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Tigecycline is a broad‐spectrum antibacterial agent. As the incidence of multidrug‐resistant bacterial infections has increased in intensive care units (ICUs) over the past decades, tigecycline is often used in ICUs. Information about tigecycline‐associated hepatotoxicity in ICU patients is limited. To investigate the potential risk factors for tigecycline‐associated hepatotoxicity in ICU patients, 148 patients from 2 centers who had received tigecycline for at least 4 days were retrospectively analyzed. Hepatotoxicity was classified according to the National Cancer Institute Common Terminology Criteria for Adverse Events (5.0) grading system. As a result, 33.8% of patients experienced hepatotoxicity events in the ICU. The multivariate analysis showed that an albumin concentration <25 g/L at baseline (odds ratio, 3.714; 95%CI, 1.082‐12.744; P =.037) and treatment duration (odds ratio, 1.094; 95%CI, 1.032‐1.160; P =.003) were significantly correlated with tigecycline‐associated hepatotoxicity. The median time to onset of hepatotoxicity was 8.0 days. The median duration ICU stay and the in‐hospital mortality rate were not different between the hepatotoxicity group and the nonhepatotoxicity group (33.5 days (interquartile range, 21.0‐72.0) vs 31.0 days (interquartile range, 21‐62.5), P =.850; 38.0% vs 43.8%; P =.504). Therefore, close monitoring of liver function is recommended for patients with baseline albumin concentrations <25 g/L or for patients who receive tigecycline therapy for >8 days. [ABSTRACT FROM AUTHOR]

This experiment prepared magnetic composite siderophores (DMPs) with strong magnetism, excellent adsorption capacity, and high specific surface area. Exploring the synergistic effect of magnetic nanoparticles and siderophores on Microcystis aeruginosa growth under iron-deficient condition, by utilizing the characteristics of the three-layer core-shell structure of DMPs. This study elucidated the potential mechanism by which DMPs promote the cyanobacterial growth through physiological indicators and transcriptome analysis. On the experiment's final day, cell density in DMPs treatment group at 2, 4, and 8 mg/L were 1.10, 1.14 and 1.16 times higher than those in the control group (Ct), respectively. Similarly, chlorophyll and photosynthetic efficiency results showed improved algae growth with increasing DMPs dosage. The microcystin content in DMPs experimental groups at low, medium, and high concentration were 0.91, 0.86, and 0.83 times that of Ct, indicating alleviation of iron deficiency stress. Additionally, based on extracellular polymers, intracellular and extracellular siderophores, and visualization techniques, DMPs nanoparticles captured free iron sources in the environment, promoting algae growth by entering algal cells and facilitating the uptake and utilization of free iron ions from the solution. During the experiment, the iron uptake and transport genes (feoA and feoB) were significantly upregulated, whereas the algal siderophore synthesis gene (pchF) and the TonB-dependent transport system gene (TonB_C) were significantly downregulated, suggesting heightened activity in intracellular iron uptake and transport. This indicates an abundance of intracellular iron, eliminating the need for secrete siderophores to overcome iron deficiency. Microcystis aeruginosa increased iron bioavailability by using iron transported through DMPs in the environment while internalizing these DMPs. This study explored the mechanism of this synergistic effect to boost algal growth, and provided new ideas for elucidating the mechanism of cyanobacterial bloom outbreaks as well as the innovative application of biotechnology. [Display omitted] • Deepening the interaction with M. aeruginosa through modifying siderophores. • The mechanism of DMPs promoting algal growth via transcriptomics. • DMPs facilitated algae to acquire free iron and were internalized by algae. • High-dose DMPs boosted photosynthesis, glycolysis and protein synthesis in algae. [ABSTRACT FROM AUTHOR]

Transportation and storage for hydrogen have garnered increasing attention in recent years because of the rapid development of low-carbon hydrogen energy. Pipeline is regarded as one of the most efficient ways for hydrogen transportation. However, many research found that the active hydrogen atoms can penetrate into the material and induce cracking on the pipe. As the initial hydrogen induced cracking is small, detecting these small defects can be challenging. To enhance the detectability of these small defects, we propose a novel capacitive sensor structure featuring two differential excitations. It is noted that the lift-off noise is always a challenging problem in electromagnetic NDE method. Fortunately, this problem can be alleviated by the proposed method. The simulation result shows the output signal will not be significantly influenced by the lift-off changing. Two metallic samples with defects are tested with a PCB-based prototype probe. Experiment result shows that the developed sensor can suppress the lift-off noise by 81.0% and increase the SNR by 592.28%, comparing to the results of a conventional sensor. A defects extraction algorithm is developed based on morphological image processing method to extract the defects from the raw data automatically. Size estimation is conducted and the average quantization error rate is 7.01% for 10 mm defects. Therefore, from the simulation and experimental results, the proposed capacitive sensing method can be a potential approach for hydrogen pipeline inspection. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a destructive fungal disease with a significant economic impact. Aegilops tauschii, the D-genome progenitor of wheat (Triticum aestivum, AABBDD), offers valuable gene pool for Pst resistance. The Ae. tauschii gene YrAS2388 confers resistance to a wide range of Pst races and encodes a typical nucleotide oligomerization domain-like receptor (NLR). The functional YrAS2388R has duplicated 3′ untranslated regions (3′UTRs). In the present study, we have developed two gene-specific kompetitive allele specific PCR (KASP) markers for YrAS2388R by comparing multiple homoeologous and paralogous genomic sequences of YrAS2388 alleles from allopolyploid wheat genomes. KASP-E5 was developed based on SNP in the exon5 sequences of YrAS2388, which behaves as a co-dominant marker. KASP-E6′ was developed based on the 3′UTR sequences of YrAS2388, which behaves as a dominant marker. These markers were validated in different types of wheat populations and showed clear functional differentiation that was completely agreement with the gel based gene-specific marker for YrAS2388R. Our results indicate that KASP-E5 and KASP-E6′ are perfect diagnostic markers for YrAS2388R and would be useful for marker assisted selection in wheat resistance breeding. [ABSTRACT FROM AUTHOR]

Based on the non-orthogonal multiple-relaxation-time lattice Boltzmann method (MRT–LBM), natural convection in a porous square cavity with a pair of isothermally hot and cold blocks inside was studied numerically in the current study. The influence of arrangements (Cases 1, 2, 3, 4, and 5), spacing ratio (S), and size ratio (A) of the hot and cold sources and the Rayleigh number (Ra) on the heat exchange efficiency were studied. The results show that different arrangements produce different heat-transfer effects. Two arrangements, hot and cold blocks placed horizontally (Case 1) and a cold block located in the upper left corner while a hot block is located in the bottom right corner (Case 4), have better heat exchange performance than the other three cases because the flow directions of hot and cold fluids are closer to that of the heat transfer. Then, the influence of the spacing between blocks and the size of the blocks on the heat transfer rate was further studied in Cases 1 and 4. Heat transfer performance improved with increasing A. Additionally, the variation in the heat transfer performance with spacing is related to the arrangement and size ratio of the blocks. For Ra = 104, 105, and 106, the best heat transfer characteristics were obtained in Case 1 when S = 0.05 and A = 0.20. For Ra = 107, Case 4 exhibited the best heat transfer effect when S = 0.35 and A = 0.20. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems. [ABSTRACT FROM AUTHOR]

The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for learning and memory and short-term changes for adaptation and synaptic computation. By studying the performance of reservoir computing (RC) models in a memory task, we revealed that short-term synaptic plasticity is fundamentally important for long-term synaptic changes in neural networks. Specifically, short-term depression (STD) greatly expands the operational range of a neural network in which it can accommodate long-term synaptic changes while maintaining system performance. This is achieved by dynamically adjusting neural networks close to a critical state. The effects of STD can be further strengthened by synaptic weight heterogeneity, resulting in networks that can tolerate very large, long-term changes in synaptic weights. Our results highlight a potential mechanism used by the brain to organize plasticity at different time scales, thereby maintaining optimal information processing while allowing internal structural changes necessary for learning and memory. [ABSTRACT FROM AUTHOR]

Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, interactions among multiple (e.g., triple and quadruple) areas cannot be reduced to pairwise interactions. Such higher order interactions (HOIs), e.g., exclusive-or (XOR) operation, are widely implemented in computation systems and are crucial for effective information processing. However, it is currently unclear whether any HOIs are present in large-scale brain functional networks when subjects are executing specific tasks. Here we analyzed functional magnetic resonance imaging (fMRI) data collected from human subjects executing various perceptual, motor, and cognitive tasks. We found that HOI strength in the macroscopic functional networks was very weak for all tasks, suggesting that major brain activities do not rely on HOIs on the macroscopic level at the timescale of hundreds of milliseconds. These weak HOIs during tasks were further investigated with a neural network model activated by external inputs, which suggested that weak pairwise interactions among brain areas organized the system without involving HOIs. Taken together, these results demonstrated the dominance of pairwise interactions in organizing coordinated activities among different brain areas to support various brain functions. • The lack of HOIs at functional brain networks was revealed during various tasks. • The weakly coupled regime of macroscopic brain networks was suggested. • The lack of HOIs provides the methodological foundation for pairwise approaches. [ABSTRACT FROM AUTHOR]

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to their efficient spike communication and their natural way to utilize kinds of synaptic plasticity rules found in brain for weight modification. However, existing SNN models for SR either had bad performance, or were trained in biologically implausible ways. In this paper, we present a biologically inspired convolutional SNN model for SR. The network adopts the time-to-first-spike coding scheme for fast and efficient information processing. A biological learning rule, spike-timing-dependent plasticity (STDP), is used to adjust the synaptic weights of convolutional neurons to form receptive fields in an unsupervised way. In the convolutional structure, the strategy of local weight sharing is introduced and could lead to better feature extraction of speech signals than global weight sharing. We first evaluated the SNN model with a linear support vector machine (SVM) on the TIDIGITS dataset and it got the performance of 97.5%, comparable to the best results of ANNs. Deep analysis on network outputs showed that, not only are the output data more linearly separable, but they also have fewer dimensions and become sparse. To further confirm the validity of our model, we trained it on a more difficult recognition task based on the TIMIT dataset, and it got a high performance of 93.8%. Moreover, a linear spike-based classifier—tempotron—can also achieve high accuracies very close to that of SVM on both the two tasks. These demonstrate that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently. [ABSTRACT FROM AUTHOR]

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME%5ftutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models. [ABSTRACT FROM AUTHOR]

Reusing agro-industrial co-products has been the research focus of sustainable development worldwide. To upcycle ethanol by-product and corncob into a balanced nutritional feed for pigs, Aspergillus niger , Trichoderma reesei , Bacillus subtilis , Bacillus coagulans , and Candida utilis were employed and investigated their nutrition and flavor improving abilities. The results showed that fermentation effectively improved the total protein content of distillers dried grains with solubles (DDGS), and the highest increasing was 96.3%, by Aspergillus niger. The essential amino acids for pig growth, including lysine, isoleucine, threonine, arginine, and valine, were increased after Aspergillus niger fermentation, and it increased by 191%, 16.0%, 91.1%, 60.2% and 200%, respectively. The leucine content decreased by 44.5% after fermentation with Aspergillus niger. It provided an alternative solution for the problem of pig inhibiting digestion by high leucine content. The highest degradation rate of cellulose was 15.7% with Trichoderma reesei, while the highest degradation rate of hemicellulose was 19.4% with Aspergillus niger. Besides, the flavor of furanal in feed fermented by Aspergillus niger was increased, which would improve the feeding rate of pig since furanal is favorite for pigs. Consequently, co-fermentation of DDGS and corncob by Aspergillus niger or Trichoderma reesei have a better effect on nutrition and flavor. The results proved a viable way of producing nutritional-improved pig feed from agro-industrial co-products of corn. • The first limiting amino acid Lys was increased to 4%, 6.20 mg/g in fermented feed. • The soluble carbohydrate was increased by 207.3% via A. niger fermentation. • Furanal in feed improved to 14.2 ng/g, would be more favorable for pigs. [ABSTRACT FROM AUTHOR]

Sea cucumbers are popular traditional tonic foods in Asia and the Middle East and are often dried for storage or sale. Rehydration of dried sea cucumber is a necessary step before further processing. The kinetics for ultrasound-assisted rehydration were mathematically described using mechanistic (Fick's diffusion) and empirical (Peleg's equation, Weibull model) models. The effective diffusivities of water transport with ultrasound were in the range of 0.182 mm2 min-1-0.197 mm2 min-1, which was greater than the value of the control, at 0.179 mm2 min-1, considering Fick's diffusion. Peleg's equation was concluded to be the best model to predict the ultrasound-assisted rehydration of dried sea cucumber. Its rehydration efficiency was increased 16-fold by ultrasound and rehydration time was reduced up to 45 h, the sea cucumbers did not show any negative effects on their texture properties after rehydration. [ABSTRACT FROM AUTHOR]

With the wide application of plastic products, microplastic pollution has become a major environmental issue of global concern. Microplastics in aquatic environments can interact with organic pollutants, causing a combined effect on submerged macrophytes. This study investigated the response mechanisms of the submerged plant Myriophyllum verticillatum and epiphytic biofilm to the antibiotic enrofloxacin, microplastics, and their combined exposure in a high nitrogen and phosphorus environment. The results indicated that Myriophyllum verticillatum was not sensitive to enrofloxacin of 1 mg L−1, while 10 and 50 mg L−1 enrofloxacin inhibited the uptake of nitrogen and phosphorus by the plants, as well as triggered oxidative stress in the plant leaves, causing irreversible damage to the plant cells. In addition, enrofloxacin altered the structure of the leaf epiphytic biofilm community. Interestingly, 1, 5, and 20 mg L−1 microplastics had no significant effect on the plant, while they facilitated the aggregation of microorganisms, increasing the abundance of the leaf epiphyte biofilm. The combination of enrofloxacin and microplastics induced a synergistic effect on Myriophyllum verticillatum. Specifically, the rate of nitrogen and phosphorus uptake by the plant was reduced, the content of photosynthetic pigments decreased, and antioxidant enzyme activity was further increased. In addition, the diversity of the leaf epiphytic biofilm community was similar to the single enrofloxacin exposure. These results demonstrated the differences between single and combined exposures and provided a new theoretical basis to evaluate the harmful effects of enrofloxacin and microplastics on submerged macrophytes. [Display omitted] • Microplastics have no ecotoxicological effects on M. verticillatum. • Microplastics contribute to the enrichment of leaf epiphytic biofilms. • Enrofloxacin has ecotoxicological effects on M. verticillatum. • Enrofloxacin alters leaf epiphytic biofilm structure. • Microplastics can exacerbate the toxicity of enrofloxacin. [ABSTRACT FROM AUTHOR]

Rehydration capacity, water distribution, and microstructure of sea cucumbers subjected to ultrasound-assisted rehydration (UAR) were investigated. Rehydration ratio and water holding capacity (WHC) were found to increase when ultrasound power was increased from 100 W to 300 W, to increase when ultrasound frequency was decreased from 45 KHz to 28 KHz. Nuclear magnetic resonance analysis revealed that water mobility increased with increasing ultrasound power, resulting in more immobilized water and free water, which facilitated better and faster water uptake. Analysis of scanning electron microscope micrographs revealed that with the accelerated water uptake, sea cucumber swelled faster and formed a structure with higher porosity which increased water uptake even faster and led to an accelerated rehydration and higher WHC. Combined with UAR, the rehydration efficiency of dried sea cucumber was increased 12-fold, and 44 h were saved in the rehydration procedure without any negative effect on texture. [ABSTRACT FROM AUTHOR]

Videogrammetric measurement (VM) becomes a focus technique at home and abroad because of its no special requirements on the test model and its non-contact way for wind tunnel testing. VM techniques and their applications for wind tunnel testing in highly developed institutes abroad are firstly introduced in detail, and some problems about the VM used in high speed intermittent wind tunnels at home are analyzed. Dynamic calibration for multi-cameras and sequential images matching technique for the mark points based on motion evaluation are developed in CARDC in order to achieve the high precision pose measurement in noise (about 130dB)/vibration environment. The potential application prospects of VM have been demonstrated by several preliminary testing cases in a 2 meter supersonic tunnel tests and a 2.4 meter transonic tunnel, including angles of attack and sideslip measurement of test model, deformation of full size embedded door and a static aero-elastic test model, which shows the precision of VM is high. On the other hand, VM can be used to determine the optical path difference (OPD) by measuring the deviation displacement field induced by the beams from center of camera to the background points crossing the flow field, provides a new way to research and measure aero-optic effects and flow display at the surface of the testing model, which is simple and needs no expensive coherent sources. [ABSTRACT FROM AUTHOR]

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results. [ABSTRACT FROM AUTHOR]

Integral equation theory (IET) provides an effective solvation model for chemical and biological systems that balances computational efficiency and accuracy. We present a new software package, the expanded package for IET‐based solvation (EPISOL), that performs 3D‐reference interaction site model (3D‐RISM) calculations to obtain the solvation structure and free energies of solute molecules in different solvents. In EPISOL, we have implemented 22 different closures, multiple free energy functionals, and new variations of 3D‐RISM theory, including the recent hydrophobicity‐induced density inhomogeneity (HI) theory for hydrophobic solutes and ion‐dipole correction (IDC) theory for negatively charged solutes. To speed up the convergence and enhance the stability of the self‐consistent iterations, we have introduced several numerical schemes in EPISOL, including a newly developed dynamic mixing approach. We show that these schemes have significantly reduced the failure rate of 3D‐RISM calculations compared to AMBER‐RISM software. EPISOL consists of both a user‐friendly graphic interface and a kernel library that allows users to call its routines and adapt them to other programs. EPISOL is compatible with the force‐field and coordinate files from both AMBER and GROMACS simulation packages. Moreover, EPISOL is equipped with an internal memory control to efficiently manage the use of physical memory, making it suitable for performing calculations on large biomolecules. We demonstrate that EPISOL can efficiently and accurately calculate solvation density distributions around various solute molecules (including a protein chaperone consisting of 120,715 atoms) and obtain solvent free energy for a wide range of organic compounds. We expect that EPISOL can be widely applied as a solvation model for chemical and biological systems. EPISOL is available at https://github.com/EPISOLrelease/EPISOL. [ABSTRACT FROM AUTHOR]

Via the non-orthogonal multiple-relaxation time Lattice Boltzmann Method, the natural convection phenomenon inside I-shape heat sink under the effect of Lorentz force is investigated. The cavity is filled with MWCNT-Fe3O4/water hybrid nanofluid, and three cold and three hot tubes exist inside the cavity at the same time. The influences of Rayleigh number (103–106), Hartmann number (0–100), magnetic field tilt angle θ (0 – π/2), and different cold and heat pipe arrangements (Case 1–Case 5) on the heat transfer and fluid flow are numerically simulated. The calculated results are shown by the variation of flow lines, isotherms and the average Nusselt number. The results show that the amplitude of the flow function is always the largest for Case 5, which is the best arrangement from the fluid motion point of view, while Case 2 has the largest Nusselt number for different Rayleigh numbers, magnetic field inclination angle θ and different Hartmann numbers, which is the best arrangement from the heat transfer efficiency point of view. However, with different cold and hot pipe arrangements, the average Nusselt number increases when the Rayleigh number and the magnetic field inclination angle θ increase. In contrast, as Hartmann number increases, the average Nusselt number decreases. The local Nusselt number of the cold and hot tubes is further investigated, comparing the discrepancy of the heat transfer performance near each wall surface. [ABSTRACT FROM AUTHOR]

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate dynamics using a discretized GME. qMSM can be constructed with much shorter MD trajectories than the MSM. However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study biomolecular conformational changes. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of qMSM in complicated biomolecules. We present a new method, the Integrative GME (IGME), in which we analytically solve the GME under the condition when the memory kernels have decayed to zero. Our IGME overcomes the challenges of the qMSM by using the time integrations of memory kernels, thereby avoiding the numerical instability caused by explicit computation of time-dependent memory kernels. Using our solutions of the GME, we have developed a new approach to compute long-time dynamics based on MD simulations in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, FIP35 WW-domain, and Taq RNA polymerase. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate biomolecular conformational changes. [ABSTRACT FROM AUTHOR]

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly. [ABSTRACT FROM AUTHOR]

Abstract: Part of understanding a molecule’s conformational dynamics is mapping out the dominant metastable, or long lived, states that it occupies. Once identified, the rates for transitioning between these states may then be determined in order to create a complete model of the system’s conformational dynamics. Here we describe the use of the MSMBuilder package (now available at http://simtk.org/home/msmbuilder/) to build Markov State Models (MSMs) to identify the metastable states from Generalized Ensemble (GE) simulations, as well as other simulation datasets. Besides building MSMs, the code also includes tools for model evaluation and visualization. [Copyright &y& Elsevier]

Abstract: We have performed simulated tempering molecular dynamics simulations to study the thermodynamics of the headpiece of the Huntingtin (Htt) protein (N17Htt). With converged sampling, we found this peptide is highly helical, as previously proposed. Interestingly, this peptide is also found to adopt two different and seemingly stable states. The region from residue 4 (L) to residue 9 (K) has a strong helicity from our simulations, which is supported by experimental studies. However, contrary to what was initially proposed, we have found that simulations predict the most populated state as a two-helix bundle rather than a single straight helix, although a significant percentage of structures do still adopt a single linear helix. The fact that Htt aggregation is nucleation dependent infers the importance of a critical transition. It has been shown that N17Htt is involved in this rate-limiting step. In this study, we propose two possible mechanisms for this nucleating event stemming from the transition between two-helix bundle state and single-helix state for N17Htt and the experimentally observed interactions between the N17Htt and polyQ domains. More strikingly, an extensive hydrophobic surface area is found to be exposed to solvent in the dominant monomeric state of N17Htt. We propose the most fundamental role played by N17Htt would be initializing the dimerization and pulling the polyQ chains into adequate spatial proximity for the nucleation event to proceed. [Copyright &y& Elsevier]

We performed molecular dynamics simulations of the collapse of a two-domain protein, the BphC enzyme, into a globular structure to examine how water molecules mediate Hydrophobic collapse of proteins. In the interdomain region, liquid water persists with a density 10 to 15% lower than in the bulk, even at small domain separations. Water depletion and hydrophobic collapse occur on a nanosecond time scale, which is two orders of magnitude slower than that found in the collapse of idealized paraffin-like plates. When the electrostatic protein-water forces are turned off, a dewetting transition occurs in the interdomain region and the collapse speeds up by more than an order of magnitude. When attractive van der Waals forces are turned off as well, the dewetting in the interdomain region is more profound, and the collapse is even faster. [ABSTRACT FROM AUTHOR]

Mutations in protein phosphatase 2A (PP2A) are connected to intellectual disability and cancer. It has been hypothesized that these mutations might disrupt the autoinhibition and phosphorylation-induced activation of PP2A. Since they are located far from both the active and substrate binding sites, it is unclear how they exert their effect. We performed allosteric pathway analysis based on molecular dynamics simulations and combined it with biochemical experiments to investigate the autoinhibition of PP2A. In the wild type (WT), the C-arm of the regulatory subunit B56δ obstructs the active and substrate binding sites exerting a dual autoinhibition effect. We find that the disease mutant, E198K, severely weakens the allosteric pathways that stabilize the C-arm in the WT. Instead, the strongest allosteric pathways in E198K take a different route that promotes exposure of the substrate binding site. To facilitate the allosteric pathway analysis, we introduce a path clustering algorithm for lumping pathways into channels. We reveal remarkable similarities between the allosteric channels of E198K and those in phosphorylation-activated WT, suggesting that the autoinhibition can be alleviated through a conserved mechanism. In contrast, we find that another disease mutant, E200K, which is in spatial proximity of E198, does not repartition the allosteric pathways leading to the substrate binding site; however, it may still induce exposure of the active site. This finding agrees with our biochemical data, allowing us to predict the activity of PP2A with the phosphorylated B56δ and provide insight into how disease mutations in spatial proximity alter the enzymatic activity in surprisingly different mechanisms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Relationship between frying methods and nutrient loss of hairtail was established. • Flavor formation of hairtail under different frying methods were analyzed. • More harmful substances generated during air frying than traditional frying. • Vacuum frying contributes to the lowest amount of HAAs of hairtail. • The balance regulation between aroma and hazards for fried hairtail was proposed. The balance regulation between characteristic aroma and hazards in high-temperature processed fish is a hot spot. This study was aimed to explore the interactive relationship between the nutritional value, microstructures, aroma, and harmful substances of hairtail under different frying methods including traditional frying (TF), air frying (AF), and vacuum frying (VF) via chemical pattern recognition. The results indicated that VF-prepared hairtail could form a crunchy mouthfeel and retain the highest content of protein (645.53 mg/g) and the lowest content of fat (242.03 mg/g). Vacuum frying reduced lipid oxidation in hairtail, resulting in the POV reaching 0.02 mg/g, significantly lower than that of TF (0.05 mg/g) and AF (0.21 mg/g), and TBARS reached 0.83 mg/g, significantly lower than that of AF (1.96 mg/g) (P < 0.05), respectively. Notable variations were observed in the aroma profile of hairtail prepared from different frying methods. Vacuum frying of hairtail resulted in higher levels of pyrazines and alcohols, whereas traditional frying and air frying were associated with the formation of aldehydes and ketones, respectively. Air frying was not a healthy way to cook hairtail which produced the highest concentration of harmful substances (up to 190.63 ng/g), significantly higher than VF (5.72 ng/g) and TF (52.78 ng/g) (P < 0.05), especially norharman (122.57 ng/g), significantly higher than VF (4.50 ng/g) and TF (32.63 ng/g) (P < 0.05). Norharman and acrylamide were the key harmful substances in hairtail treated with traditional frying. The vacuum frying method was an excellent alternative for deep-fried hairtail as a snack food with fewer harmful substances and a fine aroma, providing a theoretic guidance for preparing healthy hairtail food with high nutrition and superior sensory attraction. [ABSTRACT FROM AUTHOR]

The self-balanced loading test is a state-of-art pile testing method, but its suitability to pile bearing capacity determination in transformer substation engineering in mountainous and hilly areas is not yet clear. In this study, a two-dimensional axisymmetric numerical model is established by the PLAXIS software to simulate the behavior and bearing mechanism of shallow rock-socketed short piles based on the self-balanced loading test. The model is first validated by simulating the field tests of two adjacent piles under self-balanced loading. Then the influence factors of the load-displacement curves of piles are analyzed. Thereafter, the mechanical mechanism of the self-balanced loading tests is simulated and compared with the conventional static loading tests. It is observed that the rock modulus, rock-socketed depth of piles, and burial depth of the Osterberg Cell affect the load-displacement significantly, but the cohesion of the rocks affects little. Moreover, compared with the conventional static loading tests, the shear stress of the pile-soil interface distributes less uniformly under self-balanced loading conditions. On this basis, a bearing capacity computation method of shallow rock-socketed short piles based on the self-balanced loading test is proposed. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of weather shocks on athlete performance, focusing on soccer players in the Chinese Soccer Super League over three consecutive seasons from 2014 to 2016. Utilizing daily meteorological data, we construct the temperature and precipitation bin variables to explore the marginal effects on the physical and cognitive performance of soccer players. The empirical findings document a sizeable impact of high temperatures and heavy precipitation on players' performance indicators, including physical performance and cognitive performance. Specifically, relative to conditions with temperatures below 15 °C, the total running distances, the number of passes, and the number of fouls for soccer players significantly decrease by 7.6%, 6.0%, and 23.1%, respectively, when temperatures exceed 30 °C. The results for precipitation bins show that relative to conditions with precipitation below 0.1 mm, the total running distances, the number of passes, and the number of fouls for soccer players significantly decrease by 1.2%, 8.2%, and 4.7%, respectively, when precipitation exceeds 50 mm. Moreover, the heterogeneity analysis reveals that the negative effects of high temperatures and heavy precipitation are more pronounced and significant for older players, for matches performed away from home, and for matches performed in areas with higher temperatures or more precipitation. Evidence of player adaptations suggests that players from regions with higher temperatures or more precipitation perform better in terms of physical and cognitive abilities when playing in areas with lower temperatures or less precipitation compared to players from regions with lower temperatures or less precipitation when they compete in areas with higher temperatures or more precipitation. Finally, policy suggestions are proposed to mitigate the impact of weather shocks on soccer players and to promote the sustainability of the soccer industry. [ABSTRACT FROM AUTHOR]

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:   https://synlethdb.sist.shanghaitech.edu.cn/v2. [ABSTRACT FROM AUTHOR]

The degradation of systems in service, including pipelines, over time highlights the critical need for reliable and accurate defect detection to ensure safe operations. However, single modality-based Nondestructive Evaluation (NDE) data used in practical applications often suffers from noise contamination and errors caused by various factors like lift-off/standoff distances, probe drift, scanning speed, variation in data acquisition rates, and poor sensor sensitivity. The presence of agnostic noise types poses a challenge in extracting defect signals. To address these challenges, this paper presents an automated NDE theory-based data fusion framework aimed at enhancing the detection of surface and near-surface defects in magnetizable and conductive specimens. The Magnetic Flux Leakage (MFL) and Eddy Current (EC) based NDE sensing methods demonstrate the highly heterogeneous nature of noise distributions. Given the heterogeneity of the inspection methods, a screening rule is proposed to determine the conditions under which fusion would be beneficial. An important aspect of the proposed fusion method is registration, which ensures accurate alignment of multi-sensor image data. Two registration methods are proposed in this study as performing blind fusion without registration leads to erroneous results. The first registration method is translational, whereas the second method is registration based on linear optimal transport (OT) which proves to be effective in the boundary conditions. Finally, the registered source images from the EC and MFL modalities are fused using pixel-based fusion algorithms, including transform domain and spatial domain-based methods. Qualitative and quantitative assessments demonstrate that the registered fusion results exhibit higher accuracy and reliability compared to unregistered fused results and source images. Although the fusion method is applied to MFL and EC data in this paper, it is also suitable for other NDE modalities. • Heterogeneous Fusion. • Registration based on Optimal Transport. • Near field NDE sensors. [ABSTRACT FROM AUTHOR]

[Display omitted] • The tail exhibited lower moisture and higher protein, ash, carbohydrate content. • The middle part of eel had higher hardness and good water-holding capacity. • The tail possessed a higher proportion of n − 3/n − 6 polyunsaturated fatty acids. • DGs and MGs bond to saturated fatty acids were primarily existed near the tail. With the diversity of consumer demands for healthy food, stricter requirements have been put forward for the product quality of sea eel (Astroconger myriaster). From the common experience, remarkable differences could be observed between different parts of the sea eel. The textures, basic nutritional compositions, and lipid compositions from seven parts of sea eel were investigated to reach a comprehensive evaluation of the nutritional value. Sea eel was high-fat fish which contained 21.65% to 22.84% crude fat from part one to part seven. The seventh segment exhibited a lower moisture content (51.32%) and higher contents of protein (22.76%), ash (2.46%), carbohydrate (0.80%). The third and fourth parts remained higher hardness and good water-holding capacity. Sea eel had sixteen fatty acids, and the tail possessed a higher proportion of n − 3/n − 6 polyunsaturated fatty acids. Twenty-seven lipid species were determined in sea eel, and highly unsaturated fatty acids mainly stemmed from triglycerides, while saturated fatty acids mainly existed as diglycerides. Section six and seven were primarily diglycerides and monoglycerides. According to the characteristics of different parts, it can provide support for standardized segmentation of sea eel and quality control of different parts. [ABSTRACT FROM AUTHOR]

[Display omitted] • The tail exhibited lower moisture and higher protein, ash, carbohydrate content. • The middle part of eel had higher hardness and good water-holding capacity. • The tail possessed a higher proportion of n − 3/n − 6 polyunsaturated fatty acids. • DGs and MGs bond to saturated fatty acids were primarily existed near the tail. With the diversity of consumer demands for healthy food, stricter requirements have been put forward for the product quality of sea eel (Astroconger myriaster). From the common experience, remarkable differences could be observed between different parts of the sea eel. The textures, basic nutritional compositions, and lipid compositions from seven parts of sea eel were investigated to reach a comprehensive evaluation of the nutritional value. Sea eel was high-fat fish which contained 21.65% to 22.84% crude fat from part one to part seven. The seventh segment exhibited a lower moisture content (51.32%) and higher contents of protein (22.76%), ash (2.46%), carbohydrate (0.80%). The third and fourth parts remained higher hardness and good water-holding capacity. Sea eel had sixteen fatty acids, and the tail possessed a higher proportion of n − 3/n − 6 polyunsaturated fatty acids. Twenty-seven lipid species were determined in sea eel, and highly unsaturated fatty acids mainly stemmed from triglycerides, while saturated fatty acids mainly existed as diglycerides. Section six and seven were primarily diglycerides and monoglycerides. According to the characteristics of different parts, it can provide support for standardized segmentation of sea eel and quality control of different parts. [ABSTRACT FROM AUTHOR]

Physical contact between organelles are widespread, in part to facilitate the shuttling of protein and lipid cargoes for cellular homeostasis. How do protein‐protein and protein‐lipid interactions shape organelle subdomains that constitute contact sites? The endoplasmic reticulum (ER) forms extensive contacts with multiple organelles, including lipid droplets (LDs) that are central to cellular fat storage and mobilization. Here, we focus on ER‐LD contacts that are highlighted by the conserved protein seipin, which promotes LD biogenesis and expansion. Seipin is enriched in ER tubules that form cage‐like structures around a subset of LDs. Such enrichment is strongly dependent on polyunsaturated and cyclopropane fatty acids. Based on these findings, we speculate on molecular events that lead to the formation of seipin‐positive peri‐LD cages in which protein movement is restricted. We hypothesize that asymmetric distribution of specific phospholipids distinguishes cage membrane tubules from the bulk ER. [ABSTRACT FROM AUTHOR]

Characterization of transient intermediate or transition states is crucial for the description of biomolecular folding pathways, which is, however, difficult in both experiments and computer simulations. Such transient states are typically of low population in simulation samples. Even for simple systems such as RNA hairpins, recently there are mounting debates over the existence of multiple intermediate states. In this paper, we develop a computational approach to explore the relatively low populated transition or intermediate states in biomolecular folding pathways, based on a topological data analysis tool, MAPPER, with simulation data from large-scale distributed computing. The method is inspired by the classical Morse theory in mathematics which characterizes the topology of high-dimensional shapes via some functional level sets. In this paper we exploit a conditional density filter which enables us to focus on the structures on pathways, followed by clustering analysis on its level sets, which helps separate low populated intermediates from high populated folded/unfolded structures. A successful application of this method is given on a motivating example, a RNA hairpin with GCAA tetraloop, where we are able to provide structural evidence from computer simulations on the multiple intermediate states and exhibit different pictures about unfolding and refolding pathways. The method is effective in dealing with high degree of heterogeneity in distribution, capturing structural features in multiple pathways, and being less sensitive to the distance metric than nonlinear dimensionality reduction or geometric embedding methods. The methodology described in this paper admits various implementations or extensions to incorporate more information and adapt to different settings, which thus provides a systematic tool to explore the low-density intermediate states in complex biomolecular folding systems. [ABSTRACT FROM AUTHOR]

The 3D reference interaction site model (3DRISM) is a powerful tool to study the thermodynamic and structural properties of liquids. However, for hydrophobic solutes, the inhomogeneity of the solvent density around them poses a great challenge to the 3DRISM theory. To address this issue, we have previously introduced the hydrophobic-induced density inhomogeneity theory (HI) for purely hydrophobic solutes. To further consider the complex hydrophobic solutes containing partial charges, here we propose the D2MSA closure to incorporate the short-range and long-range interactions with the D2 closure and the mean spherical approximation, respectively. We demonstrate that our new theory can compute the solvent distributions around real hydrophobic solutes in water and complex organic solvents that agree well with the explicit solvent molecular dynamics simulations. [ABSTRACT FROM AUTHOR]

Biomolecular dynamics play an important role in numerous biological processes. Markov State Models (MSMs) provide a powerful approach to study these dynamic processes by predicting long time scale dynamics based on many short molecular dynamics (MD) simulations. In an MSM, protein dynamics are modeled as a kinetic process consisting of a series of Markovian transitions between different conformational states at discrete time intervals (called "lag time"). To achieve this, a master equation must be constructed with a sufficiently long lag time to allow interstate transitions to become truly Markovian. This imposes a major challenge for MSM studies of proteins since the lag time is bound by the length of relatively short MD simulations available to estimate the frequency of transitions. Here, we show how one can employ the generalized master equation formalism to obtain an exact description of protein conformational dynamics both at short and long time scales without the time resolution restrictions imposed by the MSM lag time. Using a simple kinetic model, alanine dipeptide, and WW domain, we demonstrate that it is possible to construct these quasi-Markov State Models (qMSMs) using MD simulations that are 5–10 times shorter than those required by MSMs. These qMSMs only contain a handful of metastable states and, thus, can greatly facilitate the interpretation of mechanisms associated with protein dynamics. A qMSM opens the door to the study of conformational changes of complex biomolecules where a Markovian model with a few states is often difficult to construct due to the limited length of available MD simulations. [ABSTRACT FROM AUTHOR]

Locating the minimum free energy paths (MFEPs) between two conformational states is among the most important tasks of biomolecular simulations. For example, knowledge of the MFEP is critical for focusing the effort of unbiased simulations that are used for the construction of Markov state models to the biologically relevant regions of the system. Typically, existing path searching methods perform local sampling around the path nodes in a pre-selected collective variable (CV) space to allow a gradual downhill evolution of the path toward the MFEP. Despite the wide application of such a strategy, the gradual path evolution and the non-trivial a priori choice of CVs are also limiting its overall efficiency and automation. Here we demonstrate that non-local perpendicular sampling can be pursued to accelerate the search, provided that all nodes are reordered thereafter via a traveling-salesman scheme. Moreover, path-CVs can be computed on-the-fly and used as a coordinate system, minimizing the necessary prior knowledge about the system. Our traveling-salesman based automated path searching method achieves a 5-8 times speedup over the string method with swarms-of-trajectories for two peptide systems in vacuum and solution, making it a promising method for obtaining initial pathways when investigating functional conformational changes between a pair of structures. [ABSTRACT FROM AUTHOR]

Markov State Model (MSM) has become a popular approach to study the conformational dynamics of complex biological systems in recent years. Built upon a large number of short molecular dynamics simulation trajectories, MSM is able to predict the long time scale dynamics of complex systems. However, to achieve Markovianity, an MSM often contains hundreds or thousands of states (microstates), hindering human interpretation of the underlying system mechanism. One way to reduce the number of states is to lump kinetically similar states together and thus coarse-grain the microstates into macrostates. In this work, we introduce a probabilistic lumping algorithm, the Gibbs lumping algorithm, to assign a probability to any given kinetic lumping using the Bayesian inference. In our algorithm, the transitions among kinetically distinct macrostates are modeled by Poisson processes, which will well reflect the separation of time scales in the underlying free energy landscape of biomolecules. Furthermore, to facilitate the search for the optimal kinetic lumping (i.e., the lumped model with the highest probability), a Gibbs sampling algorithm is introduced. To demonstrate the power of our new method, we apply it to three systems: a 2D potential, alanine dipeptide, and a WW protein domain. In comparison with six other popular lumping algorithms, we show that our method can persistently produce the lumped macrostate model with the highest probability as well as the largest metastability. We anticipate that our Gibbs lumping algorithm holds great promise to be widely applied to investigate conformational changes in biological macromolecules. [ABSTRACT FROM AUTHOR]

Tobacco is an agricultural product with extremely high economic value. Manufacturers have realized that the chemical nature of fresh tobacco leaves influences the flavor properties of tobacco products. To explore the key components that cause the differences in tobacco leaves, high-precision mass spectrometry was employed for multi-omics analysis of three groups (upper, middle, and lower parts) of tobacco leaves. The results showed that the fresh leaves from different parts of Yunyan 87 tobacco plant had significant differences. It was not unique compounds that caused these differences, but more likely variations in shared compounds. The species of glycerolipids, glycerophospholipids, and fatty acyls were the most numerous, their numbers account for 76.68% of total lipids components. The number of glycerolipids accounted for 70% of the identified key lipid components, with the majority being polyunsaturated glycerolipids. It suggested that the disparities in polyunsaturated glycerolipids were the primary cause of lipids differentiation in fresh leaves from different parts. Glycerolipids are effective storage forms of carbon and energy. Carbohydrates and carbohydrate conjugates are the main products of tobacco photosynthesis. In key components, they had the highest relative abundance in the lower leaves. In addition, terpenoids, and organic oxygen compounds, all exhibit high relative abundances in the lower leaves. Amino acids, peptides, and analogues were more abundant in the upper leaves. Differences in the chemical composition of fresh tobacco will result in different sensory qualities and styles. In addition to providing insights into the differences and diversity of tobacco leaves in various parts of the plant during growth, the results present potentially rewarding information for directional cultivation of tobacco plants, grading of tobacco leaves, and optimization of resource utilization. [Display omitted] • The chemical composition of fresh tobacco is the basis of product sensory. • Tobacco underwent a multi-omics analysis via high-precision mass spectrometry. • Screening key components to analyze fresh tobacco differences further. • The study can guide tobacco cultivation and application. [ABSTRACT FROM AUTHOR]

The three-dimensional target position is vital to automatic driving, which can efficiently detect the geometrical shape of obstacles and achieve automatic environment recognition for both indoor and outdoor scenarios. In this paper, we propose an RFID 3D localization prototype based on the polarization phase model. To eliminate the impacts of tag angle, we leverage the polarization effect to obtain polarized phases. After that, we use polarized phase decomposition to get pure phases, i.e., the phase corresponding to the line of sight path. Hence, we can calculate the raw phases using the HMFCW algorithm. Finally, we can estimate the target's 3D position according to unwrapped phase information. In this way, we implement a low-cost, high-precision, easy-for-deployment 3D position solution using commercial UHF RFID hardware. The experiments show that the method can obtain effective positioning results in various environments and achieve positioning accuracy with an average error of about 9 cm. [ABSTRACT FROM AUTHOR]

As an elementary mesh quality improvement technique, smoothing has been widely used in finite element (FE) analysis. Heuristic smoothing methods and optimization-based smoothing methods are the two main smoothing types. The former is efficient. However, it operates heuristically and may create low-quality elements. In contrast, optimization-based smoothing is very effective at improving mesh quality. However, it suffers from high computational cost since it calculates the optimal position of a free node iteratively. In this paper, we present a new smoothing method. The proposed method imitates the optimization-based smoothing based on a neural network, but it calculates the optimal position of a free node straightforwardly. Hence, the proposed method is more efficient than these optimization-based smoothing methods while being comparable in terms of mesh quality. We present various testing results to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems. Lizhe Zhu et al. use a quasi Markov State Model built from extensive molecular dynamics simulations to elucidate the dynamics of RISC-mRNA interactions. Their results provide further insight on how a target nucleotide is positioned before recognition by Argonaute. [ABSTRACT FROM AUTHOR]

In this study, functional lactic acid bacteria (LAB) specific for vegetable fermentation were screened from Chinese sauerkraut brine by designing purpose media. The selected LAB strains showed excellent tolerance to high concentrations of bile salt and acids. They can also survive passage through the simulated gastric fluid and may be able to reach to the intestine. Through the performance measurements of vegetable fermentation, one of these strains, NCU116 was found to have the best fermentation ability, and further subjected to a series of analyses including identification of physiological and biochemical characteristics, 16S rDNA and pheS sequencing. Taken together, the strain NCU116 was identified as Lactobacillus plantarum subsp. plantarum. These results laid a good foundation for the fermentation research in the future. [ABSTRACT FROM AUTHOR]

Abstract: Exploring the principle and relationship of gene transcriptional regulations (TR) has been becoming a generally researched issue. So far, two major mathematical methods, ordinary differential equation (ODE) method and Boolean map (BM) method have been widely used for these purposes. It is commonly believed that simplified BMs are reasonable approximations of more realistic ODEs, and both methods may reveal qualitatively the same essential features though the dynamical details of both systems may show some differences. In this Letter we exhaustively enumerated all the 3-gene networks and many autonomous randomly constructed TR networks with more genes by using both the ODE and BM methods. In comparison we found that both methods provide practically identical results in most of cases of steady solutions. However, to our great surprise, most of network structures showing periodic cycles with the BM method possess only stationary states in ODE descriptions. These observations strongly suggest that many periodic oscillations and other complicated oscillatory states revealed by the BM rule may be related to the computational errors of variable and time discretizations and rarely have correspondence in realistic biology transcriptional regulatory circuits. [ABSTRACT FROM AUTHOR]

Abstract: Recently, self-sustained oscillations in complex networks consisting of non-oscillatory nodes have attracted great interest in diverse natural and social fields. Oscillatory genomic regulatory networks are one of the most typical examples of this kind. Given an oscillatory genomic network, it is important to reveal the central structure generating the oscillation. However, if the network consists of large numbers of genes and interactions, the oscillation generator is deeply hidden in the complicated interactions. We apply the dominant phase-advanced driving path method proposed in Qian et al. (2010) to reduce complex genomic regulatory networks to one-dimensional and unidirectionally linked network graphs where negative regulatory loops are explored to play as the central generators of the oscillations, and oscillation propagation pathways in the complex networks are clearly shown by tree branches radiating from the loops. Based on the above understanding we can control oscillations of genomic networks with high efficiency. [Copyright &y& Elsevier]

• We introduce a state-of-the-art protocol to build the MSM. • We recommend spectral oASIS to automatically select physical features. • We review applications of MSM on studying protein functional conformational changes. • We discuss the perspective of quasi-MSM and deep learning methods for MSM. Functional conformational changes of proteins can facilitate numerous biological events in cells. The Markov state model (MSM) built from molecular dynamics simulations provide a powerful approach to study them. We here introduce a protocol that is tailor-made for constructing MSMs to study the functional conformational changes of proteins. In this protocol, one of the important steps is to select proper molecular features that can collectively describe the slowest timescales of conformational changes of interest. We recommend spectral oASIS, the modified version of oASIS, as a promising approach for automatic feature selection. Recently developed deep learning methods could also serve efficient approaches for selecting features and finding collective variables. Using DNA repair enzymes and RNA polymerases as examples, we review recent applications of MSMs to elucidate molecular mechanisms of functional conformational changes. Finally, we discuss remaining challenges and future perspectives for constructing MSMs to study functional conformational changes of proteins. [ABSTRACT FROM AUTHOR]

Markov models and master equations are a powerful means of modeling dynamic processes like protein conformational changes. However, these models are often difficult to understand because of the enormous number of components and connections between them. Therefore, a variety of methods have been developed to facilitate understanding by coarse-graining these complex models. Here, we employ Bayesian model comparison to determine which of these coarse-graining methods provides the models that are most faithful to the original set of states. We find that the Bayesian agglomerative clustering engine and the hierarchical Nyström expansion graph (HNEG) typically provide the best performance. Surprisingly, the original Perron cluster cluster analysis (PCCA) method often provides the next best results, outperforming the newer PCCA+ method and the most probable paths algorithm. We also show that the differences between the models are qualitatively significant, rather than being minor shifts in the boundaries between states. The performance of the methods correlates well with the entropy of the resulting coarse-grainings, suggesting that finding states with more similar populations (i.e., avoiding low population states that may just be noise) gives better results. [ABSTRACT FROM AUTHOR]

Cross-laminated timber (CLT) is well known as an interesting technical and economical product for modern wood structures. The use of CLT for modern construction industry has become increasingly popular in particular for residential timber buildings. Analyzing the CLT behavior in high thermal environment has attracted scholars' attention. Thermal environment greatly influences the CLT properties and load bearing capacity of CLT, and the investigation can form the basis for predicting the structural response of such CLT-based structures. In the present work, the finite element method (FEM) is employed to analyze the thermal influence on the deformation of CLT. Furthermore, several factors were taken into consideration, including board layer number, hole conformation, and hole position, respectively. In order to determine the influence, several numerical models for different calculation were established. The calculation process was validated by comparing with published data. The performance is quantified by demonstrating the temperature distribution and structural deformation. [ABSTRACT FROM AUTHOR]

Melatonin MT 1 and MT 2 receptors represent attractive drug targets for the treatment of various disorders. However, the high conservation of the melatonin binding pocket has hindered the development of subtype-selective compounds. By leveraging on the recently resolved crystal structures of MT 1 and MT 2 receptors, this study aims to elucidate the structural basis of MT 2 -selectivity of a panel of isoquinolinone derivatives. Molecular modelling and ligand docking approaches were employed to predict residues involved in forming interactions with the MT 2 -selective isoquinolinones. Seven conserved residues (Asn175, His208, Trp264, Asn268, Gly271, Tyr294 and Tyr298) were selected as targets for site-directed mutagenesis. Ca2+ mobilization, cAMP inhibition, phosphorylation of extracellular signal-regulated kinase, and ligand binding assays were performed to functionally characterize the receptor mutants in transfected CHO cells. Unlike melatonin, isoquinolinones bearing a 3-methoxybenzyloxyl substituent were unaffected by alanine substitution at His208 of MT 2. Although alanine substitutions at Tyr294 or Tyr298 reduced the potency of melatonin and some isoquinolinones on MT 2 , similar mutations on MT 1 allowed five hitherto ineffective isoquinolinones to act as agonists. An isoquinolinone antagonist bearing a 4-methoxybenzyloxyl moiety turned into an agonist at MT 2 mutants with alanine substitutions at His208, Tyr294 or Tyr298. A subset of residues is apparently involved in forming a hydrophobic binding cavity to confer selectivity upon the aromatic substituent of isoquinolinone compounds. Two conserved tyrosine residues on transmembrane helix 7 may confer ligand selectivity at MT 1 and MT 2 receptors, while a conserved histidine on transmembrane helix 5 is apparently involved in receptor activation. [ABSTRACT FROM AUTHOR]