Characterization of the key aroma compounds in four varieties of pomegranate juice by gas chromatography-mass spectrometry(GC-MS),gas chromatography-olfactometry(GC-O),odor activity value(OAV),aroma recombination,and omission tests

P omegranate(Punica granatum L.)has attracted considerable attention in world markets due to its valuable nutrients and highly appreciated sensory properties.The aroma profiles of 4 varieties of pomegranate juice,including Dahongtian(DP),Jingpitian(JP),Luyudan(LP),and Tianhonngdan(TP),were investigated via gas chromatography-mass spectrometry(GC-MS)and gas chromatography-olfactometry(GC-O)analyses.A total of 43 volatile compounds were identified by using GC-MS.Among these compounds,16 were considered as potential aroma-active compounds as detected by GC-O.These compounds belonged to the classes of terpinenes,alcohols,and aldehydes.Eleven volatile compounds were defined as the main contributors to the overall aroma of pomegranate juice due to their high odor activity values(OAVs≥1).Aroma recombination and omission tests confirmed thatβ-myrcene,1-hexanol,and(Z)-3-hexen-1-ol were the key aroma compounds,and limonene,1-octen-3-ol,linalool,and hexanal were important aroma-active compounds in DP samples.

SNP-based identification of QTLs for thousand-grain weight and related traits in wheat 8762/Keyi 5214 DH lines

As important yield-related traits,thousand-grain weight(TGW),grain number per spike(GNS)and grain weight per spike(GWS)are crucial components of wheat production.To dissect their underlying genetic basis,a double haploid(DH)population comprised of 198 lines derived from 8762/Keyi 5214 was constructed.We then used genechip to genotype the DH population and integrated the yield-related traits TGW,GNS and GWS for QTL mapping.Finally,we obtained a total of 18942 polymorphic SNP markers and identified 41 crucial QTLs for these traits.Three stable QTLs for TGW were identified on chromosomes 2D(QTgw-2D.3 and QTgw-2D.4)and 6A(QTgw-6A.1),with additive alleles all from the parent 8762,explaining 4.81–18.67%of the phenotypic variations.Five stable QTLs for GNS on chromosomes 3D,5B,5D and 6A were identified.QGns-5D.1 was from parent 8762,while the other four QTLs were from parent Keyi 5214,explaining 5.89–7.08%of the GNS phenotypic variations.In addition,a stable GWS genetic locus QGws-4A.3 was detected from the parent 8762,which explained 6.08–6.14%of the phenotypic variations.To utilize the identified QTLs,we developed STARP markers for four important QTLs,Tgw2D.3-2,Tgw2D.4-1,Tgw6A.1 and Gns3D.1.Our results provide important basic resources and references for the identification and cloning of genes related to TGW,GNS and GWS in wheat.

Sustainable Development of Energy Systems and Climate Systems:Key Issues and Perspectives

Climate change and energy security issues are prominent challenges in current energy system management,which should be governed synergistically due to the feedback relationships between them.The“Energy Systems Management and Climate Change”Special Collection Issue in the journal of Energy Engineering provide insights into the field of energy systems management and climate change.From an extended perspective,this study discusses the key issues,research methods and models for energy system management and climate change research.Comprehensive and accurate prediction of energy supply and demand,the evaluation on the energy system resilience to climate change and the coupling methodology application of both nature and social science field maybe the frontier topics around achieving sustainable development goals of energy systems.

Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

Extreme gradient boosting algorithm based urban daily traffic index prediction model:a case study of Beijing,China

The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems.Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion.Compared with traditional short-time traffic prediction,this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index(TPI).The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation,including day of week,time period,public holiday,car usage restriction policy,special events,etc.Based on long-term historical TPI data,this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm(XGBoost).The model validation results show that the model prediction accuracy can reach higher than 90%.Compared with other prediction models,including Bayesian Ridge,Linear Regression,ElatsicNet,SVR,the XGBoost model has a better performance,and proves its superiority in large high-dimensional data sets.The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Magnetic resonance imaging of extraocular rectus muscles abnormalities in acute acquired concomitant esotropia

AIM:To investigate the difference of medial rectus(MR)and lateral rectus(LR)between acute acquired concomitant esotropia(AACE)and the healthy controls(HCs)detected by magnetic resonance imaging(MRI).METHODS:A case-control study.Eighteen subjects with AACE and eighteen HCs were enrolled.MRI scanning data were conducted in target-controlled central gaze with a 3-Tesla magnetic resonance scanner.Extraocular muscles(EOMs)were scanned in contiguous image planes 2-mm thick spanning the EOM origins to the globe equator.To form posterior partial volumes(PPVs),the LR and MR cross-sections in the image planes 8,10,12,and 14 mm posterior to the globe were summed and multiplied by the 2-mm slice thickness.The data were classified according to the right eye,left eye,dominant eye,and non-dominant eye,and the differences in mean cross-sectional area,maximum cross-sectional area,and PPVs of the MR and LR muscle in the AACE group and HCs group were compared under the above classifications respectively.RESULTS:There were no significant differences between the two groups of demographic characteristics.The mean cross-sectional area of the LR muscle was significantly greater in the AACE group than that in the HCs group in the non-dominant eyes(P=0.028).The maximum cross-sectional area of the LR muscle both in the dominant and non-dominant eye of the AACE group was significantly greater than the HCs group(P=0.009,P=0.016).For the dominant eye,the PPVs of the LR muscle were significantly greater in the AACE than that in the HCs group(P=0.013),but not in the MR muscle(P=0.698).CONCLUSION:The size and volume of muscles dominant eyes of AACE subjects change significantly to overcome binocular diplopia.The LR muscle become larger to compensate for the enhanced convergence in the AACE.

A bHLH transcription factor,CsSPT,regulates high-temperature resistance in cucumber

High-temperature stress threatens the growth and yield of crops. Basic helix-loop-helix(bHLH) transcription factors(TFs) have been shown to play important roles in regulating high-temperature resistance in plants. However, the bHLH TFs responsible for high-temperature tolerance in cucumbers have not been identified. We used transcriptome profiling to screen the high temperature-responsive candidate bHLH TFs in cucumber. Here, we found that the expression of 75 CsbHLH genes was altered under high-temperature stress. The expression of the CsSPT gene was induced by high temperatures in TT(Thermotolerant) cucumber plants. However, the Csspt mutant plants obtained by the CRISPR-Cas9 system showed severe thermosensitive symptoms, including wilted leaves with brown margins and reduced root density and cell activity.The Csspt mutant plants also exhibited elevated H_(2)O_(2) levels and down-regulated photosystem-related genes under normal conditions.Furthermore, there were high relative electrolytic leakage(REC), malondialdehyde(MDA), glutathione(GSH), and superoxide radical(O_(2)^(·-)) levels in the Csspt mutant plants, with decreased Proline content after the high-temperature treatment. Transcriptome analysis showed that the photosystem and chloroplast activities in Csspt mutant plants were extremely disrupted by the high-temperature stress compared with wildtype(WT) plants. Moreover, the plant hormone signal transduction, as well as MAPK and calcium signaling pathways were activated in Csspt mutant plants under high-temperature stress. The HSF and HSP family genes shared the same upregulated expression patterns in Csspt and WT plants under high-temperature conditions. However, most bHLH, NAC, and bZIP family genes were significantly down-regulated by heat in Csspt mutant plants. Thus, these results demonstrated that CsSPT regulated the high-temperature response by recruiting photosynthesis components, signaling pathway molecules, and transcription factors. Our results provide important insights into the heat response mechanism of CsSPT in cucumber and its potential as a target for breeding heat-resistant crops.

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

Effects of baffle position in serpentine flow channel on the performance of proton exchange membrane fuel cells

This study used a three-dimensional numerical model of a proton exchange membrane fuel cell with five types of channels:a smooth channel(Case 1);eight rectangular baffles were arranged in the upstream(Case 2),midstream(Case 3),downstream(Case 4),and the entire cathode flow channel(Case 5)to study the effects of baffle position on mass transport,power density,net power,etc.Moreover,the effects of back pressure and humidity on the voltage were investigated.Results showed that compared to smooth channels,the oxygen and water transport facilitation at the diffusion layer-channel interface were added 11.53%-20.60%and 7.81%-9.80%at 1.68 A·cm^(-2)by adding baffles.The closer the baffles were to upstream,the higher the total oxygen flux,but the lower the flux uniformity the worse the water removal.The oxygen flux of upstream baffles was 8.14%higher than that of downstream baffles,but oxygen flux uniformity decreased by 18.96%at 1.68 A·cm^(-2).The order of water removal and voltage improvement was Case 4>Case 5>Case 3>Case 2>Case 1.Net power of Case 4 was 9.87%higher than that of the smooth channel.To the Case 4,when the cell worked under low back pressure or high humidity,the voltage increments were higher.The potential increment for the back pressure of 0 atm was 0.9%higher than that of 2 atm(1 atm=101.325 kPa).The potential increment for the humidity of 100%was 7.89%higher than that of 50%.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Influence of nitrogen status on fermentation performances of non-Saccharomyces yeasts:a review

Nitrogen,one of the most crucial nutrients present in grapes and musts,plays a key role in yeast activities during alcoholic fermentation.Such influences are imposed on yeast growth and fermentation performances including the formation of secondary metabolites.Saccharomyces cerevisiae,the main yeast responsible for fermentation,has been studied extensively regarding nitrogen impacts.On the other hand,a similar study for non-Saccharomyces yeasts,whose contributions to winemaking have gradually been acknowledged,remains to be fully explored,with a few studies being reported.This review starts by discussing nitrogen impacts on non-Saccharomyces yeast growth and fermentation kinetics in different case scenarios,then proceeds to summarize the nitrogen preferences of individual yeast strains with regulation mechanisms elucidated by recent studies.Detailed discussions on the influences on the production of volatile compounds and proposed pathways therein are made,followed by future work suggested as the final section.In summarizing the nitrogen impacts on non-Saccharomyces yeasts throughout alcoholic fermentation,this review will be helpful in obtaining a more comprehensive view on these non-conventional wine yeasts in terms of nutrient requirements and corresponding volatile production.Research gaps will therefore be elucidated for future research.

Visualizing Wallerian degeneration in the corticospinal tract after sensorimotor cortex ischemia in mice

Stroke can cause Wallerian degeneration in regions outside of the brain,particularly in the corticospinal tract.To investigate the fate of major glial cells and axons within affected areas of the corticospinal tract following stroke,we induced photochemical infarction of the sensorimotor cortex leading to Wallerian degeneration along the full extent of the corticospinal tract.We first used a routine,sensitive marker of axonal injury,amyloid precursor protein,to examine Wallerian degeneration of the corticospinal tract.An antibody to amyloid precursor protein mapped exclusively to proximal axonal segments within the ischemic cortex,with no positive signal in distal parts of the corticospinal tract,at all time points.To improve visualization of Wallerian degeneration,we next utilized an orthograde virus that expresses green fluorescent protein to label the corticospinal tract and then quantitatively evaluated green fluorescent protein-expressing axons.Using this approach,we found that axonal degeneration began on day 3 post-stroke and was almost complete by 7 days after stroke.In addition,microglia mobilized and activated early,from day 7 after stroke,but did not maintain a phagocytic state over time.Meanwhile,astrocytes showed relatively delayed mobilization and a moderate response to Wallerian degeneration.Moreover,no anterograde degeneration of spinal anterior horn cells was observed in response to Wallerian degeneration of the corticospinal tract.In conclusion,our data provide evidence for dynamic,pathogenic spatiotemporal changes in major cellular components of the corticospinal tract during Wallerian degeneration.

Lotus root-like RuIr alloys with close-packed(0001)branches:Strain-driven performance for acidic water oxidation

Achieving composition tunability and structure editability of nanoalloys with high level strain may be an efficient strategy to remarkably boost catalytic performance toward oxygen evolution reaction(OER)in acidic water oxidation.Herein,lotus root-like RuIr alloys with native micro-strain were constructed by an epitaxial growth of Ru-richened hcp-(0001)branches on Ir-richened fcc-(111)seeds using a polyol thermal synthesis strategy.The resultant Ru_(60)Ir_(40) alloy shows an OER overpotential of 197 mV at 10 mA cm^(-2) and a Tafel slope of 46.59 mV dec^(-1),showing no obvious activity decay for 80 h continuous chronopotentiometry test in 0.5 M H_(2)SO_(4).The related characterizations including X-ray absorption fine structure(XAFS)spectroscopy and density functional theory(DFT)calculations show that that the remarkably improved activity of the lotus root-like alloy can be attributed to the(0001)facet-triggered strain,which can efficiently optimize the electronic band structure of the active metal and the weakening of the chemisorption of oxygen-containing substances to boost OER electrocatalysis.Therefore,this work provides a new strategy to designing a class of advanced electrocatalysts with high strain using diverse nanostructures as building materials for carbon-free clean energy conversion systems.

Preweaning period is a critical window for rumen microbial regulation of average daily gain in Holstein heifer calves

Background Rumen bacterial groups can affect growth performance,such as average daily gain(ADG),feed intake,and efficiency.The study aimed to investigate the inter-relationship of rumen bacterial composition,rumen fermentation indicators,serum indicators,and growth performance of Holstein heifer calves with different ADG.Twelve calves were chosen from a trail with 60 calves and divided into higher ADG(HADG,high pre-and post-weaning ADG,n=6)and lower ADG(LADG,low pre-and post-weaning ADG,n=6)groups to investigate differences in bacterial composition and functions and host phenotype.Results During the preweaning period,the relative abundances of propionate producers,including g_norank_f_Butyricicoccaceae,g_Pyramidobacter,and g_norank_f_norank_o_Clostridia_vadin BB60_group,were higher in HADG calves(LDA>2,P<0.05).Enrichment of these bacteria resulted in increased levels of propionate,a gluconeogenic precursor,in preweaning HADG calves(adjusted P<0.05),which consequently raised serum glucose concentrations(adjusted P<0.05).In contrast,the relative abundances of rumen bacteria in post-weaning HADG calves did not exert this effect.Moreover,no significant differences were observed in rumen fermentation parameters and serum indices between the two groups.Conclusions The findings of this study revealed that the preweaning period is the window of opportunity for rumen bacteria to regulate the ADG of calves.

Bioleaching of vanadium from stone coal vanadium ore by Bacillus mucilaginosus:Influencing factors and mechanism

Vanadium and its derivatives are used in various industries,including steel,metallurgy,pharmaceuticals,and aerospace engineering.Although China has massive reserves of stone coal resources,these resources have low grades.Therefore,the effective extraction and recovery of metallic vanadium from stone coal is an important way to realize the efficient resource utilization of stone coal vanadium ore.Herein,Bacillus mucilaginosus was selected as the leaching strain.The vanadium leaching rate reached 35.5%after 20 d of bioleaching under optimal operating conditions.The cumulative vanadium leaching rate in the contact group reached 35.5%,which was higher than that in the noncontact group(9.3%).The metabolites of B.mucilaginosus,such as oxalic,tartaric,citric,and malic acids,dominated in bioleaching,accounting for 73.8%of the vanadium leaching rate.Interestingly,during leaching,the presence of stone coal stimulated the expression of carbonic anhydrase in bacterial cells,and enzyme activity increased by 1.335-1.905 U.Enzyme activity positively promoted the production of metabolite organic acids,and total organic acid content increased by 39.31 mg·L^(-1),resulting in a reduction of 2.51 in the pH of the leaching system with stone coal.This effect favored the leaching of vanadium from stone coal.Atomic force microscopy illustrated that bacterial leaching exacerbated corrosion on the surface of stone coal beyond 10 nm.Our study provides a clear and promising strategy for exploring the bioleaching mechanism from the perspective of microbial enzyme activity and metabolites.

LociScan,a tool for screening genetic marker combinations for plant variety discrimination

To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination,it is desirable to identify the optimal marker combinations.We describe a marker combination screening model based on the genetic algorithm(GA)and implemented in a software tool,Loci Scan.Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions.Among GA parameters,an increase in population size and generation number enlarged optimization depth but also calculation workload.Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time.In comparison with two other software tools,Loci Scan accommodated missing data,reduced calculation time,and offered more fitness functions.In large datasets,the sample size of training data exerted the strongest influence on calculation time,whereas the marker size of training data showed no effect,and target marker number had limited effect on analysis speed.

Dynamic response of a thermal transistor to time-varying signals

Thermal transistor,the thermal analog of an electronic transistor,is one of the most important thermal devices for microscopic-scale heat manipulating.It is a three-terminal device,and the heat current flowing through two terminals can be largely controlled by the temperature of the third one.Dynamic response plays an important role in the application of electric devices and also thermal devices,which represents the devices’ability to treat fast varying inputs.In this paper,we systematically study two typical dynamic responses of a thermal transistor,i.e.,the response to a step-function input(a switching process)and the response to a square-wave input.The role of the length L of the control segment is carefully studied.It is revealed that when L is increased,the performance of the thermal transistor worsens badly.Both the relaxation time for the former process and the cutoff frequency for the latter one follow the power-law dependence on L quite well,which agrees with our analytical expectation.However,the detailed power exponents deviate from the expected values noticeably.This implies the violation of the conventional assumptions that we adopt.

New possibility for PET plastic recycling by a tailored hydrolytic enzyme

Plastic waste puts a huge burden on the ecosystem due to the current lack of mature recycling technology.Poly(ethylene terephthalate)(PET)is one of the most produced plastics in the world.Enzymatic decomposition holds the promise of recovering monomers from PET plastic,and the monomers can be used to regenerate new PET products.However,there are still limitations in the activity and thermal stability of the existing PET hydrolases.The recent study by Lu et al.introduced a novel PET hydrolase via machine learning-aided engineering.The obtained PET hydrolase showed excellent activity and thermal stability in the hydrolysis of PET and is capable of directly degrading large amounts of postconsumer PET products.This approach provides an effective method for recycling PET waste and is expected to improve the current state of plastic pollution worldwide.