaluate the manufacturability of additive manufacturing (AM) processes that have more uncertainty-derived risks and costs than CM processes. A clear definition of the manufacturability of AM processes has not been established, and there is no standard to check whether a component is manufactured successfully by an AM process, particularly for porous complex components. This study introduces the development of a new machine learning-based method to solve the problem mentioned above. It is based on the statistical measurement of experimental samples. The proposed method can be used to perform the manufacturability analysis for periodic cellular structures printed by a selective laser melting (SLM) process. A novel definition of the manufacturability of the SLM-ed periodic cellular structure was proposed. Experimental results indicate that the developed learning model (ANN model) can achieve up to 94% classification accuracy and 96% prediction accuracy, which satisfies the application requirements of the AM industry. Moreover, the developed model can be adapted for the manufacturability analysis of different AM processes.

The intercalated disc (ICD) is a membrane structure of the cardiac muscle involved in normal heart function. Here the authors report that knockdown of the ICD-bound transmembrane protein 65 results in impaired ICD structure, abnormal cardiac electrophysiology and cardiomyopathy in mice.

The detection of the storage state of frozen meat, especially meat frozen–thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen–stored (F–S), frozen–thawed three times (F–T–3) and frozen–thawed five times (F–T–5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze–thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.

As a nutrient-rich multigrain crop, buckwheat is a typical “medicinal food homology” crop that is rich in flavonoids, including rutin and various vitamins. It has positive anti-oxidant and anti-tumour properties and lowers blood pressure. However, due to strict self-crossing characteristics, slow progress has been made in Tartary buckwheat (TB) cross-breeding, resulting in the slow breeding of new varieties of new TB varieties, which has limited the improvement of yield and quality. Therefore, mutant breeding is a rapid and effective technique for broadening and innovating TB breeding. In recent years, improving qualities related to yield, lodging resistance, and stability have become key points in TB breeding. Based on the above findings, excellent, potentially valuable TB lines with rich phenotypes were obtained for the TB mutation library via ethyl methanesulfonate (EMS), laying a foundation for creating new TB germplasms. In this study, we systematically investigated more than 10 agronomic traits of JQ2 and JQ4 mutants, including plant type, leaf colour, grain type, grain colour, grain number per plant, grain length, grain width, grain weight per plant, and 1000-grain weight. The results show that the maximum number of grains per plant was 1956, the weight was 32.84 g, and the 1000-grain weight was 30.89 g. The maximum number of grains per JQ4 plant was 2308, and the weight was 44.82 g. The maximum 1000-grain weight was 24.7 g. Among the 295 JQ2 mutants and 153 JQ4 mutants, 10 flavonoids (orientin, morin, quercetin, kaempferol, luteolin, naringin, hesperetin, myricetin, hesperidin, and rutin) were detected with near infrared spectroscopy (NIR). The mutants were divided into five groups according to the flavonoid content of the JQ2 mutants, of which the first group included 31 individual lines. and the second to fifth groups included 70, 69, 72, and 53 lines, respectively. The JQ4 mutants were divided into four classes, of which 41, 50, 32, and 30 were individual lines, respectively, with the highest rutin content being 82.06 mg/g. In summary, through systematic analysis and screening of the agronomic traits and flavonoid contents of JQ2 and JQ4 mutant seeds, we obtained three lines with a high 1000-grain weight, including two JQ2 mutant lines (30.89 g) and one JQ4 mutant line, which reached 24.70 g and ten lines with high grain weight per plant. This included 8 JQ2 mutants and 2 JQ4 mutants, as well as 72 high-rutin mutants (including 31 lines from JQ2 and 41 lines from JQ4 mutants). These elite lines provide the material basis for creating TB germplasms with excellent qualities and cultivation characteristics.

Oxidative stress-mediated excessive accumulation of ROS in the body destroys cell homeostasis and participates in various diseases. However, the relationship between oxidative stress-related genes (ORGs) and tumor microenvironment (TME) in gastric cancer remains poorly understood. For improving the treatment strategy of GC, it is necessary to explore the relationship among them. We describe the changes of ORGs in 732 gastric cancer samples from two data sets. The two different molecular subtypes revealed that the changes of ORGs were associated with clinical features, prognosis, and TME. Subsequently, the OE_score was related to RFS, as confirmed by the correlation between OE_score and TME, TMB, MSI, immunotherapy, stem cell analysis, chemotherapeutic drugs, etc. OE_score can be used as an independent predictive marker for the treatment and prognosis of gastric cancer. Further, a Norman diagram was established to improve clinical practicability. Our research showed a potential role of ORGs in clinical features, prognosis, and tumor microenvironment of gastric cancer. Our research findings broaden the understanding of gastric cancer ORGs as a potential target for individualized treatment of gastric cancer and a new direction to evaluate the prognosis.

BackgroundTertiary lymphoid structures (TLSs) are crucial in promoting and maintaining positive anti-tumor immune responses. The tumor stroma has a powerful immunosuppressive function that could exclude tumor-infiltrating lymphocytes from the tumor beds and lead to a “cold” phenotype. TLSs and tumor stroma percentage (TSP) are significantly associated with the prognosis of patients with certain cancers. However, the exact roles of TLSs and TSP and their intrinsic relationship are still largely unknown in colorectal cancer (CRC).MethodsTLSs and TSP were assessed using hematoxylin-eosin (H&E) and/or immunohistochemistry (IHC) staining from 114 CRC patients in the training set and 60 CRC patients in the external validation set. The correlation between TILs, TLS and clinicopathological characteristics and their prognostic values were assessed. Finally, we plotted a Nomogram including the TLS, TSP and tumor-node-metastasis (TNM) stage to predict the probability of recurrence-free survival (RFS) at 2- and 5-years in non-metastatic colorectal cancer (nmCRC) patients.ResultsPeritumoral TLS (P-TLS), intratumoral TLS (In-TLS) and high TSP (H-TSP, >50%) were present in 99.1%, 26.3% and 41.2% patients, respectively. H-TSP tumor tends to be associated with lower P-TLS density (P =0.0205). The low P-TLS density (< 0.098/mm2) was significantly associated with reduced RFS (HR=6.597 95% CI: 2.882-15.103, P

This work presents the design and experimental demonstration of a novel dual-storage-port nonvolatile SRAM based on back-end-of-the-line processed Hf0.5Zr0.5O2-based metal-ferroelectric-metal capacitors, which offers significant advantages over the conventional single-storage-port version without area penalty, and paves the way for implementing our proposed selector-free 3D cross-point memory.

This study provides a bibliometric analysis of political discourse in translation studies based on the Web of Science (WoS) database from 1990 to 2020. It adopts a guiding procedure integrating the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) guidelines into the bibliometric standard workflow and using VOSviewer as the visualization tool. With the help of the built-in WoS analysis tools, the annual publication trends and the most influential productive countries are examined. VOSviewer is used to explore the influential sources in the field, research themes, cited references and their intersections with other references, cited sources and their links to other sources, and most frequently cited authors. The co-occurrence analysis of keywords distinguishes eight research themes, namely, linguistic analysis, ideology and manipulation, power embodied in political communication, interpreting and critical discourse analysis (CDA), the recontextualization of political discourse in media discourse, diplomatic translation, the representation of political speeches in news translation, and conflict and politics. The co-citation analysis of the cited references focuses on discourse analysis models, research approaches, and theoretical frameworks. The co-citation of sources suggests that interdisciplinary features characterize political discourse translation studies, interacting with other disciplines through fields such as pragmatics, discourse analysis, and communication research.

MAX dimerization protein 3 (MXD3), a transcriptional regulator of the MXD3 superfamily, is a part of the MYC–MAX–MXD network. However, its role in tumors has been reported in several cancers, such as B-cell acute lymphoblastic leukemia, medulloblastoma, neuroblastoma, and glioblastoma. Based on TCGA and GEO data, our first pancancer study of MXD3 confirmed the high expression of MXD3 in cancer tissues. Our results revealed that patients suffering from cancers with higher MXD3 expression had poor OS, DSS, DFI, and PFI. We further explored the methylation status of the MXD3 gene body and gene promoter in cancer. Patients with a higher MXD3 gene body have better OS, while the prognosis of patients with a high MXD3 promoter is more complex. We also verified the differential expression of three clinical phenotypes of MXD3: age, sex, and tumor stage, in a variety of tumors, suggesting a correlation between MXD3 and clinical characteristics. We explored the negative relationship between MXD3 and TMB and MSI in most types of cancer, indicating the poor prognosis of patients with high MXD3 expression. We further investigated the relationship between MXD3 and immune infiltrating cells and identified the relationship between MXD3 and immune genes, immunosuppressive genes, and antigen-presenting genes. All of the above findings established a solid relationship between MXD3 and the immune environment and immune cells. These results demonstrated that MXD3 might also be a potential immune factor. We also found a higher expression of MXD3 and promoter according to the increasing glioma WHO grade or histologic types. Glioma patients with high MXD3 or MXD3 promoter expression had poor survival. Finally, we used IHC to verify the higher expression of MXD3 in glioma samples compared to normal samples. Our study shows that MXD3, as a poor prognostic factor, plays a significant role in many cancers, especially glioma. Although more clinical evidence for MXD3 as a clinical therapeutic target and an immunotherapy site is needed, MXD3 can play an important guiding role in multiple clinical treatments, including immunotherapy and demethylation therapy.

Classification of beef cuts is important for the food industry and authentication purposes. Traditional analytical methods are time constraints and incompatible with the modern food industry. Taking advantage of its rapidness and being nondestructive, multispectral imaging (MSI) has been widely applied to obtain a precise characterization of food and agriculture products. This study aims at developing a beef cut classification model using MSI and machine learning classifiers. Beef samples are imaged with a snapshot multi-spectroscopic camera within a range of 500–800 nm. In order to find a more accurate classification model, single- and multiple-modality feature sets are used to develop an accurate classification model with different machine learning-based classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) algorithms. The results demonstrate that the optimized LDA classifier achieved a prediction accuracy of over 90% with multiple modality feature fusion. By combining machine learning and feature fusion, the other classification models also achieved a satisfying accuracy. Furthermore, this study demonstrates the potential of machine learning and feature fusion method for meat classification by using multiple spectral imaging in future agricultural applications.

Myelodysplastic syndrome (MDS) is characterized by clonal hematopoiesis and impaired differentiation, and may develop to acute myeloid leukemia (AML). We explored the mechanism of histone methyltransferase EZH2/EHMT2 during the transformation of MDS into AML. Expression of EZH2/EHMT2 in patients and NHD13 mice was detected. EZH2 and EHMT2 were silenced or overexpressed in SKM-1 cells. The cell proliferation and cycle were evaluated. Levels of DLX5, H3K27me3, and H3K9me2 in SKM-1 cells were detected. Binding of DLX5 promoter region to H3K27me3 and H3K9me2 was examined. Levels of H3K27me3/H3K9me2 were decreased by EZH2/EHMT2 inhibitor (EPZ-6438/BIX-01294), and changes of DLX5 expression and cell proliferation were observed. EZH2 was poorly expressed in MDS patients but highly expressed in MDS-AML patients. EHMT2 was promoted in both MDS and MDS-AML patients. EZH2 expression was reduced and EHMT2 expression was promoted in NHD13 mice. NHD13 mice with overexpressing EZH2 or EHMT2 transformed into AML more quickly. Intervention of EZH2 or EHMT2 inhibited SKM-1 cell proliferation and promoted DLX5 expression. When silencing EZH1 and EZH2 in SKM-1 cells, the H3K27me3 level was decreased. EZH2 silencing repressed the proliferation of SKM-1 cells. Transcription level of DLX5 in SKM-1 cells was inhibited by H3K27me3 and H3K9me2. Enhanced DLX5 repressed SKM-1 cell proliferation. In conclusion, EZH2/EHMT2 catalyzed H3K27me3/H3K9me2 to inhibit the transcription of DLX5, thus promoting the transformation from MDS to AML.

Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

Recently, N6-methyladenosine (m6A) RNA methylation in eukaryotic mRNA has become increasingly obvious in the pathogenesis and prognosis of cancer. Moreover, tumor microenvironment is involved in the regulation of tumorigenesis. In our research, the clinical data, including 374 tumor and 50 normal patients, were obtained from The Cancer Genome Atlas (TCGA). Then 19 m6A regulators were selected from other studies. Hepatocellular carcinoma (HCC) patients were clustered in cluster1/2, according to the consensus clustering for the m6A RNA regulators. We found that m6A regulators were upregulated in cluster1. The cluster1 was associated with higher programmed death ligand 1 (PD-L1) expression level, higher immunoscore, worse prognosis, and distinct immune cell infiltration compared with cluster2. Five risk signatures were identified, including YTH N6-methyladenosine RNA-binding protein 1, YTHDF2, heterogeneous nuclear ribonucleoprotein C, WT1-associated protein, and methyltransferase-like 3, based on univariate Cox and least absolute shrinkage and selection operator regression analysis. High-risk group and low-risk group HCC patients were selected based on the risk score. Similarly, the high-risk group was extremely associated with higher PD-L1 expression level, higher grade, and worse overall survival (OS). Also, cluster1 was mainly enriched in high-risk group. Receiver operating characteristic (ROC) and a nomogram were used to predict the ability and the probability of 3- and 5-year OS of HCC patients. The time-dependent ROC curve (AUC) reached 0.77, 0.67, and 0.68 at 1, 3, and 5 years in the training dataset. Also, AUC areas of 1, 3, and 5 years were 0.7, 0.63, and 0.55 in the validation dataset. The gene set enrichment analysis showed that MTOR signaling pathway and WNT signaling pathway were correlated with cluster1 and high-risk group. Collectively, the research showed that the m6A regulators were significantly associated with tumor immune microenvironment in HCC. Risk characteristics based on m6A regulators may predict prognosis in patients with HCC and provide a new therapeutic target for improving the efficacy of immunotherapy.

Continuous monitoring of in vivo pulsatile blood flow and pulse wave velocity (PWV) is important for clinical applications. These parameters are correlated with physiological parameters, such as blood pressure and elasticity of blood vessels. A multimode fiber diffuse speckle contrast analysis (MMF-DSCA) system was developed for fast measurement of in vivo pulsatile blood flow and pulse wave velocity. With MMF and CCD sensor, the diffuse speckle could be captured and processed with higher temporal resolution of 3 ms. We also induced for the first time an MMF-DSCA for evaluation of PWV, which allows estimation of the blood pressure continuously. To validate its performance, both phantom and in vivo experiments were conducted. The results demonstrate that MMF-DSCA could achieve fast pulsatile blood flow measurement with detailed information of the pulse wave profile and velocity. Taking the advantages of being simple and cost-effective, the flexible system can be easily adapted for continuous monitoring of vital biosigns, such as heart rate, pulse wave, and blood pressure.

Tubulin alpha 1b (TUBA1B) is an important microtubule isoform that is involved in the formation of the cytoskeleton. The objective of our study was to explore the potential of TUBA1B in predicting the prognosis of HCC and patients’ response to immunotherapy. Raw data was extracted from TCGA and GEO databases, and then HCCDB, TIMER, HPA, and GEPIA websites, as well as R software, were used to perform bioinformatics analysis to investigate the potential of TUBA1B as a prognostic and immunotherapeutic marker for hepatocellular carcinoma (HCC). We found that both TUBA1B mRNA and protein were highly expressed in HCC. TUBA1B was proved to be an independent prognostic predictor of HCC. Additionally, TUBA1B expression was associated with the infiltration of several immune cells in HCC. Moreover, TUBA1B was coexpressed with immune-related genes and immune checkpoints. Patients expressing high TUBA1B responded better to immune checkpoint inhibitor (ICI) therapy. GO and KEGG analyses revealed that TUBA1B may be involved in the processes of cell cycle, spliceosome, and DNA replication. In conclusion, TUBA1B is expected to be a prognostic and immunotherapeutic marker for HCC.

Neonatal hypoxic-ischemic encephalopathy causes neurodegeneration and brain injury, leading to sensorimotor dysfunction. Xyloketal B is a novel marine compound isolated from a mangrove fungus Xylaria species (no. 2508) with unique antioxidant effects. In this study, we investigated the effects and mechanism of xyloketal B on oxygen-glucose deprivation-induced neuronal cell death in mouse primary cortical culture and on hypoxic-ischemic brain injury in neonatal mice in vivo. We found that xyloketal B reduced anoxia-induced neuronal cell death in vitro, as well as infarct volume in neonatal hypoxic-ischemic brain injury model in vivo. Furthermore, xyloketal B improved functional behavioral recovery of the animals following hypoxic-ischemic insult. In addition, xyloketal B significantly decreased calcium entry, reduced the number of TUNEL-positive cells, reduced the levels of cleaved caspase-3 and Bax proteins, and increased the level of Bcl-2 protein after the hypoxic-ischemic injury. Our findings indicate that xyloketal B is effective in models of hypoxia-ischemia and thus has potential as a treatment for hypoxic-ischemic brain injury.

Modeling of Mechanical Alloying (MA), which is a solid-state powder processing technique, is carried out by examining one widely used laboratory scale milling device, the SPEX 8000 shaker mill. It is a vibratory mill; its vial is agitated at a high frequency in a complex cycle that involves motion in three orthogonal directions. In this work, a popular dynamic simulation technique, Discrete Element Modeling, is applied to examine dynamics of a SPEX 8000 shaker ball mill based on the movement of milling balls. The computational results for energy dissipation rate inside the mill are calculated for different ball sizes and varied total ball to powder mass ratios (charge ratios). The computational results are well correlated with the experimental results tracking milling dose (used to define the degree of milling) as a function of ball sizes and charge ratios. Moreover, the numerical (theoretical) milling dose that correlates well with its experimental analog was found to depend on the energy dissipation rate of the head-on ball collisions. The numerical simulations also indicated that the milling progress is most significantly affected by milling media collisions with the energy within a specific threshold, while the collisions with smaller and greater energies are less effective. Finally, discussion shows how this novel approach of correlating specific scaling terms between experiments and simulations can be applied to other powder processing equipment.

Holistic benefits of human milk to infants, particularly brain development and cognitive behavior, have stipulated that infant formula be tailored in composition like human milk. However, the composition of human milk, especially lipids, and their effects on brain development is complex and not fully elucidated. We evaluated brain lipidome profiles in weanling rats fed human milk or infant formula using non-targeted UHPLC-MS techniques. We also compared the lipid composition of human milk and infant formula using conventional GC-FID and HPLC-ELSD techniques. The sphingomyelin class of lipids was significantly higher in brains of rats fed human milk. Lipid species mainly comprising saturated or mono-unsaturated C18 fatty acids contributed significantly higher percentages to their respective classes in human milk compared to infant formula fed samples. In contrast, PUFAs contributed significantly higher percentages in brains of formula fed samples. Differences between human milk and formula lipids included minor fatty acids such as C8:0 and C12:0, which were higher in formula, and C16:1 and C18:1 n11, which were higher in human milk. Formula also contained higher levels of low- to medium-carbon triacylglycerols, whereas human milk had higher levels of high-carbon triacylglycerols. All phospholipid classes, and ceramides, were higher in formula. We show that brain lipid composition differs in weanling rats fed human milk or infant formula, but dietary lipid compositions do not necessarily manifest in the brain lipidome.

Xyloketal B is a natural compound isolated from the mangrove fungus, Xylaria sp. in the South China Sea. In the past decade, studies have shown that xyloketal B exhibits anti-oxidative, anti-inflammatory, and anti-apoptotic abilities and may serve as a treatment for ischemic stroke. Xyloketal B has been shown to interact with both neurons and residential microglial cells and regulate a number of proteins involved in the apoptotic events during ischemia. Such mechanisms include inhibition of specific NADPH oxidase subunits, upregulation of HO-1, increase of Bcl-1/Bax ratio, and downregulation of TLR4 receptor. Both in vitro and in vivo stroke models have validated its potential in preventing ischemia-induced neuronal cell death. This review summarizes our current understanding of the effects of xyloketal B in ischemic conditions. As stroke ranks second in the causes of mortality worldwide and still lacks effective treatment, it is necessary to seek novel therapeutic options. Understanding the role of xyloketal B in ischemic stroke could reveal a new aspect of stroke treatment.

Noninvasive detection of body composition plays a significant role in the improvement of life quality and reduction in complications of the patients, and the near-infrared (NIR) spectroscopy, with the advantages of painlessness and convenience, is considered as the most promising tool for the online noninvasive monitoring of body composition. However, quite different from other fields of online detection using NIR spectroscopy, such as food safety and environment monitoring, noninvasive detection of body composition demands higher precision of the instruments as well as more rigorousness of measurement conditions. Therefore, new challenges emerge when NIR spectroscopy is applied to the noninvasive detection of body composition, which, in this paper, are first concluded from the aspects of measurement methods, measurement conditions, instrument precision, multi-component influence, individual difference and novel weak-signal extraction method based on our previous research in the cutting-edge field of NIR noninvasive blood glucose detection. Moreover, novel ideas and approaches of our group to solve these problems are introduced, which may provide evidence for the future development of noninvasive blood glucose detection, and further contribute to the noninvasive detection of other body compositions using NIR spectroscopy.