Deep neural networks may achieve excellent performance in many research fields. However, many deep neural network models are over-parameterized. The computation of weight matrices often consumes a lot of time, which requires plenty of computing resources. In order to solve these problems, a novel block-based division method and a special coarse-grained block pruning strategy are proposed in this paper to simplify and compress the fully connected structure, and the pruned weight matrices with a blocky structure are then stored in the format of Block Sparse Row (BSR) to accelerate the calculation of the weight matrices. First, the weight matrices are divided into square sub-blocks based on spatial aggregation. Second, a coarse-grained block pruning procedure is utilized to scale down the model parameters. Finally, the BSR storage format, which is much more friendly to block sparse matrix storage and computation, is employed to store these pruned dense weight blocks to speed up the calculation. In the following experiments on MNIST and Fashion-MNIST datasets, the trend of accuracies with different pruning granularities and different sparsity is explored in order to analyze our method. The experimental results show that our coarse-grained block pruning method can compress the network and can reduce the computational cost without greatly degrading the classification accuracy. The experiment on the CIFAR-10 dataset shows that our block pruning strategy can combine well with the convolutional networks. Deep neural network architectures are becoming more complex, and the number of parameters is also increasing sharply ,2. Such For coarse-grained pruning models, there are many sparse matrix compression storage approaches for optimizing the pruned weight matrices operations. Block Sparse Row (BSR) as an effective compression format for sparse matrix-vector multiplication (SpMV) acceleration is very suitable for storing sparse weight matrices with continuous data sub-blocks. We were inspired by the above two facts to design the strategy of block as the coarse-grained pruning unit in this paper. In our method, the weight matrix is divided into square sub-blocks. Whether the sub-block is retained or pruned depends on its importance in the network. After these remaining nonzero elements in the weight matrix are aggregated into blocks with regular structures, the BSR storage format is employed to compress the network and to accelerate the weight matrix computation.We conducted experiments on the MNIST, Fashion-MNIST, and CIFAR-10 benchmark datasets. It is found that some pruning models maintain accuracy when achieving a large pruning rate. At the same time, the pruned network may still achieve a reasonable performance on the classification effect compared with random sparse networks and some existing pruned models. The relationships between sparsity and accuracies and between pruning granularities and accuracies are also discussed for our method. With the increase in sparsity and pruning granularity, the accuracy has a decreasing trend. It is found that the accuracy is influenced by the sparsity and pruning granularity according to a certain rule.From the perspective of computation, our block pruning strategy can work well with the BSR storage format. The combination can improve the computational efficiency and cache hit ratio compared with the traditional Compressed Sparse Row (CSR) format.We design a coarse-grained unit called sub-block, which is applicable to the fully connected structure. Based on this structure, we propose a novel block-based division strategy and coarse-grained block pruning method for fully connected structures, which learns the weights and network structure. Accuracies with different sparsity and different pruning granularities are explored. This method can also be combined with CNNs.We exploit the BSR storage format to store the pruned blocky weight matrices, which can effectively accelerate in computation and compress in storage.We show that the cache hit ratio during network inference can be improved when the compressed model is stored by BSR. We analyze the reasons for the high computational efficiency from this aspect.Our main contributions reported in this paper are as follows:Many studies have designed different pruning guidelines on selecting the important connections. In , Han et Fine-grained pruning is a technique for pruning individual weights. In , Han et Many proposed coarse-grained pruning methods are used to regularize the structures of neural networks. They are usually applied to CNNs. In , the sumMany studies explored storage methods and computational optimization methods on pruned sparse matrices. For sparse matrix storage formats, Bell et al.  summarizIn our method, the block-based division and the coarse-grained block pruning are performed on fully connected structures to regularize the weight matrices firstly. After the model is pruned, the sparse weight matrices with dense blocks are stored in the BSR format. The block-based division structure is specified in a ×b weight matrix of each layer is divided into many n× n square sub-blocks without overlap, where n is the division granularity. In other words, the granularity is length of the side of the sub-block.For fully connected networks, the k, i = 0, 1, 2⋯and k × n < a, i × n < b. The lower-right vertex coordinates of each sub-block are k, i = 0, 1, 2⋯. Under this partition, the entire weight matrix can be regarded as a matrix arranged by a multitude of sub-blocks.The upper-left vertex coordinates of each sub-block are a × b weight matrix example after block pruning and the corresponding special pruned neural network structure. In the matrix example, the white areas represent sub-blocks that have been pruned.Based on the above block division structure, we propose a new block coarse-grained pruning method that outputs a sparse blocky sub-network. This method prunes the divided weight sub-blocks rather than individual weights. The pruning granularity is the same as the division granularity. Our weight-pruning criterion for sub-blocks is designed on the basis of the low-magnitude weight removal strategy in . In our We employed the iterative pruning proposed by  in the pEquation .(3)WgipAfter that, the model is retrained for multiple epochs to fine-tune the remaining weights and to enhance the performance. Let To visualize the iterative pruning method, the iterative process is shown in The sparse blocky structure produced by this coarse-grained pruning method has better spatial aggregation, which is much more convenient for the following computational optimization work.The CSR format is a typical sparse matrix storage method that has many advantages ,28. The However, in this paper, pruned sparse weight matrices are stored in the BSR format. BSR is similar to the CSR format, except that the elements are stored and recorded block by block. After block-based pruning, there are many compact weight blocks in the weight matrices so BSR format is more effective in storage and computation.We conducted block-based pruning experiments on the LeNet-300-100  model anWe performed coarse-grained block-based pruning iteratively on a classic LeNet-300-100 fully connected network, which is a three-layer network. This network is a very popular fully connected network. This facilitates comparisons with other classic methods.During iterative pruning, for the first two layers the pruning rate is set to 0.2, that is, 20% of remaining parameters were pruned each time. In the output layer, since the number of parameters takes a very small proportion in the whole number of parameters , the pruning rate is set to 0.1, which is the half of the pruning rate for the first two layers. Such a pruning rate setting learned from ,31 is coIn the experiments, we applied this block pruning strategy on the two datasets MNIST and Fashion-MNIST to evaluate the results.MNIST. The MNIST handwritten digital dataset, which contains 60,000 samples for training and 10,000 samples for testing, was adopted. MNIST is a typical and popular dataset that is suitable for fully connected networks.Experiments were conducted to explore accuracies with different sparsities and different pruning granularities. One of our focuses is how sparsity and pruning granularity affect accuracy. The baseline is the unpruned LeNet-300-100 fully connected network trained on the MNIST dataset. We performed iterative block pruning on the baseline model. The experimental results are shown in When the density is 8%, each model can still maintain a similar accuracy to the baseline. It can be observed from We also find that, with the same sparsity, as the pruning granularity increases, accuracies also decrease. It is caused by the limitation in search space due to the coarse-grained pruning rule. For example, test accuracy decreases from 97.85% to 97.51% (pruning granularity is from 2 × 2 to 6 × 6) when the density is 8%. When the density is 2.8%, the gap tends to be relatively obvious with the increase in pruning granularity, as shown in To validate our pruning method, the comparison between our coarse-grained pruning network and the random pruning networks is made. Two kinds of random sparse networks are investigated. One is the fine-grained random pruning sparse network, trained about 15 K epochs in . The othIn addition, our model is compared with the fine-grained pruned model of The Lottery Ticket Sparse Network, the accuracy of which is obtained from . The pruWe also compared our block pruning method with methods that employed other pruning criterions. Reference  exploiteFashion-MNIST. To verify the generalizability of the findings, we also conducted block pruning experiments on this dataset of Fashion-MNIST. The Fashion-MNIST dataset describes clothing classification images. It consists of 60,000 training images and 10,000 test images. We performed pruning experiments on Fashion-MNIST using similar experimental settings to that of MNIST. Every model with different sparsities and pruning granularities trained 1000 epochs. The results are shown in From the experimental results, the accuracy variation law shown on the Fashion-MNIST dataset is the same as that of the MNIST dataset. It can fully reflect the relationships between accuracies and sparsity and between accuracies and pruning granularities. The accuracies decrease with the increase in sparsity and pruning granularity.We also conducted comparative experiments on this dataset of Fashion-MNIST. The sparse network model proposed by  adopted In our experiments, the accuracy has a slight gap compared with that of the baseline, which is acceptable. One of the reasons is that the number of training epochs is a little small. On the other hand, our target is analyzing the relationships between sparsity and accuracies and between pruning granularities and accuracies and obtaining the sparse weight matrix structure suitable for computation rather than striving for excellent accuracy.It is also worth mentioning why fully connected structures are mainly considered in the experiment rather than CNNs. On the one hand, the concept of a coarse granularity applicable to fully connected structures is currently unclear. Therefore, we designed sub-blocks as the coarse-grained pruning units and conducted block pruning on fully connected structures. On the other hand, CNNs share weights and reduce the number of parameters. However, floating-point operations (FLOPs) are not reduced by much. Taking MNIST dataset as an example, without greatly increasing error rate, FLOPs and the number of weights of our pruned blocky network are significantly less than those of LeNet-5, as shown in In this section, we intend to explore the performance of our pruning strategy from the perspective of computational efficiency and cache hit ratio. We first conducted weight matrix-vector multiplication computation experiments during forward propagation on the MNIST dataset.The sparse matrices obtained from In the experiment, we explored the computational efficiency when our block pruning method was combined with the BSR format. The computational time of sparse matrices stored by the BSR format and the CSR format is also made a comparison. The model settings and the operating environment are the same, for fairness. To avoid cold starting and the random noise effect, the sparse matrix-vector multiplication was iterated 100 times and then the total time was recorded. The experiment was repeated four times for averaging. The experimental results are shown in From From We assume that one of the main reasons for the speedups of the BSR storage format is the higher cache hit ratio. Therefore, the number of misses of L1 cache are measured for various sparsity and pruning granularity models stored in the BSR and CSR formats in the experiments, as shown in From It also can be seen that the cache misses using the BSR storage format are, on average, 19.98% less than that using the CSR storage format. Since the data are stored by block in the format of BSR, the BSR approach makes better use of the aggregation of data in terms of the storage mechanism compared with the CSR storage format. The integrity of data access is much more enhanced, so the number of cache updates is reduced and then the cache hit ratio increases in the format of BSR.For the dataset Fashion-MNIST, we also conducted computational and cache hit ratio experiments. The performance is very similar to that of the MNIST dataset since the size, the density, the division granularities of weight matrices, and the other settings are the same as those of the MNIST dataset. This illustrated that our block pruning strategy is very computationally friendly.Though our pruning method mainly focused on the fully connected structures rather than on convolutional structures, the block pruning strategy can combine well with CNNs. In the experiments, we conducted iterative block pruning on the fully connected layers of ResNet18 on the CIFAR-10 dataset. The images in the CIFAR-10 dataset are the natural images in RGB colors.We added an extra fully connected layer with 300 neurons on top of the original fully connected layer of ResNet18. The unpruned ResNet18 model was trained until convergence with 300 epochs on CIFAR-10 as the baseline model. To focus on the pruning properties of the fully connected layers, the weights in the convolutional layers were fixed during the iterative pruning and fine-tuning. The block pruning was only performed on fully connected layers using the pruning granularity of 2 × 2. The model trained 300 epochs at every iteration. Finally, we cut off 97% of the parameters of the fully connected layers. From the experimental results, the test accuracy improved from 90.37% (baseline model) to 90.73%. This boost demonstrated that our block pruning method found the proper capacity of the network. It can eliminate the redundance and can alleviate overfitting.This study presented a special block-based division method and coarse-grained block pruning method for fully connected structures. This pruning method can also be integrated into convolutional networks well. The experimental results on the MNIST, Fashion-MNIST, and CIFAR-10 testing datasets show that some models maintain similar accuracies when reaching a large compression rate. Our block-based models do not decrease the classification accuracy compared with the existing fine-grained pruning method and other methods based on different pruning criterions. The correlations between accuracies and sparsity and between accuracies and pruning granularities were also evaluated. The increase in sparsity and pruning granularity has a negative effect on accuracy. The tradeoff between accuracy and efficiency was discussed.In terms of calculation, all compressed models stored in the format of BSR improved in computation speed compared with the CSR format. The speedups reached 1.229×–3.476×. The CPU cache hit ratio was 19.98% higher than that of the CSR storage format, on average. The experimental results show that our block-based pruning models stored in the format of BSR can ensure the continuity of data access, can improve the cache hit ratio, and can increase the calculation efficiency.In the future, we intend to design a strategy combining block-growth and block-pruning, which adjusts the network structure more flexibly to give “silent” connections a chance to be reactivated. This algorithm may enhance the accuracy further. In addition, we plan to consider how to fully take advantage of this block sparse structure to significantly reduce the FLOPs of CNNs. For a more accurate computational performance analysis, we will consider the use of Field Programmable Gate Arrays (FPGAs) to implement a special computational model.


In mammals, eight aminoacyl-tRNA synthetases (AARSs) and three AARS-interacting multifunctional proteins (AIMPs) form a multi-tRNA synthetase complex (MSC). MSC components possess extension peptides for MSC assembly and specific functions. Human cytosolic methionyl-tRNA synthetase (MRS) has appended peptides at both termini of the catalytic main body. The N-terminal extension includes a glutathione transferase (GST) domain responsible for interacting with AIMP3, and a long linker peptide between the GST and catalytic domains. Herein, we determined crystal structures of the human MRS catalytic main body, and the complex of the GST domain and AIMP3. The structures reveal human-specific structural details of the MRS, and provide a dynamic model for MRS at the level of domain orientation. A movement of zinc knuckles inserted in the catalytic domain is required for MRS catalytic activity. Depending on the position of the GST domain relative to the catalytic main body, MRS can either block or present its tRNA binding site. Since MRS is part of a huge MSC, we propose a dynamic switching between two possible MRS conformations; a closed conformation in which the catalytic domain is compactly attached to the MSC, and an open conformation with a free catalytic domain dissociated from other MSC components. Toxoplasma gondii (Trypanosoma brucei (Aminoacyl-tRNA synthetases (AARSs) catalyse specific aminoacylation of their cognate tRNAs to produce aminoacyl-tRNAs for protein biosynthesis. Among them, methionyl-tRNA synthetase (MRS) ligates a methionine to its cognate tRNAs for both translation initiation and elongation steps during protein synthesis. In eukaryotes, MRS is assembled into a complex with other AARSs. MRS and glutamyl-tRNA synthetase (ERS) form a ternary complex with Arc1p in yeast  revealed a helix-turn-helix motif (K838 to E887) and a lysine-rich peptide region. The lysine-rich cluster and WHEP domain function in tRNA binding and sequestering, respectively  were transformed into E. coli strain BL21 (DE3), and protein expression was induced using isopropyl-β-d-1-thiogalactopyranoside (IPTG) at a final concentration of 0.2 mM. Proteins expressed at 18°C were purified using Ni-NTA affinity and size-exclusion chromatography steps. MRS1–224–AIMP3 and MRS–AIMP3 complexes were obtained by co-purification (Genes for full-length human MRS (residues 1–900) and AIMP3 were previously cloned into pET30a or pQE80L , and thefication , and purTo estimate the sizes of the MRS variants, purified MRS proteins were subjected to size-exclusion chromatography using a Superdex 200 10/300 column  equilibrated in Tris–HCl pH 7.5 containing 150 mM NaCl. The molecular weights of the eluted samples were calculated based on the calibration curve of standard samples.221–834 at a concentration of 12.0 mg/ml was crystallized with 21% polyethylene glycol (PEG) 8000 as the precipitant in 0.1 M Tris–HCl (pH 8.0) and 0.2 M MgCl2 using the sitting drop vapor diffusion method at 21°C. For diffraction data collection, crystals were soaked in cryoprotectant solution containing 20% (v/v) glycerol and flash-frozen in liquid nitrogen. After a fluorescence scan, X-ray diffraction data were collected at a wavelength corresponding to the zinc absorption peak (1.28273 Å) using synchrotron 5C beam line at the Pohang Accelerator Laboratory (PAL) in Korea. Crystals of the MRS221–834 variant belonged to space groups P212121. Another dataset at a resolution of 2.0 Å was collected at a wavelength of 0.9795 Å. Datasets were processed using the HKL2000 program (1–224–AIMP3 complex were obtained in 20% PEG 8000 and 0.1 M HEPES (pH 7.5). The structure was determined by molecular replacement based on the MRS1–207–AIMP31–169 complex using the I3C structure (PDB ID: 4bvx) as template with Phenix.Phaser . Aminoacylation reactions were quenched on 3MM filter paper pre-wetted with 5% trichloroacetic acid containing 1 mM Met. After washing with 5% trichloroacetic acid and drying, radioactivity was detected by a Wallac 1409 liquid scintillation counter .MRS aminoacylation activity was assayed as previously described  at 37°C 2, 1 mM dithiothreitol, 250 μM ATP, 0.2 U/ml inorganic pyrophosphatase (Roche), 2.5 mM l-methionine (Sigma-Aldrich) and 1 μM purified MRS–AIMP3 complex or MRS221–900 at room temperature. Various concentrations of methionine or ATP were used to obtain Vmax and KM values. Reactions were stopped by addition of 180 μl Malachite Green solution . After incubation for 25 min, the absorbance at 620 nm was measured using a VesaMax Microplate Reader (Molecular Devices), and the phosphate concentration was calculated from a standard curve generated using various concentrations of inorganic phosphate.Methionine activation activity of MRS was measured by monitoring the production of pyrophosphate . Assays 221–900 and the MRS–AIMP3 complex with tRNA, pull-down assays were performed using Ni-NTA resin. Yeast total tRNA (Sigma-Aldrich) was subjected to a pull-down assay with hexa-His-tagged proteins immobilized on Ni-NTA resin. A 2.5 nmol aliquot of each protein was bound to Ni-NTA resin and washed several times with buffer . The pull-down assay was performed by adding 1.25 mg yeast tRNA, and resin was washed three times with buffer. The protein and bound tRNA were eluted with buffer containing 200 mM imidazole. UV absorption spectra of the eluted samples and buffers were measured by wavelength scanning from 320 to 230 nm using a UV-1800 UV spectrophotometer (Shimadzu). The amount of tRNA and protein was analyzed by measuring the absorbance at 260 and 280 nm. Synthesized RNA with an elongator tRNAMet sequence labeled with 6-carboxyfluorescein (6-FAM) was purchased from Integrated DNA Technologies and used for the pull-down assay. The amount of RNA eluted was estimated by fluorescence at wavelengths of 495 nm and 517 nm for excitation and emission, respectively.To examine the interactions of MRS221-900 or MRS–AIMP3 complex was titrated against 80 nM 6-FAM–RNA. Experiments were performed in phosphate-buffered saline (PBS) and samples were loaded into capillaries. To use unlabeled RNA, proteins were fluorescently labeled using a Monolith His-Tag Labeling kit . Purified MRS221–900 or MRS–AIMP3 complex (1–100 μM) was titrated against 80 nM 6-FAM–RNA. MST assays were performed with 80% light emitting diode (LED) power using a green filter and 40% MST power. Normalized fluorescence readings (thermophoresis plus T-jump) were plotted as a function of analyte concentration, and curve fitting and dissociation constant Kd calculation were performed using NanoTemper software.MST assays were performed with a Monolith NT.115 instrument (NanoTemper Technologies) . Each tiConformational changes in MRS were monitored by measuring luciferase activity using NanoLuc Binary technology (NanoBit) (Promega). Plasmids for MRS containing Small Bit (SmBit) and AIMP3 fused with SmBit or Large Bit (LgBit) of nanoluciferase were generated previously . AARS coCHO-K1 cells  were maintained in RPMI-1640 medium  supplemented with 10% (v/v) fetal bovine serum . Plasmids pBiT1.1-N [TK_LgBiT]-MRS-SmBiT and pGEM-T carrying tRNA genes were co-transfected at various ratios, and 48 h after transfection, cell-permeable luciferase substrate  was supplemented along with dilution buffer  following the manufacturer's instructions, and luminescence was measured by a Glomax 96 microplate luminometer .For L-Met deprivation and addition, 4 μg of the LgBiT-XARS1-SmBiT construct alone was transfected, and transfected cells were seeded as above. At 24 h after transfected cell seeding, L-Met-deprived RPMI-1640 media  or normal RPMI-1640 media  supplemented with 25 mM HEPES  was combined with L-Met solution  and 50 mg/l cycloheximide . A 100 μl volume of the mixture was used to replace the original media and incubated at 37°C for 10 min. Next, a mixture of 1.25 μl of Nano‐Glo live cell substrate and 23.75 μl of Nano‐Glo LCS dilution buffer were added and further incubated for 10 min. Luminescence was subsequently measured as described above.Met, 5′-AGCAGAGTGGCGCAGCGGAAGCGTGCTGGGCCCATAACCCAGAGGTCGATGGATCGAAACCATCCTCTGCTA-3′; elongator tRNAMet, 5′-GCCTCGTTAGCGCAGTAGGTAGCGCGTCAGTCTCATAATCTGAAGGTCGTGAGTTCGATCCTCACACGGGGCA-3′; tRNALys(CTT), 5′-GCCCGGCTAGCTCAGTCGGTAGAGCATGAGACTCTTAATCTCAGGGTCGTGGGTTCGAGCCCCACGTTGGGCG-3′; tRNAAsp(GTC), 5′-TCCTTGTTAGTATAGTGGTgAGTGTTTCTGCCTGTCATGTGGAGACTGGAGTTTGAGTCCCCAACAGGGAG-3′.Sequences of tRNAs used in this study were as follows: initiator tRNA221-834; Figure 221–900; 78 kDa) and both N- and C-terminal domains  were eluted as monomers. Complexes of MRS and AIMP3  and MRS without the C-terminal WHEP domain and AIMP3  were also eluted as 1:1 heterodimers.To obtain crystals of human cytosolic MRS for structure determination, we generated a series of MRS variants truncated at the N- and C-termini. Among them, the construct spanning E221 to V834  surrounding the cavity  that serves as a floor for ATP.The catalytic site in MRS is located at a crevice at the C-terminal ends of the Rossmann fold β-strands . A large cavity for the binding of methionine and ATP is present at the crevice, with helices α3, α8, and α9, and the y Figure  and B. Wtructure . At the P. abyssi MRS structures, the Rossmann fold is well-conserved , and the RMSD between two zinc knuckle structures was also 0.292 Å. In the human MRS structure, the zinc knuckles are located far from the methionine binding site, and the catalytic site is spacious enough to accommodate a methionine and an ATP molecule. For structural analysis of possible movements of the motifs at the catalytic site, the human MRS structure was superimposed on the structure of L. major MRS complexed with methionyl adenylate and pyrophosphate (L. major MRS), which serve as a floor for methionyl adenylate and pyrophosphate, superimpose well. Dramatic conformational changes are expected for the two motifs upon interaction with pyrophosphate. The KFSKS loop (KISKS loop in L. major MRS) folds into the ATP binding site to allow the lysine and serine residues to form hydrogen bonds with the pyrophosphate, and the Zn knuckles move close to the ATP binding site to cover the methionyl adenylate and pyrophosphate.When the structure of human MRS is superimposed on the hosphate  , the first zinc motif (motif1) in human MRS is brought close to the active site by bending and twisting the β-hairpin structure  is similar to the values obtained for the wild-type enzyme and other mutants (0.32–0.38 μM), the KM value of ATP for R399 (575.5 μM) is much greater than for the others (62.4–79.0 μM). These results suggest that the side chain of R399 plays a role in holding ATP. Although the R414A mutant exhibited a slight decrease in activity, the double mutant (R399A/R414A) achieved similar levels of pyrophosphate production to that of the R399A mutant (data not shown).To establish which residue is responsible for holding ATP during the catalytic reaction, four positively-charged residues that can potentially access the ATP binding site by bending and twisting the β-hairpin structure were mutated into alanine: H391, R399 and R439 located at motif1, and R414 at motif2 Figure . When thKM values of ATP for mutant and wild-type enzymes are 322.9 and 71.9 μM, respectively, and those of methionine for the mutant and wild-type enzymes are 0.58 μM and 0.35 μM, respectively. Again, the increase in the KM for ATP is much greater than that for methionine.To confirm that the bending of the β-hairpin structure is essential for catalytic activity, we introduced a disulfide bond to restrict the motion of the β-hairpin by introducing the double mutation E385C/S446C Figure . This muAquifex aeolicus  connected to the catalytic domain. The structure of the first five residues in the protein could not be determined due to poor electron density, suggesting it is flexible.As shown in Figure 2. The indole side chain of W240 is embedded in a distinct pocket in the ABD  is separate from the main catalytic body of MRS, unlike the N- and C-terminal parts of the peptide region, which are directly attached to the ABD and the catalytic Rossmann fold domain, respectively. Residues in this peptide region have relatively high B-factors and do not participate in interactions with other MRS regions, apart from an ionic interaction between R251 and E263 Figure .1–207 and MRS221–834 constructs, we generated an extended version of the MRS GST domain (MRS1–224) and determined the crystal structure of the GST domain complexed with AIMP3 at a resolution of 2.0 Å  after the N-terminal GST domain, and 12 residues (823KTSPKPAVVETV834) before the C-terminal WHEP domain. These peptides must be flexible since they are not visible in the electron density in the crystal structures of MRS1–224 and MRS221–834. Considering the lengths of the missing segments, the N- and C-terminal domains must be less than 40 Å and 35 Å away from the main body, respectively  and two additional domains at the N- and C-termini  have been determined, and two peptide regions are missing; 14 residues  is oriented toward the tRNA binding site  present in the N-terminal missing peptide, and the N-terminal GST domain may be positioned near the ABD.Since MRS is attached to MSC though the N-terminal GST domain, we investigated the relative positions of the GST domain and the catalytic domain, which depend on the location of the long flexible N-terminal linker peptide. Notably, the N-terminal peptide of MRSGST) could be located far from the catalytic main body. However, a previous electron microscopy (EM) study on the MRS–AIMP3–EPRSGST-ERS–AIMP2GST complex suggested a model in which the MRS catalytic domain is located near the tetrameric complex of GST domains  of MRS for the acceptor stem of tRNAMet. Thus, the WHEP domain is predicted to be located near the tRNA acceptor stem in MRS-tRNA complexes, meaning that both N- and C-terminal domains of MRS would face the tRNA binding surface of the ABD  of the main body is located near the KFSKS loop, and the C-terminal WHEP domain is not far from this loop. The C-terminal WHEP domain plays a role in the efficient capture of tRNA . DeletioD Figure .GST and other MSC components in the EM model of MRS complexed with AIMP3, EPRSGST-ERS and AIMP2GST . With 2.5 mM methionine, Vmax and KM values for ATP with MRS–AIMP3 were 150.3 μM/h/μM protein and 83.6 μM, respectively, while for MRS221–900 these were 155.8 μM/h/μM protein and 80.3 μM, respectively.AARS ligates an amino acid to its cognate tRNA in two steps: amino acid activation and tRNA charging. During the first step, an amino acid and an ATP molecule bind at the catalytic active site and react to yield aminoacyl-AMP and pyrophosphate products. In the closed conformation, the N-terminal GST domain of MRS may block the catalytic site. To determine whether the higher activity of MRSe Figure  and C. V221–900 could be due to greater tRNA binding ability. To investigate this, hexa-His-tagged MRS proteins were pulled down using yeast total tRNA and Ni-NTA resin. The amount of tRNA eluted with MRS was estimated from the increase in absorbance at 260 nm. Interestingly, the amount of tRNA associated with MRS221–900 was almost twice that with the MRS–AIMP3 complex, and the A260/A280 ratio indicated an increase due to nucleic acid (data not shown). Absence of the linker peptide and the GST domain around the tRNA binding site therefore enhances the binding of tRNA. Without the GST domain, MRS may bind other tRNA species, resulting in an increase in total tRNA interacting with MRS. Indeed, MRS can bind tRNALys under oxidative stress conditions, resulting in misacylation .Thus, the superior tRNA charging activity of the truncated MRScylation , and thi221MRS–AIMP900 and the MRS–AIMP3 complex for the synthetic RNA were measured by MST assay, and Kd values for the MRS–AIMP3 complex and MRS221–900 were 31.6 μM and 12.8 μM, respectively  for the RNA without 6-FAM was greater than that of the MRS–AIMP3 complex  at its N-terminus (LgBit-AIMP3) was prepared, and luciferase activity was measured with co-expression of the nanoluciferase small subunit (SmBit) at the N- or C-termini of MRS  in CHO-Ki cells. Since we already know the relative position of the MRS N-terminus relative to AIMP3 from the structure of the MRSGST-AIMP3 complex, the C-terminal position of MRS was estimated by comparison of the luciferase activity of the MRS-SmBit:AIMP3 pair with that of the SmBit-MRS:AIMP3 pair . Co-expression of MRS containing SmBit increased luciferase activity dramatically. The activities of luciferases with the subunit at the N- and C-termini of MRS were similar to each other; luciferase activity with the nanoluciferase subunit at the C-terminus of AIMP3 was similar to that with the nanoluciferase subunit at the N-terminus of AIMP3. This may be because AIMP3 is a small, compact domain in which the N- and C-termini are not far from each other. The C-terminus of MRS is therefore close to both ends of AIMP3, as well as the N-terminus of MRS, in the MRSGST-AIMP3 complex.In the closed conformation of the above MRS model, both GST and WHEP domains face the tRNA binding site. We confirmed this spatial arrangement of MRS domains r Figure . BackgroMet gene relative to expression of the gene encoding LgBit-MRS-SmBit, and luciferase activity decreased with increasing tRNA expression . Since the N- and C-terminal ends of MRS are close to each other in the closed conformation, LgBit-MRS-SmBit should exhibit luciferase activity in the absence of tRNA. However, in the presence of tRNA, binding of tRNA to MRS should convert MRS to an open conformation. Interaction with tRNA will induce MRS to release the linker peptide from the ABD, and the N-terminal GST domain will be distant from the C-terminal end, resulting in loss of luciferase activity. To increase the chance of the conformational change taking place, we increased the expression of the tRNAn Figure .Met, we measured the activity of LgBit-MRS-SmBit following overexpression of various tRNA species. Compared with controls without tRNA overexpression, when cognate tRNA was overexpressed, luciferase activity was decreased significantly . The GST domain plays a role in interacting with the N-terminal GST-C-like domain of ARC1P , and yeaThe linker between the GST domain and the catalytic core in yeast MRS is relatively short compared with its counterpart in human MRS, and the yeast primitive MSC is smaller than the human MSC. The apicomplexan MSC is assembled from five components including MRS , which h6 Å3 based on the specific volume of a protein (∼0.74 cm3/g). When we image the packing of component proteins in MSC as crystallographic packing in solution, the volume of MSC including solvent molecules in the void space between components can be calculated to be 3.75 × 106 Å3 by applying a Matthew's coefficient of 2.5 per Dalton. This represents a cubic volume with a length of 150 Å. By comparison, approximate volumes of hexahedrons containing the MSC measured by EM and SAXS are 3.0 × 106 Å3 and 5.0 × 106 Å3, respectively. These volumes indicate compact and expanded forms of MSC, respectively.Many MSC component proteins have flexible linkers between their structured domains . AIMP1 aThe MSC may adopt both compact and extended conformations due to dynamic motions of MSC components, as shown for MRS. In a living cell, the MSC must be dynamic for tRNA charging and interaction with other cellular components. Long linker peptides between compact structural domains allow conformational changes that are essential for function, and dynamic motions in large, complex protein assemblies such as MSCs are particularly important.221–834 and the MRS1–224–AIMP3 complex) have been deposited with the Protein Data bank under accession numbers 5GL7 and 4BVY, respectively.Atomic coordinates and structure factors for the reported crystal structures (MRS


Poxviridae), the smallpox vaccine, is a complex process involving multiple distinct cellular membranes and resulting in multiple different forms of infectious virion. Efficient release of enveloped virions, which promote systemic spread of infection within hosts, requires the VACV protein E2 but the molecular basis of E2 function remains unclear and E2 lacks sequence homology to any well-characterised family of proteins. We solved the crystal structure of VACV E2 to 2.3 Å resolution, revealing that it comprises two domains with novel folds: an N-terminal annular (ring) domain and a C-terminal globular (head) domain. The C-terminal head domain displays weak structural homology with cellular (pseudo)kinases but lacks conserved surface residues or kinase features, suggesting that it is not enzymatically active, and possesses a large surface basic patch that might interact with phosphoinositide lipid headgroups. Recent deep learning methods have revolutionised our ability to predict the three-dimensional structures of proteins from primary sequence alone. VACV E2 is an exemplar ‘difficult’ viral protein target for structure prediction, being comprised of multiple novel domains and lacking sequence homologues outside Poxviridae. AlphaFold2 nonetheless succeeds in predicting the structures of the head and ring domains with high and moderate accuracy, respectively, allowing accurate inference of multiple structural properties. The advent of highly accurate virus structure prediction marks a step-change in structural virology and beckons a new era of structurally-informed molecular virology.The morphogenesis of vaccinia virus (VACV, family  Poxviridae, a family of DNA viruses producing large and complex enveloped virions [Vaccinia virus (VACV) is the prototype member of the  virions . The fam virions . While aOrthopoxviruses produce two distinct types of infectious virion, mature virions  and enveloped virions . MVs are formed in cytoplasmic viral factories, where the genome-containing viral core and lateral bodies are wrapped by a single lipid membrane derived from the endoplasmic reticulum . MVs areDuring IEV egress at least three viral proteins are involved in the activation of kinesin-1-dependent transport of IEVs. These include an integral membrane protein A36 and two cytoplasmic proteins, F12 and E2. Kinesin-1 is a tetramer of two heavy chains, comprising the microtubule-binding motor domain and a long coiled-coil dimerization domain, and two light chains, each comprising a tetratricopeptide repeat (TPR) domain and a coiled-coil domain that mediates dimerization plus heavy-chain association. A36 is associated with the outer IEV envelope  and possEscherichia coli) and insect cell systems were largely unsuccessful, a mammalian expression system was pursued. Small-scale expression tests using transient transfection of codon optimised E2 into human embryonic kidney (HEK)293 T cells confirmed successful purification using cobalt affinity chromatography of VACV E2 tagged at the carboxy terminus with a decahistidine tag. Large-scale expression was thus performed via transient transfection of Freestyle 293 F cells cultured in suspension and purification via cobalt affinity chromatography and size exclusion chromatography (SEC) followed by anion exchange chromatography, yielding ~0.75 mg of highly pure E2 per litre of cultured cells  wide at its narrowest point . While thttps://robetta.bakerlab.org/), and the top five models obtained using each programme have been deposited in the University of Cambridge Data Repository (https://doi.org/10.17863/CAM.77496). Both AF2 and RTF accurately predicted the presence of two domains in the E2 structure, successfully identifying that the head of E2 would have a compact globular fold while the ring would have an extended helical conformation.Recently the application of deep learning technologies to the prediction of protein structures has made the resultant models significantly more accurate with regards to the backbone conformation , although the side chain conformation prediction remains more challenging . The lacα atoms and a Global Distance Test Total Score (GDT_TS) of 87.2, which is remarkably accurate , consistent with accurate prediction of the gross topology but significant differences in the relative orientation of secondary structural elements  than that of RTF . Furthermore, RTF demonstrated more heterogeneity in the conformation of the head domain relative to the ring. Neither AF2 nor RTF predicted the same relative domain orientation as seen in the experimental structure . Surprisingly, correlation was higher for the ring domain (ρ=0.9475) than for the globular head domain (ρ=0.8945), perhaps owing to its higher surface-area to volume ratio, but overall the AF2 model is clearly capable of predicting those residues that are buried in the core of the protein and should be avoided for mutagenic studies.While the above analysis confirms that the AF2 model is closer to the crystal structure of E2 than the RTF model, the obvious use-case of ing DALI   domain and C-terminal globular (head) domain. While the fold of the head domain shares weak homology with cellular (pseudo)kinases and contains a large basic surface patch that may bind phosphoinositide headgroups, the lack of strong structural homology hampers attempts to infer E2 function by analogy to other proteins. Being a multi-domain protein with novel domain folds and limited availability of homologous protein sequences, VACV E2 is an excellent test for the ability of modern deep-learning algorithms to predict ‘difficult’ viral protein structures. The results are impressive, with both AF2 and RTF correctly predicting the overall fold of both domains and AF2 predicting the head domain structure with very high precision. AF2 models will prove a significant resource for the molecular virology community, allowing the identification of structural homologies in the absence of identifiable sequence homology, the exploration of protein surface features, and accelerating the experimental determination of novel viral protein structures.Click here for additional data file.


Following this approach, prototype solid-state soft sensors were designed and fabricated, a dedicated neural network was built to extract the sensory information. Stretching and twisting were measured using the same sensor even at large deformations. In addition, exploiting the distinctive responses against temperature change, we could estimate environmental temperatures simultaneously. Results are promising for the proposed solid-state multimodal approach of soft sensors for multifunctional perception under extreme conditions.Soft robots, with their unique and outstanding capabilities of environmental conformation, natural sealing against elements, as well as being insensitive to magnetic/electrical effects, are ideal candidates for extreme environment applications. However, sensing for soft robots in such harsh conditions would still be challenging, especially under large temperature change and complex, large deformations. Existing soft sensing approaches using liquid-metal medium compromise between large deformation and environmental robustness, limiting their real-world applicability. In this work, we propose a multimodal solid-state soft sensor using hydrogel and silicone. By exploiting the conductance and transparency of hydrogel, we could deploy both optical and resistive sensing in one sensing component. This novel combination enables us to benefit from the  Soft robotics is receiving increasing attention among the researchers and becoming ubiquitous in various fields, including manipulation, human–robot interaction, medical devices, wearable exoskeleton, etc., for its intrinsic compliance, safety, and adaptive continuum deformation S. . ConsideWith reference to the crucial demand of such a sensing unit for soft robotic application, researchers have done fruitful work on soft sensors made of elastic material, achieving easy embedding with the soft actuators and deformation perception . State-oIn this article, we propose a novel solid-state soft-robotic sensor to perceive various modes of deformation, to be effective and robust under severe ambient factors, and to enable the environmental temperature estimation beyond self-state sensing. The proposed multimodal soft-robotic sensor (MHS Sensor), as shown in In this work, investigation on the sensing performance in terms of the resistance changing, optical intensity loss under different deformation is conducted respectively. Temperature estimation is also validated in an incubator. The pattern recognition for deformation classification is achieved by artificial neural network (ANN) for real-time deformation perception. The novel design and experimental results of the MHS sensor are presented and discussed in detail to validate the investigations.1) Large deformation range. The sensing unit should be able to conform to large, continuum deformation, such as stretching and twisting. Therefore, conventional rigid mechanism should be eliminated, and sensing mechanism with soft material is preferred for its compliance and elasticity.2) Multiple sensing principles. To broaden the limited functionality of one simple sensing principle, the sensing unit should be able to integrate multiple sensing principles for utilizing different sensing properties, such as electrical and optical properties. Innovative measures are required to ensure the different mechanisms being able to coexist and coordinate properly within the integrated sensing modular without sacrificing the compactness.3) Multifunctional perception. With a compact form factor, the sensing system would be desirable to sense multiple types of stimulus, such as different deformations or thermal stimulus. The remarkable ability of both proprio and environmental perception within the compact unit would grant the sensor with wide application potential.4) Embedment and customization. As a modularized and integrated sensing unit, it should be encapsulated individually via standard fabrication techniques. In addition, the sensing unit should be size-customizable and easy to embed in the robots for different application scenarios.Numerous extreme application scenarios in soft robotics field have shown the critical demand of a multifunctional integrated sensor unit for proprioception of the large continuum and multi-degree-of-freedom deformation, with ambient environmental information perception, such as the aquatic field with varying temperatures, etc. Apart from the stretching, twisting movements of the robots were shown to be difficult to measure, which is very common in robotic hand or joint motions S. . The couTo achieve the multifunctional perception of large temperature change and complex, large deformations, the MHS Sensor with elastic cladding and multifunctional core medium is proposed, as shown in As the electrical medium, free ions residing in the hydrogel help the current transmitting through the material for resistance detecting, as shown in To achieve the stretching and twisting sensing, the two properties are utilized, which are resistance and optical intensity. When being stretched, the change of the geometrical parameters of the hydrogel core causes the resistive variation , compareThe different responses of the electrical and optical properties under thermal stimulus grant the MHS Sensor with potentials to work under a range of temperatures and estimate the ambient temperature as shown in Overall, the proposed combination of the silicone cladding and the hydrogel core could fulfill the requirements of the soft sensor with the electrical and optical signal varying respectively with the deformation. Detecting and processing such two signals could recognize the mode and the magnitude of the deformation. The thermal characteristics of the proposed sensor would bring benefits of environmental perception.For a cylindrical shape hydrogel with length For the ionic hydrogel, the relationship between the conductivity According to the Arrhenius equation and Vogel–Fulcher–Tammann (VFM) equation, the temperature dependence of any thermal activation process can be described asture [Q. ],R=ρLS=1Thus, the relationship of the core’s resistance and the geometrical parameters as well as the ambient temperature could be derived. Through the relation equation, the deformation could be estimated in terms of the mode and the magnitude.Apart from being the conductive medium for detecting the resistance changing, hydrogel could be the optical medium for its inherent transparency. The total internal reflection of certain wavelengths would happen through the core hydrogel and the photodiode could sense the optical intensity at the distal end of the MHS Sensor. Such optical intensity would vary with the amount of the total internal reflection due to deformation. The optical intensity transmitted to the distal end through the hydrogel could be expressed as [H. ],A=ecL=lBy analyzing the two signal modalities generated by the MHS Sensor under different deformation, the deformation type and magnitude can be obtained. Furthermore, the two properties of the hydrogel  could derive additional perception, i.e., the ambient temperature estimation. When subjected to a thermal stimulus, the resistance of the hydrogel changes, while the transparency and the refractive rate stay unaffected. Thus, the optical intensity transmitted to the end of the sensor would not vary with the temperature. When subjected to a deformation with a temperature higher/lower than the room temperature, the resistance of the sensor is affected by both deformation and temperature. By using the optical information as a reference, the temperature influence can be decoupled from the deformation; thus, the ambient temperature could be estimated. In this way, the multifunctional perception  can be realized by the compact design of the sensor, consisting of silicone cladding and hydrogel core.via current amplifier and digital converter, the signal is outputted to the MCU connected to the PC. Thus, the direct collection of the two signals information could be processed by the PC and further analysis on the deformation and environmental factors modes could be done.To analyze the electrical and optical signal generated from the sensor’s deformation or environmental stimulus, a collecting and processing system has been designed, as shown as To validate the deformation sensing of the MHS Sensor, prototypes were fabricated and tested. An appropriative testing platform was set up for stretching and twisting tests. Results and discussion are presented in detail.The proposed solid-state MHS Sensor consists of three parts: the elastic cladding, the hydrogel core, and the encapsuling ends. The detailed fabrication procedures are illustrated in To investigate the electrical and optical behavior of the MHS Sensor under different deformation modes, experimental apparatuses for stretching and twisting test respectively were developed, as shown in Furthermore, to investigate the MHS Sensor’s sensibility under a range of ambient temperatures, an incubator (LY-2150) was utilized to conduct resistance and optical intensity-strain relation tests under different constant ambient temperatures, as shown in With the three sets of the experimental apparatus, the relationships of resistance and optical intensity and the strain and twisting angle could be investigated for further analysis on the deformation type, magnitude, and the temperature characteristics of the MHS Sensor.To validate the MHS Sensor’s capability of detecting deformation within large range, the stretching deformation was tested and both electrical and optical signals were collected. At room temperature (∼25°C), three groups of stretching tests were conducted with constant stepper motor speed, which means that the stretching was speed constant and continuous. The electrical and optical signals were outputted according to the strain by the aforementioned processing circuits. The testing results are shown in Highly repeatable varying processes were observed among different groups of experiments. In addition, the results also show the remarkable stretching capability of the MHS Sensor that the strain could reach up to 150%. Such capability is realized by the hyper elastic silicone cladding, the strong adhesion, and the deformability of the hydrogel core.Compared with the variations of the resistance, the optical intensity changes tend to be more obvious at the first 100% strain and less obvious afterward. This should be caused by that the optical intensity deteriorated to almost undetectable level at the distal end during the last 50% strain period. The MHS Sensor of a smaller diameter showed higher sensitivity of resistive variation while lower sensitivity of optical variation, leading to a selectable design strategy to balance the resistive and optical sensitivity by designating the appropriate diameter. With the validation of the resistance-strain and optical-strain relationships, the MHS Sensor’s features under stretching deformation could be analyzed.To investigate the relationship of the MHS Sensor’s properties under twisting, three groups of tests were conducted. At the room temperature, the sensor was twisted by the stepper motor with a constant speed from 0 to 540° , which was beyond the critical twisting angle once buckling happened. The results are shown in To validate the reproducibility of the experimental results, repetitive tests of the MHS sensor were conducted. In this part, stretching and bending loops of a fabricated prototype were done for eight times, respectively. Stretching repetitive tests were done with the range of 0–150% strain, and bending tests were from 70 to 140 bending angles. Resistance and optical signals were both collected. The results are shown in Apart from the repetitive tests, duration tests were conducted in terms of stretching. Each 500 cycles of 50% strain, 100% strain, and 150% strain were done collecting the resistance signals and optical signals, as shown in Although there is a silicone cladding to surround and protect the hydrogel inside, separating it with the environment, the dehydrating may affect the performance of the sensor. Therefore, the feasibility of the proposed sensor working in an environment with different humidity situations was tested. The results are shown in The sensors for soft robots are facing a critical application demand, which is operating in complex and harsh environmental factors. For instance, the ambient temperatures vary in a large range under water. To achieve multifunctional perception, the work further explored the performance of the MHS sensor at a variety of ambient temperatures. The incubator was adopted to generate a constant-temperature environment. Then the stretching tests of the three MHS Sensors with different diameters were conducted inside the incubator and the results are shown in The hydrogel was first tested in a −18°C freezer for 2 h. No significant phrase change was obserbed, that the hydrogel still kept elastic and transparent under 0°Cwithout being frozen. From the results shown in In terms of the optical intensity, the larger diameter enhanced the light transmission to the photodiode and the diameter decrease caused by the stretching made the optical intensity deteriorate rapidly, illustrated by the earlier arrival of zero intensity of 2-mm-diameter sensor. Unlike the resistance characteristics, the optical intensity did not change with ambient temperatures , 0.5/0mmThe temperature perception could be achieved by finding temperature Benefit from the proposed dual-modality sensing mechanism, different deformation patterns of the MHS Sensor could be separated with the utilization of ANN. In this section, a simple classifier was trained to predict the deformation mode of the MHS Sensor with a dataset collected under four passive movements: bending, compression, stretching, and twisting .The continuous signals were split into 60-ms data segments with a 15-ms overlap between adjacent segments. The dataset was randomly divided into training dataset and testing dataset with a 90:10 split. 53,255 segments were used for the model training while 5,918 segments were only used for evaluating the classifier. Five features were extracted from each data segment. They are the Arithmetic Mean (M), Median (MED), Standard Deviation (SD) of the electrical and optical signals separately, and the Covariance (CDV) value between the two signals. A 9D feature vector was formatted eventually and constructed an input/output pair with a 4D label as the reference output. Accordingly, the light-weight ANN was built with a simple multilayer perceptron structure .As Overall, through the experiments of stretching, twisting, and temperature, the sensing mechanism of the MHS Sensor could be validated and utilized after calibration. The experimental results and observations are of remarkable importance to the novel method of sensing deformations on robotics. The proposed soft sensor brought three main contributions: 1) substantially conforming to the large and complex deformation, being beneficial from its inherent elastic characteristics of both the cladding and the core; 2) being capable of sensing stretching and twisting with the integrated and compact design, utilizing both the electrical and optical properties variation when changing its geometrical parameters by deformation; 3) being able to compensate the thermal stimulus to the electrical properties of the hydrogel, meantime achieving the temperature estimation.This work tackled the challenges in robotic sensing for large and complex deformation by offering a soft and multimodal approach to the sensing unit. The proposed MHS Sensor is entire solid state, and was investigated, showing its superior performance in large range stretching and twisting with the specific design. The MHS Sensor utilizes silicone molding and the hydrogel deploying techniques to provide a highly integrated and compact sensing system. Experiments were conducted on stretching, twisting platforms and in an incubator, revealing underlying mechanisms and distinctive characteristics of the proposed MHS Sensor. With such a design, the sensor could sense both proprio large and complex deformation and environmental temperature, achieving multifunctional perception. Furthermore, the deformation recognition was achieved by ANN with high accuracy.This work also offers insights into robotic sensing system development with the solution to conform to and detect the coupled deformation, using property-change principles with multiple modalities. Moreover, the soft fabrication and hydrogellic approaches enable the capability of customization for sensor design due to varying demands of the specific applications. Such prodigious customized and individual unit could help design the whole robotic system more targetedly. In addition, the proposed sensor achieves extra perception within limited form factor, which could inspire novel ideas of compact sensor design. Such achievements pave the way for durable and compelling robotic sensing system in extreme application scenarios.Future works include further investigation on more deformation modes, such as compression and coupled deformation. The concentration and the mechanical deformation brought by the temperature change should be investigated in a detailed manner. Furthermore, the adhesion between the hydrogel and the silicone cladding should be extra ensured with special techniques.


Coccinia abyssinica (Lam.) Cogn.) is an indigenous tuber crop produced in southwest part of Ethiopia. As an indigenous and underutilized tuber, limited scientific information is available about the characteristics of dried anchote powder. In this study, attempts were made to investigate the starch composition and functional properties of flours produced from the raw and pretreated tuber dried at different temperatures . The results showed that both pretreatment and drying temperature significantly (p < .05) affected the amylose/amylopectin ratio, pH, total soluble solids (TSS), water absorption capacity (WAC), oil absorption capacity (OAC), water absorption index (WAI), water solubility index (WSI), swelling power (SP), total polyphenols content (TPC), and total flavonoid content (TFC). The treatment combinations result in varied ranges of pH (5.70–6.47), TSS (5.37–10.8 °Brix), WAC (2.42–4.21 g/g), OAC (0.94–1.44 g/g), WAI (3.40–5.42 g/g), WSI (11.40%–20.37%), SP (4.56–7.20 g/g), foaming capacity (FC) (3.31%–33.33%), foam stability (FS) (1.89%–20.00%), amylose content (AC) (14.18%–36.11% ), TPC (0.22–0.80 mg GAE/g), and TFC content (0.12–0.44 mg CE/g). The blanched and boiled anchote flours dried at relatively lower drying temperature exhibited better WAC, SP, and WAI than the raw. Considering the determined parameters, the flour from the tuber can be used as an ingredient in different food formulations.Anchote ( Coccinia abyssinica (Lam.) Cogn.) tuber flours dried at different temperatures were evaluated. The results showed that both pretreatment and drying temperature significantly (p < .05) affected the amylose/amylopectin ratio, pH, TSS, WAC, OAC, WAI, WSI, SP, TPC, and TFC of the flours. The blanched and boiled anchote tuber flours dried at lower temperature exhibited better functional property relative to the raw.Starch composition and functional properties of raw and pretreated  anchote ( The most common edible part of anchote is its tuber, but its leaf and young fruit are also consumed in some areas  in 2018.The anchote tuber used for this study was 2.2A 3 × 3 factorial experiment in a completely randomized design was employed to run the experiment. The first factor included predrying treatments in three levels (untreated (raw), blanched, and boiled tubers), the second factor was drying temperature in three levels , and the experiment was replicated three times.2.3Labquip, Leicester LE67 5FT, England) at 60, 80, and 100°C until constant weight was obtained. Dried tuber slices were then ground into flour, homogenized, sieved through a 500‐μm mesh, packed in a moisture‐proof polyethylene bag, and stored at −4°C until analysis.Anchote flour samples were prepared as per the primary steps shown in Figure 2.42.4.1SIGMA 2‐16KC, India) at 3000 g and 4°C for 10 min. The supernatant was then decanted, the sediment was resuspended in five parts of distilled water, and centrifugation was carried out. The washing step using distilled water was repeated four times. The sediment was finally dried in a hot air convective oven ) at 40°C for 16 h, ground, and sieved through a 100‐mesh sieve, and the starch flour were preserved for further analysis.Starch extraction was carried out using the alkaline method described by Tattiyakul et al. . One par2.4.2V‐630; JASCO, Japan) at 620 nm. The absorbance of the blank solution was deducted from each sample, and percent amylose content was determined using the following equation, and the difference estimated in the amylopectin content (%).A is the absorbance read at 620 nm, 3.06 is the conversion factor calculated as milligram amylose in unit absorbance, and 20 is the dilution factor.Amylose and amylopectin of anchote starch were determined according to Arueya and Ojesanmi . Accordi2.52.5.1pH‐016; HINOTEK, China) calibrated against standards of pH 7 and 4. Five grams of each sample was dispersed in 25 ml of distilled water to prepare the sample. The solution was stirred constantly for 30 min, and pH measurement was taken with frequent shaking until stable reading.The pH of the flour samples was determined using a pH meter (2.5.2°Brix) of the flour sample was estimated using 10% aqueous solution of each flour using a hand refractometer  according to the method described in Thriveni et al.  . Then, the swollen sample was separated by decantation and weighed. Finally, WAC was calculated as g H2O/g dry flour using the following equation:m2 is the sample's weight with the absorbed water and m1 is the initial weight of the dry flour sample.According to the method described by Witono et al. , Water a2.5.4g for 30 min. Then, the clear supernatant was decanted, and the sediment was weighed. Finally, OAC was calculated using the following equation:m2 = sample's weight with the absorbed oil, whereas m1 = initial weight of the dry flour.Oil absorption capacity (OAC) was determined according to the method described by Achy et al. . One gra2.5.5g for 15 min. The supernatant was poured into a tarred evaporating dish to be used for WSI (g/g) determination. The remaining sediment was weighed, and WAI was calculated as grams of the gel obtained per gram of solid flour.The water absorption index (WAI) was determined according to the method described by Yousf et al. . The anc2.5.6The water solubility index (WSI) was determined according to the method reported by Yousf et al. . The sup2.5.7g for 15 min at room temperature (21°C), the precipitated part was weighted, and SP was calculated using the following equation:The swelling power (SP) of the flour samples was determined according to the procedure reported by Kusumayanti et al. . A 0.1 g2.5.8The foaming capacity (FC) and foam stability (FS) of anchote flours were evaluated according to the method reported by Chandra et al. . One graThe foam volume was recorded 1 h after whipping to determine foam stability as a percent of initial foam volume.2.5.93nh Spectrophotometer, NR110, China), recording coordinates L*, a*, and b* values. The L* stands for lightness, the a* indicates redness, and the b* represents yellowness  was calculated concerning the raw sample from the color parameters L*, a*, and b* as described in the following equation:Lo, ao, and bo are L*, a*, b* values for the control (raw) sample.The color of the flour samples was measured with a colorimeter (2.62.6.1HY‐2(A); Speed Adjusting Multi‐purpose Vibrator, China) as optimized and reported . Finally, the TPC present in the samples was determined using gallic acid (GAE) standard  (R2 = .999) as gallic acid equivalent (GAE) in concentration ranges of 12.50–100.00 mg/L and expressed as milligram of GAE/g of the flour sample.The total polyphenols content (TPC) of anchote flour extracts was estimated according to the Folin–Ciocalteu method as adopted from Tanvir et al. . Accordi2.6.3V‐630; JASCO, Japan). Finally, the TFC was determined against (+)‐catechin standard ( Sigma‐Aldrich) (R2=0.988) as (+)‐catechin equivalent (CE) in a concentration range of 1.00–100.00 μg/ml, and the result was expressed as milligram of CE/g of anchote flour.The Total flavonoid content (TFC) of anchote flour extracts was estimated using the colorimetric method adopted from Tanvir et al. . Briefly2.6.42, 2′–diphenyl‐1‐picrylhydrazyl) radical. The radical scavenging activity of the sample extracts was measured using DPPH, according to Woldegiorgis et al.  by monitoring the decrease in absorbance at 517 nm. l‐ascorbic acid was used as positive control with the same concentrations as above. The inhibition of radical DPPH in percent (%) was then calculated by using the following equation:A0 is the absorbance of the control reaction and A1 is the absorbance in the presence of all of the extract samples and reference standards.The total antioxidant activity of anchote flour was estimated using DPPH  using Minitab . The results were expressed as means ± standard deviation of the triplicate treatment. Pearson's correlation coefficient was used to measure the relationship between the flour's properties and the strength and direction of the relationship.The data were subjected to ANOVA, and the significance of the difference between means was determined with Tukey's method at a 95% confidence level (33.1p < .05) decrease in amylose content from raw to blanched and then boiled samples. For instance, at the drying temperature of 60°C, the flour sample from the boiled tuber exhibited a 37% reduction in amylose content than flour from the raw sample dried at the same temperature. Predrying treatments also showed a significant effect on amylopectin content. However, the decrease in amylose was accompanied by an increase in percentage of amylopectin content. Similar findings also showed a reduction in amylose content by boiling in cocoyam (Xanthosoma maffa (Scoth))  and aerial yam (Dioscorea Bulbifera)  tubers , mainly for the raw and blanched samples   was comparable with previous reports for cassava (23.79%–48.79%), yam (29.5%–30.0%), and water yam (23.35%–33.28%) . On the contrary, the flour's pH was significantly (p < .05) decreased with increasing drying temperature, except for the blanched sample. This study illustrated that the flours under this study were slightly acidic (pH < 7) compared with that of sweet potato flour (5.8–6.2)  affected the TSS. Blanching and boiling increased the TSS with the highest observed values of 10.8 and 9.2 °Brix, respectively. This finding is in agreement with the report of Iheagwara et al.  into simple molecules such as carbon dioxide and water at higher drying temperatures  the WAC of the flours. With an increase in heat treatment during predrying treatment, the WAC increased (boiled > blanched > raw); however, with an increase in drying temperature, the WAC decreased (60 > 80 > 100°C). WAC values in this study are in close agreement with the report of Shebabaw   OAC. Blanching and boiling reduced OAC as compared to the raw sample, which agrees with the results reported for Dioscorea bulbifera  and cocoyam  flours  is compared with other tuber crops, it is comparable with the report of Akubor  for raw 3.2.5p < .05) the WAI of the flours. The WAI of the boiled anchote flour is the highest (60°C), and that of the raw sample is the lowest. The higher WAI of boiled anchote tuber indicates the flour's tendency to be gelatinized and well‐cooked  affected by predrying treatments and drying temperature, the highest value for boiled anchote flour and the lowest for the raw (Table Dioscorea bulbifera) flour from 17.31% to 36.85% after boiling , plantain (3.88%), maize (0.70%), and baking wheat (4.67%) flours  increased the SP of the resulting flours. Similar increases were reported for the SP of raw (6.75 g/g) and blanched (8.04 g/g) cocoyam flour . The result revealed that higher SP was recorded in the boiled flour and lower in the raw. The SP of anchote flour fall in the range reported for sweet potato varieties (4.5–7.9 g/g)  different from both blanched and boiled flours. The highest FC was observed in the raw anchote flour dried at 60°C (33.33%), whereas the lowest was in boiled flour dried at 100°C (3.31%). This variation implies that FC of the flour is critically affected by heat treatments during predrying treatment and by drying temperature. Results in this study are also in agreement with the results of Harijono et al.  affected FS at 60°C, while drying temperature significantly (p < .05) affected FS of the raw tuber flour. Increasing drying temperature reduced the FS of the raw tuber flours. Harijono et al. (The findings showed that the highest FS was observed in the raw anchote flour dried at 60°C 20.0%), while the lowest FS was observed in boiled flour dried at 80°C , redness (a*), yellowness (b*), and total color change (ΔE) values for the raw and pretreated anchote flours are presented in Table L* values of the raw anchote flour were significantly (p < .05) lower than in the pretreated flours, which implies that blanching/boiling positively impacted the flour's brightness. Similarly, an increase in drying temperature significantly (p < .05) increased the brightness of the flour. Better lightness  for pretreated samples as compared to the raw might be due to the less effect of enzymatic browning, which could be caused by phenolase enzyme  affected by drying temperature. The decrease in redness after blanching/boiling is in line with what was reported for potato flour  flour  in anchote flour due to blanching and boiling of the tuber ranged from 3.62 to 28.26 (Table E significantly differed (p < .05), suggesting a difference in the color of the control and the pretreatments of anchote flour. This observation agrees with what was reported for blanched taro flour  decreased with increasing drying temperature. This coincides with that reported in lily bulb flours in which ΔE linearly decreased with increasing drying temperature ) flour in which TPC of 0.78, 0.46, and 0.29 mg/100 g for the raw, blanched, and boiled products, respectively  level of raw and pretreated anchote flours is presented in Figure Dioscorea alata (6.8 mg/g), and even greater TPC was reported in sweet potato (10.5–119 mg GAE/g)  is lower than in the previous report for anchote accessions Ayalew, . The dif3.3.2p < .05) affected the TFC of the flours. The TFC was reduced by blanching/boiling and with increasing drying temperature. This finding agrees with the report of Ukom et al.  and sweet potato (6–178 mg QE/g) tubers’ extracts  and SP (r = .798), while negatively correlated with OAC (r = −.794). This relation agrees with the negative correlation reported between the WAC and OAC in rice and maize flours  negative correlation with WAI (r = −.735) and SP (r = −.730), where WAI exhibited a strong positive association with SP (r = .787). The L* showed a strong negative correlation with TPC (r = −.808) of the flours. The FC and FS of the flours were highly correlated (r = .908), and both of them showed a positive correlation with TPC and TFC. This might be because of the dependence of TFC on protein content  and WAC (r = −.76) of the anchote flour, which is in agreement with the reports of Babu and Parimalavalli  increase one of the parameters WAC, WAI, and SP would increase the other and vice versa. These parameters increased by blanching and boiling compared with the raw. Similarly, any change in either WSI, FC, FS, AC, or TPC of the flours due to pretreatment and drying temperature would change the others in a similar trend.4L* values of the flours. Tuber pretreatments (blanching/boiling) can be recommended in food application if these functional properties are considered for commercial use. In contrast, if the interest is to get better amylose, FC, FS, TPC, TFC, and more total antioxidant capacity, it is better to use flours from raw tuber without pretreatments. In general, an increase in drying temperature resulted in a more negative impact on the measured parameters. The lower the drying temperature (60°C) is, the better the result for measured parameters, regardless of pretreatments.Both predrying treatments and drying temperatures exhibited an impact on the amylose and amylopectin ratio. Relatively higher amylose ration was observed in a flour from raw anchote, which can be used to produce firmer, stiffer, and less stick food products. The higher the amylose ratio could also correspond with the presence of more resistant starch for less digestibility of the product for better health benefits associated with noncommunicable chronic diseases. However, the lower amylose from pretreated flours can be used as a thickener and binder in food industries. Predrying treatments also showed a relatively positive impact on TSS, WAC, WAI, WSI, SP, and The authors have no conflict of interest to declare.Adugna Mosissa Bikila: Conceptualization ; Data curation (lead); Formal analysis ; Funding acquisition ; Investigation (lead); Methodology ; Project administration ; Software ; Validation ; Visualization ; Writing – original draft (lead); Writing – review & editing . Yetenayet Bekele Tola: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration (lead); Resources ; Software ; Supervision ; Validation ; Visualization ; Writing – review & editing . Tarekegn Berhanu Esho: Project administration (supporting); Supervision (supporting); Validation (supporting); Visualization (supporting); Writing – review & editing . Sirawdink Fikreyesus Forsido: Conceptualization ; Formal analysis (supporting); Funding acquisition (supporting); Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision (supporting); Validation ; Visualization ; Writing – review & editing . Desta Fekadu Mijena: Data curation (supporting); Resources (supporting).This study did not involve any human or animal testing.


Spin‐torque memristors are proposed in 2009, and can provide fast, low‐power, and infinite memristive behavior for neuromorphic computing and large‐density non‐volatile memory. However, the strict requirements of combining high magnetoresistance, stable domain wall pinning and current‐induced switching in a single device pose difficulties in physical implementation. Here, a nanoscale spin‐torque memristor based on a perpendicular‐anisotropy magnetic tunnel junction with a CoFeB/W/CoFeB composite free layer structure is experimentally demonstrated. Its tunneling magnetoresistance is higher than 200%, and memristive behavior can be realized by spin‐transfer torque switching. Memristive states are retained by strong domain wall pinning effects in the free layer. Experiments and simulations suggest that nanoscale vertical chiral spin textures can form around clusters of W atoms under the combined effect of opposite Dzyaloshinskii–Moriya interactions and the Ruderman–Kittel–Kasuya–Yosida interaction between the two CoFeB free layers. Energy fluctuation caused by these textures may be the main reason for the strong pinning effect. With the experimentally demonstrated memristive behavior and spike‐timing‐dependent plasticity, a spiking neural network to perform handwritten pattern recognition in an unsupervised manner is simulated. Due to advantages such as long endurance and high speed, the spin‐torque memristors are competitive in the future applications for neuromorphic computing. Nanoscale spin memristors are realized based on magnetic tunnel junctions with large tunneling magnetoresistance ratio. Strong domain wall pinning effects in the free layer with an atom‐thick tungsten insertion is shown to be the origin of the memristive behavior. Spike‐timing‐dependent plasticity of the device is studied. A spiking neural network is built, and handwritten pattern recognition is numerically demonstrated. However, most of them suffer from certain limitations, such as finite endurance or relatively low switching speed, which hinder their real applications in systems requiring long endurance such as neural networks with “on‐chip” learning. Spintronic devices, in which the state is modulated by magnetic variation and thus promise a much longer endurance, provide an alternative solution. The concept of a spin‐torque memristor based on the current‐induced magnetic domain wall motion in the free layer of a magnetic tunnel junction (MTJ) was first proposed in 2009. Nevertheless, a real device with nanoscale dimension and all‐spin‐torque operation, for example, without the assistance of an external magnetic field, is still missing. The intermediate tunneling magnetoresistance (TMR) is difficult to stabilize against thermal activation or stimulating currents, especially in devices with nanoscale dimensions. A free layer in the partially switched state with domain walls usually has higher energy than monodomain states because both Heisenberg exchanges and magnetic anisotropy favor a collinear spin texture. Several possible solutions have been proposed, such as creating an intermediate state with the assistance of shape anisotropy, manipulating memristive switching through domain wall pinning in some complex geometries, or by engineering the reference layer. However, these solutions require a large device size or an external magnetic field to realize memristive behaviors. The interfacial Dzyaloshinskii–Moriya interaction (DMI), a form of antisymmetric exchange that favors a chiral spin texture, makes it possible to obtain intrinsically stable noncollinear magnetic structures in nanometer‐scale magnet and provides new possibilities to realize memristive MTJs with nanoscale dimensions.Memristors are considered to be essential elements for realizing neuromorphic computing.2, 3 This spin texture leads to the fluctuations of the domain wall surface energy, which may be one reason for the strong domain wall pinning effects. The spike‐timing‐dependent plasticity (STDP) functionality is experimentally validated. A compact model of the spin‐torque memristors is created according to experimental data and is integrated into a spiking neural network (SNN), which was then employed for the Mixed National Institute of Standards and Technology database (MINST) handwritten pattern recognition. Arabic handwritten numeral can be recognized with our devices. Thanks to advantages such as long endurance, the spin‐torque memristor studied in this work provides a new opportunity for the application of spintronic devices in neuromorphic computing with “on‐chip” learning.In this work, we experimentally demonstrate a nanoscale spin‐torque memristor based on a perpendicular‐anisotropy MTJ with W‐inserted free layer. A high TMR ratio, a low resistance‐area product (RA), and spin‐polarized‐current‐induced switching are achieved. The memristive behavior is proved to originate from strong domain wall pinning effects in the free layer. Measurements with nitrogen‐vacancy (NV) color center in diamond indicate that the saturated magnetization is quite homogeneous in the free layer of MTJ stacks. Whereas, experiments and simulations suggest that a type of vertical chiral spin vortex could form around clusters of W atoms under the combined effect of Ruderman–Kittel–Kasuya–Yosida (RKKY) interactions and interfacial DMIs.16 Thi22.1Figure  A transmission electron microscopy (TEM) image of the multilayer stack is presented in Figure 2 are obtained at room temperature. The switching current is on the order of MA cm−2.The MTJ film with composite free layer is patterned into circular nanopillars with a 200‐nm radius (R) using electron beam (e‐beam) lithography and Ar ion milling and is instrumented with gold electrodes, as shown in Figure TP= 100 ms are applied and after each pulse, the resistance is immediately measured with a 0.01‐V reading voltage, as shown in Figure  Intermediate states are reproduced, which correspond well with those in Figure TP= 200 ns are applied to observe the STT‐switching and more intermediate states appeared, as shown in Figure TP= 200 ns switching voltage pulses. One can find that once the applied voltage increases over a threshold value, the resistance of the device varies quasi‐continuously, which is typical memristive behaviors.To investigate the stability of the intermediate states, voltage pulses with a duration The stability of the intermediate states against external magnetic fields is checked. First, the resistance‐magnetic field hysteresis loop in a device with R = 200 nm is measured at a low temperature, as shown in Figure 2.2 A stable intermediate state is rare in traditional single‐free layer MTJs with submicron dimensions since once magnetic switching begins, the nucleated domain wall moves forward immediately and leads to the complete switching. Here, we get a different process in the composite free layer MTJs.The magnetoresistance of an MTJ depends on the relative magnetic state of the free layer with respect to the pinned layer.24 A sFigure  indicating a strong pinning effect in the composite free layer film. The velocity of the field‐induced domain wall motion is measured and it leaves the thermally activated creep regime until the driving field reaches ≈μ0HC = 16 mT, as shown in Figure  This value is consistent with the field . The deposition and annealing conditions remained the same. As seen from the hysteresis loop of this film i and dwdt=f, where v and i are the voltage and current. w can be a set of state variables and R and f can in general be explicit functions of time. Obviously, the behavior of the present device consists of this definition, where w can be defined as the domain wall position. Theoretically, the number of intermediate states depends on the pinning sites distribution. Increasing the density and strength of pinning sites allows to obtain more intermediate states in a device with finite size.According to Leon Chua's definition, a memristor can be described as,  of time.28 Obv2.3 For example, we measured the domain wall motion velocity in a W/CoFeB (1 nm)/MgO film for comparison and found the intrinsic pinning field is 4.1 mT, as shown in Figure The domain wall pinning effect in a heavy metal/CoFeB/MgO stack is usually very weak.25 For The NV center is placed 20.7±6.7 nm above a magnetically saturated FL‐film and the distribution of stray fields along the NV center quantization axis is obtained, as shown in Figure Scanning microscopy with NV center in diamond is used to check the magnetic quality of the FL‐film.30 TheFigure  Based on the experimental data reported by S. Parkin and our fitting results according to the RKKY law, two ferromagnets neighboring a W spacer begin to exhibit an antiferromagnetic coupling when the W‐thickness reaches ≈0.44 nm, reaching a peak at a W‐thickness of 0.55 nm, corresponding to approximately two atomic layers. The antiferromagnetic coupling between CoFeB layers separated by 0.6‐nm thick W is also observed in a recent study. Therefore, although the two free layers of our MTJ stack exhibit globally ferromagnetic exchange, the stochastic overlapping of the W atoms (W clusters) may result in antiferromagnetic exchange in some local regions.According to the energy‐dispersive X‐ray spectroscopy (EDS) mapping Figure 4a, the sFigure 31 Bas and also at a CoFeB/MgO interface. This interaction is particularly large for a Fe‐rich CoFeB composition, as in our case, and promotes a chiral magnetic texture in the magnetic layer. We have measured the DMI in films with different symmetry breaking via asymmetric domain wall motion when both the perpendicular field and in‐plane field are applied. The DMI is about 0.45 mJ m−2 and favors a left‐handed chirality in a MgO/CoFeB(1.7 nm)/W film; while DMI reaches 0.65 mJ m−2 and favors a right‐handed chirality in a W/CoFeB(1 nm)/MgO film (see Supporting Information 1 (online)). These results suggest that in the composite free layer of MTJs, opposite chiralities are favored for the domain walls in the Up‐layer and Lw‐layer. On the one hand, because of the global ferromagnetic coupling of the two free layers, the total DMI cancels out (only 0.02 mJ m−2 is detected in the FL‐film) and the domain wall configuration shows no chirality (see the Supporting Information 1 (online)). In contrast, when the exchange coupling between the Up‐layer and Lw‐layer is weak or even antiferromagnetic in a local region where W atoms overlap, the domain wall chiralities in the two free layers should be determined by the corresponding DMIs. The magnetization of the domain walls center in the two free layers are opposite, and a chiral vortex could form around the W cluster.On the other hand, considerable DMIs can arise at a W/CoFeB interface because of the spin‐orbit coupling,36 and as shown in Figure Jex is set to be 1 mJ m−2 (0.01 mJ m−2), where a positive (negative) sign of Jex means ferromagnetic (antiferromagnetic) coupling. The interfacial DMI constant D is set to 0.5 mJ m−2 in both cases, with a negative (positive) sign in the Up‐layer (Lw‐layer). The results confirm the above analysis: a coupled Bloch domain wall forms in the two free layers with strong coupling, while a vertical vortex forms under weak coupling, the chirality of which is dominated by the DMIs in each layer.The magnetic state of the two 1‐nm‐thick ferromagnetic layers separated by a 0.2‐nm spacer is simulated using the OOMMF code,42 as −2, the domain wall profile transforms to a chiral vortex, and the domain wall surface energy drops rapidly. In fact, the formation of the chiral vortex domain wall minifies both the energy associated with DMIs and with dipole–dipole interactions. According to the fitting result based on the data reported by S. Parkin, the interlayer coupling decreases from 0.6 mJ m−2 to −0.03 mJ m−2 as the thickness of the W increases from 0.2 to 0.55 nm in ferromagnet/W/ferromagnet structure. When a moving domain wall encounters a W cluster, the domain wall will locally transform into a chiral vortex, as illustrated in Figure  It should be noted that, according to our model, even a partial rotation of the domain wall angle could cause a large fluctuation of wall energy  and duration (TP = 200 ns), as shown in Figure Due to the memristive and non‐volatile magnetoresistance, this two‐terminal MTJ can be used as synapse for neuromorphic computing.45 The is investigated based on these devices. Two sequences of pulses ( TP= 200 ns) with opposite polarity, the amplitude of which ramped below the threshold switching voltage, are used as pre‐spike. A couple of opposite‐voltage pulses (TP = 200 ns and ±0.2 V), with a delay time Δt from the center of pre‐spike, are used as post‐spike, as shown in Figure t is defined as positive (negative) when the pre‐spike stimulus is applied before (after) the post‐spike. Since the amplitude of both the pre‐ and post‐spike stimulus are below the switching voltage of the device, the resistance variation is determined only by the polarity and amplitude of the pick of the across voltage, which depends uniquely on the delay time Δt. Figure t = 80 µs. We define the synaptic weight as the ratio of the initial resistance to the resistance after spiking. Then, we perform a set of tests to observe the weight changes under varying delay time Δt, ranging from −110 to 140 µs, and the results are given in Figure t>0) the post‐spike, the synaptic weight increases, mimicking the potentiation of synapse in a neural system, vice versa; The smaller |Δt|, meaning the higher correlation between the two spikes, the deeper potentiation/depression of synapse.Then, STDP, a fundamental learning function for artificial neural networks,48 is 2.5τ for each given voltage, as shown in Figure τ is observed to be linear to the voltage, consistent with the domain wall pinning‐dominated switching model. A noise factor is added into the device model considering the device's variations and stochastic nature of the domain wall depinning effect. Some examples of the switching behaviors of the device model are given in Figure  as shown in Figure  Figure Rap to1/Rp. Increasing the number of epochs from 0 to 5, 20th, and 50, the weight of the depressed synapses (purple lines) gradually converge to the lowest conductance and the potentiated synapses (yellow lines) converge to the highest conductance. After 50th epochs of unsupervised training, the neural network is trained and the letters are recognizable in the synaptic layer. The specified system simulation method and intermediate data could be found in the Supplementary Materials.In order to investigate the availability and performance of the device in spiking neuromorphic networks, a device‐system simulation with a hierarchical framework is performed. A behavioral model of the device that matches the experimental data is developed and then integrated as synapse in the network, interfacing with the Leaky‐Integrate‐Fire neurons. For both the AP‐P and P‐AP switching, the variation of the resistance along with the stimulating time can be phenomenologically fitted with an exponential function, with a characteristic time constant ng model.49 A n These disadvantages could be overcome by using multiple devices in series as one synapse or implementing sense amplifiers in the synaptic array in real applications. Since the response speed of the device increases in an exponential manner with the applied voltage, a spiking time reduced to nanoseconds can be expected if the voltage is slightly increased. In fact, tens nanometers of STT‐induced domain wall motion are possible within this time scale (10). Considering a spiking voltage of about 0.6 V and the average device resistance of 130 Ω, the power consumption per spike is estimated to be in the order of 10 pJ for a single device. It is worth noting that the memristive behavior of our devices is achieved through magnetic switching, without ion motion or ionic valence changes. Their endurance is expected to be more than 1015 cycles, which is promising for neuromorphic computing that requires high reliability on devices, for example, neural networks with “on‐chip” learnings.The resistance of the presented spin memristor is around hundreds of Ohms and the ON/OFF ratio is lower than conventional metal‐oxide memristors.28 The3In conclusion, a spin‐torque memristor with a high TMR ratio, a low RA, a low working current, and a nanoscale size is obtained by engineering an atomic‐thickness W spacer in the free layer of a MTJ. A memristive TMR is achieved during STT‐induced switching in both directions. By comparing the intrinsic pinning field for domain wall motion in the free layer films and the filed required to break the intermediate TMR at low temperature, it is proved that the memristive behavior of the device originates from strong domain wall pinning effects. NV center measurements prove that the magnetization of the free layer is quite homogenous in a saturated state. Experiments and micromagnetic simulations show that a chiral vortex domain wall could form at a cluster of W atoms because of the opposing interfacial DMIs and the RKKY interaction. The energy fluctuation induced by the domain wall structural transition between trivial coupled Bloch configuration and vertical chiral vortex configuration may be one reason for the strong pinning effect. The STDP functionality of the device has been experimentally demonstrated based on its synaptic property. A compact model is developed based on experimental data and system‐level simulation is performed, showing that our spin‐torque memristor can be performant and competitive for neuromorphic computing, such as unsupervised learning.4−9 Torr. Circular nanopillars were patterned by e‐beam lithography, followed by Ar ion milling and SiO2 insulation. After the lift‐off procedure, Ti/Au electrodes were evaporated for measurements.The MTJ films were deposited by a Singulus TIMARIS 200 mm magnetron sputtering machine at a base pressure of 3.75 × 10Fundamental properties of the MTJ stack and the FL stack, including the saturation magnetization and the perpendicular anisotropy, were both measured via VSM. Besides, the domain structure and domain wall motion in the FL film were observed using a Kerr microscope with a 400‐nm resolution. A fast‐perpendicular field with a rise time of sub‐microsecond, which is produced by a small magnetic coil, is used to measure the domain wall motion velocities in the magnetic film. The strength of DMIs in films with different structure asymmetries are measured via analyzing domain wall motion velocities when out of plane magnetic fields and in‐plane fields are applied simultaneously (see Supporting Information 1).The setup for the electrical characterization of the spin‐torque memristor consists of a Lake Shore CRX‐VF cryogenic probe station, Keithley 4200, Keithley 6221 current sources, and 2182 nanovolt meters. The resistance of the device is measured using four‐probe method. The applied voltage and current are directly recorded to calculate the resistance of the device by the equation: R = V / I. Origin Software is used for data processing and analysis.Micromagnetic simulations based on the OOMMF code were performed to observe the magnetic texture and domain wall pinning effect in the dual FLs. Simulations based on Mumax3 software were performed to quantify the effect of the demagnetizing field on the domain wall structure. Both simulations are based on solving the Landau–Lifshitz–Gilbert equation.In order to simulate the implementation of SNN based on the proposed spin‐torque memristors device, a hybrid device‐system co‐simulation framework is utilized. A “top‐down”  and “bottom‐up” (device modeling) simulation framework written by Python is developed to evaluate the performance of the STDP synaptic function based on the memristive behavior over a subset of standard MNIST handwritten dataset.The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supplemental Movie S1Click here for additional data file.