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• Lanzhou lily polysaccharides with low molecular weight were extracted from the bulbs by freeze-thaw method for the first time. • The polysaccharides structure was characterized by different physical and chemical experiments. • Cell proliferation, cytokines, and other experiments prove that polysaccharides have immunoactivation of RAW264.7. • RNA-seq and RNAi experiment revealed the immune regulation mechanism of the polysaccharides, it can activate the corresponding signaling pathways in the downstream of the G protein coupled receptor subunit. Exploring immunomodulatory effects of polysaccharides mechanisms is highly significant. In this study, Lanzhou lily (Lilium davidii var. unicolor) polysaccharides (LP) of 6.80 × 103 Da were extracted using the freeze-thaw method. For analytical purposes, structure characterization was conducted using fourier transform infrared spectrometer (FTIR), ultraviolet spectrophotometer (UV–Vis), scanning electron microscope (SEM), and transmission electron microscopy (TEM). Results showed that LP exhibited characteristics of polysaccharide functional groups. Moreover, findings on its immune activity showed that LP can stimulate macrophage RAW264.7, promote macrophage proliferation, secrete nitric oxide (NO), tumor necrosis factor -α (TNF-α), and interleukin -6 (IL-6). We use RNA-Seq to explore how LP mechanisms effect macrophages. Results revealed that LP likely plays an immunomodulatory role through cytokines secretion, cytokines that effectuate G-protein coupled receptor subunits, and activating downstream immune-related signaling pathways. This study can be used as a reference for the further development of LP while also offering an in-depth understanding of mechanistic immunoregulatory activity. [ABSTRACT FROM AUTHOR]

Abstract: ELOVL4 was first identified as a disease-causing gene in Stargardt macular dystrophy (STGD3, MIM 600110.) To date, three ELOVL4 mutations have been identified, all of which result in truncated proteins which induce autosomal dominant juvenile macular degenerations. Based on sequence homology, ELOVL4 is thought to be another member within a family of proteins functioning in the elongation of long chain fatty acids. However, the normal function of ELOVL4 is unclear. We generated Elovl4 knockout mice to determine if Elovl4 loss affects retinal development or function. Here we show that Elovl4 knockout mice, while perinatal lethal, exhibit normal retinal development prior to death at day of birth. Further, postnatal retinal development in Elovl4 heterozygous mice appears normal. Therefore haploinsufficiency for wildtype ELOVL4 in autosomal dominant macular degeneration likely does not contribute to juvenile macular degeneration in STGD3 patients. We found, however, that Elovl4 +/− mice exhibit enhanced ERG scotopic and photopic a and b waves relative to wildtype Elovl4 +/+ mice suggesting that reduced Elovl4 levels may impact retinal electrophysiological responses. [Copyright &y& Elsevier]

• We propose a CNN-based automatic diagnosis method to classify the gray matter density images of first-episode psychosis (FEP), bipolar disorder (BD) and healthy controls (HC). • We design a novel CNN architecture to classify first-episode psychosis (FEP), bipolar disorder (BD) and healthy controls (HC). • The proposed CNN architecture achieves the best precision (99.24%), recall (99.22%), F1-score (99.23%), accuracy (99.15%) and AUC (99.40%). • It can also provide a biological basis for clinical diagnosis and treatment of FEP, BD, and HC. Neuroimaging data driven machine learning based predictive modeling and pattern recognition has been attracted strongly attention in biomedical sciences. Machine learning based diagnosis techniques are widely applied in diagnosis of neurological diseases. However, machine learning techniques are difficult to effectively extract deep information in neuroimaging data, resulting in low classification accuracy of mental illnesses. To address this problem, we propose a deep learning based automatic diagnosis first-episode psychosis (FEP), bipolar disorder (BD) and healthy controls (HC) method. Specifically, we design a convolutional neural network (CNN) framework to automatically diagnosis based on structural magnetic functional imaging (sMRI). Our dataset consists of 89 FEP patients, 40 BD patients and 83 HC. A three-way classifier (FEP vs. BD vs. HC) and three binary classifiers (FEP vs. BD, FEP vs. HC, BD vs. HC) are trained based on their gray matter volume images. Experiment results show that the performance of CNN-based method outperforms the classic classifiers both in two and three categories classification task. Our research reveals that abnormal gray matter volume is one of the main characteristics for discriminating FEP, BD and HC. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IES) can effectively regulate and optimize dynamic loads by utilizing load forecasting, which intelligently manages energy scheduling. Nevertheless, the insufficient attention given by existing research to loads with multiple frequency scales and dynamic coupling relationships in the IES may lead to a reduced accuracy in load forecasting. To address this issue, a novel load forecasting model using structured dynamic-inner latent variables and broad learning system (SDiLV-BLS) is proposed. Firstly, the dynamic coupling reconstruction method is introduced to restructure the data of various load types into multimodal data with coupling relationships across multiple frequency scales, serving as input for the forecasting model. Then, the structured dynamic-inner latent variables (SDiLV) decouple the multimodal data to obtain dynamic features that have regression properties and simplify the complexity of the input features for the forecasting model. Finally, the broad learning system (BLS) is employed to reveal the structured geometric patterns in the extracted features, which achieves accurate load forecasting. Case studies show that by employing the coupling feature selection strategy in SDiLV-BLS, there is an improvement in forecasting accuracy compared to the strategy of selecting only load features of the same type and the strategy of selecting all load type features. Furthermore, by selecting coupling features, the average root mean square error (RMSE) and mean absolute error (MAE) of SDiLV-BLS are reduced by 21.66% and 22.06%, respectively, compared to the original BLS in multi-step ahead forecasting. [ABSTRACT FROM AUTHOR]

As the integration of wind energy into the power system increases, accurate wind speed prediction becomes crucial to ensure the reliable and economically efficient operation of the grid. The non-stationary, chaotic, and nonlinear characteristics of wind speed pose significant challenges for prediction models in uncovering the dynamic evolution process. To address these challenges, we proposed a wind speed prediction method based on the dynamic spectral regression broad learning system coupled with multimodal information (DSR-BLS). Firstly, we proposed a frequency density clustering-based mode decomposition (FDCMD) algorithm, which automatically transforms the non-stationary wind speed into multiple relatively stationary modal components. Next, we proposed the dynamic spectral regression (DSR) algorithm, which is based on dynamic-inner latent variable modeling and spectral regression. DSR can extract features and reconstruct the dynamic characteristics of wind speed through non-uniform embedding in the phase space. Finally, DSR-BLS is proposed to enhance the deterministic point prediction accuracy of the original BLS by utilizing multimodal features and dynamic features. The experiments show that DSR-BLS outperforms the comparative prediction methods in multi-step ahead prediction results. [ABSTRACT FROM AUTHOR]

An abundance of microfibril-associated glycoprotein 3-like (MFAP3L) significantly correlates with distant metastasis in colorectal cancer (CRC), although the mechanism has yet to be explained. In this study, we observed that MFAP3L knock-down resulted in reduced CRC cell invasion and hepatic metastasis. We evaluated the cellular location and biochemical functions of MFAP3L and found that this protein was primarily localized in the nucleus of CRC cells and acted as a protein kinase. When EGFR translocated into the nucleus upon stimulation with EGF, MFAP3L was phosphorylated at Tyr287 within its SH2 motif, and the activated form of MFAP3L phosphorylated ERK2 at Thr185 and Tyr187. Moreover, the metastatic behavior of CRC cells in vitro and in vivo could be partially explained by activation of the nuclear ERK pathway through MFAP3L phosphorylation. Hence, we experimentally demonstrated for the first time that MFAP3L likely participates in the nuclear signaling of EGFR and ERK2 and acts as a novel nuclear kinase that impacts CRC metastasis. [ABSTRACT FROM AUTHOR]

Highlights: [•] We demonstrate that p53 is a direct target of miR-375. [•] miR-375 desensitizes cells to ionizing radiation and etoposide. [•] miR-375 abrogates the cell cycle arrest and apoptosis after DNA damage. [•] miR-375 regulates cellular response to stress through inactivation of the p53 pathway. [ABSTRACT FROM AUTHOR]

• Facile synthesis of 3D transition-metal core-shell structure electrocatalyst is realized. • Synergy between porous nanosheets and metal nanoparticles rich in defects. • 3D Flower-like transition-metal electrocatalyst exhibit excellent activity for electrocatalysis. The development of high efficiency, low-budget and excellent stability of electrocatalysts is a critical factor to improve water decomposition. Herein, a highly active 3D bulk catalysts of core-shell nanostructures, in which Flower-like CuCoMoOx nanosheets embellished with CoCu alloy nanoparticles are evenly wrapped on Cu NWs cores supported on CF, toward overall water splitting is firstly reported. The coaction of CoCu alloy nanoparticles and CuCoMoOx nanosheets with 3D hierarchical and porous nanoarchitecture can accelerate the ionization of H 2 O molecules and formation of H 2. Meanwhile, the connection nanosheets of Cu NWs with noteworthy conductivity, resulting in rapid electron transport, exhibits outstanding both HER and OER activities and remarkable overall water splitting characteristics in the alkaline medium. As an electrocatalyst for HER and OER, the low overpotential of 75 mV and 315 mV at 100 mA cm−2, and low voltage of 1.66 V for overall water splitting are achieved, even superior to the benchmark of IrO 2 /CF (+) // /Pt/C/CF(-) in 1 M KOH. This work has a significant reference to hydrogen production for the electrolytic water industry. [Display omitted] [ABSTRACT FROM AUTHOR]

Pyruvate kinase M2 (PKM2) serves as a key enzyme that promotes aerobic glycolysis. This study investigated the function of PKM2 in tumor growth and maintenance in gastric cancer (GC). Histological staining was applied to detect PKM2 expression in GC tissues. PCR and western blotting were used to measure PKM2 expression in GC cells. PKM2 was knocked down to examine the biological behavior of tumors, glycometabolism, and apoptosis. PKM2 was upregulated in GC tissues (65%, 34/52) compared with that in adjacent normal tissues (27%, 10/37). Moreover, PKM2 knockdown inhibited proliferation of BGC823 GC cells, and elevated PKM2 levels were associated with poor survival of GC patients. Furthermore, knockdown of PKM2 altered the biological behavior of BGC823 cells through induction of apoptosis. In conclusion, the results of this study indicated that inhibition of PKM2 could represent a novel strategy for gastric cancer treatment. [ABSTRACT FROM AUTHOR]