Production traits and fertility of reciprocal hybrids between Argopecten irradians irradians and A.i.concentricus

The two bay scallop subspecies,Argopecten irradians irradians(NN)and A.i.concentricus(SS),are fast growing and major cultured bivalves in China.However,their relatively small sizes and decreasing production traits caused by long-term inbreeding have been major concerns to the industry in the last two decades.Hybridization between the two bay scallop subspecies may provide a new approach to breed a new variety with superior production traits for the industry.For this end,in this study,we hybridized the two bay scallop subspecies in order to obtain a new strain that incorporates the genes of both subspecies.No significant difference was found in fertilization rate,hatching rate and metamorphosis rate between the purebred and crossbred cohorts(NN♀×SS♂,denoted as NS;SS♀×NN♂,denoted as SN).Both mating strategy(intra-vs.inter-population crosses)and egg origin had significant effects on growth and survival at the larval stage.Heterosis was observed in the crossbred and was more pronounced in older stages.Genetic diversity of the reciprocal hybrids,especially that of SN,was increased compared with the purebred cohorts.Almost all hybrids were completely fertile and able to reproduce by selffertilization or by backcrossing with either parent.Apparently,male sterile individuals whose gonads were fully occupied by the ovary part at mature stage were found in the hybrids for the first time.The hybrids,especially SN,may provide precious germplasm resources for the production of ternary hybrids with the Peruvian scallop,A.purpuratus.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.Nevertheless,the conventional"trial and error"method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive.Fortunately,the advancement of machine learning brings new opportunities for electrocatalysts discovery and design.By analyzing experimental and theoretical data,machine learning can effectively predict their hydrogen evolution reaction(HER)performance.This review summarizes recent developments in machine learning for low-dimensional electrocatalysts,including zero-dimension nanoparticles and nanoclusters,one-dimensional nanotubes and nanowires,two-dimensional nanosheets,as well as other electrocatalysts.In particular,the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted.Finally,the future directions and perspectives for machine learning in electrocatalysis are discussed,emphasizing the potential for machine learning to accelerate electrocatalyst discovery,optimize their performance,and provide new insights into electrocatalytic mechanisms.Overall,this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

Effects of acute and chronic thermal stress on survival,apoptosis,and transcriptional responses of Scapharca broughtonii

Ocean warming is altering the habitats of marine invertebrates,which has resulted in an increased physiological stress to marine molluscs,especially those intertidal bivalves,such as the ark shell Scapharca broughtonii.We investigated the physiological and transcriptional responses of ark shells to acute and chronic thermal stress results showed that at 33℃,a significantly higher cumulative mortality(55.7%)occurred under acute thermal stress than chronic thermal stress.The apoptosis rate of hemocytes was sustained at higher levels and the necrosis rate was increased significantly in a time-dependent manner under acute thermal stress.However,under chronic thermal stress,the apoptosis and necrosis rates exhibited similar change trends:a rapid increase followed by a gradual decline and sustained at a relatively high level until the end of the experiment.The expressions of heat shock protein genes(HSP20 and HSP90),apoptosis-related genes(TRAF6,GRP78,NIX,and Casp-3),antioxidative-related genes(GST and MRP)and cellular detoxification-related genes(HbⅡB,NOS-1,HO-1,and ENO-1)were upregulated significantly under both acute and chronic thermal stresses.These results demonstrated that the anti-apoptotic system,antioxidant defense system,cellular detoxification system,and heat shock proteins(HSPs)played vital roles for ark shells in response to thermal stress.As acute thermal stress can result in irreversible damage to marine molluscs,it is thus advised that chronic thermal stress should be used to select thermal-resistant ark shell strains.

Cerium-modified Ni-La2O3/ZrO2 for CO2 methanation

The key point in CO2 methanation is to improve the activity at low temperature and the stability.For this purpose,a new cerium-modified Ni-La2O3/ZrO2 catalyst was prepared using La1-xCexNiO3/ZrO2 with perovskite phase as the precursor,which was obtained by citrate complexation combined with an impregnation method.The resulting catalyst was characterized through Nitrogen adsorption and desorption,X-ray diffraction (XRD),Transmission electron microscopy (TEM),Hydrogen temperature programmed reduction (H2-TPR),Temperature-programmed desorption of CO2 (CO2-TPD) and that of H2 (H2-TPD),and X-ray photoelectron spectroscopy (XPS) techniques,and the catalytic performances for CO2 methanation was investigated.Cerium modification could improve the effective activation of CO2,thus enhancing the activity at low temperature for CO2 methanation.The metal Ni nanoparticles prepared using this method were highly dispersed and showed excellent resistance to sintering,leading to very good stability,which could be attributed to the following:Ni nanoparticles could be confined by cerium-modified La2O3;La2O3could be confined by the cerium ions at the La2O3/ZrO2 interface;and the cerium ions were confined by ZrO2.

Novel Pt-Ni Bimetallic Catalysts Pt(Ni)-LaFeO_3/SiO_2 via Lattice Atomic-Confined Reduction for Highly Efficient Isobutane Dehydrogenation

In this study, a series of novel Pt-Ni bimetallic catalysts supported on LaFeO_3/SiO_2 with different amounts of Ni were prepared by the lattice atomic-confined reduction of LaFe_(1-x)(Ni, Pt)_xO_3/SiO_2 perovskite precursors and applied in isobutane dehydrogenation to isobutene reaction. The catalysts were characterized by X-ray diffraction, H_2-temperature-programmed reduction, Brunauer-Emmett-Teller analysis, transmission electron microscopy, energy dispersive X-ray, CO chemisorption, X-ray photoelectron spectroscopy, and thermogravimetric analysis. The as-synthesized Pt-Ni bimetallic catalysts possessed smaller most probable particle size with tunable Pt-Ni interaction, depending on the Ni content. The catalyst with Ni content of 3.0 wt% showed excellent activity and stability(the isobutane conversion and isobutene selectivity remained at about 38% and 92%, respectively, after 310 min) for the isobutane dehydrogenation reaction. It also provided approximately six times turnover frequency of the catalyst without Ni. The excellent activity and stability of the 3.0 wt% Ni-containing catalyst can be attributed to its small metal nanoparticles with high dispersion and suitable Pt-Ni interaction. Moreover, the Pt(Ni)-LaFeO_3/SiO_2 catalyst with Ni content of 3.0 wt% had been run for more than 35 h without obvious loss of activity,indicating its long-term stability, and the decrease in the Pt-Ni interaction that accompanied the formation of the FeNi alloy phase was thought to be responsible for the slight decrease in activity.

Rough Set Theory over Fuzzy Lattices

Rough set theory, proposed by Pawlak in 1982, is a tool for dealing with uncertainty and vagueness aspects of knowledge model. The main idea of rough sets corresponds to the lower and upper approximations based on equivalence relations. This paper studies the rough set and its extension. In our talk, we present a linear algebra approach to rough set and its extension, give an equivalent definition of the lower and upper approximations of rough set based on the characteristic function of sets, and then we explain the lower and upper approximations as the colinear map and linear map of sets, respectively. Finally, we define the rough sets over fuzzy lattices, which cover the rough set and fuzzy rough set,and the independent axiomatic systems are constructed to characterize the lower and upper approximations of rough set over fuzzy lattices,respectively,based on inner and outer products. The axiomatic systems unify the axiomization of Pawlak’s rough sets and fuzzy rough sets.

Rhizoma Drynariae-derived nanovesicles reverse osteoporosis by potentiating osteogenic differentiation of human bone marrow mesenchymal stem cells via targeting ERα signaling

Although various anti-osteoporosis drugs are available,the limitations of these therapies,including drug resistance and collateral responses,require the development of novel anti-osteoporosis agents.Rhizoma Drynariae displays a promising anti-osteoporosis effect,while the effective component and mechanism remain unclear.Here,we revealed the therapeutic potential of Rhizoma Drynariae-derived nanovesicles(RDNVs)for postmenopausal osteoporosis and demonstrated that RDNVs potentiated osteogenic differentiation of human bone marrow mesenchymal stem cells(hBMSCs)by targeting estrogen receptor-alpha(ERα).RDNVs,a natural product isolated from fresh Rhizoma Drynariae root juice by differential ultracentrifugation,exhibited potent bone tissue-targeting activity and anti-osteoporosis efficacy in an ovariectomized mouse model.RDNVs,effectively internalized by hBMSCs,enhanced proliferation and ERαexpression levels of hBMSC,and promoted osteogenic differentiation and bone formation.Mechanistically,via the ERαsignaling pathway,RDNVs facilitated mRNA and protein expression of bone morphogenetic protein 2 and runt-related transcription factor 2 in hBMSCs,which are involved in regulating osteogenic differentiation.Further analysis revealed that naringin,existing in RDNVs,was the active component targeting ERαin the osteogenic effect.Taken together,our study identified that naringin in RDNVs displays exciting bone tissue-targeting activity to reverse osteoporosis by promoting hBMSCs proliferation and osteogenic differentiation through estrogen-like effects.