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This study aims to quantitatively analyze the three-dimensional microstructure of ultra-thin HDF paving boards and molding boards, in order to explore the modeling methods of HDF and attempt to predict its mechanical properties more accurately through theoretical and simulation analyses. In this study, non-destructive analysis of the internal microstructure was conducted using computed tomography (CT) imaging technology. The characteristics of microstructural evolution during the hot compression molding process were explored, and all analyzed data were utilized for 3D reconstruction and simulation model construction. Ultra-thin fiberboards with different fiber ratios were modeled through volume compression and experimentally simulated in tension. The results were compared with experimentally determined results for verification purposes. Additionally, differences in surface roughness, water absorption, and expansion of ultra-thin fiberboards with varying fiber ratios were analyzed. The findings indicate that different fiber ratios have a significant impact on the mechanical properties of ultra-thin fiberboards; tensile strength increases with an increase in medium-length fibers content when fibers are pulled or split, highlighting the key role played by the strength of the fibers themselves. Furthermore, it was observed that surface roughness and water-absorbing expansion rate are lower when a higher proportion of fine fibers is present. The results from this study hold great significance for gaining a deeper understanding of the composition, structure, and properties of ultra-thin fiberboards as well as exploring modeling methods for high-density fiberboards. They also provide guidance for optimizing material formulations and preparation processes while improving performance and quality standards for poplar-ultra-thin fiberboards. • We quantitatively analysed the three-dimensional microstructure of ultra-thin fibreboard paving slabs and moulded panels. • We explored the modelling method of high-density fibreboard and obtained a model of high-density fibreboard with a volume fraction of 90%. • We explored the effects of different fibre ratios on the mechanical properties of ultra-thin fibreboards. [ABSTRACT FROM AUTHOR]

Hypercholesterolemia is frequently diagnosed in patients with primary biliary cholangitis (PBC). However, its association with the prognosis and lipid metabolism is unknown. In this study, we aimed to investigate the prognostic value of baseline total cholesterol (TC) levels in PBC and characterized the associated lipid metabolism. Five hundred and thirty-one patients with PBC without prior cirrhosis-related complications were randomly divided into the derivation and validation cohorts at a ratio of 7:3. Complete clinical data were obtained and analyzed. The endpoints were defined as liver-related death, liver transplantation, and cirrhosis-related complications. Lipidomics was performed in 89 patients and 28 healthy controls. Baseline TC was independently associated with poor liver-related outcomes, and adjusted C-statistics were 0.80 (95% confidence interval [CI]: 0.74–0.85) and 0.88 (95% CI: 0.78–0.91) in the derivation and validation cohorts, respectively. The predictive ability of TC for disease outcomes was stable over time and comparable with the Globe score. The 200 mg/dL cut-off optimally divided patients into low- and high-TC groups. A combination of TC and Globe score provided a more accurate stratification of patients into risk subgroups. Lipidomics indicated an up-regulation of lipid families in high-TC patients. Pathway analysis of 66 up-regulated lipids revealed the dysregulation of glycerophospholipid and sphingolipid metabolism in high-TC patients, which were associated with poor liver-related outcomes. Our results indicate that patients with PBC having baseline TC levels above 200 mg/dL have unique lipidome characteristics and are at a higher risk of poor liver-related outcomes. [Display omitted] [ABSTRACT FROM AUTHOR]

Anthracite, a high-rank coal, is an important fossil energy and resource, widely used in combustion and gasification. To improve the utilization efficiency of anthracite, it is important to study its structures. In our previous work, we found that the alkali-oxygen oxidation of anthracite could yield benzene carboxylic acids (BCAs). However, none of the existing structural models of anthracite captures the yield distribution of BCAs. In this work, we studied the structures of Hebi anthracite based on its BCAs combined with multi analysis techniques and proposed a structural model of the coal. We first oxidized the coal to yield BCAs, whose parent structure distributions we then investigated. Then, based the results of ultimate analysis and 13C NMR, we obtained the structural parameters. We then proposed corresponding aromatic clusters based on the parent structures of BCAs. Lastly, we constructed a structural model of the coal based on the analyses of ultimate analysis, 13C NMR, XPS and the contents of carboxyl and phenolic hydroxyl groups. The model has a molecular formula of C 1966 H 882 N 22 O 76 S 3 and a molecular weight of 26094 amu. The model not only reflects the characteristics of anthracite intuitively, but also agrees with the results of BCAs, elemental analysis, carboxyl and phenolic hydroxyl group analyses, XPS analysis, and 13C NMR analysis. The work provides new insights in the structural characteristics of anthracite. • Benzene carboxylic acids were obtained from Hebi anthracite via oxidation. • A structural model of Hebi anthracite was proposed based on the distributions of their yields. • The model has a molecular formula of C 1966 H 882 N 22 O 76 S 3 and a molecular weight of 26094 amu. • The model can reflect the characteristics of anthracite. [ABSTRACT FROM AUTHOR]

One critical component of the immune system that prevents breast cancer cells from forming distant metastasis is natural killer (NK) cells participating in immune responses to tumors. Ginsenoside Rh2 (GRh2) as one of the major active ingredients of ginseng has been employed in treatment of cancers, but the function of GRh2 in modulating the development of breast cancer remains elusive. This study was to dissect the effect of GRh2 against breast cancer and its potential mechanisms associated with NK cells, both in vitro and in vivo. MDA-MB-231 and 4T1 cells were used to establish in situ and hematogenous mouse models. MDA-MB-231 and MCF-7 were respectively co-cultured with NK92MI cells or primary NK cells in vitro. Anti-tumor efficacy of GRh2 was verified by immunohistochemistry (IHC), Cell Counting Kit-8 (CCK8), high resolution micro-computed tomography (micro-CT) scanning of lungs and hematoxylin and eosin (H&E) staining. Lactate dehydrogenase (LDH) cytotoxicity assay, flow cytometry, in vivo depletion of NK cells, enzyme-linked immunosorbent assay (ELISA), western blot, quantitative reverse transcription polymerase chain reaction (qRT-PCR), immunofluorescence and cell transfection were performed for investigating the anti-tumor mechanisms of GRh2. Molecular docking, microscale thermophoresis (MST) and cellular thermal shift assay (CETSA) were employed to determine the binding between endoplasmic reticulum protein 5 (ERp5) and GRh2. We demonstrated that GRh2 exerted prominent impacts on retarding the growth and metastasis of breast cancer through boosting the cytotoxic function of NK cells, as validated by the elevated release of perforin, granzyme B and interferon- γ (IFN- γ). Mechanistical studies revealed that GRh2 was capable of diminishing the expression of ERp5 and GRh2 directly bound to ERp5 in MDA-MB-231 cells as well as on a recombinant protein level. GRh2 prevented the formation of soluble MICA (sMICA) and upregulated the expression level of MICA in vivo and in vitro. Importantly, the reduced lung metastasis of breast cancer by GRh2 was almost abolished upon the depletion of NK cells. Moreover, GRh2 was able to insert into the binding pocket of ERp5 directly. We firstly demonstrated that GRh2 played a pivotal role in augmenting NK cell activity by virtue of modulating the NKG2D-MICA signaling axis via directly binding to ERp5, and may be further optimized to a therapeutic agent for the treatment of breast cancer. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate diagnostic performance of radiomics models of MRI in the detection of differentiation degree (DD) and lymph node metastases (LNM) of extrahepatic cholangiocarcinoma (ECC). We retrospectively enrolled 100 patients with ECC confirmed by pathology from January 2011 to December 2018. Three hundred radiomics features were extracted from each region of interest using MaZda software. Next, the radiomics model was developed by incorporating the optimal radiomics signatures and ADC values of tumors to predict DD (model A) and LNM (model B) of ECC, respectively, through the random forest algorithm. After which, the performance of the radiomics models were further evaluated. The model A showed better performance in both training and testing cohorts to discriminate high and medium-low differentiation groups of ECC, with an average AUC of 0.78 and 0.80, respectively. The model B also yielded the good average AUC of 0.80 and 0.90 to predict the LNM of ECC in training and testing cohorts. The radiomics models based on MRI performed well in predicting DD and LNM of ECC and have significant potential in clinical noninvasive diagnosis and in the prediction of ECC. [ABSTRACT FROM AUTHOR]

Background: Most cases of extrahepatic cholangiocarcinoma (ECC) show various degrees of hyperintensity on diffusion-weighted imaging (DWI) and hypointensity on apparent diffusion coefficient (ADC). However, the factors influencing ADC values in ECC have not yet been explored extensively. Therefore, this study explored the independent factors influencing ADC values in ECC.Methods: A total of 88 patients with ECC confirmed by surgical pathology were retrospectively assessed. All patients underwent abdominal magnetic resonance imaging (MRI) at 3.0 T and ADC values of the tumor were measured. The correlation between ADC values and multiple clinicopathological features in ECC was analyzed, and independent factors influencing the ADC values in ECC were explored further.Results: The ADC value of the tumor showed a significant difference in different perineural invasion group (p = 0.048), microvascular invasion group (p = 0.001), vascular endothelial growth factor expression group (p < 0.001), lymphatic status group (p = 0.003), and differentiation degree (DD) group (p < 0.001). However, there were no significant differences in different sex (p = 0.715), tumor location (p = 0.659), and degree of T stage (p = 0.879). Moreover, ADC value was negatively correlated with microvascular density (r = -0.725, p < 0.001) and lesion size (r = -0.244, p = 0.023). Nevertheless, there was no correlation between ADC value and patient age (r = 0.026, p = 0.812). Further regression analysis indicated that ADC value was independently associated with pathological DD of ECC (R2 = 0.678, p < 0.001) but not with other clinicopathological factors (p > 0.05).Conclusion: ADC value of ECC is independently correlated with pathological DD of ECC, indicating that ADC value is a potential imaging biomarker for predicting the degree of ECC malignancy. [ABSTRACT FROM AUTHOR]

Titanium nitride nanoparticles (TiN NPs) are proposed as a novel catalyst towards the V(II)/V(III) redox pair for the negative electrode in vanadium redox flow batteries (VRFB). Electrochemical properties of TiN NPs were characterized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results show that TiN NPs demonstrate better electrochemical activity and reversibility for the processes of V(II)/V(III) redox couples as compared with the graphite NPs. TiN NPs facilitate the charge transfer in the V(II)/V(III) redox reaction. Performance of a VRFB using a TiN NPs coated carbon paper as a negative electrode is much higher than that of a VRFB with a raw carbon paper electrode. The columbic efficiency (CE), the voltage efficiency (VE) and the energy efficiency (EE) of the VRFB single cell at charge-discharge current density of 30 mA/cm 2 are 91.74%, 89.11% and 81.74%, respectively. During a 50 charge-discharge cycles test, the CE values of VRFB with TiN NPs consistently remain higher than 90%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Acacetin weakens the proliferation, migration and invasion of osteosarcoma cells. • Acacetin promotes the apoptosis of osteosarcoma cells. • Xenograft mouse osteosarcoma model confirms the inhibitory effect in vivo. • The signal pathway is predicted by network pharmacology and confirmed by the experiments. Osteosarcoma (OS) is the most common primary malignant bone tumor with high lung metastasis and mortality rate. Neo-adjuvant chemotherapy combined with limb salvage is the main treatment strategy for OS. However, because of the tolerance to conventional chemotherapy and the cell toxicity of chemotherapy drugs, new drugs with lower toxicity for OS are urgently needed. Acacetin is a natural flavonoid compound that has inhibitory effects on many kinds of tumors. Herein, we explored the impact of acacetin on OS cell growth in vitro and in vivo. We discovered that acacetin suppressed the proliferation, migration and invasion, while promoted the apoptosis of OS cells. Notably, acacetin had little impact on proliferation and apoptosis of human normal cells. Furthermore, we confirmed that acacetin repressed the growth and metastasis of OS in xenograft mouse model in vivo. Mechanically, we discovered that acacetin activated JNK pathway and inactivated Wnt/β-catenin pathway. β-catenin over-expression or JNK inhibitor SP600125 was able to partially neutralize acacetin-induced inhibitory effect on OS cells. To sum up, our study implies that acacetin may inhibit tumor growth of human OS cells through regulating Wnt/β-catenin and JNK signaling pathways. [ABSTRACT FROM AUTHOR]

[Display omitted] • Graphene induced conformation changes of SARS-CoV-2 spike RBD are studied by MD simulations. • Graphene significantly affects the secondary structure of the three key sites binding with ACE2. • Both π–π stacking and hydrophobic interactions orchestrate RBD adsorption. • To develop graphene-based protective equipments to defeat SARS-CoV-2 is possible. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely spread in the world, causing more than two million deaths and seriously threatening human life. Effective protection measures are important to prevent the infection and spreading of the virus. To explore the effects of graphene on the virus adsorption and its biological properties, the adsorption process of the receptor binding domain (RBD) of SARS-CoV-2 on graphene has been investigated by molecular dynamics simulations in this paper. The results show that RBD can be quickly adsorbed onto the surface of graphene due to π - π stacking and hydrophobic interactions. Residue PHE486 with benzene ring has stronger adsorption force and the maximum contact area with graphene. Graphene significantly affects the secondary structure of RBD area, especially on the three key sites of binding with human ACE2, GLY476, PHE486 and ASN487. The binding free energy of RBD and graphene shows that the adsorption is irreversible. Undoubtedly, these changes will inevitably affect the pathogenicity of the virus. Therefore, this study provides a theoretical basis for the application of graphene in the protection of SARS-CoV-2, and also provides a reference for the potential application of graphene in the biomedical field. [ABSTRACT FROM AUTHOR]

Abstract: Through structure-based virtual screening, some dozen of benzene sulfonamides with novel scaffolds are identified as potent inhibitors against carbonic anhydrase (CA) IX with IC50 values ranging from 2.86 to 588.34nM. Among them, compounds 1 and 9 show high selectivity against tumor-target CA IX over CA II (the selectivity ratios are 21.3 and 136.6, respectively). The possible binding poses of hit compounds are also explored and the selectivity is elucidated by molecular docking simulations. The hit compounds discovered in this work would provide novel scaffolds for further hit-to-lead optimization. [Copyright &y& Elsevier]

Abstract: In this study, the preparation and trophic characterization of perilla chewable tablet were investigated. Perilla chewable tablet was prepared according to the following process: mixing perilla raw materials with excipients, making wet granules, drying, tabletting and coating. The optimal formula was determined as follows: 8% perilla powder, 2.5% perilla extract powder, 20% isomaltooligosaccharide, 20% microcrystalline cellulose, 44.4% lactose, 0.5% essence of perilla, 0.1% sucralose, 2% erythritol, 2% vitamin C, 0.5% magnesium stearate. Results from nutrient analysis showed that perilla chewable tablet was rich in essential vitamins and mineral substances, which are good for human health. [Copyright &y& Elsevier]

Abstract: The objective of this study was to investigate the enzymatic preparation method of soybean oligosaccharides (S0S) from okara by using Viscozyme L, characterize the physicochemical properties as well as antioxidant activity of S0S. The effect of Viscozyme L treatment on the preparation of S0S from okara was researched by orthogonal test. Under the fixed extraction time of 2.5 h and the ratio of okara to water of 1:15 (w/v), the optimal enzymatic preparation conditions of S0S by using Viscozyme L were identified as pH of 3.5, temperature of 45 °C, enzyme dosage of 3% (w/v) with a maximum yield of 10% (w/w). S0S were found to be crude oligosaccharides containing 56.5% neutral sugar, having a low molecular weight distribution range between 860 and 9380 Da, composing of galactose, xylose, rhamnose, arabinose, mannose and glucose. Furthermore, S0S showed the free radical scavenging activity with IC50 value of 1.81 mg/mL. These results suggest that S0S are novel oligosaccharides derived from okara, and could be improve value-added utilization of okara as antioxidants in functional foods and nutraceuticals. [Copyright &y& Elsevier]

Clustering is one of the major exploratory techniques for gene expression data analysis. Only with suitable similarity metrics and when datasets are properly preprocessed, can results of high quality be obtained in cluster analysis. In this study, gene expression datasets with external evaluation criteria were preprocessed as normalization by line, normalization by column or logarithm transformation by base-2, and were subsequently clustered by hierarchical clustering, k-means clustering and self-organizing maps (SOMs) with Pearson correlation coefficient or Euclidean distance as similarity metric. Finally, the quality of clusters was evaluated by adjusted Rand index. The results illustrate that k-means clustering and SOMs have distinct advantages over hierarchical clustering in gene clustering, and SOMs are a bit better than k-means when randomly initialized. It also shows that hierarchical clustering prefers Pearson correlation coefficient as similarity metric and dataset normalized by line. Meanwhile, k-means clustering and SOMs can produce better clusters with Euclidean distance and logarithm transformed datasets. These results will afford valuable reference to the implementation of gene expression cluster analysis. [ABSTRACT FROM AUTHOR]

• This article first discovered that CAR can inhibit the growth of osteosarcoma. • In this article, while studying the effect of CAR on osteosarcoma, it also studies its toxic and side effects on normal cells. • This article also studies the role of CAR in vitro and in vivo. • This study confirmed the mechanism of CAR inhibiting osteosarcoma through P 38 and JNK signaling pathway inhibitors. Osteosarcoma (OS) is the most common type of bone malignant tumors. Clinical commonly used therapeutic drugs of OS treatment are prone to toxic and side effects, so it is very urgent to develop new drugs with low toxicity and low side effects. As a Chinese herbal medicine, Cardamonin (CAR) (C 16 H 14 O 4) has inhibitory effects in various tumors. In the present study, we investigated the effects of CAR on OS cells in vitro and in vivo. We found that CAR inhibited cell proliferation, reduced migration, decreased invasion, and induced G2 / M arrest of OS cells. Notably, we demonstrated that CAR had no obvious effect on proliferation and apoptosis of normal cells. Besides, CAR repressed tumor growth of OS cells in xenograft mouse model. Mechanically, we found that CAR increased the phosphorylation level of P38 and JNK. In summary, our research validates that CAR may inhibit the proliferation, migration, and invasion of OS and promote apoptosis possibly by activating P38 and JNK Mitogen-activated protein kinase (MAPK) signaling pathway. [ABSTRACT FROM AUTHOR]

• An automatic fundamental contour extraction of dugong chirps was presented. • Five categories of dugong chirps were identified. • A specific classification criterion was proposed for the automatic and effective identification of chirp types. • Our results enrich the known repertoire of dugong chirps, and may be useful in future passive acoustic monitoring studies. A steady decrease in the Dugong (Dugong dugon) population has been notices worldwide. Considering the social function and utilization rate, dugong chirp classification has become an important bioacoustics research topic, which may help marine biologists monitor the change of their population, study their behavioral context in different environments and optimise the protection policy. However, the objective classification of dugong chirps has not been adequately addressed in the literature. In this paper, an automatic fundamental contour extraction using the tempo-frequency variables to describe the contours of the fundamental frequency of the chirps is presented. With the automatic fundamental contour extraction, the time–frequency information of dugong chirps can be effectively represented, which gives a good classification performance. Following the principles used for the classification of dolphin whistles, three variables were chosen and performed with hierarchical cluster analysis. Based on these results and further visual examination, five categories of chirps were identified. A simple classification criterion was proposed which could be used in the automatic and effective identification of chirp types. Our findings enrich the known repertoire of dugong chirps, and may be useful in future passive acoustic monitoring studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • The novel Ginkgo biloba extract (nGBE) improved long-term neurofunction after cerebral ischemia. • nGBE reduced microglia-related inflammation. • nGBE promoted white matter integrity. Ginkgo biloba L. leaf extract (GBE) has been added in many commercial herbal formulations such as EGb 761 and Shuxuening Injection to treat cardiovascular diseases and stroke worldwide. However, the comprehensive effects of GBE on cerebral ischemia remained unclear. Using a novel GBE (nGBE), which consists of all the compounds of traditional (t)GBE and one new compound, pinitol, we investigated its effect on inflammation, white matter integrity, and long-term neurological function in an experimental stroke model. Both transient middle cerebral artery occlusion (MCAO) and distal MCAO were conducted in male C57/BL6 mice. We found that nGBE significantly reduced infarct volume at 1, 3, and 14 days after ischemia. Sensorimotor and cognitive functions were superior in nGBE treated mice after MCAO. nGBE inhibited the release of IL-1β in the brain, promoted microglial ramification, and regulated the microglial M1 to M2 phenotype shift at 7 days post injury. In vitro analyses showed that nGBE treatment reduced the production of IL-1β and TNFα in primary microglia. Administration of nGBE also decreased the SMI-32/MBP ratio and enhanced myelin integrity, thus exhibiting improved white matter integrity at 28 days post stroke. These findings demonstrate that nGBE protects against cerebral ischemia by inhibiting microglia-related inflammation and promoting white matter repair, suggesting that nGBE is a promising therapeutic strategy for long-term recovery after stroke. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop and test radiomics models based on magnetic resonance imaging (MRI) to preoperatively and respectively predict the T stage, perineural invasion, and microvascular invasion of extrahepatic cholangiocarcinoma (eCCA) through a non-invasive approach. This research included 101 eCCA patients (29–83 years; 45 females and 56 males) between August 2011 and December 2019. Radiomics features were retrospectively extracted from T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map using MaZda software. The region of interest was manually delineated in the largest section on four MRI images as ground truth while keeping 1–2 mm margin to tumor border, respectively. Pretreatment, dimension reduction method, and classifiers were used to establish radiomics signatures for assessing three pathological characteristics of eCCA. Finally, independent training and testing datasets were used to assess radiomics signature performance based on receiver operating characteristic curve analysis, accuracy, precision, sensitivity, and specificity. This study extracted 1208 radiomics features from four MRI images of each patient. The best performing radiomics signatures for assessing the T stage, perineural invasion, and microvascular invasion were respectively produced by L1_normalization + linear discriminant analysis (LDA) + logistic regression, Box_Cox transformer + LDA + K-nearest neighbor, and L2_normalization + LDA + AdaBoost. The area under the curve values of the radiomics signatures for predicting the training and testing cohorts in each subgroup were respectively 1 and 0.962 (T stage), 1 and 1 (both perineural invasion and microvascular invasion). These proposed radiomic models based on MR images had powerful performance and high potential in predicting T stage, perineural, and microvascular invasion of eCCA. Prognostic study. [Display omitted] • MRI radiomics models were built to predict T stage, perineural, and microvascular invasion. • MRI-based radiomics model achieved AUC 1 and 0.962 (train and test) for predicting T stage. • The AUCs were 1 and 1 (train, test) in perineural and microvascular invasion models, respectively. [ABSTRACT FROM AUTHOR]

• The magnesium alloy and 304 stainless steel were successfully joined via nanosecond pulsed laser welding. • Highest tensile shear force of 298.7 N was obtained at the speed of 30 mm/s. • Three fracture modes produced at various welding speeds were clarified. • The welding temperature field of magnesium/steel joint was simulated. Nanosecond pulse laser welding was performed on AZ31B magnesium alloy and 304 stainless steel to investigate the impact of welding speed on the joining process. The temperature field of the magnesium/steel laser welding process was simulated using COMSOL software. The findings revealed that a welding speed of 10 mm/s resulted in significant spattering and larger porosity defects in the joint due to excessive heat input. However, when the welding speed was increased to 30 mm/s, these defects disappeared, and the porosity decreased to a minimum, leading to an increased bonding area at the interface. As the speed increased, the heat input decreased, making it more challenging for the porosity to escape from the molten pool and resulting in the formation of larger pores. The shear force test results indicated that the highest shear force was 298.7 N at a welding speed of 30 mm/s. The reduction in porosity and greater penetration depth of the magnesium alloy contributed to the desired mechanical performance. Additionally, the fracture modes were classified as button pullout failure (BPF), base material tearing failure (BTF), and interface failure (IF). The outermost weld seam served as the initial fracture path for both BPF and BTF modes, with BTF ultimately fracturing in the steel base material during tearing. Oxide inclusions, porosity, and the angle of distortion contributed to the fracture path of IF. [ABSTRACT FROM AUTHOR]

The albumin-bilirubin (ALBI) grade has exhibited an equal excellence with the Child-Pugh (C-P) grade in predicting overall survival (OS) of patients with hepatocellular carcinoma (HCC). However, available published results of the ALBI grade in predicting the prognosis of HCC are still limited. The goal of this study is to perform a systematic review and meta-analysis of the available data to comprehensively evaluate the ALBI grade in predicting OS of patients with HCC. Multiple databases were systematically searched for eligible studies. Studies analyzing the relationship between the ALBI grade and survival outcome were identified. Hazard ratio (HR) with 95% confidence interval (CI) was calculated to assess the risk. All statistical analyses were conducted by R version 3.3.1 (The R Foundation for Statistical Computing, Vienna, Austria). A total of 8 studies were enrolled in the meta-analysis. The pooled estimates demonstrated a significant relationship between elevated ALBI grade and inferior OS in patients with HCC (grade 1 vs 2: HR = 1.71, 95% CI: 1.52-1.92; grade 1 vs 3: HR = 3.81, 95% CI: 2.75-5.29.). In addition, the same tendency was observed when performing subgroup analysis, including treatment strategies (surgical resection, transcatheter arterial chemoembolization, radiofrequency ablation, and sorafenib) and study regions (Japan, Europe, China, and the USA). Moreover, the ALBI grade was able to classify patients with C-P grade A into 2 distinct prognostic cohorts—ALBI grade 1 and ALBI grade 2—with distinguishing survival outcomes (surgical resection: grade 1 vs 2: HR = 1.74, 95% CI: 1.55-2.06, P <. 001; sorafenib: grade 1 vs 2: HR = 1.54, 95% CI: 1.30-1.82, P <. 001). The ALBI grade has the potency of becoming an independent prognostic factor in patients with HCC. More well-designed studies should be performed to evaluate the ALBI grade as a complementary prognostic tool to current staging systems in routine clinical practice. [ABSTRACT FROM AUTHOR]

The synthesis and antileukemia evaluation of a series of dual HDAC/COX-2 inhibitors are reported. [Display omitted] • A series of dual HDAC/COX-2 inhibitors were designed and synthesized. • Compound 11 was identified as an effective agent to reduce the viability of different leukemia cell lines, particularly NALM6 cells (acute lymphoblastic leukemia cells). • Compound 11 inhibited the growth of NALM6 via cell cycle arrest and induction of apoptosis. • Compound 11 induced apoptotic cell death by activating PARP cleavage. • Compound 11 had the potential as a promising chemotherapeutic agent for acute lymphoblastic leukemia. Acute lymphoblastic leukemia (ALL) is the most common malignancy in children. Here, we exploited the synergy between histone deacetylase inhibitors (HDACi) and cyclooxygenase 2 (COX-2) inhibitors by generating and testing a series of hybrid Celecoxib-HDAC inhibitors (selenium-containing analogues of Celecoxib) on ALL cells, of which compound 11 exhibited significant inducement to kill NALM6 cells with an average IC 50 of 9.95 ± 0.44 μM compared with control Celecoxib at 28.58 ± 1.44 μM and inhibited NALM6 cells growth via the inhibition of the cell cycle in G2 phase. Furthermore, compound 11 induced apoptosis by activating PARP cleavage. Taken together, compound 11 possessed the potential to be developed further as a chemotherapeutic agent for ALL. [ABSTRACT FROM AUTHOR]

• Four types of defects in rubber timber are performed by improved YOLOX. • Improve the feature fusion module of YOLOX by adding ECA attention mechanism and ASSF multi-feature adaptive fusion. • Improve the confidence loss function and change BCE loss to Focal loss. • The regression of the target box was performed using EIOU loss. Deep learning has achieved certain results in the field of wood surface defect detection. To address the problems of low accuracy of the detection results of surface defects on boards, slow detection speed and large number of model parameters, this article take advantage of computer vision to improve the feature fusion module of YOLOX target detection algorithm, by adding efficient channel attention (ECA) mechanism, adaptive spatial feature fusion mechanism (ASFF) and improve the confidence loss and localization loss functions as Focal loss and Efficient Intersection over Union (EIoU) loss, to enhance the feature extraction ability and detection accuracy of the algorithm. Considering the depth and width of the model, the depth-separable convolution and optional multi-version algorithm are used to reduce the model parameters and computational effort to seek the optimal model. Experiments show that the improved model detects four types of defects in rubber timber with a considerable improvement and has significant advantages over other target detection algorithms. [ABSTRACT FROM AUTHOR]

Infrared spectroscopy has been widely used in quantitative analysis due to its advantages of rapidity, high efficiency and no pollution. With the development of deep learning, Convolutional Neural Network (CNN) promotes the further development of quantitative analysis methods. Spectral pretreatment is a key part to remove noise before the network training, which is able to improve the accuracy of the CNN model. However, the parameters adjustment and the signal information loss always exist in the preprocessing, which limits the application scope of CNN model. In this paper, a combined Time-Frequency Analysis and CNN (TFA-CNN) method for quantitative analysis in infrared spectroscopy is proposed. The pretreatment process of infrared spectra is integrated with CNN to avoid the loss of information, guaranteeing a high accuracy quantitative analysis without parameter adjustment. The experiment results show that the proposed TFA-CNN method has better accuracy in quantitative analysis of infrared spectroscopy, compared with traditional quantitative analysis methods. Furthermore, the TFA-CNN method has proved to be effective in a relatively small dataset. The proposed method is simple and universal, which has a broader application prospect. [ABSTRACT FROM AUTHOR]

• The Markov, FLUS, and InVEST models were combined for their respective strengths. • The cultivated land occupation by construction land leads to the reduction of carbon storage. • 200 m is the optimal grid scale in carbon storage prediction. • The NTS, FSS, HUS, and ESS were used to predict future changes in carbon storage. • ESS increases carbon storage by 1.51 Tg between 2020 and 2035. Carbon storage services play an important role in maintaining ecosystem stability. Land use/cover change (LUCC) is the dominant factor generating changes in the ecosystem carbon storage. Demonstrating the impact of LUCC on regional carbon storage changes and predicting future carbon storage under different land use scenarios is of great significance for promoting regional carbon peak and carbon neutrality goals. Taking the main urban area of Chongqing as an example, this study analyzes carbon storage changes from 2000 to 2020 and the response to LUCC. The Markov-FLUS model is employed to predict the land use pattern of the main urban area in 2035 under four scenarios, and the InVEST model is used to assess carbon storage in 2035 under different scenarios. The results can be summarized as follows: 1) In the past 20 years, the area of cultivated land in the main urban areas of Chongqing decreased by 743.29 km2, and the area of construction land increased by 773.48 km2. About 18.8 % of the main urban area of Chongqing was transferred, the conversion of cultivated land to construction land being the most important type of transfer. 2) In 2000, 2005, 2010, 2015, and 2020, the carbon storage in the main urban area was 59.85, 59.29, 57.90, 56.95, and 54.07 Tg, respectively, showing an annually decreasing trend with a cumulative decrease of 5.78 Tg. The cultivated land occupation by construction land is the leading factor for the rapid decrease of carbon storage. The spatial distribution of carbon storage in the main urban area differs significantly, exhibiting a low in the middle and high in the surrounding areas. 3) In 2035, the carbon storage in the main urban area shows different degrees of decline in the Natural Trend Scenario (NTS), the Food Security Scenario (FSS) and the High Urbanization Scenario (HUS), decreasing by 3.37, 0.59, and 5.25 Tg, respectively. The only increase by 1.51 Tg is found in the Ecological Security Scenario (ESS). Therefore, under the background of the "Dual Carbon" targets and the important positioning of Chongqing as an important ecological barrier in the Yangtze River Basin, the ESS can be considered in the future development planning of the main urban area of Chongqing, which can both increase carbon sink and ensure ecological security. [ABSTRACT FROM AUTHOR]

• A method that can accomplish both detection and classification tasks of three marine mammal calls in one-step with Convolutional neural networks is proposed. • The proposed method can reach high identification precise and recall rate. • The identification process is fast and can be done in real-time. • The proposed method shows the potential to be applied in passive acoustic monitoring systems in the future. Animal vocalization is one of the most important ways to identify the existence and occurrence of marine mammals. Accurately detecting and identifying marine mammal species in real time is significant in the field of marine mammal research. In this study, we developed a novel method based on convolutional neural networks (CNNs) to precisely identify the calls of bottlenose dolphins (Tursiops), dugongs (Dugong dugong), and the Indo-Pacific humpback dolphin (Sousa chinensis). Compared with traditional detection and classification techniques, the proposed method is a one-step algorithm that combines detection and classification. It can automatically learn the features of different marine mammal calls and position the target calls with bounding boxes in a spectrogram. Furthermore, the whole process can be conducted in real time. With this method, the raw data are cut into fixed lengths and spectrograms are created. Next, the annotated sound data are sent to the CNNs to train an identification model. Experimental results showed that the proposed method achieved >91% precision and >84% recall of test data with real-time speed. In the future, this method can be applied to passive acoustic observation equipment for quick, accurate pre-detection of marine mammal calls. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a method of utilizing the carbonized micron wood fiber (CMWF) as the filter material is presented and a corresponding numerical simulation method is applied. Furthermore, a kind of detachable diesel particulate filters (DPF) is developed, a diesel engine bench test is conducted and the influence elements upon Δp/H are researched. Theory and test show that the smaller the average micro pore diameter is, the larger the focal length of parabola is, which means that the numerical simulation method can be consistent with the test very well. Meanwhile, a lot of tests prove that the DPF filters with bigger pore diameter and longer length should take priority over the others, together with the increase of the filter length, the PM arresting efficiency will be improved and Δp/H also climbs up rapidly and a kind of modified CMWF DPF, of which the micro pore diameter reduces gradually from entry to exit end, possesses high filtration efficiency and low pressure drop and be suited to filter the exhaust gases of diesel. [Copyright &y& Elsevier]

Diabetic nephropathy (DN) is a common disease, and patients often do not have satisfactory treatments. We investigated therapeutic effects of Fuxin Granules(FX) on DN and potential molecular mechanisms. We orally administered doses of FX to db/db mice for 10 weeks and measured total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol. H&E, PAS, Masson, and Oil Red O staining were used to observe the structure of kidneys and calculate indices of kidney function. We used pharmacological analysis to investigate potential mechanisms of FX. Relative mRNA and protein levels in the TGF-β1/Smad, TGF-β1/Smad, and VEGF/VEGFR2 pathways were examined. TC, TG, and LDL-C were markedly reduced, lipid accumulation was low, fibrosis reduced, kidney atrophy improved, kidney lipid droplet number significantly reduced, and glomerular filtration function improved by FX treatment. Multi-channel therapeutic effects in DN through the TGF-β1/Smad and VEGF/VEGFR2 signaling pathways occurred, and FX substantially reduced expression of TGF-β1 in the glomeruli. FX significantly inhibited TGF-β1, Smad2/3 total protein levels, Smad2/3 phosphorylation mRNA levels of TGF-β1, Smad2, and Smad3. eNOS, VEGFA, and VEGFR2 expression was regulated, levels of VEGFA and VEGFR2 were decreased, and FX increased eNOS. FX ameliorated symptoms of DN, resulting in marked improvement in hyperglycemia and hyperlipidemia and optimized structure and function of kidneys in db/db mice. FX efficacy was associated with the TGF-β1/Smad and VEGF/VEGFR2 signaling pathways. We verified this potential mechanism and hope that this study will provide benefits for the clinical treatment of DN. [Display omitted] • Fuxin Granules attenuate symptoms of hyperglycemia and hyperlipidemia in db/db mice. • Fuxin Granules aid kidney lipid droplet deposition and renal function in db/db mice. • Fuxin Granules play a multi-channel regulatory and therapeutic role. • Fuxin Granules inhibit ECM storage in db/db mice by TGF-β1/Smad pathway activation. • Fuxin Granules protect renal function in db/db mice through the VEGF/VEGFR2 pathway. [ABSTRACT FROM AUTHOR]

Abstract: Neuroligins are postsynaptic membrane cell-adhesion molecules which bind to β-neurexins, a family of proteins that act as neuronal cell surface receptors. To explore the possibility that structural variants in the β-neurexin genes predispose to autism, the coding regions and associated splice junctions of three β-neurexin genes were scanned with detection of virtually all mutations-SSCP (DOVAM-S) in 72 Caucasian patients with autism. In addition, segments of the neurexin 1β gene were sequenced in 131 additional Caucasian and 61 Afro-American patients with autism from South Carolina and the Midwest. Two putative missense structural variants were identified in the neurexin 1β gene in four Caucasian patients with autism and not in 535 healthy Caucasian controls (4/203 vs. 0/535, P =0.0056). Initial family data suggest that incomplete penetrance may occur. In addition, no structural variant was found in the neurexin 2β gene and the neurexin 3β gene. In the context of all available data, we conclude that mutations of the neurexin 1β gene may contribute to autism susceptibility. [Copyright &y& Elsevier]

• Based on image feature extraction technology to build aggregate feature data set. • A fusion feature importance analysis method is proposed. • Establish a random forest and XGBoost aggregate shape classification model. • The ability to recognize the 3D features of aggregates in 2D images are improved. Aggregate plays the role of skeleton filling in asphalt pavements. The shape of the aggregate affects the embedded structure between the aggregates, thus affecting the performance of asphalt concrete. In this study, extreme gradient boosting (XGBoost) classification is used to study the automatic shape classification of aggregates. The expression of main and microscopic features of aggregate was improved by transforming aggregate images into data, and a feature importance analysis method based on method fusion is proposed to select the feature parameters of aggregate morphology. Based on cross-validation, the XGBoost classification model was trained by optimizing the super parameter combination to complete the classification of aggregate shapes. Compared with the random forest model, the results show that the proposed method can effectively classify aggregate shapes. It is also proved that the two-dimensional images can reflect the three-dimensional features of the aggregate to some extent. This method provides a certain theoretical basis for the automatic classification of aggregate, and simultaneously it has important practical significance to promote the intelligent production of asphalt mixtures. [ABSTRACT FROM AUTHOR]