Measurements of argon metastable density using the tunable diode laser absorption spectroscopy in Ar and Ar/O2

Densities of Ar metastable states 1s5 and 1s3 are measured by using the tunable diode laser absorption spectroscopy（TDLAS） in Ar and Ar/O2 mixture dual-frequency capacitively coupled plasma（DF-CCP）. We investigate the effects of high-frequency（HF, 60 MHz） power, low-frequency（LF, 2 MHz） power, and working pressure on the density of Ar metastable states for three different gas components（0%, 5%, and 10% oxygen mixed in argon）. The dependence of Ar metastable state density on the oxygen content is also studied at different working pressures. It is found that densities of Ar metastable states in discharges with different gas components exhibit different behaviors as HF power increases. With the increase of HF power, the metastable density increases rapidly at the initial stage, and then tends to be saturated at a higher HF power. With a small fraction（5% or 10%） of oxygen added in argon plasma, a similar change of the Ar metastable density with HF power can be observed, but the metastable density is saturated at a higher HF power than in the pure argon discharge. In the DF-CCP, the metastable density is found to be higher than in a single frequency discharge, and has weak dependence on LF power. As working pressure increases, the metastable state density first increases and then decreases,and the pressure value, at which the density maximum occurs, decreases with oxygen content increasing. Besides, adding a small fraction of oxygen into argon plasma will significantly dwindle the metastable state density as a result of quenching loss by oxygen molecules.

Fundamental study towards a better understanding of low pressure radio-frequency plasmas for industrial applications

Two classic radio-frequency(RF) plasmas, i.e., the capacitively and the inductively coupled plasmas(CCP and ICP),are widely employed in material processing, e.g., etching and thin film deposition, etc. Since RF plasmas are usually operated in particular circumstances, e.g., low pressures(m Torr-Torr), high-frequency electric field(13.56 MHz-200 MHz),reactive feedstock gases, diverse reactor configurations, etc., a variety of physical phenomena, e.g., electron resonance heating, discharge mode transitions, striated structures, standing wave effects, etc., arise. These physical effects could significantly influence plasma-based material processing. Therefore, understanding the fundamental processes of RF plasma is not only of fundamental interest, but also of practical significance for the improvement of the performance of the plasma sources. In this article, we review the major progresses that have been achieved in the fundamental study on the RF plasmas,and the topics include 1) electron heating mechanism, 2) plasma operation mode, 3) pulse modulated plasma, and 4) electromagnetic effects. These topics cover the typical issues in RF plasma field, ranging from fundamental to application.

Time-resolved radial uniformity of pulse-modulated inductively coupled O_(2)/Ar plasmas

Time-resolved radial uniformity of pulse-modulated inductively coupled O_(2)/Ar plasma has been investigated by means of a Langmuir probe as well as an optical probe in this paper. The radial uniformity of plasma has been discussed through analyzing the nonuniformity factor β(calculated by the measured n_e, lower β means higher plasma radial uniformity). The results show that during the active-glow period, the radial distribution of ne exhibits an almost flat profile at the beginning phase, but it converts into a parabola-like profile during the steady state. The consequent evolution for β is that when the power is turned on, it declines to a minimum at first, and then it increases to a maximum, after that, it decays until it keeps constant. This phenomenon can be explained by the fact that the ionization gradually becomes stronger at the plasma center and meanwhile the rebuilt electric field(plasma potential and ambipolar potential) will confine the electrons at the plasma center as well. Besides, the mean electron energy( <ε>_(on)) at the pulse beginning decreases with the increasing duty cycle. This will postpone the plasma ignition after the power is turned on. This phenomenon has been verified by the emission intensity of Ar(λ = 750.4 nm). During the after-glow period, it is interesting to find that the electrons have a large depletion rate at the plasma center. Consequently, ne forms a hollow distribution in the radial direction at the late stage of after-glow. Therefore, β exhibits a maximum at the same time. This can be attributed to the formation of negative oxygen ion(O^(-)) at the plasma center when the power has been turned off.

Phase shift effects of radio-frequency bias on ion energy distribution in continuous wave and pulse modulated inductively coupled plasmas

A retarding field energy analyzer（RFEA） is used to measure the time-averaged ion energy distributions（IEDs） on the substrate in both continuous wave（CW） and synchronous pulse modulated radio-frequency（RF） inductively coupled Ar plasmas（ICPs）.The effects of the phase shift θ between the RF bias voltage and the RF source on the IED is investigated under various discharge conditions.It is found that as θ increases from 0 to π,the IED moves towards the low-energy side,and its energy width becomes narrower.In order to figure out the physical mechanism,the voltage waveforms on the substrate are also measured.The results show that as θ increases from 0 to π,the amplitude of the voltage waveform decreases and,meanwhile,the average sheath potential decreases as well.Specifically,the potential drop in the sheath on the substrate exhibits a maximum value at the same phase（i.e.,θ = 0） and a minimum value at the opposite phase（i.e.,θ = π）.Therefore,when ions traverse across the sheath region above the substrate,they obtain less energies at lower sheath potential drop,leading to lower ion energy.Besides,as θ increases from π to 2π,the IEDs and their energy widths change reversely.

Measurement of electronegativity during the E to H mode transition in a radio frequency inductively coupled Ar/O2 plasma

This paper presents the evolution of the electronegativity with the applied power during the E to H mode transition in a radio frequency(rf)inductively coupled plasma(ICP)in a mixture of Ar and O2.The densities of the negative ion and the electron,as well as their ratio,i.e.,the electronegativity,are measured as a function of the applied power by laser photo-detachment combined with a microwave resonance probe,under different pressures and O2 contents.Meanwhile,the optical emission intensities at Ar 750.4 nm and O 844.6 nm are monitored via a spectrograph.It was found that by increasing the applied power,the electron density and the optical emission intensity show a similar trench,i.e.,they increase abruptly at a threshold power,suggesting that the E to H mode transition occurs.With the increase of the pressure,the negative ion density presents opposite trends in the E-mode and the H-mode,which is related to the difference of the electron density and energy for the two modes.The emission intensities of Ar 750.4 nm and O 844.6 nm monotonously decrease with increasing the pressure or the O2 content,indicating that the density of high-energy electrons,which can excite atoms,is monotonically decreased.This leads to an increase of the negative ion density in the H-mode with increasing the pressure.Besides,as the applied power is increased,the electronegativity shows an abrupt drop during the E-to H-mode transition.

Dynamic control of lipid provisioning in plant-fungal symbiosis

Arbuscular mycorrhizal(AM)fungi form a type of endomycorrhiza that helps plants capture nutrients from the soil.Lipid provisioning plays a critical role in maintaining the AM fungi within the plant cortical cells during AM symbiosis.The coordination of lipid provisioning with AM symbiosis is regulated by the lysin motif(LysM)and leucine-rich repeat receptor-like kinases(RLKs),including DOES NOT MAKE INFECTIONS 2(DMI2),through transcriptional programming(Shi et al.,2023).However,key components of the complete signaling pathway remain to be identified.

The best practice for microbiome analysis using R

With the gradual maturity of sequencing technology,many microbiome studies have published,driving the emergence and advance of related analysis tools.R language is the widely used platform for microbiome data analysis for powerful functions.However,tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data.How to choose suitable,efficient,convenient,and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers.We have organized 324 common R packages for microbiome analysis and classified them according to application categories(diversity,difference,biomarker,correlation and network,functional prediction,and others),which could help researchers quickly find relevant R packages for microbiome analysis.Furthermore,we systematically sorted the integrated R packages(phyloseq,microbiome,MicrobiomeAnalystR,Animalcules,microeco,and amplicon)for microbiome analysis,and summarized the advantages and limitations,which will help researchers choose the appropriate tools.Finally,we thoroughly reviewed the R packages for microbiome analysis,summarized most of the common analysis content in the microbiome,and formed the most suitable pipeline for microbiome analysis.This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub,which can help beginners to learn,also help analysts compare and test different tools.This paper systematically sorts the application of R in microbiome,providing an important theoretical basis and practical reference for the development of better microbiome tools in the future.All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.

Sequence-based Functional Metagenomics Reveals Novel Natural Diversity of Functional CopA in Environmental Microbiomes

Exploring the natural diversity of functional genes/proteins from environmental DNA in high throughput remains challenging.In this study,we developed a sequence-based functional metagenomics procedure for mining the diversity of copper(Cu)resistance gene copA in global microbiomes,by combining the metagenomic assembly technology,local BLAST,evolutionary trace analysis(ETA),chemical synthesis,and conventional functional genomics.In total,87 metagenomes were collected from a public database and subjected to copA detection,resulting in 93,899 hits.Manual curation of 1214 hits of high confidence led to the retrieval of 517 unique CopA candidates,which were further subjected to ETA.Eventually,175 novel copA sequences of high quality were discovered.Phylogenetic analysis showed that almost all these putative CopA proteins were distantly related to known CopA proteins,with 55 sequences from totally unknown species.Ten novel and three known copA genes were chemically synthesized for further functional genomic tests using the Cu-sensitive Escherichia coli(DcopA).The growth test and Cu uptake determination showed that five novel clones had positive effects on host Cu resistance and uptake.One recombinant harboring copA-like 15(copAL15)successfully restored Cu resistance of the host with a substantially enhanced Cu uptake.Two novel copA genes were fused with the gfp gene and expressed in E.coli for microscopic observation.Imaging results showed that they were successfully expressed and their proteins were localized to the membrane.The results here greatly expand the diversity of known CopA proteins,and the sequence-based procedure developed overcomes biases in length,screening methods,and abundance of conventional functional metagenomics.

Microbiome research outlook:past,present,and future

With its critical role in human health and disease,the microbiome has transformed modern biology.Over the past few years,microbiome research has evolved rapidly,with microbiologists gradually shifting their focus from cataloging microorganisms in the human microbiome to understanding their functional roles and how they interact with the host.Here,we present the global trends in microbiome research and summarize the past and current work on microbiome published in Protein&Cell.

Root microbiota shift in rice correlates with resident time in the field and developmental stage

Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant's life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field(e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.

A practical guide to amplicon and metagenomic analysis of microbiome data

Advances in high-throughput sequencing(HTS)have fostered rapid developments in the field of microbiome research,and massive microbiome datasets are now being generated.However,the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field.Here,we systematically summarize the advantages and limitations of microbiome methods.Then,we recommend specific pipelines for amplicon and metagenomic analyses,and describe commonly-used software and databases,to help researchers select the appropriate tools.Furthermore,we introduce statistical and visualization methods suitable for microbiome analysis,including alpha-and betadiversity,taxonomic composition,difference comparisons,correlation,networks,machine learning,evolution,source tracing,and common visualization styles to help researchers make informed choices.Finally,a stepby-step reproducible analysis guide is introduced.We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

Recently duplicated sesterterpene(C25) gene clusters in Arabidopsis thaliana modulate root microbiota

Land plants co-speciate with a diversity of continually expanding plant specialized metabolites(PSMs) and root microbial communities(microbiota).Homeostatic interactions between plants and root microbiota are essential for plant survival in natural environments.A growing appreciation of microbiota for plant health is fuelling rapid advances in genetic mechanisms of controlling microbiota by host plants.PSMs have long been proposed to mediate plant and single microbe interactions.However,the effects of PSMs,especially those evolutionarily new PSMs,on root microbiota at community level remain to be elucidated.Here,we discovered sesterterpenes in Arabidopsis thaliana,produced by recently duplicated prenyltransferase-terpene synthase(PT-TPS) gene clusters,with neo-functionalization.A single-residue substitution played a critical role in the acquisition of sesterterpene synthase(sesterTPS) activity in Brassicaceae plants.Moreover,we found that the absence of two root-specific sesterterpenoids,with similar chemical structure,significantly affected root microbiota assembly in similar patterns.Our results not only demonstrate the sensitivity of plant microbiota to PSMs but also establish a complete framework of host plants to control root microbiota composition through evolutionarily dynamic PSMs.

A guide to human microbiome research: study design, sample collection, and bioinformatics analysis

The purpose of this review is to provide medical researchers,especially those without a bioinformatics background,with an easy-to-understand summary of the concepts and technologies used in microbiome research.First,we define primary concepts such as microbiota,microbiome,and metagenome.Then,we discuss study design schemes,the methods of sample size calculation,and the methods for improving the reliability of research.We emphasize the importance of negative and positive controls in this section.Next,we discuss statistical analysis methods used in microbiome research,focusing on problems with multiple comparisons and ways to compare β-diversity between groups.Finally,we provide step-by-step pipelines for bioinformatics analysis.In summary,the meticulous study design is a key step to obtaining meaningful results,and appropriate statistical methods are important for accurate interpretation of microbiome data.The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.

EVenn:Easy to create repeatable and editable Venn diagrams and Venn networks online

Effective visualization of features generated in next-generation sequencing or other experimental assays is of great interest.These features include operational taxonomy units(OTUs)in microbiome analysis(Zhang et al.,2019),genes,single nucleotide variants,and gene ontology information in transcriptome,genome,and epigenome analysis(Liu et al.,2018),ingredients in traditional Chinese formulas(Li et al.,2020).

Active ammonia-oxidizing bacteria and archaea in wastewater treatment systems

Ammonia-oxidizing bacteria(AOB)and archaea(AOA)are two microbial groups mediating nitrification,yet little is presently known about their abundances and community structures at the transcriptional level in wastewater treatment systems(WWTSs).This is a significant issue,as the numerical abundance of AOA or AOB at the gene level may not necessarily represent their functional role in ammonia oxidation.Using amo A genes as molecular markers,this study investigated the transcriptional abundance and community structure of active AOA and AOB in 14 WWTSs.Quantitative PCR results indicated that the transcriptional abundances of AOB amo A(averaged:1.6×10^(8)copies g^(-1)dry sludge)were higher than those of AOA(averaged:3.4×10^(7)copies g^(-1)dry sludge)in all WWTSs despite several higher abundances of AOA amo A at the gene level.Moreover,phylogenetic analysis demonstrated that Nitrosomonas europaea and unknown clusters accounted for 37.66%and 49.96%of the total AOB amo A transcripts,respectively,suggesting their dominant role in driving ammonia oxidation.Meanwhile,AOA amo A transcripts were only successfully retrieved from 3 samples,and the Nitrosospaera sister cluster dominated,accounting for 83.46%.Finally,the substrate utilization kinetics of different AOA and AOB species might play a fundamental role in shaping their niche differentiation,community composition,and functional activity.This study provides a basis for evaluating the relative contributions of ammonia-oxidizing microorganisms(AOMs)to nitrogen conversions in WWTSs.

Host-Associated Quantitative Abundance Profiling Reveals the Microbial Load Variation of Root Microbiome

Plant-associated microbes are critical for plant growth and survival under natural environmental conditions.To date,most plant microbiome studies involving high-throughput amplicon sequencing have focused on the relative abundance of microbial taxa.However,this technique does not assess the total microbial load and the abundance of individual microbes relative to the amount of host plant tissues.Here,we report the development of a host-associated quantitative abundance profiling(HA-QAP)method that can accurately examine total microbial load and colonization of individual root microbiome members relative to host plants by the copy-number ratio of microbial marker gene to plant genome.We validate the HAQAP method using mock experiments,perturbation experiments,and metagenomic sequencing.The HA-QAP method eliminates the generation of spurious outputs in the classical method based on microbial relative abundance,and reveals the load of root microbiome to host plants.Using the HA-QAP method,we found that the copy-number ratios of microbial marker genes to plant genome range from 1.07 to 6.61 for bacterial 16S rRNA genes and from 0.40 to 2.26 for fungal internal transcribed spacers in the root microbiome samples from healthy rice and wheat.Furthermore,using HA-QAP we found that an increase in total microbial load represents a key feature of changes in root microbiome of rice plants exposed to drought stress and of wheat plants with root rot disease,which significantly influences patterns of differential taxa and species interaction networks.Given its accuracy and technical feasibility,HA-QAP would facilitate our understanding of genuine interactions between root microbiome and plants.

Endogenous Small RNA Clusters in Plants

In plants, small RNAs（sRNAs） usually refer to non-coding RNAs（ncRNAs） with lengths of 20–24 nucleotides. sRNAs are involved in the regulation of many essential processes related to plant development and environmental responses. sRNAs in plants are mainly grouped into microRNAs（miRNAs） and small interfering RNAs（siRNAs）, and the latter can be further classified into trans-acting siRNAs（ta-siRNAs）, repeat-associated siRNAs（ra-siRNAs）, natural anti-sense siRNAs（nat-siRNAs）, etc. Many sRNAs exhibit a clustered distribution pattern in the genome. Here, we summarize the features and functions of cluster-distributed sRNAs, aimed to not only provide a thorough picture of sRNA clusters（SRCs） in plants, but also shed light on the identification of new classes of functional sRNAs.

The role of genotype and diet in shaping gut microbiome in a genetic vitamin A deficient mouse model

Multifactors have been reported to affect the gut microbiome, including genotype, age, diet, and nutrition.However, few reports have investigated the relative capacity of different factors to shape the gut microbiome in a single study. Our design used a genetic vitamin A-deficient mouse model, the Rbp4^(-/-)mouse,feeding with the low vitamin A diets at different ages of initiation(4 or 7 weeks) for 28 days. Fecal samples were collected for bacterial profiling at seven time points after diet controlling. With Rbp4 depletion,Akkermansia decreased and Bacteroides increased, whereas Desulfovibrio, Barnesiella, Clostridium_Xl Va,and Lactobacillus fluctuated. The bacterial community swiftly adjusted with the vitamin A-deficient diet administration and gradually changed(e.g., decrease of Barnesiella and increase of Desulfovibrio). Age exerted a relatively weaker but long-last influence. At an earlier age to feed a vitamin A-deficient diet, a higher microbial dysbiosis index will be valued. Of note, the shaping effects of diet and age on the bacterial community varied with the difference of genotype, which might indicate a greater role of genotype than diet and age in shaping the gut microbiome.