More Like this

1. SHOOT MERISTEMLESS trafficking controls axillary meristem formation, meristem size and organ boundaries in Arabidopsis

   https://doi.org/10.1111/tpj.13504  

   Balkunde, Rachappa ; Kitagawa, Munenori ; Xu, Xianfeng Morgan ; Wang, Jing ; Jackson, David ( March 2017 , The Plant Journal)

   The shoot stem cell niche, contained within the shoot apical meristem (SAM) is maintained in Arabidopsis by the homeodomain proteinSHOOT MERISTEMLESS(STM).STMis a mobile protein that traffics cell‐to‐cell, presumably through plasmodesmata. In maize, theSTMhomologKNOTTED1 shows clear differences betweenmRNAand protein localization domains in theSAM. However, theSTM mRNAand protein localization domains are not obviously different in Arabidopsis, and the functional relevance ofSTMmobility is unknown. Using a non‐mobile version ofSTM(2xNLS‐YFP‐STM), we show thatSTMmobility is required to suppress axillary meristem formation during embryogenesis, to maintain meristem size, and to precisely specify organ boundaries throughout development.STMand organ boundary genesCUP SHAPED COTYLEDON1(CUC1),CUC2andCUC3regulate each other during embryogenesis to establish the embryonicSAMand to specify cotyledon boundaries, andSTMcontrolsCUCexpression post‐embryonically at organ boundary domains. We show that organ boundary specification by correct spatial expression ofCUCgenes requiresSTMmobility in the meristem. Our data suggest thatSTMmobility is critical for its normal function in shoot stem cell control.

    

   more » « less
2. Short-Term Long-Term Compute-In-Memory Architecture: A Hybrid Spin/CMOS Approach Supporting Intrinsic Consolidation

   https://doi.org/10.1109/JXCDC.2020.2983450  

   Sheikhfaal, Shadi ; DeMara, Ronald F. ( March 2020 , IEEE Journal on Exploratory Solid-State Computational Devices and Circuits)

   Biological memory structures impart enormous retention capacity while automatically providing vital functions for chronological information management and update resolution of domain and episodic knowledge. A crucial requirement for hardware realization of such cortical operations found in biology is to first design both Short-Term Memory (STM) and Long-Term Memory (LTM). Herein, these memory features are realized via a beyond-CMOS based learning approach derived from the repeated input information and retrieval of the encoded data. We first propose a new binary STM-LTM architecture with composite synapse of Spin Hall Effect-driven Magnetic Tunnel Junction (SHE-MTJ) and capacitive memory bit-cell to mimic the behavior of biological synapses. This STM-LTM platform realizes the memory potentiation through a continual update process using STM-to-LTM transfer, which is applied to Neural Networks based on the established capacitive crossbar. We then propose a hardware-enabled and customized STM-LTM transition algorithm for the platform considering the real hardware parameters. We validate the functionality of the design using SPICE simulations that show the proposed synapse has the potential of reaching ~30.2pJ energy consumption for STM-to-LTM transfer and 65pJ during STM programming. We further analyze the correlation between energy, array size, and STM-to-LTM threshold utilizing the MNIST dataset. 

   more » « less
3. Detecting and distinguishing Majorana zero modes with the scanning tunnelling microscope

   https://doi.org/10.1038/s42254-021-00328-z  

   Jäck, Berthold ; Xie, Yonglong ; Yazdani, Ali ( January 2021 , Nature Reviews Physics)

   null (Ed.)

   One of the most exciting areas of research in quantum condensed matter physics is the push to create topologically protected qubits using non-Abelian anyons. The focus of these efforts has been Majorana zero modes (MZMs), which are predicted to emerge as localized zero-energy states at the ends of 1D topological superconductors. A key role in the search for experimental signatures of these quasiparticles has been played by the scanning tunnelling microscope (STM). The power of high-resolution STM techniques is perhaps best illustrated by their application in identifying MZMs in 1D chains of magnetic atoms on the surface of a superconductor. In this platform, STM spectroscopic mapping has demonstrated the localized nature of MZM zero-energy excitations at the ends of such chains, and experiments with superconducting and magnetic STM tips have been used to uniquely distinguish them from trivial edge modes. Beyond the atomic chains, STM has also uncovered signatures of MZMs in 2D materials and topological surface and boundary states, when they are subjected to the superconducting proximity effect. Looking ahead, future STM experiments may be able to demonstrate the non-Abelian statistics of MZMs. 

   more » « less
4. Auxin Response Factors promote organogenesis by chromatin-mediated repression of the pluripotency gene SHOOTMERISTEMLESS

   https://doi.org/10.1038/s41467-019-08861-3  

   Chung, Yuhee ; Zhu, Yang ; Wu, Miin-Feng ; Simonini, Sara ; Kuhn, Andre ; Armenta-Medina, Alma ; Jin, Run ; Østergaard, Lars ; Gillmor, C. Stewart ; Wagner, Doris ( December 2019 , Nature Communications)

   Abstract Specification of new organs from transit amplifying cells is critical for higher eukaryote development. In plants, a central stem cell pool maintained by the pluripotency factor SHOOTMERISTEMLESS (STM), is surrounded by transit amplifying cells competent to respond to auxin hormone maxima by giving rise to new organs. Auxin triggers flower initiation through Auxin Response Factor (ARF) MONOPTEROS (MP) and recruitment of chromatin remodelers to activate genes promoting floral fate. The contribution of gene repression to reproductive primordium initiation is poorly understood. Here we show that downregulation of the STM pluripotency gene promotes initiation of flowers and uncover the mechanism for STM silencing. The ARFs ETTIN (ETT) and ARF4 promote organogenesis at the reproductive shoot apex in parallel with MP via histone-deacetylation mediated transcriptional silencing of STM . ETT and ARF4 directly repress STM , while MP acts indirectly, through its target FILAMENTOUS FLOWER ( FIL ). Our data suggest that – as in animals- downregulation of the pluripotency program is important for organogenesis in plants. 

   more » « less
5. Coupled support tensor machine classification for multimodal neuroimaging data

   https://doi.org/10.1002/sam.11587  

   Li, Peide ; Sofuoglu, Seyyid Emre ; Aviyente, Selin ; Maiti, Tapabrata ( May 2022 , Statistical Analysis and Data Mining: The ASA Data Science Journal)

   Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities. Multimodal modeling helps to explain the interdependence between heterogeneous data sources, discovers new insights that may not be available from a single modality, and improves decision‐making. Recently, coupled matrix–tensor factorization has been introduced for multimodal data fusion to jointly estimate latent factors and identify complex interdependence among the latent factors. However, most of the prior work on coupled matrix–tensor factors focuses on unsupervised learning and there is little work on supervised learning using the jointly estimated latent factors. This paper considers the multimodal tensor data classification problem. A coupled support tensor machine (C‐STM) built upon the latent factors jointly estimated from the advanced coupled matrix–tensor factorization is proposed. C‐STM combines individual and shared latent factors with multiple kernels and estimates a maximal‐margin classifier for coupled matrix–tensor data. The classification risk of C‐STM is shown to converge to the optimal Bayes risk, making it a statistically consistent rule. C‐STM is validated through simulation studies as well as a simultaneous analysis on electroencephalography with functional magnetic resonance imaging data. The empirical evidence shows that C‐STM can utilize information from multiple sources and provide a better classification performance than traditional single‐mode classifiers.

    

   more » « less