The progress of artificial intelligence and the development of large-scale neural networks have significantly increased computational costs and energy consumption. To address these challenges, researchers are exploring low-power neural network implementation approaches and neuromorphic computing systems are being highlighted as potential candidates. Specifically, the development of high-density and reliable synaptic devices, which are the key elements of neuromorphic systems, is of particular interest. In this study, an 8 × 16 memcapacitor crossbar array that combines the technological maturity of flash cells with the advantages of NAND flash array structure is presented. The analog properties of the array with high reliability are experimentally demonstrated, and vector-matrix multiplication with extremely low error is successfully performed. Additionally, with the capability of weight fine-tuning characteristics, a spiking neural network for CIFAR-10 classification via off-chip learning at the wafer level is implemented. These experimental results demonstrate a high level of accuracy of 92.11%, with less than a 1.13% difference compared to software-based neural networks (93.24%).

Maintaining the intracellular iron concentration within the homeostatic range is vital to meet cellular metabolic needs and reduce oxidative stress. Previous research revealed that the haloarchaeon Halobacterium salinarum encodes four diphtheria toxin repressor (DtxR) family transcription factors (TFs) that together regulate the iron response through an interconnected transcriptional regulatory network (TRN). However, the conservation of the TRN and the metal specificity of DtxR TFs remained poorly understood. Here we identified and characterized the TRN of Haloferax volcanii for comparison. Genetic analysis demonstrated that Hfx. volcanii relies on three DtxR transcriptional regulators (Idr, SirR, and TroR), with TroR as the primary regulator of iron homeostasis. Bioinformatics and molecular approaches revealed that TroR binds a conserved cis-regulatory motif located ∼100 nt upstream of the start codon of iron-related target genes. Transcriptomics analysis demonstrated that, under conditions of iron sufficiency, TroR repressed iron uptake and induced iron storage mechanisms. TroR repressed the expression of one other DtxR TF, Idr. This reduced DtxR TRN complexity relative to that of Hbt. salinarum appeared correlated with natural variations in iron availability. Based on these data, we hypothesize that variable environmental conditions such as iron availability appear to select for increasing TRN complexity.

Archaea are a domain of microbial life whose member species play a critical role in the global carbon cycle, climate regulation, the human microbiome, and persistence in extreme habitats. In particular, hypersaline-adapted archaea are important, genetically tractable model organisms for studying archaeal genetics, genomics, and biochemistry. As the archaeal research community grows, keeping track of the genetic integrity of strains of interest is necessary. In particular, routine genetic manipulations and the common practice of sharing strains between labs allow mutations to arise in lab stocks. If these mutations affect cellular processes, they may jeopardize the reproducibility of work between research groups and confound the results of future studies. In this work, we examine DNA sequences from 60 strains across two species of archaea. We identify shared and unique mutations occurring between and within strains. Independently, we trace the lineage of each strain, identifying which genetic manipulations lead to observed off-target mutations. While overall divergence across labs is minimal so far, our work highlights the need for labs to continue proper strain husbandry.

Abstract In this work, low-power RRAM (Resistive random-access memory) devices were characterized by a SiO 2 layer serving as an oxygen scavenging barrier, which suppresses conductive filament overgrowth and reduces operation current and power consumption. Additionally, the integration of an AlO x /TiO y overshoot suppression layer enabled intrinsic self-compliance and forming-free operation. XPS analysis confirmed the oxygen composition of the oxygen-rich TiO y , and it was also verified that the higher oxygen composition in TiO y suppresses filament formation, which decreases the operation current. Consequently, the SiO 2 layer decreases the LRS current significantly by 10 3 times. Furthermore, the device demonstrates the endurance of 6 × 10 3 cycles and retention over 10 4 s, maintaining analog multi-bit operation. When the proposed device was integrated into an 8 × 8 passive array and programmed with the designated weights, a low mean absolute error (MAE) of 10.8 nA was achieved. Additionally, vector-matrix multiplication operations demonstrated excellent accuracy, with over 99% of results falling within an error margin of 10%. Based on this low MAE, MNIST image classification simulations were conducted, yielding classification accuracy exceeding 96%.

Microplastics are pervasive pollutants in aquatic ecosystems, yet their effects on fish tissues remain insufficiently characterized. Our study investigates the impact of polystyrene microplastics (0.5 and 2 μm) on the gill and intestinal tissues of goldfish (<i>Carassius auratus</i>), with a focus on inflammatory responses and pathogen susceptibility. Following two weeks of exposure, histological and molecular analyses revealed increased filament cartilage thickness in gills, enhanced villus thickness and goblet cell numbers in intestines, and upregulation of immune- and oxidative stress-related genes. Exposure to 0.5 μm microplastics significantly reduced survival after <i>Edwardsiella piscicida</i> infection, indicating increased vulnerability to pathogens. These findings highlight the immunotoxic effects of microplastics and their potential to compromise fish health in contaminated environments.

A surge in the number of anthropogenic pollutants has been caused by increasing industrial activities. Nanoplastics are spotlighted as a new aquatic pollutant that are a threat to microbes and larger organisms. Our previous study showed that the subinhibitory concentrations of aquatic pollutants such as phenol and formalin act as signaling molecules and modulate global gene expression and metabolism. In this study, we aimed to investigate the impact of a new type of anthropogenic contaminant, polystyrene (PS) nanoplastics, on the expression of key virulence factors in zoonotic pathogen Edwardsiella piscicida and the assessment of potential changes in the susceptibility of zebrafish as a model host. The TEM data indicated a noticeable change in the cell membrane indicating that PS particles were possibly entering the bacterial cells. Transcriptome analyses performed to identify the differentially expressed genes upon PS exposure revealed that the genes involved in major virulence factor type VI secretion system (T6SS) were down-regulated. However, the expression of T6SS-related genes was recovered from the PS adapted E. piscicida when nanoplastics are free. This demonstrated the hypervirulence of pathogen in infection assays with both cell lines and in vivo zebrafish model. Therefore, this study provides experimental evidence elucidating the direct regulatory impact of nanoplastics influx into aquatic ecosystems on fish pathogenic bacteria, notably influencing the expression of virulence factors.

Batch normalization (BN) is a technique used to enhance training speed and generalization performance by mitigating internal covariate shifts. However, implementing BN in hardware presents challenges due to the need for an additional complex circuit to normalize, scale and shift activations. We proposed a hardware binary neural network (BNN) system capable of BN in hardware, which is consist of an AND-type flash memory array as a synapse and a voltage sense amplifier (VSA) as a neuron. In this system, hardware BN was implemented using a voltage shifter by adjusting the threshold of the binary neuron. To validate the effectiveness of the proposed hardware-based BNN system, we fabricated a charge trap flash with a gate stack of SiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>/Si<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub>N<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub>/SiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>. The electrical characteristics were modelled by using BSIM3 model parameters so that the proposed circuit was successfully demonstrated by a SPICE simulation. Moreover, variation effects of the voltage shifter were also analyzed using Monte Carlo simulation. Finally, the performance of the proposed system was proved by incorporating the SPICE results into a high-level simulation of binary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LeNet-5</i> for MNIST pattern recognition, resulting in the improvement of the proposed system in terms of power and area, compared to the previous studies.

The progress of artificial intelligence and the development of large-scale neural networks have significantly increased computational costs and energy consumption. To address these challenges, researchers are exploring low-power neural network implementation approaches and neuromorphic computing systems are being highlighted as potential candidates. Specifically, the development of high-density and reliable synaptic devices, which are the key elements of neuromorphic systems, is of particular interest. In this study, an 8 × 16 memcapacitor crossbar array that combines the technological maturity of flash cells with the advantages of NAND flash array structure is presented. The analog properties of the array with high reliability are experimentally demonstrated, and vector-matrix multiplication with extremely low error is successfully performed. Additionally, with the capability of weight fine-tuning characteristics, a spiking neural network for CIFAR-10 classification via off-chip learning at the wafer level is implemented. These experimental results demonstrate a high level of accuracy of 92.11%, with less than a 1.13% difference compared to software-based neural networks (93.24%).

Maintaining the intracellular iron concentration within the homeostatic range is vital to meet cellular metabolic needs and reduce oxidative stress. Previous research revealed that the haloarchaeon Halobacterium salinarum encodes four diphtheria toxin repressor (DtxR) family transcription factors (TFs) that together regulate the iron response through an interconnected transcriptional regulatory network (TRN). However, the conservation of the TRN and the metal specificity of DtxR TFs remained poorly understood. Here we identified and characterized the TRN of Haloferax volcanii for comparison. Genetic analysis demonstrated that Hfx. volcanii relies on three DtxR transcriptional regulators (Idr, SirR, and TroR), with TroR as the primary regulator of iron homeostasis. Bioinformatics and molecular approaches revealed that TroR binds a conserved cis-regulatory motif located ∼100 nt upstream of the start codon of iron-related target genes. Transcriptomics analysis demonstrated that, under conditions of iron sufficiency, TroR repressed iron uptake and induced iron storage mechanisms. TroR repressed the expression of one other DtxR TF, Idr. This reduced DtxR TRN complexity relative to that of Hbt. salinarum appeared correlated with natural variations in iron availability. Based on these data, we hypothesize that variable environmental conditions such as iron availability appear to select for increasing TRN complexity.