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Self-supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking-based pre-trained language models are not designed for generative design, and their black-box nature makes it difficult to interpret their design logic. Here a Blank-filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network-based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank-filling language model for text generation and has demonstrated unique advantages in learning the "materials grammars" together with high-quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7% charge neutrality and 84.8% balanced electronegativity, which are more than four and eight times higher compared to a pseudo-random sampling baseline. The probabilistic generation process of BLMM allows it to recommend materials tinkering operations based on learned materials chemistry, which makes it useful for materials doping. The model is applied to discover a set of new materials as validated using the Density Functional Theory (DFT) calculations. This work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user-friendly web app for tinkering materials design has been developed and can be accessed freely at www.materialsatlas.org/blmtinker.
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D-glucaric acid is a platform chemical of great importance and the consolidated bioprocessing (CBP) of lignocellulose by the microbial consortium of Trichoderma reesei C10 and Saccharomyces cerevisiae LGA-1C3S2 features prospects in biomanufacturing it. Here we compared some representative lignocelluloses in Northwest China including corn stover, wheat straw and switchgrass, and the leading pretreatments including steam explosion, subcritical water pretreatment, sodium hydroxide pretreatment, aqueous ammonia pretreatment, lime pretreatment, and diluted sulfuric acid pretreatment. It was found that sodium hydroxide pretreated switchgrass (SHPSG) was the best substrate for D-glucaric acid production, resulting in the highest D-glucaric acid titers, 11.69 ± 0.73 g/L in shake flask and 15.71 ± 0.80 g/L in 10L airlift fermenter, respectively. To the best of our knowledge, this is the highest D-glucaric acid production titer from lignocellulosic biomass. This work offers a paradigm of producing low-cost D-glucaric acid for low-carbon polyethylene 2,5-furandicarboxylate (PEF) and a reference on developing biorefinery in Northwest China.
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Objectives: Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods. Methods: The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms. Results: In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur. Conclusions: To sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.
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Rice tiller angle is a key agronomic trait that has significant effects on the establishment of a high-yield rice population. However, the molecular mechanism underlying the control of rice tiller angle remains to be clarified. Here, we characterized the novel tiller-angle gene LAZY4 (LA4) in rice through map-based cloning. LA4 encodes a C3H2C3-type RING zinc-finger E3 ligase localized in the nucleus, and an in vitro ubiquitination assay revealed that the conserved RING finger domain is essential for its E3 ligase activity. We found that expression of LA4 can be induced by gravistimulation and that loss of LA4 function leads to defective shoot gravitropism caused by impaired asymmetric auxin redistribution upon gravistimulation. Genetic analysis demonstrated that LA4 acts in a distinct pathway from the starch biosynthesis regulators LA2 and LA3, which function in the starch-statolith-dependent pathway. Further genetic analysis showed that LA4 regulates shoot gravitropism and tiller angle by acting upstream of LA1 to mediate lateral auxin transport upon gravistimulation. Our studies reveal that LA4 regulates shoot gravitropism and tiller angle upstream of LA1 through a novel pathway independent of the LA2-LA3-mediated gravity-sensing mechanism, providing new insights into the rice tiller-angle regulatory network.
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Membrane distillation (MD) is considered a promising technology for desalination. In the MD process, membrane pores are easily contaminated and wetted, which will degrade the permeate flux and salt rejection of the membrane. In this work, SiC ceramic membranes were used as the supports, and an Al2O3 micro-nano structure was constructed on its surface. The surface energy of Al2O3@SiC micro-nano composite membranes was reduced by organosilane grafting modification. The effective deposition of Al2O3 nanoflowers on the membrane surface increased membrane roughness and enhanced the anti-fouling and anti-wetting properties of the membranes. Simultaneously, the presence of nanoflowers also regulated the pore structures and thus decreased the membrane pore size. In addition, the effects of Al2(SO4)3 concentration and sintering temperature on the surface morphology and performance of the membranes were investigated in detail. It was demonstrated that the water contact angle of the resulting membrane was 152.4°, which was higher than that of the pristine membrane (138.8°). In the treatment of saline water containing 35 g/L of NaCl, the permeate flux was about 11.1 kgâ m-2â h-1 and the salt rejection was above 99.9%. Note that the pristine ceramic membrane cannot be employed for MD due to its larger membrane pore size. This work provides a new method for preparing superhydrophobic ceramic membranes for MD.
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OBJECTIVES: The evidence connecting polyunsaturated fatty acids (PUFAs) to biliary problems is still highly contested and speculative despite the fact that biliary diseases are common and PUFAs have long been studied for their potential health benefits. This work used Mendelian randomization (MR) techniques in conjunction with genome-wide association study (GWAS) data to clarify the causal relationships between PUFAs and biliary tract diseases. METHODS: We compiled data on PUFAs, including Omega-3 fatty acids, Omega-6 fatty acids, and the ratio of Omega-6 to Omega-3 fatty acids (Omega-6:Omega-3), using GWAS. MR was used to examine biliary tract problems (cholecystitis, cholelithiasis, gallbladder cancer, primary biliary cholangitis, primary sclerosing cholangitis, and disorders of gallbladder, biliary tract and pancreas). Single nucleotide polymorphisms significantly associated with PUFAs were selected as instrumental variables to estimate causal effects on biliary tract diseases. The final results were analyzed using five MR analysis techniques. Inverse variance weighting (IVW) was used as the primary outcome. And IVW was utilized in conjunction with the other MR analysis techniques (MR-Egger, weighted median, simple mode, and weighted mode). Additionally, we evaluated heterogeneity and horizontal multiplicity using the MR-Egger intercept test and Cochrane's Q test, respectively. Finally, to increase the accuracy and precision of the study outcomes, we carried out a number of sensitivity analyses. RESULTS: We found that Omega-3 fatty acids reduced the risk of cholecystitis (OR: 0.851, P = 0.009), cholelithiasis (OR: 0.787, P = 8.76e-5), and disorders of gallbladder, biliary tract and pancreas (OR: 0.842, P = 1.828e-4) but increased the primary biliary cholangitis (OR: 2.220, P = 0.004). There was no significant association between Omega-3 fatty acids and risk of gallbladder cancer (OR: 3.127, P = 0.530) and primary sclerosing cholangitis (OR: 0.919, P = 0.294). Omega-6 fatty acids were associated with a reduced risk of cholecystitis (OR: 0.845, P = 0.040). However, they were not linked to an increased or decreased risk of cholelithiasis (OR: 0.878, P = 0.14), gallbladder cancer (OR: 4.670, P = 0.515), primary sclerosing cholangitis (OR: 0.993, P = 0.962), primary cholestatic biliary cholangitis (OR: 1.404, P = 0.509), or disorders of gallbladder, biliary tract and pancreas. Omega-6:Omega-3 fatty acids were linked to a greater risk of cholecystitis, cholelithiasis, and disorders of gallbladder, biliary tract and pancreas (OR:1.168, P = 0.009, OR:1.191, P = 1.60e-6, and OR:1.160, P = 4.11e-6, respectively). But (OR: 0.315, P = 0.010) was linked to a decreased risk of primary biliary cholangitis. Not linked to risk of primary sclerosing cholangitis (OR: 1.079, P = 0.078) or gallbladder cancer (OR: 0.046, P = 0.402). According to the MR-Egger intercept, our MR examination did not appear to be impacted by any pleiotropy (all P > 0.05). Additionally, sensitivity studies validated the accuracy of the calculated causation. CONCLUSION: Inconsistent causative relationships between PUFAs and biliary tract diseases were revealed in our investigation. However, Omega-3 fatty acids were found to causally lower the risk of cholecystitis, cholelithiasis, and disorders of gallbladder, biliary tract and pancreas. Omega-3 fatty acids increased the risk of primary biliary cholangitis in a causative way. Omega-3 fatty acids with the risk of gallbladder cancer and primary sclerosing cholangitis did not have any statistically significant relationships. Omega-6 fatty acids were not significantly causally connected with the risk of cholelithiasis, gallbladder cancer, primary sclerosing cholangitis, or disorders of gallbladder, biliary tract and pancreas. However, they did play a causative role in lowering the risk of cholecystitis. Omega-6:Omega-3 fatty acids decreased the risk of primary biliary cholangitis but increased the risk of cholecystitis, gallstone disease, and disorders of gallbladder, biliary tract and pancreas. They had no effect on the risk of gallbladder cancer or primary sclerosing cholangitis. Therefore, additional research should be done to examine the probable processes mediating the link between polyunsaturated fatty acids and the risk of biliary tract diseases.
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Doenças Biliares , Ácidos Graxos Ômega-3 , Ácidos Graxos Ômega-6 , Ácidos Graxos Insaturados , Estudo de Associação Genômica Ampla , Análise da Randomização Mendeliana , Polimorfismo de Nucleotídeo Único , Humanos , Doenças Biliares/genéticaRESUMO
Plasmonic nanoparticles are highly tunable light-harvesting materials with a wide array of applications in photonics and catalysis. More recently, there has been interest in using aerosolized plasmonic nanoparticles for cloud formation, airborne photocatalysts, and molecular sensors, all of which take advantage of the large scattering cross sections and the ability of these particles to support intense local field enhancement ("hot spots"). While extensive research has investigated properties of plasmonic particles in the solution phase, surfaces, and films, aerosolized plasmonics are relatively unexplored. Here, we demonstrate how the capping ligand, suspension solvent, and atomization conditions used for aerosol generation control the steady-state optical properties of aerosolized Silica@Au plasmonic nanoshells. Our experimental results, supported with spectral simulations, illustrate that ligand coverage and atomization conditions control the degree of solvent retention and thus the spectral characteristics and potential access to surfaces for catalysis in the aerosol phase, opening a new regime for tunable applications of plasmonic metamaterials.
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BACKGROUND: The association of single nucleotide polymorphism of KCNQ1 gene rs2237895 with type 2 diabetes mellitus (T2DM) is currently controversial. It is unknown whether this association can be gene realized across different populations. AIM: To determine the association of KCNQ1 rs2237895 with T2DM and provide reliable evidence for genetic susceptibility to T2DM. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library, Medline, Baidu Academic, China National Knowledge Infrastructure, China Biomedical Liter-ature Database, and Wanfang to investigate the association between KCNQ1 gene rs2237895 and the risk of T2DM up to January 12, 2022. Review Manager 5.4 was used to analyze the association of the KCNQ1 gene rs2237895 polymorphism with T2DM and to evaluate the publication bias of the selected literature. RESULTS: Twelve case-control studies (including 11273 cases and 11654 controls) met our inclusion criteria. In the full population, allelic model [odds ratio (OR): 1.19; 95% confidence interval (95%CI): 1.09-1.29; P < 0.0001], recessive model (OR: 1.20; 95%CI: 1.11-1.29; P < 0.0001), dominant model (OR: 1.27. 95%CI: 1.14-1.42; P < 0.0001), and codominant model (OR: 1.36; 95%CI: 1.15-1.60; P = 0.0003) (OR: 1.22; 95%CI: 1.10-1.36; P = 0.0002) indicated that the KCNQ1 gene rs2237895 polymorphism was significantly correlated with susceptibility to T2DM. In stratified analysis, this association was confirmed in Asian populations: allelic model (OR: 1.25; 95%CI: 1.13-1.37; P < 0.0001), recessive model (OR: 1.29; 95%CI: 1.11-1.49; P = 0.0007), dominant model (OR: 1.35; 95%CI: 1.20-1.52; P < 0.0001), codominant model (OR: 1.49; 95%CI: 1.22-1.81; P < 0.0001) (OR: 1.26; 95%CI: 1.16-1.36; P < 0.0001). In non-Asian populations, this association was not significant: Allelic model (OR: 1.06, 95%CI: 0.98-1.14; P = 0.12), recessive model (OR: 1.04; 95%CI: 0.75-1.42; P = 0.83), dominant model (OR: 1.06; 95%CI: 0.98-1.15; P = 0.15), codominant model (OR: 1.08; 95%CI: 0.82-1.42; P = 0.60. OR: 1.15; 95%CI: 0.95-1.39; P = 0.14). CONCLUSION: KCNQ1 gene rs2237895 was significantly associated with susceptibility to T2DM in an Asian population. Carriers of the C allele had a higher risk of T2DM. This association was not significant in non-Asian populations.
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Introduction: Continuous strawberry cropping often causes soil-borne diseases, with 20 calcium cyanamide being an effective soil fumigant, pig manure can often be used as soil organic fertilizer. Its impact on soil microorganisms structure, however, remains unclear. Methods: This study investigated the effectiveness of calcium cyanamide and pig manure in treating strawberry soil, specifically against strawberry anthracnose. We examined the physical and chemical properties of the soil and the rhizosphere microbiome and performed a network analysis. Results: Results showed that calcium cyanamide treatment significantly reduces the mortality rate of strawberry in seedling stage by reducing pathogen abundance, while increasing actinomycetes and Alphaproteobacteria during the harvest period. This treatment also enhanced bacterial network connectivity, measured by the average connectivity of each Operational Taxonomic Unit (OTU), surpassing other treatments. Moreover, calcium cyanamide notably raised the levels of organic matter, available potassium, and phosphorus in the soil-key factors for strawberry disease resistance and yield. Discussion: Overall, applying calcium cyanamide to soil used for continuous strawberry cultivation can effectively decrease anthracnose incidence. It may be by changing soil physical and chemical properties and enhancing bacterial network stability, thereby reducing the copy of anthracnose. This study highlights the dual benefit of calcium cyanamide in both disease control and soil nutrient enhancement, suggesting its potential as a valuable tool in sustainable strawberry farming.
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PURPOSE: To explore imaging biomarkers predictive of intratumoral haemorrhage for lesions intended for elective stereotactic biopsy. METHOD: This study included a retrospective cohort of 143 patients with 175 intracranial lesions intended for stereotactic biopsy. All the lesions were randomly split into a training dataset ( n =121) and a test dataset ( n =54) at a ratio of 7:3. Thirty-four lesions were defined as "hemorrhage-prone tumors" as haemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1 Weighted Imaging and T2 Weighted Imaging images. Features informative of haemorrhage were then selected by the LASSO algorithm, and an Support Vector Machine model was built with selected features. The Support Vector Machine model was further simplified by discarding features with low importance and calculating them using a "permutation importance" method. The model's performance was evaluated with confusion matrix-derived metrics and area under curve (AUC) values on the independent test dataset. RESULTS: Nine radiomics features were selected as haemorrhage-related features of intracranial tumours by the LASSO algorithm. The simplified model's sensitivity, specificity, accuracy, and AUC reached 0.909, 0.930, 0.926, and 0.949 (95% CI: 0.865-1.000) on the test dataset in the discrimination of "hemorrhage-prone tumors". The permutation method rated feature "T2_gradient_firstorder_10Percentile" as the most important, the absence of which decreased the model's accuracy by 10.9%. CONCLUSION: Radiomics features extracted on contrast-enhanced T1 Weighted Imaging and T2 Weighted Imaging sequences were predictive of future haemorrhage of intracranial tumours with favourable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.
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Neoplasias Encefálicas , Imageamento por Ressonância Magnética , Humanos , Estudos Retrospectivos , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Masculino , Feminino , Imageamento por Ressonância Magnética/métodos , Pessoa de Meia-Idade , Idoso , Adulto , Técnicas Estereotáxicas , Biópsia , Máquina de Vetores de Suporte , Algoritmos , Idoso de 80 Anos ou mais , Adulto Jovem , RadiômicaRESUMO
BACKGROUND: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.
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The aim of the study was to develop soybean polysaccharide (SSPS) -carboxylated nanocellulose (CNC) blending films with enhanced mechanical and barrier properties to be used as a tear-free and edible packaging materials. The films were formed by casting method, with CNC as the strengthening unit and glycerol as the plasticizer. The effect of CNC on structural and physical performances of the SSPS-CNC films were studied. SEM indicated that the film will stratify with excess CNC (10 %), but the film remains intact and compact. Incorporation of CNC into SSPS films did not change peak position in the XRD pattern significantly. Hydrogen bonds among SSPS, glycerol and CNC were indicated by the FTIR spectra. The compounding of CNC greatly lessened the light transmittance and hydrophilicity (CA increased from 55.42° to 70.67°), but perfected the barrier (WVP decreased from 3.595 × 10-10 to 2.593 × 10-10 g m-1 s-1 Pa-1) and mechanical properties (TS improved from 0.806 to 1.317 MPa). The results of packaging dehydrated vegetable indicated that the SSPS-8CNC film can effectively inhibit the packaged cabbage absorption water vapor. As a consequence, SSPS film perfected by CNC is hopeful to pack dehydrated vegetables in instant foods.
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Nanocompostos , Verduras , Glycine max , Glicerol , Nanocompostos/química , Polissacarídeos/química , Embalagem de Alimentos/métodosRESUMO
BACKGROUND: Bone marrow-derived endothelial progenitor cells (EPCs) play a dynamic role in maintaining the structure and function of blood vessels. But how these cells maintain their growth and angiogenic capacity under bone marrow hypoxic niche is still unclear. This study aims to explore the mechanisms from a perspective of cellular metabolism. METHODS: XFe96 Extracellular Flux Analyzer was used to analyze the metabolic status of EPCs. Gas Chromatography-Mass Spectrometry (GC-MS) was used to trace the carbon movement of 13C-labeled glucose and glutamine under 1 % O2 (hypoxia) and â¼20 % O2 (normoxia). Moreover, RNA interference, targeting isocitrate dehydrogenase-1 (IDH1) and IDH2, was used to inhibit the reverse tricarboxylic acid (TCA) cycle and analyze metabolic changes via isotope tracing as well as changes in cell growth and angiogenic potential under hypoxia. The therapeutic potential of EPCs under hypoxia was investigated in the ischemic hindlimb model. RESULTS: Compared with normoxic cells, hypoxic cells showed increased glycolysis and decreased mitochondrial respiration. Isotope metabolic tracing revealed that under hypoxia, the forward TCA cycle was decreased and the reverse TCA cycle was enhanced, mediating the conversion of α-ketoglutarate (α-KG) into isocitrate/citrate, and de novo lipid synthesis was promoted. Downregulation of IDH1 or IDH2 under hypoxia suppressed the reverse TCA cycle, attenuated de novo lipid synthesis (DNL), elevated α-KG levels, and decreased the expression of hypoxia inducible factor-1α (HIF-1α) and vascular endothelial growth factor A (VEGFA), eventually inhibiting the growth and angiogenic capacity of EPCs. Importantly, the transplantation of hypoxia-cultured EPCs in a mouse model of limb ischemia promoted new blood vessel regeneration and blood supply recovery in the ischemic area better than the transplantation of normoxia-cultured EPCs. CONCLUSIONS: Under hypoxia, the IDH1- and IDH2-mediated reverse TCA cycle promotes glutamine-derived de novo lipogenesis and stabilizes the expression of α-KG and HIF-1α, thereby enhancing the growth and angiogenic capacity of EPCs.
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Células Progenitoras Endoteliais , Animais , Camundongos , Medula Óssea/metabolismo , Hipóxia Celular , Células Progenitoras Endoteliais/metabolismo , Glutamina/metabolismo , Hipóxia/metabolismo , Subunidade alfa do Fator 1 Induzível por Hipóxia/genética , Subunidade alfa do Fator 1 Induzível por Hipóxia/metabolismo , Isquemia/metabolismo , Isótopos/metabolismo , Lipídeos , Lipogênese , Fator A de Crescimento do Endotélio Vascular/metabolismoRESUMO
Artificial light harvesting, a process that involves converting sunlight into chemical potential energy, is considered to be a promising part of the overall solution to address urgent global energy challenges. Conjugated polyelectrolyte complexes (CPECs) are particularly attractive for this purpose due to their extended electronic states, tunable assembly thermodynamics, and sensitivity to their local environment. Importantly, ionically assembled complexes of conjugated polyelectrolytes can act as efficient donor-acceptor pairs for electronic energy transfer (EET). However, to be of use in material applications, we must understand how modifying the chemical structure of the CPE backbone alters the EET rate beyond spectral overlap considerations. In this report we investigate the dependence of the EET efficiency and rate on the electronic structure and excitonic wave function of the CPE backbone. To do so, we synthesized a series of alternating copolymers where the electronic states are systematically altered by introducing comonomers with electron withdrawing and electron-rich character while keeping the linear ionic charge density nearly fixed. We find evidence that the excitonic coupling may be significantly affected by the exciton delocalization radius, in accordance with analytical models based on the line-dipole approximation and quantum chemistry calculations. Our results imply that care should be taken when selecting CPE components for optimal CPEC EET. These results have implications for using CPECs as key components in water-based light-harvesting materials, either as standalone assemblies or as adsorbates on nanoparticles and thin films.
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This study aimed to examine the interaction mechanism of polyphenol protein in a heat-treated aqueous solution system using epigallocatechin gallate (EGCG) and whey protein (WP) as raw materials. Further, we hypothesized the binding characteristics of these two compounds. The results were as follows: The quenching mechanism between WP and EGCG was characterized as static quenching. As the temperature increased, the binding constant and the binding force between EGCG and WP both increased. The number of binding sites (denoted as n) between WP and EGCG was approximately 1. Hence, WP provided a single site to bind to EGCG to form a complex. The main binding modes between WP and EGCG were hydrophobic and electrostatic interactions, and they were spontaneously combined into complexes (ΔG < 0). This study provided a basis for the interaction between WP and EGCG under different heating conditions and their combination mode.
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OBJECTIVES: Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT. MATERIALS AND METHODS: RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT. RESULTS: We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively. CONCLUSIONS: The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.
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Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose the Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering with molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer.
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Background: Patients with untreated cerebral arteriovenous malformations (AVMs) are at risk of intracerebral hemorrhage. However, treatment to prevent AVM hemorrhage carries risks. Objective: This study aimed to analyze the AVM nidus-related hemodynamic features and identify the risk factors for subsequent hemorrhage. Methods: We retrospectively identified patients with untreated AVMs who were assessed at our institution between March 2010 and March 2021. Patients with ≥6 months of treatment-free and hemorrhage-free follow-up after diagnosed by digital subtraction angiography were included in subsequent examinations. The hemodynamic features were extracted from five contrast flow-related parameter maps. The Kaplan-Meier analyses and Cox proportional hazards regression models were used to find the potential risk factors for subsequent hemorrhage. Results: Overall, 104 patients with a mean follow-up duration of 3.37 years (median, 2.42 years; range, 6-117 months) were included in study, and the annual risk of rupture was 3.7%. Previous rupture (hazard ratio [HR], 4.89; 95% confidence interval [CI], 1.16-20.72), deep AVM location (HR, 4.02; 95% CI, 1.01-15.99), higher cerebral blood volume (HR, 3.35; 95% CI, 1.15-9.74) in the nidus, and higher stasis index (HR, 1.54; 95% CI, 1.06-2.24) in the nidus were associated with subsequent hemorrhage in untreated AVMs. Conclusion: Higher cerebral blood volume and stasis index in the nidus suggest increased blood inflow and stagnant blood drainage. The combination of these factors may cause subsequent hemorrhage of AVMs.
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Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first-principles free energy calculations to predict the ground-state crystal structure of a given material composition. These ab initio algorithms are currently too slow for predicting complex material structures. Inspired by the AlphaFold algorithm for protein structure prediction, herein, we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model for predicting the atomic contact map of a target material followed by three-dimensional (3D) structure reconstruction using genetic algorithms. Extensive experiments on 20 benchmark structures showed that our AlphaCrystal algorithm can predict structures close to the ground truth structures, which can significantly speed up the crystal structure prediction and handle relatively large systems.
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BACKGROUND: The prognosis and survival of lung adenocarcinoma (LUAD) patients are still not promising despite recent breakthroughs in treatment. Endoplasmic reticulum stress (ERS) is a self-protective mechanism resulting from an imbalance in quality control of unfolded proteins when cells are stressed, which plays an active role in lung cancer development, but the relationship between ERS and the pathological characteristics and clinical prognosis of LUAD patients remains unclear. METHODS: LASSO and Cox regression were applied based on sequencing information to construct the model, which was validated to be robust. The risk scores of the patients were calculated using the formula provided by the model, and the patients were divided into high and low-risk groups according to the median cut-off of risk scores. Cox regression analysis identifies independent prognostic factors for these patients, and enrichment analysis of prognosis-related genes was also performed. The relationship between risk scores and tumor mutation burden (TMB), cancer stem cell index, and drug sensitivity was explored. RESULTS: We constructed a 13-gene prognostic model for LUAD patients. Patients in the high-risk group had worse overall survival, lower immune score and ESTIMATE score, higher TMB, higher cancer stem cell index, and higher sensitivity to conventional chemotherapeutic agents. In addition, we constructed a nomogram that predicts 5-year survival in LUAD patients, which helps clinicians to foresee the prognosis from a new perspective. CONCLUSIONS: Our results highlight the association of ERS with LUAD and the potential use of ERS in guiding treatment.