KbhbXG: A Machine learning architecture based on XGBoost for prediction of lysine ß-Hydroxybutyrylation (Kbhb) modification sites.

Lysine ß-hydroxybutyrylation is an important post-translational modification (PTM) involved in various physiological and biological processes. In this research, we introduce a novel predictor KbhbXG, which utilizes XGBoost to identify ß-hydroxybutyrylation modification sites based on protein sequence information. The traditional experimental methods employed for the identification of ß-hydroxybutyrylated sites using proteomic techniques are both costly and time-consuming. Thus, the development of computational methods and predictors can play a crucial role in facilitating the rapid identification of ß-hydroxybutyrylation sites. Our proposed KbhbXG model first utilizes machine learning algorithm XGBoost to predict ß-hydroxybutyrylation modification sites. On the independent test set, KbhbXG achieves an accuracy of 0.7457, specificity of 0.7771, and an impressive area under the curve (AUC) score of 0.8172. The high AUC score achieved by our method demonstrates its potential for effectively identifying novel ß-hydroxybutyrylation sites, thereby facilitating further research and exploration of the ß-hydroxybutyrylation process. Also, functional analyses have revealed that different organisms preferentially engage in distinct biological processes and pathways, which can provide valuable insights for understanding the mechanism of ß-hydroxybutyrylation and guide experimental verification. To promote transparency and reproducibility, we have made both the codes and dataset of KbhbXG publicly available. Researchers interested in utilizing our proposed model can access these resources at https://github.com/Lab-Xu/KbhbXG.

Altered gray matter volume in children with newly diagnosed type 1 diabetes mellitus.

BACKGROUND: Type 1 diabetes mellitus (T1DM) affects the development of cognitive function in children, which may be due to deficits in brain structures or functions. It is unclear whether children with T1DM experience alterations in the gray matter (GM) structure at the initial stages of the disease. This study investigated GM structure alterations in children with newly diagnosed T1DM. METHODS: Based on 3D T1-weighted MR images, we investigated the gray matter volume (GMV) of 35 newly diagnosed T1DM children and 35 age- and sex-matched healthy controls using voxel-based morphometry. The brain regions with significant differences in GMV between the newly diagnosed T1DM children and the controls were extracted and the correlation with clinical data was assessed. RESULTS: Compared with the control group, children with newly diagnosed T1DM had a lower GMV in the right inferior and middle temporal gyri, right lingual gyrus, and left superior frontal gyrus. In T1DM subjects, the GMV of the right middle temporal gyrus was positively correlated with IQ but was negatively correlated with HbA1c. CONCLUSIONS: Our findings provide compelling evidence that GM abnormalities occur during early disease stages in T1DM children, which may be a potential neurobiological mechanism underlying cognitive deficits. IMPACT: Using an efficient method to analyze gray matter changes in T1DM is very important. The anterior, posterior, and temporal brain regions are susceptible to T1DM in children. Recent glucose variability may affect regional gray matter volume in children with newly diagnosed T1DM. Structural changes were documented in the gray matter of the brain even at the early stages of the disease in children with T1DM.

Can lymphovascular invasion be predicted by contrast-enhanced CT imaging features in patients with esophageal squamous cell carcinoma? A preliminary retrospective study.

BACKGROUND: To investigate the value of contrast-enhanced CT (CECT)-derived imaging features in predicting lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC) patients. METHODS: One hundred and ninety-seven patients with postoperative pathologically confirmed esophageal squamous cell carcinoma treated in our hospital between January 2017 and January 2019 were enrolled in our study, including fifty-nine patients with LVI and one hundred and thirty-eight patients without LVI. The CECT-derived imaging features of all patients were analyzed. The CECT-derived imaging features were divided into quantitative features and qualitative features. The quantitative features consisted of the CT attenuation value of the tumor (CTVTumor), the CT attenuation value of the normal esophageal wall (CTVNormal), the CT attenuation value ratio of the tumor-to-normal esophageal wall (TNR), the CT attenuation value difference between the tumor and normal esophageal wall (ΔTN), the maximum thickness of the tumor measured by CECT (Thickness), the maximum length of the tumor measured by CECT (Length), and the gross tumor volume measured by CECT (GTV). The qualitative features consisted of an enhancement pattern, tumor margin, enlarged blood supply or drainage vessels to the tumor (EVFDT), and tumor necrosis. For the clinicopathological characteristics and CECT-derived imaging feature analysis, the chi-squared test was used for categorical variables, the Mann-Whitney U test was used for continuous variables with a nonnormal distribution, and the independent sample t-test was used for the continuous variables with a normal distribution. The trend test was used for ordinal variables. The association between LVI status and CECT-derived imaging features was analyzed by univariable logistic analysis, followed by multivariable logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: The CTVTumor, TNR, ΔTN, Thickness, Length, and GTV in the group with LVI were higher than those in the group without LVI (P < 0.05). A higher proportion of patients with heterogeneous enhancement pattern, irregular tumor margin, EVFDT, and tumor necrosis were present in the group with LVI (P < 0.05). As revealed by the univariable logistic analysis, the CECT-derived imaging features, including CTVTumor, TNR, ΔTN and enhancement pattern, Thickness, Length, GTV, tumor margin, EVFDT, and tumor necrosis were associated with LVI status (P < 0.05). Only the TNR (OR 8.655; 95% CI 2.125-37.776), Thickness (OR 6.531; 95% CI 2.410-20.608), and tumor margin (OR 4.384; 95% CI 2.004-9.717) were independent risk factors for LVI in the multivariable logistic regression analysis. The ROC curve analysis incorporating the above three CECT-derived imaging features showed that the area under the curve obtained by the multivariable logistic regression model was 0.820 (95% CI 0.754-0.885). CONCLUSION: The CECT-derived imaging features, including TNR, Thickness, tumor margin, and their combination, can be used as predictors of LVI status for patients with ESCC.

A Modular Grad-Div Stabilization Method for Time-Dependent Thermally Coupled MHD Equations.

In this paper, we consider a fully discrete modular grad-div stabilization algorithm for time-dependent thermally coupled magnetohydrodynamic (MHD) equations. The main idea of the proposed algorithm is to add an extra minimally intrusive module to penalize the divergence errors of velocity and improve the computational efficiency for increasing values of the Reynolds number and grad-div stabilization parameters. In addition, we provide the unconditional stability and optimal convergence analysis of this algorithm. Finally, several numerical experiments are performed and further indicated these advantages over the algorithm without grad-div stabilization.

Optimal Convergence Analysis of Two-Level Nonconforming Finite Element Iterative Methods for 2D/3D MHD Equations.

Several two-level iterative methods based on nonconforming finite element methods are applied for solving numerically the 2D/3D stationary incompressible MHD equations under different uniqueness conditions. These two-level algorithms are motivated by applying the m iterations on a coarse grid and correction once on a fine grid. A one-level Oseen iterative method on a fine mesh is further studied under a weak uniqueness condition. Moreover, the stability and error estimate are rigorously carried out, which prove that the proposed methods are stable and effective. Finally, some numerical examples corroborate the effectiveness of our theoretical analysis and the proposed methods.

Error Analysis of a PFEM Based on the Euler Semi-Implicit Scheme for the Unsteady MHD Equations.

In this article, we mainly consider a first order penalty finite element method (PFEM) for the 2D/3D unsteady incompressible magnetohydrodynamic (MHD) equations. The penalty method applies a penalty term to relax the constraint "∇·u=0", which allows us to transform the saddle point problem into two smaller problems to solve. The Euler semi-implicit scheme is based on a first order backward difference formula for time discretization and semi-implicit treatments for nonlinear terms. It is worth mentioning that the error estimates of the fully discrete PFEM are rigorously derived, which depend on the penalty parameter ϵ, the time-step size τ, and the mesh size h. Finally, two numerical tests show that our scheme is effective.

Recovery-Based Error Estimator for Natural Convection Equations Based on Defect-Correction Methods.

In this paper, we propose an adaptive defect-correction method for natural convection (NC) equations. A defect-correction method (DCM) is proposed for solving NC equations to overcome the convection dominance problem caused by a high Rayleigh number. To solve the large amount of computation and the discontinuity of the gradient of the numerical solution, we combine a new recovery-type posteriori estimator in view of the gradient recovery and superconvergent theory. The presented reliability and efficiency analysis shows that the true error can be effectively bounded by the recovery-based error estimator. Finally, the stability, accuracy and efficiency of the proposed method are confirmed by several numerical investigations.

Penalty Virtual Element Method for the 3D Incompressible Flow on Polyhedron Mesh.

In this paper, a penalty virtual element method (VEM) on polyhedral mesh for solving the 3D incompressible flow is proposed and analyzed. The remarkable feature of VEM is that it does not require an explicit computation of the trial and test space, thereby bypassing the obstacle of standard finite element discretizations on arbitrary mesh. The velocity and pressure are approximated by the practical significative lowest equal-order virtual element space pair (Xh,Qh) which does not satisfy the discrete inf-sup condition. Combined with the penalty method, the error estimation is proved rigorously. Numerical results on the 3D polygonal mesh illustrate the theoretical results and effectiveness of the proposed method.

Linear Full Decoupling, Velocity Correction Method for Unsteady Thermally Coupled Incompressible Magneto-Hydrodynamic Equations.

We propose and analyze an effective decoupling algorithm for unsteady thermally coupled magneto-hydrodynamic equations in this paper. The proposed method is a first-order velocity correction projection algorithms in time marching, including standard velocity correction and rotation velocity correction, which can completely decouple all variables in the model. Meanwhile, the schemes are not only linear and only need to solve a series of linear partial differential equations with constant coefficients at each time step, but also the standard velocity correction algorithm can produce the Neumann boundary condition for the pressure field, but the rotational velocity correction algorithm can produce the consistent boundary which improve the accuracy of the pressure field. Thus, improving our computational efficiency. Then, we give the energy stability of the algorithms and give a detailed proofs. The key idea to establish the stability results of the rotation velocity correction algorithm is to transform the rotation term into a telescopic symmetric form by means of the Gauge-Uzawa formula. Finally, numerical experiments show that the rotation velocity correction projection algorithm is efficient to solve the thermally coupled magneto-hydrodynamic equations.

Highly Selective Production of Ethylene by the Electroreduction of Carbon Monoxide.

Conversion of carbon monoxide to high value-added ethylene with high selectivity by traditional syngas conversion process is challenging because of the limitation of Anderson-Schulz-Flory distribution. Herein we report a direct electrocatalytic process for highly selective ethylene production from CO reduction with water over Cu catalysts at room temperature and ambient pressure. An unprecedented 52.7 % Faradaic efficiency of ethylene formation is achieved through optimization of cathode structure to facilitate CO diffusion at the surface of the electrode and Cu catalysts to enhance the C-C bond coupling. The highly selective ethylene production is almost without other carbon-based byproducts (e.g. C1 -C4 hydrocarbons and CO2 ) and avoids the drawbacks of the traditional Fischer-Tropsch process that always delivers undesired products. This study provides a new and promising strategy for highly selective production of ethylene from the abundant industrial CO.

Ramipril and resveratrol co-treatment attenuates RhoA/ROCK pathway-regulated early-stage diabetic nephropathy-associated glomerulosclerosis in streptozotocin-induced diabetic rats.

Clinical studies have shown that hyperglycemia can induce early-stage diabetic nephropathy (DN). Furthermore, oxidative stress, tubular epithelial-mesenchymal transition and extracellular matrix accumulation promote the progression of DN to chronic kidney disease and tubulointerstitial fibrosis. It is necessary to initiate treatment at the early stages of DN or even during the early stages of diabetes. In this work, rats with streptozotocin (STZ)-induced diabetes mellitus (DM) presented early DN symptoms within 45 days, and collagen accumulation in the glomerulus of the rats was primarily mediated through the RhoA/ROCK pathway instead of the TGF-ß signaling pathway. Resveratrol (15 mg/kg/day) and ramipril (10 mg/kg/day) co-treatment of STZ-induced DN rats showed that glomerulosclerosis in early-stage DN was reversible (P < .05 compared with that in STZ-induced DM rats). The results of this study support early intervention in diabetes or DN as a more efficient therapeutic strategy.

Chemical Insights into the Design and Development of Face-Centered Cubic Ruthenium Catalysts for Fischer-Tropsch Synthesis.

Ruthenium is a promising low-temperature catalyst for Fischer-Tropsch synthesis (FTS). However, its scarcity and modest specific activity limit its widespread industrialization. We demonstrate here a strategy for tuning the crystal phase of catalysts to expose denser and active sites for a higher mass-specific activity. Density functional theory calculations show that upon CO dissociation there are a number of open facets with modest barrier available on the face-centered cubic (fcc) Ru but only a few step edges with a lower barrier on conventional hexagonal-closest packed (hcp) Ru. Guided by theoretical calculations, water-dispersible fcc Ru catalysts containing abundant open facets were synthesized and showed an unprecedented mass-specific activity in the aqueous-phase FTS, 37.8 molCO·molRu-1·h-1 at 433 K. The mass-specific activity of the fcc Ru catalysts with an average size of 6.8 nm is about three times larger than the previous best hcp catalyst with a smaller size of 1.9 nm and a higher specific surface area. The origin of the higher mass-specific activity of the fcc Ru catalysts is identified experimentally from the 2 orders of magnitude higher density of the active sites, despite its slightly higher apparent barrier. Experimental results are in excellent agreement with prediction of theory. The great influence of the crystal phases on site distribution and their intrinsic activities revealed here provides a rationale design of catalysts for higher mass-specific activity without decrease of the particle size.

Carbon induced selective regulation of cobalt-based Fischer-Tropsch catalysts by ethylene treatment.

Various carbonaceous species were controllably deposited on Co/Al2O3 catalysts using ethylene as carbon source during the activation process for Fischer-Tropsch synthesis (FTS). Atomic, polymeric and graphitic carbon were distinguished by Raman spectroscopy, thermoanalysis and temperature programmed hydrogenation. Significant changes occurred in both the catalytic activity and selectivity toward hydrocarbon products after ethylene treatment. The activity decreased along with an increase in CH4 selectivity, at the expense of a remarkable decrease of heavy hydrocarbon production, resulting in enhanced selectivity for the gasoline fraction. In situ XPS experiments show the possible electron transfer from cobalt to carbon and the blockage of metallic cobalt sites, which is responsible for the deactivation of the catalyst. DFT calculations reveal that the activation barrier (Ea) of methane formation decreases by 0.61 eV on the carbon-absorbed Co(111) surface, whereas the Ea of the CH + CH coupling reaction changes unnoticeably. Hydrogenation of CHx to methane becomes the preferable route among the elementary reactions on the Co(111) surface, leading to dramatic changes in the product distribution. Detailed coke-induced deactivation mechanisms of Co-based catalysts during FTS are discussed.

Qualitative and Semiquantitative Analysis of Fecal Bifidobacterium Species in Centenarians Living in Bama, Guangxi, China.

Centenarians constitute a significant subpopulation in the Bama County of Guangxi province in China. The beneficial effects of intestinal microbiota, especially bifidobacteria of centenarians, have been widely accepted; however, knowledge about Bifidobacterium species in centenarians is not adequate. The aim of this study was to investigate the quantity and prevalence of fecal Bifidobacterium in healthy longevous individuals. Fecal samples from eight centenarians from Bama (aged 100 to 108 years), eight younger elderlies from Bama (aged 80 to 99 years), and eight younger elderlies from Nanning (aged 80 to 99 years) were analyzed using denaturing gradient gel electrophoresis, species-specific clone library, and quantitative polymerase chain reaction technology (qPCR). A total of eight different Bifidobacterium species were detected. B. dentium, B. longum, B. thermophilum, B. pseudocatenulatum/B. catenulatum, and B. adolescentis were common in fecal of centenarians and young elderly. B. minimum, B. saecularmay/B. pullorum/B. gallinarum, and B. mongoliense were found in centenarians but were absent in the younger elderlies. In addition, Bifidobacterium species found in centenarians were different from those found in Bama young elderly and Nanning young elderly, and the principal differences were the significant increase in the population of B. longum (P < 0.05) and B. dentium (P < 0.05) and the reduction in the frequency of B. adolescentis (P < 0.05), respectively. Centenarians tend to have more complex fecal Bifidobacterium species than young elderlies from different regions.

Leveraging machine learning for prediction of antibiotic resistance genes post thermal hydrolysis-anaerobic digestion in dairy waste.

Anaerobic digestion holds promise as a method for removing antibiotic resistance genes (ARGs) from dairy waste. However, accurately predicting the efficiency of ARG removal remains a challenge. This study introduces a novel appproach utilizing machine learning to forecast changes in ARG abundances following thermal hydrolysis-anaerobic digestion (TH-AD) treatment. Through network analysis and redundancy analyses, key determinants of affect ARG fluctuations were identified, facilitating the development of machine learning models capable of accurately predicting ARG changes during TH-AD processes. The decision tree model demonstrated impressive predictive power, achieving an impessive R2 value of 87% against validation data. Feature analysis revealed that the genes intI2 and intI1 had a critical impact on the absolute abundance of ARGs. The predictive model developed in this study offers valuable insights for improving operational and managerial practices in dairy waste treatment facilities, with the ultimate goal of mitigating the spread of antibiotic resistance.

Identifying neural correlates of balance impairment in traumatic brain injury using partial least squares correlation analysis.

Objective.Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment. However, such stratification is not straightforward, and it warrants a data-driven approach.Approach.We conducted a study to assess the balance control using a computerized posturography platform in 17 individuals with TBI and 15 age-matched healthy controls. We stratified the TBI participants into balance-impaired and non-impaired TBI usingk-means clustering of either center of pressure (COP) displacement during a balance perturbation task or Berg Balance Scale score as a functional outcome measure. We analyzed brain functional connectivity using the imaginary part of coherence across different cortical regions in various frequency bands. These connectivity features are then studied using the mean-centered partial least squares correlation analysis, which is a multivariate statistical framework with the advantage of handling more features than the number of samples, thus making it suitable for a small-sample study.Main results.Based on the nonparametric significance testing using permutation and bootstrap procedure, we noticed that the weakened theta-band connectivity strength in the following regions of interest significantly contributed to distinguishing balance impaired from non-impaired population, regardless of the type of stratification:left middle frontal gyrus, right paracentral lobule, precuneus, andbilateral middle occipital gyri. Significance.Identifying neural regions linked to balance impairment enhances our understanding of TBI-related balance dysfunction and could inform new treatment strategies. Future work will explore the impact of balance platform training on sensorimotor and visuomotor connectivity.

Crystallographic dependence of CO activation on cobalt catalysts: HCP versus FCC.

Identifying the structure sensitivity of catalysts in reactions, such as Fischer-Tropsch synthesis from CO and H2 over cobalt catalysts, is an important yet challenging issue in heterogeneous catalysis. Based on a first-principles kinetic study, we find for the first time that CO activation on hexagonal close-packed (HCP) Co not only has much higher intrinsic activity than that of face centered-cubic (FCC) Co but also prefers a different reaction route, i.e., direct dissociation with HCP Co but H-assisted dissociation on the FCC Co. The origin is identified from the formation of various denser yet favorable active sites on HCP Co not available for FCC Co, due to their distinct crystallographic structure and morphology. The great dependence of the activity on the crystallographic structure and morphology of the catalysts revealed here may open a new avenue for better, stable catalysts with maximum mass-specific reactivity.

Platinum-modulated cobalt nanocatalysts for low-temperature aqueous-phase Fischer-Tropsch synthesis.

Fischer-Tropsch synthesis (FTS) is an important catalytic process for liquid fuel generation, which converts coal/shale gas/biomass-derived syngas (a mixture of CO and H2) to oil. While FTS is thermodynamically favored at low temperature, it is desirable to develop a new catalytic system that could allow working at a relatively low reaction temperature. In this article, we present a one-step hydrogenation-reduction route for the synthesis of Pt-Co nanoparticles (NPs) which were found to be excellent catalysts for aqueous-phase FTS at 433 K. Coupling with atomic-resolution scanning transmission electron microscopy (STEM) and theoretical calculations, the outstanding activity is rationalized by the formation of Co overlayer structures on Pt NPs or Pt-Co alloy NPs. The improved energetics and kinetics from the change of the transition states imposed by the lattice mismatch between the two metals are concluded to be the key factors responsible for the dramatically improved FTS performance.

Biomimetic ion nanochannels as a highly selective sequential sensor for zinc ions followed by phosphate anions.

A novel biomimetic ion-responsive multi-nanochannel system is constructed by covalently immobilizing a metal-chelating ligand, 2,2'-dipicolylamine (DPA), in polyporous nanochannels prepared in a polymeric membrane. The DPA-modified multi-nanochannels show specific recognition of zinc ions over other common metal ions, and the zinc-ion-chelated nanochannels can be used as secondary sensors for HPO4(2-) anions. The immobilized DPA molecules act as specific-receptor binding sites for zinc ions, which leads to the highly selective zinc-ion response through monitoring of ionic current signatures. The chelated zinc ions can be used as secondary recognition elements for the capture of HPO4(2-) anions, thereby fabricating a sensing nanodevice for HPO4(2-) anions. The success of the DPA immobilization and ion-responsive events is confirmed by measurement of the X-ray photoelectron spectroscopy (XPS), contact angle (CA), and current-voltage (I-V) characteristics of the systems. The proposed nanochannel sensing devices display remarkable specificity, high sensitivity, and wide dynamic range. In addition, control experiments performed in complex matrices suggest that this sensing system has great potential applications in chemical sensing, biotechnology, and many other fields.

Identifying active surface phases for metal oxide electrocatalysts: a study of manganese oxide bi-functional catalysts for oxygen reduction and water oxidation catalysis.

Progress in the field of electrocatalysis is often hampered by the difficulty in identifying the active site on an electrode surface. Herein we combine theoretical analysis and electrochemical methods to identify the active surfaces in a manganese oxide bi-functional catalyst for the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). First, we electrochemically characterize the nanostructured α-Mn(2)O(3) and find that it undergoes oxidation in two potential regions: initially, between 0.5 V and 0.8 V, a potential region relevant to the ORR and, subsequently, between 0.8 V and 1.0 V, a potential region between the ORR and the OER relevant conditions. Next, we perform density function theory (DFT) calculations to understand the changes in the MnO(x) surface as a function of potential and to elucidate reaction mechanisms that lead to high activities observed in the experiments. Using DFT, we construct surface Pourbaix and free energy diagrams of three different MnO(x) surfaces and identify 1/2 ML HO* covered Mn(2)O(3) and O* covered MnO(2), as the active surfaces for the ORR and the OER, respectively. Additionally, we find that the ORR occurs through an associative mechanism and that its overpotential is highly dependent on the stabilization of intermediates through hydrogen bonds with water molecules. We also determine that OER occurs through direct recombination mechanism and that its major source of overpotential is the scaling relationship between HOO* and HO* surface intermediates. Using a previously developed Sabatier model we show that the theoretical predictions of catalytic activities match the experimentally determined onset potentials for the ORR and the OER, both qualitatively and quantitatively. Consequently, the combination of first-principles theoretical analysis and experimental methods offers an understanding of manganese oxide oxygen electrocatalysis at the atomic level, achieving fundamental insight that can potentially be used to design and develop improved electrocatalysts for the ORR and the OER and other important reactions of technological interest.