Experimental Study of Ultralight (<300 kg/m 3

A type of ultralight (<300 kg/m3) foamed concrete (FC), which can be used as a new energy-conservation and environmental-protection building material and is particularly suitable for the thermal-insulation engineering of building external walls, was produced. The influences of different mixing amounts of fly ash, fly ash activator, WC (WC) ratio, and foaming agent (FA) on the compressive strength of FC were reported. The experimental study indicated that (1) the addition of fly ash reduced the strength of the FC and that the appropriate mixing amount of fly ash in this ultralight FC system should not exceed 45%; (2) with the increasing of fly ash activator, the strength of the FC sample is notably enhanced and the appropriate mixing amount of fly ash activator is 2.5%; (3) the optimized proportion of WC ratio is 0.45, and the FC that was produced according to this proportion has relatively high compressive strength; (4) by increasing the mixing amount of FA, the compressive strength of the FC notably decreases, and the optimal mixing amount of FA in this experiment is 3.5%

Climatic Signals in Wood Property Variables of Picea Crassifolia

Little attention has been given to climatic signals in wood properties. In this study, ring width(RW), annual average microfibril angle (MFA), annual average tracheid radial diameter (TRD), andannual average density (DEN), as the annual and intra-annual wood property variables, were measured at high resolution by SilviScan-3 on dated Picea crassifolia trees. Dendroclimatological methods were used to analyze climatic signals registered in wood property variables. RW, MFA, and TRD negatively correlated with temperature and positively correlated with precipitation in the growing season, whereas the reverse was true for DEN. Climatic signals recorded in the earlywood were similar to those measured for the full width of the annual rings. Climatic signals recorded in latewood were very weak except for latewood MFA. This study showed that wood property variables could be extensive resources for learning more about the influences of climate on tree growth and how trees adapt to ongoing climate change

Updates on precision medicine of pancreatic neuroendocrine tumor

Since the concept of precision medicine proposed in 2011, the treatment of solid tumors has entered era of precision medicine led by gene testing. As a rare tumor, the incidence rate of pancreatic neuroendocrine tumor (PanNET) is increasing gradually. In the past, clinicopathological factors such as stage and grade system were used as criteria in the diagnosis and prognostic prediction of PanNET patients, and there were few biomarkers guiding the selection of PanNET diagnosis and treatment. As the diagnosis, treatment and prognosis of PanNET have been updated these years, and genomics and molecular testing are wildly used in PanNET research, can precision bring new changes to the diagnosis, treatment and prognostic prediction of PanNET? This article reviewed the current status of PanNET precision therapy through the latest literature

ImmuneAPP for Hla-I Epitope Prediction and Immunopeptidome Analysis

Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies

Galaxy Morphology Classification Using Multi-Scale Convolution Capsule Network

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multi-scale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers through using a multi-branch structure to extract multi-scale hidden features of galaxy images. In order to further explore the hidden information in the features, the multi-scale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieving 97% accuracy, 96% precision, 98% recall, and 97% F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation were accomplished in this study. We visualized the morphological features for the part of sample set, which using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The results shows that the model has the better generalization ability and robustness, it can be effectively used in the galaxy morphological classification

Prognostic Significance of Altered ATRX/DAXX Gene in Pancreatic Neuroendocrine Tumors: A Meta-Analysis

BackgroundPancreatic neuroendocrine tumors (PanNETs) are a heterogeneous group of neoplasms with increasing incidence and unpredictable behavior. Whole-exome sequencing recently has shown very frequent somatic mutations in the alpha-thalassemia/mental retardation X-linked (ATRX) and death domain-associated protein (DAXX) genes in PanNETs. And the prognostic significance of altered ATRX/DAXX genes in PanNETs patients have been revealed in several reports. However, many of these include small sample size and hold controversial opinions. To increase statistical power, we performed a systematic review and meta-analysis to determine a pooled conclusion. We examined the impact of altered ATRX/DAXX genes mainly on overall survival (OS), disease-free survival (DFS) and relapse-free survival (RFS) in PanNETs.MethodsEligible studies were identified and quality was assessed using multiple search strategies (last search May 2021). Data were collected from studies about prognostic significance of altered ATRX/DAXX in PanNETs. Studies were pooled, and combined hazard ratios (HRs) with 95% confidence intervals (CIs) were used to estimate strength of the associations.ResultsFourteen studies involving 2313 patients treated for PanNETs were included. After evaluating for publication bias, disease-free survival and relapse-free survival was significantly shortened in patients with altered ATRX/DAXX gene, with combined HR 5.05 (95% confidence interval (CI): 1.58-16.20, P = 0.01) and 3.21 (95% confidence interval (CI): 1.44-7.16, P &lt; 0.01) respectively. However, the combined data showed there were no difference between patients with altered ATRX/DAXX gene or not in overall survival, with a combined HR 0.71 (95% confidence interval (CI): 0.44-1.15, P = 0.23). We also performed a subgroup analysis with metastatic patients in overall survival, showing a combined HR 0.22 (95% confidence interval (CI): 0.11-0.48, P = 0.96). The small number of studies and paucity of multivariate analyses are the limitations of our study.ConclusionsThis is the first rigorous pooled analysis assessing ATRX/DAXX mutation as prognostic biomarkers in PanNETs. Patients with altered ATRX/DAXX gene would have poor DFS according to the combined data. And altered ATRX/DAXX genes in metastatic patients showed a trend towards improved overall survival, although the difference did not reach statistical significance

Effect of Extreme Acid Combined with Heat Induction on Structure and Properties of Soybean Protein Isolate Microgel

Soybean protein isolate microgel (SPIM) was prepare by extreme acid combined with heat induction. The structural changes and molecular interactions of the protein, and the microstructure and gel properties of the microgel were explored by fluorescence spectroscopy, infrared spectroscopy, and atomic force microscopy, and the effects of different heat induction temperatures (25, 45, 55, 65, 75 and 85 ℃) on the structure and properties of the microgel were evaluated. The results showed that the relative content of β-sheet increased during the formation of SPIM, and electrostatic interaction, hydrophobic interaction and hydrogen bonding were involved in the self-assembly of microgels. In addition, with increasing temperature, the surface hydrophobicity index of SPIM increased first and then decreased, and that the thermal stability gradually increased. Compared with extreme acid, the specific surface area, emulsifying activity, emulsion stability and water-holding capacity of the microgel formed by extreme acid combined with heat induction at 75 ℃ were significantly increased (P < 0.05). Overall, extreme acid combined with heat induction is an effective method to regulate the structure and properties of protein microgels, and the quality of microgels can be improved by precise temperature control