Effective recovery of the nitritation process through hydrogen peroxide.

This study successfully achieved stable nitritation by adding hydrogen peroxide (H2O2) to the nitrification sludge whose nitritation stability had been destroyed. The batch experiment demonstrated that, the activity of ammonia-oxidizing bacteria (AOB) was restored more rapidly than that of nitrite oxidizing bacteria (NOB) after the addition of H2O2, thereby selectively promoting AOB enrichment and NOB washout. When the H2O2 concentration was 6.25 mg/L, the NOB activity was significantly reduced and the nitrite accumulation rate (NAR) was more than 95% after 18 cycles of nitrifying sludge restoration. As a result, H2O2 treatment enabled a nitrifying reactor to recover stable nitritation performance via H2O2 treatment, with the NAR and ammonia removal efficiency (ARE) both exceeding 90%. High-throughput sequencing analysis revealed that H2O2 treatment was successful in restoring nitritation, as the relative abundance of Nitrosomonas in the nitrifying reactor increased from 6.43% to 41.97%, and that of Nitrolancea decreased from 17.34% to 2.37%. Recovering nitritation by H2O2 inhibition is a low operational cost, high efficiency, and non-secondary pollution nitritation performance stabilization method. By leveraging the varying inhibition degrees of H2O2 on AOB and NOB, stable nitrification can be efficiently restored at a low cost and without causing secondary pollution.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes.

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

Chasing collective variables using temporal data-driven strategies.

The convergence of free-energy calculations based on importance sampling depends heavily on the choice of collective variables (CVs), which in principle, should include the slow degrees of freedom of the biological processes to be investigated. Autoencoders (AEs), as emerging data-driven dimension reduction tools, have been utilised for discovering CVs. AEs, however, are often treated as black boxes, and what AEs actually encode during training, and whether the latent variables from encoders are suitable as CVs for further free-energy calculations remains unknown. In this contribution, we review AEs and their time-series-based variants, including time-lagged AEs (TAEs) and modified TAEs, as well as the closely related model variational approach for Markov processes networks (VAMPnets). We then show through numerical examples that AEs learn the high-variance modes instead of the slow modes. In stark contrast, time series-based models are able to capture the slow modes. Moreover, both modified TAEs with extensions from slow feature analysis and the state-free reversible VAMPnets (SRVs) can yield orthogonal multidimensional CVs. As an illustration, we employ SRVs to discover the CVs of the isomerizations of N-acetyl-N'-methylalanylamide and trialanine by iterative learning with trajectories from biased simulations. Last, through numerical experiments with anisotropic diffusion, we investigate the potential relationship of time-series-based models and committor probabilities.

Discovering Reaction Pathways, Slow Variables, and Committor Probabilities with Machine Learning.

A significant challenge faced by atomistic simulations is the difficulty, and often impossibility, to sample the transitions between metastable states of the free-energy landscape associated with slow molecular processes. Importance-sampling schemes represent an appealing option to accelerate the underlying dynamics by smoothing out the relevant free-energy barriers, but require the definition of suitable reaction-coordinate (RC) models expressed in terms of compact low-dimensional sets of collective variables (CVs). While most computational studies of slow molecular processes have traditionally relied on educated guesses based on human intuition to reduce the dimensionality of the problem at hand, a variety of machine-learning (ML) algorithms have recently emerged as powerful alternatives to discover meaningful CVs capable of capturing the dynamics of the slowest degrees of freedom. Considering a simple paradigmatic situation in which the long-time dynamics is dominated by the transition between two known metastable states, we compare two variational data-driven ML methods based on Siamese neural networks aimed at discovering a meaningful RC model─the slowest decorrelating CV of the molecular process, and the committor probability to first reach one of the two metastable states. One method is the state-free reversible variational approach for Markov processes networks (VAMPnets), or SRVs─the other, inspired by the transition path theory framework, is the variational committor-based neural networks, or VCNs. The relationship and the ability of these methodologies to discover the relevant descriptors of the slow molecular process of interest are illustrated with a series of simple model systems. We also show that both strategies are amenable to importance-sampling schemes through an appropriate reweighting algorithm that approximates the kinetic properties of the transition.

Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning.

Accurate evaluation of protein-ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 µs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.

Enhancing sampling with free-energy calculations.

In recent years, considerable progress has been made to enhance sampling and help address biological questions, including, but not limited to conformational transitions in biomolecules and protein-ligand reversible binding, hitherto intractable by brute-force computer simulations. Many of these advances result from the development of a palette of methods aimed at exploring rare events through reliable free-energy calculations. The advent of new, often conceptually related methods has also rendered difficult the choice of the best suited option for a given problem. Here, we focus on geometrical transformations and algorithms designed to enhance sampling along adequately chosen progress variables, tracing their theoretical foundations, and showing how they are connected and can be blended together for improved performance.

When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy.

Accurate determination of binding free energy is pivotal for the study of many biological processes and has been applied in a number of theoretical investigations to compare the affinity of severe acute respiratory syndrome coronavirus 2 variants toward the host cell. Diversity of these variants challenges the development of effective general therapies, their transmissibility relying either on an increased affinity toward their dedicated human receptor, the angiotensin-converting enzyme 2 (ACE2), or on escaping the immune response. Now that robust structural data are available, we have determined with utmost accuracy the standard binding free energy of the receptor-binding domain to the most widespread variants, namely, Alpha, Beta, Delta, and Omicron BA.2, as well as the wild type (WT) in complex either with ACE2 or with antibodies, namely, S2E12 and H11-D4, using a rigorous theoretical framework that combines molecular dynamics and potential-of-mean-force calculations. Our results show that an appropriate starting structure is crucial to ensure appropriate reproduction of the binding affinity, allowing the variants to be compared. They also emphasize the necessity to apply the relevant methodology, bereft of any shortcut, to account for all the contributions to the standard binding free energy. Our estimates of the binding affinities support the view that while the Alpha and Beta variants lean on an increased affinity toward the host cell, the Delta and Omicron BA.2 variants choose immune escape. Moreover, the S2E12 antibody, already known to be active against the WT (Starr et al., 2021; Mlcochova et al., 2021), proved to be equally effective against the Delta variant. In stark contrast, H11-D4 retains a low affinity toward the WT compared to that of ACE2 for the latter. Assuming robust structural information, the methodology employed herein successfully addresses the challenging protein-protein binding problem in the context of coronavirus disease 2019 while offering promising perspectives for predictive studies of ever-emerging variants.

Sulfammox forwarding thiosulfate-driven denitrification and anammox process for nitrogen removal.

The coupled process of thiosulfate-driven denitrification (NO3-→NO2-) and Anammox (TDDA) was a promising process for the treatment of wastewater containing NH4+-N and NO3--N. However, the high concentration of SO42- production limited its application, which needs to be alleviated by an economical and effective way to promote the application of TDDA process. In this study, TDDA process was started in a relatively short time by stepwise replacing nitrite with nitrate and operated continuously for 146 days. Results presented that the average total nitrogen removal efficiency of 82.18% can be acquired at a high loading rate of 1.98 kg N/(m3·d) with maximum nitrogen removal efficiency up to 87.04%. It was observed that the increase of S/N ratio improved the denitrification efficiency and slightly inhibit the Anammox process. Batch tests showed that Sulfammox process appeared in TDDA process under certain conditions, further contributing 2.59% nitrogen removal and 10.46% sulfur removal (14.42 mg/L NH4+-N and 37.68 mg/L SO42--S were removed). This finding was mainly attributed to the reduction of sulfate in TDDA system to elemental S0 or HS-, which subsequently was used as an electron donor to realize the recycling of sulfate (SO42--S) pollutants and promote the sulfur-nitrogen (S-N) cycle. High-throughput analysis displayed that Anammox bacteria (Candidatus_Kuenenia), Sulfur-oxidizing bacteria (Thiobacillus) with relatively high abundance of 5.37%, 7.74%, respectively, guaranteeing the excellent nitrogen and sulfate removal performance in the reactor. The enrichment of phyla Chloroflexi (31.79%), Proteobacteria (31.82%), class Ignavibacteriales (10.55%), genus Planctomycetes (13.57%) further verified the exitence of Sulfammox process in the TDDA reactor. This study provides a new perspective for the practical application of TDDA in terms of reducing the production of high concentration SO42- and saving operational cost and strengthening deeply nitrogen removal.

Hazardous Shortcuts in Standard Binding Free Energy Calculations.

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Silencing of B7-H4 induces intracellular oxidative stress and inhibits cell viability of breast cancer cells via downregulating PRDX3.

Breast cancer (BC) is the most common malignancy in women worldwide, accounting for 15.5% of total cancer deaths. B7-H4 belongs to the B7 family members and plays an important role in the development of a variety of cancers, while Peroxiredoxin III (PRDX3) is an antioxidant protein found in mitochondria. Aberrant expression of B7-H4 or PRDX3 has been implicated in the tumorigenesis of various cancers. However, the functional roles of B7-H4 and PRDX3 in BC and the underlying mechanisms remain unclear. In this research, we found that silencing of B7-H4 by siRNA could lead to not only cell viability inhibition but also the downregulation of PRDX3 in MCF-7 and T47D cells. In order to reveal the roles of PRDX3 in the B7-H4 pathway, we firstly transfected siRNA specifically targeting PRDX3 into MCF-7 and T47D cells, and the results showed that silencing of PRDX3 also inhibited the viability of MCF-7 and T47D cells significantly, accompanied by the increase of reactive oxygen species (ROS) levels. Then we overexpressed the expression of PRDX3 by transfecting PRDX3 expression plasmids into B7-H4 knocking-down cells of MCF-7 and T47D. The results showed that compared with the control groups (MCF-7 or T47D/siNC+pcDNA3.1 vector), cell viabilities were significantly inhibited in RNAi groups (MCF-7 or T47D/siB7-H4+pcDNA3.1 vector), and mildly inhibited in revertant groups (MCF-7 or T47D/siB7-H4+pcDNA3.1 PRDX3), meanwhile, ROS levels significantly elevated in RNAi groups and had no significant changes in revertant groups. All these results indicate that silencing of B7-H4 increases intracellular ROS levels and affects cell viability by modulating the expression of PRDX3 in BC cells, which may provide a potential strategy and therapeutic target for the treatment of BC.

[Promoting Nitrogen Removal in ANAMMOX Biofilm Reactor by Fe2+ Under Low Nitrogen Concentration].

The feasibility for nitrogen removal in a two-stage ANAMMOX biofilm reactor promoted by Fe2+ under low nitrogen concentration was investigated. The results showed that the ANAMMOX reaction could be effectively promoted by a ρ(Fe2+) of 5, 10, and 15 mg·L-1. A ρ(Fe2+) of 10 mg·L-1 presented the highest promotion for the ANAMMOX reaction, with the highest nitrogen removal efficiency (NRE) of 81.71% under a ρ(TN) of 150 mg·L-1and a nitrogen loading rate (NLR) of 0.62 kg·(m3·d)-1. Fe2+ promoted the secretion of extracellular polymeric substance (EPS) and the synthesis of heme c in the ANAMMOX system. Batch test results further verified the positive effects by Fe2+on the activity of anaerobic ammonium oxidizing bacteria (AnAOB). The specific ANAMMOX activity (SAA) of 10 mg·L-1 ρ(Fe2+) was 3.6 times as high as that of the control group[ρ(Fe2+)=0 mg·L-1], whereas the activity of AnAOB was significantly inhibited with ρ(Fe2+) increased to 20 mg·L-1. High-throughput sequencing results showed that the addition of Fe2+ increased the abundance of Candidatus_Kuenenia. When ρ(Fe2+) was 10 mg·L-1, the relative abundance of Candidatus_Kuenenia in reactor 1 and reactor 2 increased to 16.18% and 4.22%, respectively. The stable operation of the two-stage ANAMMOX biofilm process promoted by Fe2+provides an alternative technology for low-strength nitrogen wastewater.

Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations.

Designing a reliable computational methodology to calculate protein:ligand standard binding free energies is extremely challenging. The large change in configurational enthalpy and entropy that accompanies the association of ligand and protein is notoriously difficult to capture in naive brute-force simulations. Addressing this issue, the present protocol rests upon a rigorous statistical mechanical framework for the determination of protein:ligand binding affinities together with the comprehensive Binding Free-Energy Estimator 2 (BFEE2) application software. With the knowledge of the bound state, available from experiments or docking, application of the BFEE2 protocol with a reliable force field supplies in a matter of days standard binding free energies within chemical accuracy, for a broad range of protein:ligand complexes. Limiting undesirable human intervention, BFEE2 assists the end user in preparing all the necessary input files and performing the post-treatment of the simulations towards the final estimate of the binding affinity.

A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls.

The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand.

MLCV: Bridging Machine-Learning-Based Dimensionality Reduction and Free-Energy Calculation.

Importance-sampling algorithms leaning on the definition of a model reaction coordinate (RC) are widely employed to probe processes relevant to chemistry and biology alike, spanning time scales not amenable to common, brute-force molecular dynamics (MD) simulations. In practice, the model RC often consists of a handful of collective variables (CVs) chosen on the basis of chemical intuition. However, constructing manually a low-dimensional RC model to describe an intricate geometrical transformation for the purpose of free-energy calculations and analyses remains a daunting challenge due to the inherent complexity of the conformational transitions at play. To solve this issue, remarkable progress has been made in employing machine-learning techniques, such as autoencoders, to extract the low-dimensional RC model from a large set of CVs. Implementation of the differentiable, nonlinear machine-learned CVs in common MD engines to perform free-energy calculations is, however, particularly cumbersome. To address this issue, we present here a user-friendly tool (called MLCV) that facilitates the use of machine-learned CVs in importance-sampling simulations through the popular Colvars module. Our approach is critically probed with three case examples consisting of small peptides, showcasing that through hard-coded neural network in Colvars, deep-learning and enhanced-sampling can be effectively bridged with MD simulations. The MLCV code is versatile, applicable to all the CVs available in Colvars, and can be connected to any kind of dense neural networks. We believe that MLCV provides an effective, powerful, and user-friendly platform accessible to experts and nonexperts alike for machine-learning (ML)-guided CV discovery and enhanced-sampling simulations to unveil the molecular mechanisms underlying complex biochemical processes.

Interfering with B7-H4 expression can inhibit proliferation of breast cancer cells by down-regulating E2F family related transcription factors / 中国肿瘤生物治疗杂志

@#[摘 要] 目的：探讨干扰B7-H4表达对乳腺癌细胞增殖、凋亡、周期以及相关下游分子表达的影响。方法：利用脂质体转染技术分别将特异性靶向B7-H4的siRNA（siB7-H4）及其阴性对照（siNC）转染至对数生长期的乳腺癌T47D和MCF-7细胞，分别命名为T47D-siB7-H4、T47D-siNC、MCF-7-siB7-H4和MCF-7-siNC组。用qPCR法和WB法验证siRNA干扰效果及其对细胞周期分子cyclin D1表达的影响，CCK-8法和FCM分别检测干扰B7-H4表达对T47D和MCF-7细胞增殖、周期和凋亡的影响，qPCR法检测B7-H4干扰对E2F家族相关转录因子表达的影响。结果：成功构建干扰B7-H4表达的乳腺癌T47D和MCF-7细胞。与T47D-siNC和MCF-7-siNC组相比，T47D-siB7-H4和MCF-7-siB7-H4组细胞中B7-H4 mRNA和蛋白表达水平均显著降低、细胞增殖能力显著降低（均P<0.01），并伴有G1/S期细胞周期阻滞以及cyclin D1表达下调（均P<0.01），但细胞凋亡率差异无统计学意义（均P>0.05）。与T47D-siNC相比，干扰B7-H4后T47D细胞中E2F1、E2F2、E2F7和E2F8 mRNA水平有不同程度的降低（均P<0.01）；与MCF-7-siNC相比，干扰B7-H4后MCF-7细胞中E2F1、E2F2、E2F3、E2F7和E2F8 mRNA水平均有不同程度的降低（P<0.05或P<0.01）。结论：干扰乳腺癌细胞B7-H4表达可下调cyclin D1和E2F家族相关转录因子的表达，导致细胞周期阻滞并抑制细胞增殖。

CD73 Severed as a Potential Prognostic Marker and Promote Lung Cancer Cells Migration via Enhancing EMT Progression.

To investigate the expression levels and prognostic value of CD73 in lung cancer. And moreover, to identify the effect and potential mechanism of CD73 on lung cancer cells proliferation and migration. CD73 expression levels in lung cancer were analyzed base on GEPIA2 and GEO database. GEPIA2 and Kaplan-Meier Plotter (KM Plotter) was used to analyzed the correlation between CD73 expression and prognosis. GEO dataset were analyzed via GEO2R. CD73 overexpression cell model was construction via recombinant lentivirus transfection into A549 and NCI-H520 cells. CCK8 assay were used to investigate cells proliferation. Migration and invasion ability were evaluated by scratch and transwell methods. Base on GEPIA2, GSE32683, GSE116959 and GSE37745 dataset, we found that CD73 expression were significant higher in tumor tissues of lung adenocarcinoma (LUAD) compared with that in non-tumor normal tissues and in lung squamous cell carcinoma (LUSC), while there were no significant difference of CD73 expression between LUSC and normal control tissues. Interestingly, a high CD73 level predict poor overall survival (OS) of LUSC. However, GEPIA2 and KM plotter showed the opposite conclusion of prognostic value of CD73 in LUAD. By using cell experiments, we found that CD73 overexpression promoted proliferation and migration of LUAD A549 cells. However, there was no significant effect of CD73 overexpression on LUSC NCI-H520 cells. Furthermore, CD73 overexpression facilitates epithelial to mesenchymal transition (EMT) progression of A549 cells. In conclusion, our results indicated that CD73 expression were increased in LUAD and might be an poor prognostic marker for LUSC patients. CD73 play an important role in LUAD cells proliferation and migration. These data allowed to support CD73 as a therapeutic target for LUAD.

Overcoming Free-Energy Barriers with a Seamless Combination of a Biasing Force and a Collective Variable-Independent Boost Potential.

Amid collective-variable (CV)-based importance-sampling algorithms, a hybrid of the extended adaptive biasing force and the well-tempered metadynamics algorithms (WTM-eABF) has proven particularly cost-effective for exploring the rugged free-energy landscapes that underlie biological processes. However, as an inherently CV-based algorithm, this hybrid scheme does not explicitly accelerate sampling in the space orthogonal to the chosen CVs, thereby limiting its efficiency and accuracy, most notably in those cases where the slow degrees of freedom of the process at hand are not accounted for in the model transition coordinate. Here, inspired by Gaussian-accelerated molecular dynamics (GaMD), we introduce the same CV-independent harmonic boost potential into WTM-eABF, yielding a hybrid algorithm coined GaWTM-eABF. This algorithm leans on WTM-eABF to explore the transition coordinate with a GaMD-mollified potential and recovers the unbiased free-energy landscape through thermodynamic integration followed by proper reweighting. As illustrated in our numerical tests, GaWTM-eABF effectively overcomes the free-energy barriers in orthogonal space and correctly recovers the unbiased potential of mean force (PMF). Furthermore, applying both GaWTM-eABF and WTM-eABF to two biologically relevant processes, namely, the reversible folding of (i) deca-alanine and (ii) chignolin, our results indicate that GaWTM-eABF reduces the uncertainty in the PMF calculation and converges appreciably faster than WTM-eABF. Obviating the need of multiple-copy strategies, GaWTM-eABF is a robust, computationally efficient algorithm to surmount the free-energy barriers in orthogonal space and maps with utmost fidelity the free-energy landscape along selections of CVs. Moreover, our strategy that combines WTM-eABF with GaMD can be easily extended to other biasing-force algorithms.

The benefits of autotrophic nitrogen removal from high concentration of urea wastewater through a process of urea hydrolysis and partial nitritation in sequencing batch reactor.

For the sake of high efficiency and saving operational cost for high-concentration urea wastewater treatment, a novel two-stage partial nitritation (PN)-anammox process containing simultaneous urea hydrolysis and PN in sequencing batch reactor (SBR) was investigated. Although the influent urea concentration increased from 500 to 1200 mg/L, the SBR simultaneously achieved urea removal efficiency higher than 98% and stable PN with effluent NO2--N/NH4+-N ratio of 1.0-1.3 without any extra alkalinity addition. The intracellular hydrolysis was the dominant mechanism for urea removal and persistent free ammonia inhibition on nitrite-oxidizing bacteria was the main reason for nitrite accumulation of 97.92% in SBR. The subsequent anammox reactor showed efficient nitrogen removal performance with average ammonium removal efficiency, nitrogen removal efficiency and maximum nitrogen removal loading rate of 98.08%, 81.45% and 1.05 kg N·m-3·d-1 respectively. High-throughput sequencing results indicated Gemmatimonadetes became the most abundant bacterial phylum related to potential intracellular urea hydrolysis and displayed obvious ammonium-oxidizing bacteria enrichment and nitrite-oxidizing bacteria inhibition in SBR, and the dominant anammox bacteria (Candidatus_Kuenenia) in anammox reactor. The proposed process was proven to be promising for high-concentration urea wastewater treatment, facilitating the sustainable development of the urea industry in the future.

BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations.

Accurate absolute binding free-energy estimation in silico, following either an alchemical or a geometrical route, involves several subprocesses and requires the introduction of geometric restraints. Human intervention, for instance, to define the necessary collective variables, prepare the input files, monitor the simulation, and perform post-treatments is, however, tedious, cumbersome, and prone to errors. With the aim of automating and streamlining free-energy calculations, especially for nonexperts, version 2.0 of the binding free energy estimator (BFEE2) provides both standardized alchemical and geometrical workflows and obviates the need for extensive human intervention to guarantee complete reproducibility of the results. To achieve the largest gamut of protein-ligand and, more generally, of host-guest complexes, BFEE2 supports most academic force fields, such as CHARMM, Amber, OPLS, and GROMOS. Configurational files are generated in the NAMD and Gromacs formats, and all the post-treatments are performed in an automated fashion. Moreover, convergence of the free-energy calculation can be monitored from the intermediate files generated during the simulation. All in all, BFEE2 is a foolproof, versatile tool for accurate absolute binding free-energy calculations, assisting the end-user over a broad range of applications.

Assessment of transmission capacity of influenza and effect evaluation of suspension measures in schools and nurseries / 中国学校卫生

Objective@#To estimate the transmission capacity of influenza clustering in schools and nurseries, and to evaluate the effect of suspension measures, providing a basis for formulating disease management strategies and control measures.@*Methods@#The SEIAR dynamics model was used to simulate the epidemic data, calculating the basic regeneration coefficient R 0 of the epidemic to evaluate the epidemic transmission capacity, and calculating the cumulative incidence rate of the epidemic to evaluate the prevention and control effect of the suspension measures.@*Results@#The basic regeneration coefficient R 0 was 8.44(8.01,8.89) without intervention. There were statistically significant differences in R 0 of influenza epidemic among different types of school(F=9.52, P<0.01). The R 0 of influenza epidemic in primary and secondary schools were higher than that in nurseries(P<0.05). R 0 of influenza A was higher than that of influenza B(t=2.71, P<0.01). R 0 of influenza A(H3) was higher than of influenza B(Victoria)(P<0.05). The cumulative incidence of the outbreaks which were suspended for 4 days and 7 days was significantly lower than that in the non-suspensions(P<0.05). However, there was no significant difference in the cumulative incidence of the outbreaks between the 4-day suspension and the 7-day suspension(P>0.05).@*Conclusion@#Transmission capacity of school-based influenza epidemic is high, especially among primary and secondary schools. When the epidemic situation of infected class meets the suspension standard, it is recommended to suspend classes for 4 days.