Partial function prediction of sulfate-reducing bacterial community from the rhizospheres of two typical coastal wetland plants in China

Sulfate-reducing bacteria(SRB)are ubiquitous anaerobic microorganisms that play signifi cant roles in the global biogeochemical cycle.Coastal wetlands,one of the major habitats of SRB,exhibit high sulfate-reducing activity and thus play signifi cant roles in organic carbon remineralization,benthic geochemical action,and plant-microbe interactions.Recent studies have provided credible evidence that the functional rather than the taxonomic composition of microbes responds more closely to environmental factors.Therefore,in this study,functional gene prediction based on PacBio single molecular real-time sequencing of 16S rDNA was applied to determine the sulfate-reducing and organic substrate-decomposing activities of SRB in the rhizospheres of two typical coastal wetland plants in North and South China:Zostera japonica and Scirpus mariqueter.To this end,some physicochemical characteristics of the sediments as well as the phylogenetic structure,community composition,diversity,and proportions of several functional genes of the SRB in the two plant rhizospheres were analyzed.The Z.japonic a meadow had a higher dissimilatory sulfate reduction capability than the S.mariqueter-comprising saltmarsh,owing to its larger proportion of SRB in the microbial community,larger proportions of functional genes involved in dissimilatory sulfate reduction,and the stronger ability of the SRB to degrade organic substrates completely.This study confi rmed the feasibility of applying microbial community function prediction in research on the metabolic features of SRB,which will be helpful for gaining new knowledge of the biogeochemical and ecological roles of these bacteria in coastal wetlands.

In silico protein function prediction:the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes,rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation.Within the context of protein research,an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings.Due to the exorbitant costs and limited throughput inherent in experimental investigations,computational models offer a promising alternative to accelerate protein function annotation.In recent years,protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks.This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction.In this review,we elucidate the historical evolution and research paradigms of computational methods for predicting protein function.Subsequently,we summarize the progress in protein and molecule representation as well as feature extraction techniques.Furthermore,we assess the performance of machine learning-based algorithms across various objectives in protein function prediction,thereby offering a comprehensive perspective on the progress within this field.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.

QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially.However,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems operate.Here,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)numbers.QAUST uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence motifs.These three pieces of information are combined by consensus averaging to make the final prediction.Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark set.The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading.We further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

Effect of Bacillus amyloliquefaciens and Bacillus subtilis on fermentation, dynamics of bacterial community and their functional shifts of whole-plant corn silage

Background:Bacillus amyloliquefaciens(BA)and Bacillus subtilis(BS)are usually used as feed supplements directly or bacterial inoculants in biological feeds for animals.However,few research have reported the effects of BA and BS on fermentation characteristics and bacterial community successions of whole-plant corn silage during ensiling.If the BA and BS inoculants have positive effects on silages,then they could not only improve fermentation characteristics,but also deliver BA or BS viable cells to ruminants,which would play its probiotic effect.Therefore,the objectives of this study were to investigate the effects of BA and BS on the fermentation,chemical characteristics,bacterial community and their metabolic pathway of whole-plant corn silage.Results:Freshly chopped whole-plant corn was inoculated without or with BA and BS,respectively,and ensiled for1,3,7,14 and 60 d.Results showed that BA and BS inoculations increased lactic acid concentrations of whole-plant corn silages compared with control,and BA inoculation decreased acetic acid concentrations,whereas BS inoculation decreased fiber contents and increased crude protein(CP)content.Higher water-soluble carbohydrate contents and lower starch contents were observed in BA-and BS-inoculated silages compared with that in control.The decreased CP content and increased non-protein nitrogen content were observed in BA-inoculated silage,which was consistent with the higher amino acid metabolism abundances observed in BA-inoculated silage.In addition,it was noteworthy that BA and BS inoculations increased the metabolism of cofactors and vitamins,and decreased the relative abundances of drug resistance:antimicrobial pathways.We also found that the bacterial metabolism pathways were clearly separated into three clusters based on the ensiling times of whole-plant corn silage in the present study.There were no significant differences in bacterial community compositions among the three groups during ensiling.However,BA and BS inoculations decreased the relative abundances of undesirable bacteria such as Acetobacter and Acinetobacter.Conclusion:Our findings suggested that the BS strain was more suitable as silage inoculants than the BA strain in whole-plant corn silage in this study.

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

Incremental multivariable predictive functional control and its application in a gas fractionation unit

The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

Predictive functional control of integrating process based on impulse response

The predictive model is built according to the characteristics of the impulse response of integrating process. In order to eliminate the permanent offset between the setpoint and the process output in the presence of the load disturbance, a novel error compensation method is proposed. Then predictive functional control of integrating process is designed. The method given generates a simple control structure, which can significandy reduce online computation. Furthermore, the tuning of the controller is fairly straightforward. Simulation results indicate that the designed control system is relatively robust to the parameters variation of the process.

Time function of surface subsidence based on Harris model in mined-out area

The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve model in consideration of the shortage of current surface subsidence time functions. By analyzing the characteristics of the new time function, we found that it could meet the dynamic process, the velocity change process and the acceleration change process during surface subsidence. Then its rationality had been verified through project cases. The results show that the proposed time function model can give a good reflection of the regularity of surface subsidence in mined-out area and can accurately predict surface subsidence. And the prediction data of the model are a little greater than measured data on condition of proper measured data quantity, which is safety in the engineering. This model provides a new method for the analysis of surface subsidence in mined-out area and reference for future prediction, and it is valuable to engineering application.

Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation

In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)

Active disturbance rejected predictive functional control for space vehicles with RCS

Reaction control system(RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states(off/on). Along with inevitable internal uncertainties and external disturbances in practice, this inherent nonlinear character always hinders space vehicles autopilot from pursuing precise tracking performance. Compared to most of pre-existing methodologies that passively suppress the uncertainties and disturbances, a design based on predictive functional control(PFC) and generalized extended state observer(GESO) is firstly proposed for three-axis RCS control system to actively reject that with no requirement for additional fuel consumption. To obtain a high fidelity predictive model on which the performance of PFC greatly depends, the nonlinear coupling multiple-input multiple-output(MIMO) flight dynamics model is parameterized as a state-dependent coefficient form. And based on that, a MIMO PFC algorithm in state space domain for a plant of arbitrary orders is deduced in this paper.The internal uncertainties and external disturbances are lumped as a total disturbance, which is estimated and cancelled timely to further enhance the robustness. The continuous control command synthesised by above controller-rejector tandem is finally modulated by pulse width pulse frequency modulator(PWPF) to on-off signals to meet RCS requirement. The robustness and feasibility of the proposed design are validated by a series of performance comparison simulations with some prominent methods in the presence of significant perturbations and disturbances, as well as measurement noise.

Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall

Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.

Rheological Behaviour for Polymer Melts and Concentrated Solutions Part Ⅲ: A New Multiple Entanglement Model to Predict the Dependence of Linear Viscoelastic Function (η_0, Ψ_(10)~0,η_(ext)~0) on the Ranges of Primary Molecular Weights and th

It is shown theoretically that the viscoelasticity of polymer melts is determined by three combining factorst they are the primary molecular weight and its distribution, the number of entanglement sites on polymer chain and the sequence distribution of constituent chains in entanglement spacings. A unified quantity for the three combing factors is the average constrained dimensional number of constituent chains in the long entanglement spacings (v). A new relation of v to the primary molecular weight and the number of testing polymers were derived from the multiple entanglement and reptation model, and a new method for determining v was proposed. The dependences of linear viscoelastic functions on the primary molecular weight and its distribution were derived by the statistical method. When Mn=6Me to 18 Me, the values of (v) can range from 3.33 to 3.70. Their values are in a good agreement with the experiment data, and it can slightjy vary with the different species of polymers and the different ranges of molecular weight of polymers

Structure elucidation and in vitro rat intestinal fermentation properties of a novel sulfated glucogalactan from Porphyra haitanensis

This study was to investigate the structure and rat fecal microbial fermentation properties of a polysaccharide fraction(PHP2)isolated from the red marine alga Porphyra haitanensis.PHP2 was characterized as a sulfated glucogalactan,with a hypothetical backbone structure of→4)Gα(1→6)G4 Sβ(1→4)Glc(1→and a side chain of Man(1→6)Glc.PHP2 had an irregular spherical chain conformation.The 16 S r RNA sequence analysis revealed that PHP2 modulated the rat fecal micro-flora composition,with a similar effect to inulin,changing the dominant genus(Lactobacillus and Escherichia-Shigella)and promoting the growth of organisms that degrade sulfur-containing polysaccharides,such as Desulfovibrio,Ruminococcaceae_UCG-005,and Ruminococcus_2.PHP2 can promote production of acetic,propionic and butyric acid by rat fecal micro-flora.Prediction of metabolic function suggested that PHP2 could modulate cholesterol metabolism.The sulfated glucogalactan fermentation behavior may be associated with its monosaccharide composition,chain branching and chain conformation.PHP2 appeared to have considerable potential as functional food,and was associated with sulfur-containing polysaccharides in general.

Additive predictions of aboveground stand biomass in commercial logs and harvest residues for rotation age Pinus radiata plantations in New South Wales,Australia

Two systems of additive equations were developed to predict aboveground stand level biomass in log products and harvest residue from routinely measured or predicted stand variables for Pinus radiata plantations in New South Wales,Australia.These plantations were managed under three thinning regimes or stand types before clear-felling at rotation age by cut-to-length harvesters to produce sawlogs and pulpwood.The residue material following a clear-fell operation mainly consisted of stumps,branches and treetops,short off-cut and waste sections due to stem deformity,defects,damage and breakage.One system of equations did not include dummy variables for stand types in the model specification and was intended for more general use in plantations where stand density management regimes were not the same as the stand types in our study.The other system that incorporated dummy variables was for stand type-specific applications.Both systems of equations were estimated using 61 plot-based estimates of biomass in commercial logs and residue components that were derived from systems of equations developed in situ for predicting the product and residue biomass of individual trees.To cater for all practical applications,two sets of parameters were estimated for each system of equations for predicting component and total aboveground stand biomass in fresh and dry weight respectively.The two sets of parameters for the system of equations without dummy variables were jointly estimated to improve statistical efficiency in parameter estimation.The predictive performances of the two systems of equations were benchmarked through a leave-one-plot-out cross validation procedure.They were generally superior to the performance of an alternative two-stage approach that combined an additive system for major components with an allocative system for sub-components.As using forest harvest residue biomass for bioenergy has increasingly become an integrated part of forestry,reliable estimates of product and residue biomass will assist harvest and management planning for clear-fell operations that integrate cut-to-length log production with residue harvesting.

DFT Predictions on Structures and Stabilities of Eleven-vertex nido- and closo-Heteroboranes

Based on the octadecahedron of eleven-vertex closo-borane, the eleven-vertex closo-heteroborane was suggested with nonmetallic atoms instead of the different nonequivalent boron, and the stabilities were predicted at G96PW91/6-31＋G（3d,2p） level. The small heteroatoms, C, N, O, preferentially occupy vertex 2 with the absolutely lowest relative energy to form the high stabilization closo-heteroboranes. They cap four-membered rings to satisfy the geometrical demand of short B--Z bonds. The electron attractions from the vicinal boron atoms make the frameworks shrink. Differently, Si and Ge preferentially substitute for boron at vertex 1 with six tight B--Z bonds and form stabilized molecules. P, As, S, and Se tend to occupy vertex 4 and the optimized structures belong to the nido configura- tions. In contrast to high electronegative heteroatoms, S and Se transfer less negative charges to framework and the electropositive heteroatoms, Si and Ge transfer more negative charges to framework to form the delocalization structures. The HOMO-LUMO gaps show that most of predicted clusters possess chemical stabilities. The substitutions of heteroatoms for boron atoms in eleven-vertex closo-heteroboranes are consistent with the topological charge stabilization rule proposed by Gimarc.

FUZZY APPROACHING SET AND FUZZYAPPROACHING FUNCTIONAL MAPPING

By establishing the concepts of fuzzy approaching set and fuzzy approaching functional mapping and making research on them, a new method for time series prediction is introduced.

Anatomical changes in the somatosensory system after large sensory loss predict strategies to promote functional recovery after spinal cord injury

Among cases of spinal cord injury are injuries involving the dorsal column in the cervical spinal cord that interrupt the major cutaneous afferents from the hand to the cuneate nucleus（Cu）in the brainstem.Deprivation of touch and proprioceptive inputs consequently impair skilled hand use.

iORbase:A database for the prediction of the structures andfunctions of insect olfactory receptors

Insect olfactory receptors(iORs)with atypical 7-transmembrane domains,unlike Chordata olfactory receptors,are not in the GPCR protein family.iORs selectively bind to volatile ligands in the environment and affect essential insect behaviors.In this study,we constructed a new platform(iORbase,https://www.iorbase.com)for the structural and functional analysis of iORs based on a combined algorithm for gene annotation and protein structure prediction.Moreover,it provides the option to calculate the binding affinities and binding residues between iORs and pheromone molecules by virtual screening of docking.Furthermore,iORbase supports the automatic structural and functional prediction of user-submitted iORs or pheromones.iORbase contains the well-analyzed results of approximately 6000 iORs and their 3D protein structures identified from 59 insect species and 2077 insect pheromones from the literature,as well as approximately 12 million pairs of simulated interactions between functional iORs and pheromones.We also built 4 online modules,iORPDB,iInteraction,iModelTM,and iOdorTool to easily retrieve and visualize the 3D structures and interactions.iORbase can help greatly improve the experimental efficiency and success rate,identify new insecticide targets,or develop electronic nose technology.This study will shed light on the olfactory recognition mechanism and evolutionary characteristics from the perspectives of omics and macroevolution.

Response of fungal communities to afforestation and its indication for forest restoration

Soil fungi in forest ecosystems have great potential to enhance host plant growth and systemic ecological functions and services.Reforestation at Saihanba Mechanized Forest Farm,the world's largest artificial plantation,has been integral to global forest ecosystem preservation since the 1950s.To better assess the ecological effects of soil microbiology after afforestation,fungal diversity and community structure(using Illumina sequencing)from forests dominated by Larix gmelinii var.principis-rupprechtii,Pinus sylvestris var.mongolica and Picea asperata,and from grassland were surveyed.In total,4,540 operational taxonomic units(OTUs)were identified,with Mortierella and Solicoccozyma being the dominant genera of grassland soil and Inocybe,Cortinarius,Piloderma,Tomentella,Sebacina,Hygrophorus and Saitozyma dominating the plantation soil.Principle coordinate analysis(PCoA)and co-occurrence networks revealed differences in fungal structure after afforestation.Significantly,more symbiotroph guilds were dominated by ectomycorrhizal fungi in plantations under the prediction of FUNGuild.The community composition and diversity of soil fungi were significantly influenced by pH via redundancy analysis(RDA)and the Mantel test(p<0.01).This finding emphasizes that soil pH has a strong effect on the transition of fungal communities and functional taxa from grassland to plantation,providing a novel indicator for forest restoration.