The interaction between temperature and precipitation on the potential distribution range of Betula ermanii in the alpine treeline ecotone on the Changbai Mountain

Alpine treeline ecotones are highly sensitive to climate warming.The low temperature-determined alpine treeline is expected to shift upwards in response to global warming.However,little is known about how temperature interacts with other important factors to influence the distribution range of tree species within and beyond the alpine treeline ecotone.Hence,we used a GF-2 satellite image,along with bioclimatic and topographic variables,to develop an ensemble suitable habitat model based on the species distribution modeling algorithms in Biomod2.We investigated the distribution of suitable habitats for B.ermanii under three climate change scenarios(i.e.,low(SSP126),moderate(SSP370)and extreme(SSP585)future emission trajectories)between two consecutive time periods(i.e.,current-2055,and 2055-2085).By 2055,the potential distribution range of B.ermanii will expand under all three climate scenarios.The medium and high suitable areas will decline under SSP370 and SSP585scenarios from 2055 to 2085.Moreover,under the three climate scenarios,the uppermost altitudes of low suitable habitat will rise to 2,329 m a.s.l.,while the altitudes of medium and high suitable habitats will fall to 2,201 and2,051 m a.s.l.by 2085,respectively.Warming promotes the expansion of B.ermanii distribution range in Changbai Mountain,and this expansion will be modified by precipitation as climate warming continues.This interaction between temperature and precipitation plays a significant role in shaping the potential distribution range of B.ermanii in the alpine treeline ecotone.This study reveals the link between environmental factors,habitat distribution,and species distribution in the alpine treeline ecotone,providing valuable insights into the impacts of climate change on high-elevation vegetation,and contributing to mountain biodiversity conservation and sustainable development.

Growth monitoring of greenhouse lettuce based on a convolutional neural network

Growth-related traits,such as aboveground biomass and leaf area,are critical indicators to characterize the growth of greenhouse lettuce.Currently,nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features.In this study,a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network(CNN).Taking lettuce images as the input,a CNN model was trained to learn the relationship between images and the corresponding growth-related traits,i.e.,leaf fresh weight(LFW),leaf dry weight(LDW),and leaf area(LA).To compare the results of the CNN model,widely adopted methods were also used.The results showed that the values estimated by CNN had good agreement with the actual measurements,with R^(2) values of 0.8938,0.8910,and 0.9156 and normalized root mean square error(NRMSE)values of 26.00,22.07,and 19.94%,outperforming the compared methods for all three growth-related traits.The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno.Generalization tests were conducted by using images of Tiberius from another growing season.The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits,with R2 values of 0.9277,0.9126,and 0.9251 and NRMSE values of 22.96,37.29,and 27.60%.The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.

A Method Based on Knowledge Distillation for Fish School Stress State Recognition in Intensive Aquaculture

Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture.Recent advances have been made in fish behavior analysis based on deep learning.However,most existing methods with top performance rely on considerable memory and computational resources,which is impractical in the real-world scenario.In order to overcome the limitations of these methods,a new method based on knowledge distillation is proposed to identify the stress states of fish schools.The knowledge distillation architecture transfers additional inter-class information via a mixed relative loss function,and it forces a lightweight network(GhostNet)to mimic the soft probabilities output of a well-trained fish stress state recognition network(ResNeXt101).The fish school stress state recognition model’s accuracy is improved from 94.17%to 98.12%benefiting from the method.The proposed model has about 5.18 M parameters and requires 0.15 G FLOPs(floating-point operations)to process an image of size 224×224.Furthermore,fish behavior images are collected in a land-based factory,and a dataset is constructed and extended through flip,rotation,and color jitter augmentation techniques.The proposed method is also compared with other state-of-the-art methods.The experimental results show that the proposed model is more suitable for deployment on resource-constrained devices or real-time applications,and it is conducive for real-time monitoring of fish behavior.

Discovery of a potent and highly selective inhibitor of SIRT6 against pancreatic cancer metastasis in vivo

Pancreatic cancer,one of the most aggressive malignancies,has no effective treatment due to the lack of targets and drugs related to tumour metastasis.SIRT6 can promote the migration of pancreatic cancer and could be a potential target for antimetastasis of pancreatic cancer.However,highly selective and potency SIRT6 inhibitor that can be used in vivo is yet to be discovered.Here,we developed a noveSIRT6 allosteric inhibitor,compound 11e,with maximal inhibitory potency and an IC_(50) value of 0.98±0.13μmol/L.Moreover,compound 11e exhibited significant selectivity against other histone deacetylases(HADC1-11 and SIRT1-3)at concentrations up to 100μmol/L.The allosteric site and the molecular mechanism of inhibition were extensively elucidated by cocrystal complex structure and dynamic structural analyses.Importantly,we confirmed the antimetastatic function of such inhibitors in four pancreatic cancer cell lines as well as in two mouse models of pancreatic cancer liver metastasis.To our knowledge,this is the first study to reveal the in vivo effects of SIRT6 inhibitors on liver metastatic pancreatic cancer.It not only provides a promising lead compound for subsequent inhibitor developmentargeting SIRT6 but also provides a potential approach to address the challenge of metastasis in pancreatic cancer.

A novel SIRT6 activator ameliorates neuroinflammation and ischemic brain injury via EZH2/FOXC1 axis

Ischemic stroke is the second leading cause of death worldwide with limited medications and neuroinflammation was recognized as a critical player in the progression of stroke,but how to control the overactive neuroinflammation is still a long-standing challenge.Here,we designed a novel SIRT6 activator MDL-811 which remarkably inhibited inflammatory response in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages and primary mouse microglia,which were abolished by silencing SIRT6.RNA-seq screening identified the forkhead box C1 (Foxc1) is a key gene evoked by MDL-811stimulation and is required for the anti-inflammatory effects of MDL-811.We found MDL-811-activated SIRT6 directly interacted with enhancer of zeste homolog 2 (EZH2) and promoted deacetylation of EZH2 which could bind to the promoter of Foxc1 and upregulate its expression to modulate inflammation.Moreover,our data demonstrated that MDL-811 not only ameliorated sickness behaviors in neuroinflammatory mice induced by LPS,but also markedly reduced the brain injury in ischemic stroke mice in addition to promoting long-term functional recovery.Importantly,MDL-811 also exhibited strong anti-inflammatory effects in human monocytes isolated from ischemic stroke patients,underlying an interesting translational perspective.Taken together,MDL-811 could be an alternative therapeutic candidate for ischemic stroke and other brain disorders associated with neuroinflammation.

Mechanism of allosteric activation of SIRT6 revealed by the action of rationally designed activators

The recent discovery of activator compounds binding to an allosteric site on the NAD+-dependent protein lysine deacetylase,sirtuin 6(SIRT6)has attracted interest and presents a pharmaceutical target for aging-related and cancer diseases.However,the mechanism underlying allosteric activation of SIRT6 by the activator MDL-801 remains largely elusive because no major conformational changes are observed upon activator binding.By combining molecular dynamics simulations with biochemical and kinetic analyses of wild-type SIRT6 and its variant M136 A,we show that conformational rotation of 2-methyl-4-fluoro-5-bromo substituent on the right phenyl ring(R-ring)of MDL-801,which uncovers previously unseen hydrophobic interactions,contributes to increased activating deacetylation activity of SIRT6.This hypothesis is further supported by the two newly synthesized MDL-801 derivatives through the removal of the 5-Br atom on the R-ring(MDL-801-D1)or the restraint of the rotation of the R-ring(MDL-801-D2).We further propose that the 5-Br atom serves as an allosteric driver that controls the ligand allosteric efficacy.Our study highlights the effect of allosteric enzyme catalytic activity by activator binding and provides a rational approach for enhancing deacetylation activity.

Identification of an allosteric hotspot for additive activation of PPARγ in antidiabetic effects

Thiazolidinediones(TZDs),such as rosiglitazone(RSG),which activates peroxisome proliferator activated receptor-y(PPARy),are a potent class of oral antidiabetic agents with good durability.However,the clinical use of TZDs is challenging because of their side effects,including weight gain and hepatotoxicity.Here,we found that bavachinin(BVC),a lead natural product,additively activates PPARγ with lowdose RSG to preserve the maximum antidiabetic effects while reducing weight gain and hepatotoxicity in db/db mice caused by RSG monotherapy.Structural and biochemical assays demonstrated that an unexplored hotspot around Met329 and Ser332 in helix 5 is triggered by BVC cobinding to RSG-bound PPARy,thereby allosterically stabilizing the active state of the activation-function 2 motif responsible for additive activation with RSG.Based on this hotspot,we discovered a series of new classes of allosteric agonists inducing the activity of TZDs in the same manner as BVC.Together,our data illustrate that the hotspot of PPARγ is druggable for the discovery of new allosteric synergists,and the combination thera py of allosteric synergists and TZD drugs may provide a potential alternative approach to the treatment of type 2 diabetes mellitus.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells

SIRT6 belongs to the conserved NAD^(+)-dependent deacetylase superfamily and mediates multiple biological and pathological processes.Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics,which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases.Here,developing a reversed allosteric strategy Allo Reverse,we identified a cryptic allosteric site,Pocket Z,which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD^(+).Based on Pocket Z,we discovered an SIRT6 allosteric inhibitor named JYQ-42.JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation,with an IC50 of 2.33μmol/L.JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production.JYQ-42,to our knowledge,is the most potent and selective allosteric SIRT6 inhibitor.This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.

Purpurolide C-based microneedle promotes macrophage-mediated diabetic wound healing via inhibiting TLR4-MD2 dimerization and MYD88 phosphorylation

Delayed wound healing in diabetes is a global challenge,and the development of related drugs is a clinical problem to be solved.In this study,purpurolide C(PC),a small-molecule secondary metabolite of the endophytic fungus Penicillium purpurogenum,was found to promote diabetic wound healing.To investigate the key regulation targets of PC,in vitro RNA-seq,molecular docking calcula-tions,TLR4-MD2 dimerization SDS-PAGE detection,and surface plasmon resonance(SPR)were per-formed,indicating that PC inhibited inflammatory macrophage activation by inhibiting both TLR4-MD2 dimerization and MYD88 phosphorylation.Tlr4 knockout in vivo attenuated the promotion effect of PC on wound healing.Furthermore,a delivery system consisting of macrophage liposome and GelMA-based microneedle patches combined with PC(PC@MLIP MN)was developed,which overcame the poor water solubility and weak skin permeability of PC,so that successfully punctured the skin and delivered PC to local tissues,and accurately regulated macrophage polarization in diabetic wound management.Overall,PC is an anti-inflammatory small molecule compound with a well-defined structure and dualtarget regulation,and the PC@MLIP MN is a promising novel biomaterial for the management of diabetic wound.

A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture

Ammonia nitrogen is one of the key parameters in determining the aquaculture water quality condition in pond.The high level of ammonia nitrogen is likely to cause deterioration of water quality and mass death of cultured subjects.Therefore,accurate detection of the cultured water ammonia nitrogen content is crucially important for aquaculture management.While,at present,the accuracy of equipment for measuring the ammonia nitrogen content of aquaculture water in real time cannot meet the requirements for aquaculture.In this paper,the soft computing method is firstly proposed to predict the ammonia nitrogen content in aquaculture water in real time.This method includes empirical mode decomposition(EMD),improved particle swarm optimization(IPSO)and extreme learning machine(ELM).To evaluate the performance of the soft computing techniques,three different statistic indicators were used,including root mean square error(RMSE),the mean absolute error(MAE),and the mean absolute percentage error(MAPE)to compare three artificial soft computing methods.Results showed that the EMD-IPSO-ELM soft computing method showed the best performance among other studied methods in the ammonia nitrogen real time prediction.The EMD-IPSO-ELM model provides moderately and roughly accurately real time prediction value of ammonia nitrogen in aquaculture water.

Rhodauricanol A, an analgesic diterpenoid with an unprecedented5/6/5/7 tetracyclic system featuring a unique16-oxa-tetracyclo[11.2.1.0^(1,5).0^(7,13)]hexadecane core from Rhododendron dauricum

A novel diterpenoid with an unprecedented 5/6/5/7 tetracyclic system, rhodauricanol A(1), five new grayanane-derived diterpenoids, dauricanols A-E(2-6), and five known ones(7-11) were isolated from the flowers of Rhododendron dauricum. Rhodauricanol A(1) possesses a unique 5/6/5/7 tetracyclic ring system featuring a 16-oxa-tetracyclo[11.2.1.0^(1,5).0^(7,13)]hexadecane core. Dauricanols A-C(2-4) are the first1,3-dioxolane conjugates of grayanane diterpenoids and 5-hydroxymethylfurfural and vanillin, respectively, and dauricanols D(5) and E(6) represent the first examples of 6-deoxy-1,5-seco-grayanane diterpenoids. Their structures were determined by spectroscopic methods, quantum chemical calculation including ^(13)C NMR-DP4+ analysis and ECD calculation, and single-crystal X-ray diffraction analysis. Plausible biosynthetic pathways for 1-4 were proposed. All the isolates showed significant analgesic activities,and dauricanols B(3) and C(4) showed more potent analgesic activities than the positive control, morphine.

Meta-analysis in the production chain of aquaculture: A review

Meta-analysis is a statistical analysis of the data obtained from multiple studies and pro-vides a quantitative synthesis of research results.It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known.However,due to the complexity of the environment and problems associated with the use of model in aquaculture,it remain few guidelines for the design,implementation or interpretation of meta-analysis in the field of aquaculture.Here,we first briefly reviewed the history of meta-analysis,then summarized the applications of meta-analysis in terms of major procedures,standards,and methods.Next,we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality.Overall,there is a large room for improving yield along the production chain.Large contributions can be found in breeding,feed,and farm management.For example,improving breeding can increase yield by 5.6%to 49%,depending on fish species and type of improvements.This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture.Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.