Search

Background: Inflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify immune-related diagnostic candidate genes for IDD, and search for potential pathogenesis and therapeutic targets for IDD. Methods: Gene expression datasets were obtained from the Gene Expression Omnibus (GEO). Differential expression immune genes (Imm-DEGs) were identified through weighted gene correlation network analysis (WGCNA) and linear models for microarray data analysis (Limma). LASSO algorithm was used to identify feature genes related to IDD, which were compared with core node genes in PPI network to obtain hub genes. Based on the coefficients of hub genes, a risk model was constructed, and the diagnostic value of hub genes was further evaluated through receiver operating characteristic (ROC) analysis. Xcell, an immunocyte analysis tool, was used to estimate the infiltration of immune cells. Finally, nucleus pulposus cells were co-cultured with macrophages to create an M1 macrophage immune inflammatory environment, and the changes of hub genes were verified. Results: Combined with the results of WGCNA and Limma gene differential analysis, a total of 30 Imm-DEGs were identified. Imm-DEGs enriched in multiple pathways related to immunity and inflammation. LASSO algorithm identified 10 feature genes from Imm-DEGs that significantly affected IDD, and after comparison with core node genes in the PPI network of Imm-DEGs, 6 hub genes (NR1H3, SORT1, PTGDS, AGT, IRF1, TGFB2) were determined. Results of ROC curves and external dataset validation showed that the risk model constructed with the 6 hub genes had high diagnostic value for IDD. Immunocyte infiltration analysis showed the presence of various dysregulated immune cells in the degenerative nucleus pulposus tissue. In vitro experimental results showed that the gene expression of NR1H3, SORT1, PTGDS, IRF1, and TGFB2 in nucleus pulposus cells in the immune inflammatory environment was up-regulated, but the change of AGT was not significant. Conclusions: The hub genes NR1H3, SORT1, PTGDS, IRF1, and TGFB2 can be used as immunorelated biomarkers for IDD, and may be potential targets for immune regulation therapy for IDD.

Abstract In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Motivated by the recent observation of Klein tunneling in 8-Pmmn borophene, we delve into the phenomenon in β12 borophene by employing tight-binding approximation theory to establish a theoretical mode. The tight-binding model is a semi-empirical method for establishing the Hamiltonian based on atomic orbitals. A single cell of β12 borophene contains five atoms and multiple central bonds, so it creates the complexity of the tight-binding model Hamiltonian of β12 borophene. We investigate transmission across one potential barrier and two potential barriers by changing the width and height of barriers and the distance between two potential barriers. Regardless of the change in the barrier heights and widths, we find the interface to be perfectly transparent for normal incidence. For other angles of incidence, perfect transmission at certain angles can also be observed. Furthermore, perfect and all-angle transmission across a potential barrier takes place when the incident energy approaches the Dirac point. This is analogous to the “super”, all-angle transmission reported for the dice lattice for Klein tunneling across a potential barrier. These findings highlight the significance of our theoretical model in understanding the complex dynamics of Klein tunneling in borophene structures.

Ribosome biogenesis is a fundamental process in eukaryotic cells. NOTCHLESS (NLE) is involved in 60S ribosome biogenesis in yeast, but its role in Arabidopsis (A. thaliana) remains exclusive. Here, we found that Arabidopsis NLE (AtNLE) is highly conservative in phylogeny, which encoding a WD40-repeat protein. AtNLE is expressed in actively dividing tissues. AtNLE-GFP is localized in the nucleus. AtNLE physically interacts with the MIDAS domain of AtMDN1, a protein involved in the biogenesis of the 60S ribosomal subunit in Arabidopsis. The underexpressing mutant nle-2 shows short roots and reduced cell number in the root meristem. In addition, the null mutant nle-1 is embryo lethal, and defective embryos are arrested at the early globular stage. This work suggests that AtNLE interacts with AtMDN1, and AtNLE functions in root and embryo development.

As an emerging computing architecture, the computing-in-memory (CIM) exhibits significant potential for energy efficiency and computing power in artificial intelligence applications. However, the intrinsic non-idealities of CIM devices, manifesting as random interference on the weights of neural network, may significantly impact the inference accuracy. In this paper, we propose a novel training algorithm designed to mitigate the impact of weight noise. The algorithm strategically minimizes cross-entropy loss while concurrently refining the feature representations in intermediate layers to emulate those of an ideal, noise-free network. This dual-objective approach not only preserves the accuracy of the neural network but also enhances its robustness against noise-induced degradation. Empirical validation across several benchmark datasets confirms that our algorithm sets a new benchmark for accuracy in CIM-enabled neural network applications. Compared to the most commonly used forward noise training methods, our approach yields approximately a 2% accuracy boost on the ResNet32 model with the CIFAR-10 dataset and a weight noise scale of 0.2, and achieves a minimum performance gain of 1% on ResNet18 with the ImageNet dataset under the same noise quantization conditions.

The sandstone layer connectivity in coal measure strata is one of the key factors in CBM escape in underlying coal seams, which lacks systematic research currently. This study aimed to explore sandstone layer connectivity and its control on CBM accumulation, taking the Lower Shihezi Formation in Qinan Coal Mine, Xuzhou–Suzhou Region, China, as a case study; to do so, we studied the No. 7 coal CBM unit, the pore-rich sandstone layers, and their connectivity modes by performing sequence stratigraphic analysis on the borehole cores, the logging data, and the theory of sequence stratigraphy and sedimentology, combined with the accumulation characteristics of the No. 7 coal seam CBM. This study shows the following: (1) The sequence stratigraphic framework of the Lower Shihezi Formation in the research region consists of two third-class sequences and six system tracts. (2) The No. 7 coal seam CBM unit includes the CBM formation layer, the connectivity layer, and the stable capping layer. (3) There are 10 types of parasequences in the No. 7 coal connectivity layer, and the pore-rich sandstone layers are all located in the connectivity layer and connected in three modes (vertical connectivity, lateral connectivity, and non-connectivity). (4) The connectivity modes and the thickness of pore-rich sandstone layers control the CBM accumulation in the region. Where the pore-rich sandstone layers are thickest and display vertical connectivity, the strong CBM desorption and escape lead to low CBM; where the pore-rich sandstone layers are thinnest and unconnected, the weak to no CBM desorption and escape result in high CBM. (5) Three models for sandstone layer connectivity and its control on CBM accumulation include the CBM weak accumulation model with a strong source supply, large basin subsidence, and undercompensation deposition; the CBM moderate accumulation model with a moderate source supply, moderate basin subsidence, and overcompensation to isostatic compensation deposition; and the CBM strong accumulation model with a weak source supply, small basin subsidence, and undercompensation deposition.

Abstract We report a theoretical study of electronic transport properties of α-T3 lattice nanoribbons in the presence of uniform electric and magnetic fields. Landau levels with an unexcepted fashion are obtained in the system, and unique flat bands are observed due to the crossed electric and magnetic fields. We found that the nondispersive flat band of α-T3 lattice is distorted and split to many dispersive energy levels when electric and magnetic fields are applied. A double constriction structure of α-T3 lattice is considered to investigate the quantum transport in the flat band, and novel quantum transport properties are obtained, which shows great differences from conventional Dirac electrons. Our results show that the flat bands of α-T3 lattice can also contribute to the quantum transport properties and play an important role in the development of novel Dirac electron device.

Analog resistive random-access memory (RRAM)-based computation-in-memory (CIM) technology is promising for constructing artificial intelligence (AI) with high energy efficiency and excellent scalability. However, the large overhead of analog-to-digital converters (ADCs) is a key limitation. In this work, we propose a novel LINKAGE architecture that eliminates PE-level ADCs and leverages an analog data transfer module to implement inter-array data processing. A blockwise dataflow is further proposed to accelerate convolutional neural networks (CNNs) to speed up compute-intensive layers and solve the unbalanced pipeline problem. To obtain accurate and reliable benchmark results, key component modules, such as straightforward link (SFL) modules and Tile-level ADCs, are designed in standard 28 nm CMOS technology. The evaluation shows that LINKAGE outperforms the conventional ADC/DAC-based architecture with a 2.07×∼11.22× improvement in throughput, 2.45×∼7.00× in energy efficiency, and 22%–51% reduction in the area overhead while maintaining accuracy. Our LINKAGE architecture can achieve 22.9∼24.4 TOPS/W energy efficiency (4b-IN/4b-W) and 1.82 ∼4.53 TOPS throughput with the blockwise method. This work demonstrates a new method for significantly improving the energy efficiency of CIM chips, which can be applied to general CNNs/FCNNs.

Sound localization is one of the many learning tasks accomplished by the brain based on the binaural signals of the ears. Here, Wu et al demonstrate in-situ learning of sound localization function using a memristor array, with dramatic improvements in energy efficiency.

Reservoir computing has demonstrated high-level performance, however efficient hardware implementations demand an architecture with minimum system complexity. The authors propose a rotating neuron-based architecture for physically implementing all-analog resource efficient reservoir computing system.

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis. These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units (CPUs) and graphics processing units (GPUs). Computation-in-memory (CIM) provides a possible solution for highly efficient morphology operations. In this study, we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromorphic computation on a multi-array-based memristor system. Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors. Moreover, hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit (IC) manufacturing and medical image analysis. In addition, we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism. The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed, respectively, exhibiting the advantage of the proposed memristor-based morphology operation.

The phenomenon of coalbed-methane synclinal accumulation in the Qinshui Basin has been widely reported, but it has mainly been observed in the core block of the Qinshui Syncline. The questions arise: does this phenomenon exist in the wing of the Qinshui Syncline and, if so, what is the mechanism behind it? Further study is required to answer these questions. This paper focuses on the South Anze No. 3 coal seam in the Qinshui Basin as an example. It conducts a systematic sorting of coalbed-methane geological characteristics and an analysis of the effects of structural assemblage characteristics, genetic mechanisms, and structural control on coalbed-methane accumulation. Additionally, it examines the basin structure and evolution during the critical period of the Qinshui Basin, as well as the gas geological characteristics of adjacent areas, in order to discuss the gas-rich mechanism of the syncline in the Qinshui Basin. Key insights obtained from the study include the following: (i) The whole South Anze is a nosing structure that plunges from west to east and superposes secondary folds and faults in different directions. Four deformation zones can be identified based on the characteristics of structural assemblage, including NEN-oriented compressive structures, ENE-trend shear fractures, EW-trend compressive fractures, and EW-trend compressive folds. The formation of structural assemblage in the study area is attributed to the compression in the Indosinian and Yanshanian, and the fault inversion in the Himalayan period. (ii) The ENE-trend shear fracture deformation area located in the nosing uplift is a low CBM (coalbed methane) content area due to gas diffusion during the Himalayan extension. The syncline in the combination of NEN-trend and EW-trend “ejective folds” in the west and south of the study area is a high-value area of coalbed-methane content. It is further verified that the law of syncline gas accumulation in the Qinshui Basin is also applicable to the wing of the Qinshui Syncline. (iii) Since the formation of the Qinshui Syncline, the main coal seam has been in an extensional environment below the neutral plane, resulting in the main dissipation of coalbed methane. During its geological history, surface water penetrated the aquifer above the main coal seam through two channels: the extensional area above the neutral plane of the adjacent anticline and the shear fracture. A hydrostatic pressure seal is formed in the Qinshui Syncline and the secondary syncline is superimposed upon it, which is the cause of gas enrichment in the syncline of the Qinshui Basin. (iv) Weak deformation in the syncline basin is the focus of global coalbed-methane exploration and development. The mechanism proposed in this paper can provide ideas and references for further understanding of coalbed-methane enrichment in this type of basin.

In plant cells, multiple paralogs from ribosomal protein (RP) families are always synchronously expressed, which is likely contributing to ribosome heterogeneity or functional specialization. However, previous studies have shown that most RP mutants share common phenotypes. Consequently, it is difficult to distinguish whether the phenotypes of the mutants have resulted from the loss of specific genes or a global ribosome deficiency. Here, to investigate the role of a specific RP gene, we employed a gene overexpression strategy. We found that Arabidopsis lines overexpressing RPL16D (L16D-OEs) display short and curled rosette leaves. Microscopic observations reveal that both the cell size and cell arrangement are affected in L16D-OEs. The severity of the defect is positively correlated with RPL16D dosage. By combining transcriptomic and proteomic profiling, we found that overexpressing RPL16D decreases the expression of genes involved in plant growth, but increases the expression of genes involved in immune response. Overall, our results suggest that RPL16D is involved in the balance between plant growth and immune response.

The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.

Flexible pressure sensors based on polymer elastomers filled with conductive fillers show great advantages in their applications in flexible electronic devices. However, integratable high-sensitivity pressure sensors remain understudied. This work improves the conductivity and sensitivity of PDMS-Fe/Ni piezoresistive composites by introducing silver flakes and magnetic-assisted alignment techniques. As secondary fillers, silver flakes with high aspect ratios enhance the conductive percolation network in composites. Meanwhile, a magnetic field aligns ferromagnetic particles to further improve the conductivity and sensitivity of composites. The resistivity of the composite decreases sharply by 1000 times within a tiny compression strain of 1%, indicating excellent sensing performance. On the basis of this, we demonstrate an integratable miniature pressure sensor with a small size (2 × 2 × 1 mm), high sensitivity (0.966 kPa−1), and wide sensing range (200 kPa). Finally, we develop a flexible E-skin system with 5 × 5 integratable sensor units to detect pressure distribution, which shows rapid real-time response, high resolution, and high sensitivity.

We investigated spin-dependent thermoelectric transport in zigzag phosphorene nanoribbons with a ferromagnetic stripe. We explored the possibility to enhance the spin thermopower via modifications of the edge states in zigzag ribbons. Two methods are proposed to modulate the edge transport: one is applying gate voltages on the edges; the other is including notches on the ribbon edges. The transport gap is enlarged by the edge-state modifications, which enhance the charge and spin Seebeck coefficients almost twofold. Our results suggest phosphorene to be a promising material for thermoelectric applications and open a possibility to design a tunable spin-thermoelectric device.

Abstract Neuromorphic systems consisting of artificial neurons and synapses can process complex information with high efficiency to overcome the bottleneck of von Neumann architecture. Artificial neurons are essentially required to possess functions such as leaky integrate‐and‐fire and output spike. However, previous reported artificial neurons typically have high operation voltage and large leakage current, leading to significant power consumption, which is contrary to the energy‐efficient biological model. Here, an oscillatory neuron based on Ag filamentary threshold switching memristor (TS) that has a low operation voltage (108 cycles). Being connected to an external resistor or a resistive switching memristor (RS) as synaptic weight, the TS clearly demonstrates self‐oscillation behavior once the input pulse voltage exceeds the threshold voltage. Meanwhile, the oscillation frequency is proportional to the input pulse voltage and the conductance of RS synapse, which can be used to integrate the weighted sum current. As an energy‐efficient memristor‐based spiking neural network, this combination of TS oscillatory neuron with RS synapse is further evaluated for image recognition achieving an accuracy of 79.2 ± 2.4% for CIFAR‐10 subset.

Using chips that mimic the human brain to perform cognitive tasks, namely neuromorphic computing, calls for low power and high efficiency hardware. Here, Yaoet al. show on-chip analogue weight storage by integrating non-volatile resistive memory into a CMOS platform and test it in facial recognition.

The auditory pathway consists of multiple stages, from the cochlear nucleus to the auditory cortex. Neurons acting at different stages have different functions and exhibit different response properties. It is unclear whether these stages share a common encoding mechanism. We trained an unsupervised deep learning model consisting of alternating sparse coding and max pooling layers on cochleogram-filtered human speech. Evaluation of the response properties revealed that computing units in lower layers exhibited spectro-temporal receptive fields (STRFs) similar to those of inferior colliculus neurons measured in physiological experiments, including properties such as sound onset and termination, checkerboard pattern, and spectral motion. Units in upper layers tended to be tuned to phonetic features such as plosivity and nasality, resembling the results of field recording in human auditory cortex. Variation of the sparseness level of the units in each higher layer revealed a positive correlation between the sparseness level and the strength of phonetic feature encoding. The activities of the units in the top layer, but not other layers, correlated with the dynamics of the first two formants (F1, F2) of all phonemes, indicating the encoding of phoneme dynamics in these units. These results suggest that the principles of sparse coding and max pooling may be universal in the human auditory pathway.

The parallel updating scheme of RRAM-based analog neuromorphic systems based on sign stochastic gradient descent (SGD) can dramatically accelerate the training of neural networks. However, sign SGD can decrease accuracy. Also, some non-ideal factors of RRAM devices, such as intrinsic variations and the quantity of intermediate states, may significantly damage their convergence. In this paper, we analyzed the effects of these issues on the parallel updating scheme and found that it performed poorly on the task of MNIST recognition when the number of intermediate states was limited or the variation was too large. Thus, we propose a weighted synapse method to optimize the parallel updating scheme. Weighted synapses consist of major and minor synapses with different gain factors. Such a method can be widely used in RRAM-based analog neuromorphic systems to increase the number of equivalent intermediate states exponentially. The proposed method also generates a more suitable ΔW, diminishing the distortion caused by sign SGD. Unlike when several RRAM cells are combined to achieve higher resolution, there are no carry operations for weighted synapses, even if a saturation on the minor synapses occurs. The proposed method also simplifies the circuit overhead, rendering it highly suitable to the parallel updating scheme. With the aid of weighted synapses, convergence is highly optimized, and the error rate decreases significantly. Weighted synapses are also robust against the intrinsic variations of RRAM devices.

The NAC (for NAM, ATAF1,2, and CUC2) proteins family are plant-specific transcription factors, which play important roles in leaf development and response to environmental stresses. In this study, an NAC gene, DRL1, isolated from grapevine Vitis vinifera L. “Yatomi Rose”, was shown to be involved in leaf senescence. The quantity of DRL1 transcripts decreased with advancing leaf senescence in grapevine. Overexpressing the DRL1 gene in tobacco plants significantly delayed leaf senescence with respect to chlorophyll concentration, potential quantum efficiency of photosystem II (Fv/Fm), and ion leakage. Moreover, exogenous abscisic acid (ABA) markedly reduced the expression of DRL1, and the ABA and salicylic acid (SA) concentration was lower in the DRL1-overexpressing transgenic plants than in the wild-type plants. The DRL1 transgenic plants exhibited reduced sensitivity to ABA-induced senescence but no significant change in the sensitivity to jasmonic acid-, SA- or ethylene-induced senescence. Transcriptomic analysis and RNA expression studies also indicated that the transcript abundance of genes associated with ABA biosynthesis and regulation, including 9-cis-epoxycarotenoid dioxygenase (NCED1), NCED5, zeaxanthin epoxidase1 (ZEP1), ABA DEFICIENT2 (ABA2), ABA4, and ABA INSENSITIVE 2 (ABI2), was markedly reduced in the DRL1-overexpressing plants. These results suggested that DRL1 plays a role as a negative regulator of leaf senescence by regulating ABA synthesis.

In grapevine, the MYB transcription factors play an important role in the flavonoid pathway. Here, a R2R3-MYB transcription factor, VvMYBC2L2, isolated from Vitis vinifera cultivar Yatomi Rose, may be involved in anthocyanin biosynthesis as a transcriptional repressor. VvMYBC2L2 was shown to be a nuclear protein. The gene was shown to be strongly expressed in root, flower and seed tissue, but weakly expressed during the fruit development in grapevine. Overexpressing the VvMYBC2L2 gene in tobacco resulted in a very marked decrease in petal anthocyanin concentration. Expression analysis of flavonoid biosynthesis structural genes revealed that chalcone synthase (CHS), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin reductase (LAR) and UDP glucose flavonoid 3-O-glucosyl transferase (UFGT) were strongly down-regulated in the VvMYBC2L2-overexpressed tobacco. In addition, transcription of the regulatory genes AN1a and AN1b was completely suppressed in transgenic plants. These results suggested that VvMYBC2L2 plays a role as a negative regulator of anthocyanin biosynthesis.