The dynamic metabolic profile of Qi-Yu-San-Long decoction in rat urine using UPLC-QTOF-MS^(E) coupled with a post-targeted screening strategy

Qi-Yu-San-Long decoction(QYSLD)is a traditional Chinese medicine that has been clinically used in the treatment of non-small-cell lung cancer(NSCLC)for more than 20 years.However,to date,metabolicrelated studies on QYSLD have not been performed.In this study,a post-targeted screening strategy based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight full information tandem mass spectrometry(UPLC-QTOF-MS^(E))was developed to identify QYSLD-related xenobiotics in rat urine.The chemical compound database of QYSLD constituents was established from previous research,and metabolites related to these compounds were predicted in combination with their possible metabolic pathways.The metabolites were identified by extracted ion chromatograms using predicted m/z values as well as retention time,excimer ions,and fragmentation behavior.Overall,85 QYSLD-related xenobiotics(20 prototype compounds and 65 metabolites)were characterized from rat urine.The main metabolic reactions and elimination features of QYSLD included oxidation,reduction,decarboxylation,hydrolysis,demethylation,glucuronidation,sulfation,methylation,deglycosylation,acetylation,and associated combination reactions.Of the identified molecules,14 prototype compounds and 58 metabolites were slowly eliminated,thus accumulating in vivo over an extended period,while five prototypes and two metabolites were present in vivo for a short duration.Furthermore,one prototype and five metabolites underwent the process of“appearing-disappearing-reappearing”in vivo.Overall,the metabolic profile and characteristics of QYSLD in rat urine were determined,which is useful in elucidating the active components of the decoction in vivo,thus providing the basis for studying its mechanism of action.

Molecular Mechanisms of Intracellular Delivery of Nanoparticles Monitored by an Enzyme‑Induced Proximity Labeling

Achieving increasingly finely targeted drug delivery to organs,tissues,cells,and even to intracellular biomacromolecules is one of the core goals of nanomedicines.As the delivery destination is refined to cellular and subcellular targets,it is essential to explore the delivery of nanomedicines at the molecular level.However,due to the lack of technical methods,the molecular mechanism of the intracellular delivery of nanomedicines remains unclear to date.Here,we develop an enzyme-induced proximity labeling technology in nanoparticles(nano-EPL)for the real-time monitoring of proteins that interact with intracellular nanomedicines.Poly(lactic-co-glycolic acid)nanoparticles coupled with horseradish peroxidase(HRP)were fabricated as a model(HRP(+)-PNPs)to evaluate the molecular mechanism of nano delivery in macrophages.By adding the labeling probe biotin-phenol and the catalytic substrate H_(2)O_(2)at different time points in cellular delivery,nano-EPL technology was validated for the real-time in situ labeling of proteins interacting with nanoparticles.Nano-EPL achieves the dynamic molecular profiling of 740 proteins to map the intracellular delivery of HRP(+)-PNPs in macrophages over time.Based on dynamic clustering analysis of these proteins,we further discovered that different organelles,including endosomes,lysosomes,the endoplasmic reticulum,and the Golgi apparatus,are involved in delivery with distinct participation timelines.More importantly,the engagement of these organelles differentially affects the drug delivery efficiency,reflecting the spatial–temporal heterogeneity of nano delivery in cells.In summary,these findings highlight a significant methodological advance toward understanding the molecular mechanisms involved in the intracellular delivery of nanomedicines.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

Numerical simulation of wheel wear evolution for heavy haul railway

The prediction of the wheel wear is a fundamental problem in heavy haul railway. A numerical methodology is introduced to simulate the wheel wear evolution of heavy haul freight car. The methodology includes the spatial coupling dynamics of vehicle and track, the three-dimensional rolling contact analysis of wheel-rail, the Specht's material wear model, and the strategy for reproducing the actual operation conditions of railway. The freight vehicle is treated as a full 3D rigid multi-body model. Every component is built detailedly and various contact interactions between parts are accurately simulated, taking into account the real clearances. The wheel-rail rolling contact calculation is carried out based on Hertz's theory and Kalker's FASTSIM algorithm. The track model is built based on field measurements. The material loss due to wear is evaluated according to the Specht's model in which the wear coefficient varies with the wear intensity. In order to exactly reproduce the actual operating conditions of railway,dynamic simulations are performed separately for all possible track conditions and running velocities in each iterative step.Dimensionless weight coefficients are introduced that determine the ratios of different cases and are obtained through site survey. For the wheel profile updating, an adaptive step strategy based on the wear depth is introduced, which can effectively improve the reliability and stability of numerical calculation. At last, the wear evolution laws are studied by the numerical model for different wheels of heavy haul freight vehicle running in curves. The results show that the wear of the front wheelset is more serious than that of the rear wheelset for one bogie, and the difference is more obvious for the outer wheels. The wear of the outer wheels is severer than that of the inner wheels. The wear of outer wheels mainly distributes near the flange and the root; while the wear of inner wheels mainly distributes around the nominal rolling circle. For the outer wheel of front wheelset of each bogie, the development of wear is gradually concentrated on the flange and the developing speed increases continually with the increase of traveled distance.

TRIGONOMETRIC CURVE MODIFICATION OF SCROLL PROFILE AND ITS EFFECT ON PERFORMANCES OF A SCROLL COMPRESSOR

A modification of central profile with trigonometric curve is proposed based on the theory of engagement of scroll compressor. General modification equations for central profile of a pair of scrolls are given and various modification patterns are discussed. The equidistant method is employed to calculate the volume of a sealed chamber and a set of general equations is represented. Modification parameters affecting geometric and dynamic property of a scroll compressor are analyzed systematically, and the relations between them are accurately determined. The condition for transforming a trigonometric curve modification into an arc-curve modification is explained. The conclusions can also be applied to other scroll fluid machines.

Computing Open-Loop Optimal Control of the q-Profile in Ramp-Up Tokamak Plasmas Using the Minimal-Surface Theory

The q-profile control problem in the ramp-up phase of plasma discharges is consid- ered in this work. The magnetic diffusion partial differential equation （PDE） models the dynamics of the poloidal magnetic flux profile, which is used in this work to formulate a PDE-constrained op-timization problem under a quasi-static assumption. The minimum surface theory and constrained numeric optimization are then applied to achieve suboptimal solutions. Since the transient dy- namics is pre-given by the minimum surface theory, then this method can dramatically accelerate the solution process. In order to be robust under external uncertainties in real implementations, PID （proportional-integral-derivative） controllers are used to force the actuators to follow the computational input trajectories. It has the potential to implement in real-time for long time discharges by combining this method with the magnetic equilibrium update.

Runtime Engine for Dynamic Profile Guided Stride Prefetching

Stride prefetching is recognized as an important technique to improve memory access performance. The prior work usually profiles and/or analyzes the program behavior offline, and uses the identified stride patterns to guide the compilation process by injecting the prefetch instructions at appropriate places. There are some researches trying to enable stride prefetching in runtime systems with online profiling, but they either cannot discover cross-procedural prefetch opportunity, or require special supports in hardware or garbage collection. In this paper, we present a prefetch engine for JVM （Java Virtual Machine）. It firstly identifies the candidate load operations during just-in-time （JIT） compilation, and then instruments the compiled code to profile the addresses of those loads. The runtime profile is collected in a trace buffer, which triggers a prefetch controller upon a protection fault. The prefetch controller analyzes the trace to discover any stride patterns, then modifies the compiled code to inject the prefetch instructions in place of the instrumentations. One of the major advantages of this engine is that, it can detect striding loads in any virtual code places for both regular and irregular code, not being limited with plain loop or procedure scopes. Actually we found the cross-procedural patterns take about 30% of all the prefetchings in the representative Java benchmarks. Another major advantage of the engine is that it has runtime overhead much smaller （the maximal is less than 4.0%） than the benefits it brings. Our evaluation with Apache Harmony JVM shows that the engine can achieve an average 6.2% speed-up with SPECJVM98 and DaCapo on Intel Pentium 4 platform, in spite of the runtime overhead.

Investigation into keyhole-weld pool dynamic behaviors based on HDR vision sensing of real-time K-TIG welding process through a steel/glass sandwich

To obtain a deep insight into keyhole tungsten inert gas welding,it is necessary to observe the dynamic behavior of the weld pool and keyhole.In this study,based on the steel/glass sandwich and high dynamic range camera,a vision system is developed and the keyhole-weld pool profiles are captured during the real-time welding process.Then,to analyze the dynamic behavior of the weld pool and keyhole,an image processing algorithm is proposed to extract the compression depth of the weld pool and the geometric parameters of the keyhole from the captured images.After considering the variations of these parameters over time,it was found that the front and rear lengths of the keyhole were dynamically adjusted internally and had opposite trends according to the real-time welding status while the length of the keyhole was in a quasi-steady state.The proposed vision-based observation method lays a solid foundation for studying the weld forming process and improving keyhole tungsten inert gas welding.

Semi-tensor product approach to networked evolutionary games

In this paper a comprehensive introduction for modeling and control of networked evolutionary games （NEGs） via semi-tensor product （STP） approach is presented. First, we review the mathematical model of an NEG, which consists of three ingredients： network graph, fundamental network game, and strategy updating rule. Three kinds of network graphs are considered, which are i） undirected graph for symmetric games; ii） directed graph for asymmetric games, and iii） d-directed graph for symmetric games with partial neighborhood information. Three kinds of fundamental evolutionary games （FEGs） are discussed, which are i） two strategies and symmetric （S-2）; ii） two strategies and asymmetric （A-2）; and iii） three strategies and symmetric （S-3）. Three strategy updating rules （SUR） are introduced, which are i） Unconditional Imitation （UI）; ii） Fermi Rule（FR）; iii） Myopic Best Response Adjustment Rule （MBRA）. First, we review the fundamental evolutionary equation （FEE） and use it to construct network profile dynamics （NPD）of NEGs. To show how the dynamics of an NEG can be modeled as a discrete time dynamics within an algebraic state space, the fundamental evolutionary equation （FEE） of each player is discussed. Using FEEs, the network strategy profile dynamics （NSPD） is built by providing efficient algorithms. Finally, we consider three more complicated NEGs： i） NEG with different length historical information, ii） NEG with multi-species, and iii） NEG with time-varying payoffs. In all the cases, formulas are provided to construct the corresponding NSPDs. Using these NSPDs, certain properties are explored. Examples are presented to demonstrate the model constructing method, analysis and control design technique, and to reveal certain dynamic behaviors of NEGs.

Trimester-specific urinary metabolome alterations associated with gestational diabetes mellitus:A study in different pregnancy stages

Gestational diabetes mellitus(GDM),a frequently-occurring disease during pregnancy,may cause some adverse healthy outcome of both mother and offspring.However,the knowledge about metabolite alterations during the pathogenesis and development process is limited.Here,a large longitudinal nontargeted metabolomics study of 195 pregnant women(64 women with subsequently developed GDM and131 healthy controls)was conducted.Each participant provided urine samples at three timepoints during early,middle and late pregnancy,respectively.The metabolic profiles of 585 urine samples(195×3)were measured by using ultra-high performance liquid chromatography coupled with Orbitrap high-resolution mass spectrometry.Among the 56 identified metabolites,the levels of eight metabolites increased and three ones decreased in the first trimester,the concentration of one metabolite increased and those of 20 decreased in the second trimester,as well as the levels of five metabolites increased and two decreased in the third trimester.After false discovery rate correction,the levels of valine and 5-acetamidovalerate in GDM group significantly increased in the first trimester,the levels of 1-methylguanine and 1,3-dihydro-(2 H)-indol-2-one significantly decreased in the second trimester and three metabolites(threonine,OH-octanedioyl-carnitine and pimelylcarnitine)increased and N-acetyltryptophan decreased in the third trimester,respectively.Six metabolites,such as pantothenic acid and threonine,had significant interaction effects between gestational stage(different trimester)and group(GDM or control).The differential metabolites were involved in“tryptophan metabolism”,“purine metabolism”,“valine,leucine and isoleucine degradation”and other pathways.The findings may provide insights into further pathogenesis study of GDM.