An All‑In‑One Multifunctional Touch Sensor with Carbon‑Based Gradient Resistance Elements

Human–machine interactions using deep-learning methods are important in the research of virtual reality,augmented reality,and metaverse.Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes,signal crosstalk,propagation delay,and demanding configuration requirements.Here,an all-inone multipoint touch sensor(AIOM touch sensor)with only two electrodes is reported.The AIOM touch sensor is efficiently constructed by gradient resistance elements,which can highly adapt to diverse application-dependent configurations.Combined with deep learning method,the AIOM touch sensor can be utilized to recognize,learn,and memorize human–machine interactions.A biometric verification system is built based on the AIOM touch sensor,which achieves a high identification accuracy of over 98%and offers a promising hybrid cyber security against password leaking.Diversiform human–machine interactions,including freely playing piano music and programmatically controlling a drone,demonstrate the high stability,rapid response time,and excellent spatiotemporally dynamic resolution of the AIOM touch sensor,which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

Female-biased Dispersal of the Emei Moustache Toad (Leptobrachium boringii) under Local Resource Competition

Dispersal is an important area of ecological and evolutionary research.Although many studies have been conducted in mammals and birds,dispersal pattern in amphibians is still unclear.To verify dispersal patterns of amphibians,an endangered species the Emei Moustache Toad(Leptobrachium boringii)was selected.In this study,we analyzed six genetic parameters:inbreeding coefficient(F_(IS)),gene diversity(H_S),the mean of corrected assignment index(mAI_C),the variance of corrected assignment index(vAI_C),relatedness(r)for all three years together and each year separately based on eight highly polymorphic microsatellite loci.Data in totaled across years and each year for 581 individuals captured during 2013–2015 revealed a significant female-biased dispersal pattern.Significantly higher F_(IS) and H_S in females,and lower mAI_C and r for each year separately in females support that L.boringii displays femalebiased dispersal,although r for the total dataset and vAI_C tests did not show significant differences between the sexes.Female-biased dispersal patterns may be explained by the local resource competition hypothesis.

Transcriptomic and Physiological Analyses of Pigment Accumulation in Eucommia ulmoides‘Hongye’

Eucommia ulmoides‘Hongye’is a new ornamental variety of E.ulmoides with excellent red or purple foliage.We found that E.ulmoides‘Hongye’exhibited a gradual change from green to red colour under light conditions.However,the colouring mechanism in the leaves of E.ulmoides‘Hongye’remains unclear.In this study,we compared the pigment content and leaf colour index of E.ulmoides‘Hongye’at five stages with those of E.ulmoides‘Xiaoye’,which was used as the control variety.The transcriptome sequencing data of the first-period(H1,green)and fifth-period(H5,red)leaves were also analysed and compared.The corresponding gene regulation in anthocyanin-related metabolic pathways was then analysed.Physiological results indicated that the contents of flavonoids and anthocyanins in red leaves(H5)were significantly higher than those in green leaves(H1),whereas the chlorophyll content in red leaves(H5)was lower than that in green leaves(H1).Moreover,the carotenoid content did not significantly differ between the two varieties.A transcriptome analysis identified 4240 differentially expressed genes(DEGs),and 20 of these genes were found to be involved in flavonoid and anthocyanin biosynthesis pathways.The results provide a reference for further study of the leaf colouration mechanism in E.ulmoides.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology.Driven by a combination of highthroughput sequencing,molecular biology and data science,great advances have been made in omics technologies at every step along the central dogma,especially in genome assembling,genome annotation,epigenomic profiling,and transcriptome profiling.These advances further revolutionized three directions of development.One is genetic dissection of complex traits in crops,along with genomic prediction and selection.The second is comparative genomics and evolution,which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery.The third direction is the development of deep learning approaches for the rational design of biological sequences,especially proteins,for synthetic biology.All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Genome-wide identification of agronomically important genes in outcrossing crops using OutcrossSeq

Many important crops(e.g.,tuber,root,and tree crops)are cross-pollinating.For these crops,no inbred lines are available for genetic study and breeding because they are self-incompatible,clonally propagated,or have a long generation time,making the identification of agronomically important genes difficult,particularly in crops with a complex autopolyploid genome.In this study,we developed a method,OutcrossSeq,for mapping agronomically important loci in outcrossing crops based on whole-genome low-coverage resequencing of a large genetic population,and designed three computation algorithms in OutcrossSeq for different types of outcrossing populations.We applied OutcrossSeq to a tuberous root crop(sweet potato,autopolyploid),a tree crop(walnut tree,highly heterozygous diploid),and hybrid crops(double-cross populations)to generate high-density genotype maps for the outcrossing populations,which enable precise identification of genomic loci underlying important agronomic traits.Candidate causative genes at these loci were detected based on functional clues.Taken together,our results indicate that OutcrossSeq is a robust and powerful method for identifying agronomically important genes in heterozygous species,including polyploids,in a cost-efficient way.The OutcrossSeq software and its instruction manual are available for downloading at www.xhhuanglab.cn/tool/OutcrossSeq.html.