Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module. In the circuit, the process of output excitement and addiction in stochastic feedback is achieved. The memory of interval between the two rewards is formed. The circuit can adapt to complex scenarios with these functions. In addition, hunger and satiety are introduced to realize the interaction between biological behavior and exploration desire, which enables the circuit to continuously reshape its memories and actions. The process of operant conditioning theory for automatic learning is accomplished. The study of operant conditioning can serve as a reference for more intelligent brain-inspired neural systems.

Diallyl Trisulfide Acts as a Soil Disinfestation Against the Ilyonectria destructans through Inducing the Burst of Reactive Oxygen Species.

Soil-borne diseases represent an impediment to the sustainable development of agriculture. A soil-borne disease caused by Ilyonectria destructans severely impacts Panax species, and soil disinfestation has proven to be an effective management approach. Here, diallyl trisulfide (DATS), derived from garlic, exhibited pronounced inhibitory effects on the growth of I. destructans in vitro tests and contributed to the alleviation of soil-borne diseases in the field. A comprehensive analysis demonstrated that DATS inhibits the growth of I. destructans by activating detoxifying enzymes, such as GSTs, disrupting the equilibrium of redox reactions. A series of antioxidant amino acids were suppressed by DATS. Particularly noteworthy is the substantial depletion of glutathione by DATS, resulting in the accumulation of ROS, ultimately culminating in the inhibition of I. destructans growth. Briefly, DATS could effectively suppress soil-borne diseases by inhibiting pathogen growth through the activation of ROS, and it holds promise as a potential environmentally friendly soil disinfestation.

NNT-AS1 in CAFs-derived exosomes promotes progression and glucose metabolism through miR-889-3p/HIF-1α in pancreatic adenocarcinoma.

It is metabolic and signaling crosstalk between stromal cells and tumors in the tumor microenvironment, which influences several aspects of tumor formation and drug resistance, including metabolic reprogramming. Despite considerable findings linking lncRNAs in HIF-1-related regulatory networks to cancer cell, little emphasis has been given to the role in communication between cancer-associated fibroblasts (CAFs) and tumor cells. Previously, we observed that NNT-AS1 was substantially expressed in CAFs cells and CAFs exosomes, and subsequently investigated the influence of CAFs exosomal NNT-AS1 on glucose metabolism, proliferation, and metastasis of pancreatic ductal adenocarcinoma (PDAC) cells. Transmission electron microscopy was used to examine exosomes secreted by PDAC patient-derived CAFs. qRT-PCR was used to evaluate the expression of NNT-AS1, miR-889-3p, and HIF-1. The role of CAFs-derived exosomal NNT-AS1 in PDAC cell progression and metabolism have been identified. Dual luciferase reporter assays examined the binding between NNT-AS1, miR-889-3p, and HIF-1. After PDAC cells co-culture exosomes secreted by CAFs, we found that they alter glucose metabolism, proliferation, and metastasis. In PDAC cells, CAF-derived exosomal lncRNA NNT-AS1 acted as a molecular sponge for miR-889-3p. Furthermore, HIF-1 could be targeted by miR-889-3p and was controlled by NNT-AS1. This study explores the mechanism by which NNT-AS1 influences the interaction of CAFs on glycolytic remodeling, proliferation, and metastasis of tumor cells through regulating miR-889-3p/HIF-1α, which also helps discover new clinical treatment targets for PDAC.

Atomic-Level Tailoring of the Electronic Metal-Support Interaction Between Pt-Co3O4 Interfaces for High Hydrogen Evolution Performance.

Atomic-level modulation of the metal-oxide interface is considered an effective approach to optimize the electronic structure and catalytic activity of metal catalysts but remains highly challenging. Here, we employ the atomic layer deposition (ALD) technique together with a heteroatom doping strategy to effectively tailor the electronic metal-support interaction (EMSI) at the metal-oxide interface on the atomic level, thereby achieving high hydrogen evolution performance and Pt utilization. Theoretical calculations reveal that the doping of N atoms in Co3O4 significantly adjusts the EMSI between Pt-Co3O4 interfaces and, consequently, alters the d-band center of Pt and optimizes the adsorption/desorption of reaction intermediates. This work sheds light on the atomic-level regulation and mechanistic understanding of the EMSI in metal-oxide, while providing guidance for the development of advanced EMSI electrocatalysts for various future energy applications.

Causal relationship between gut microbiota and polycystic ovary syndrome: a literature review and Mendelian randomization study.

Introduction: Numerous studies have suggested an association between gut microbiota and polycystic ovarian syndrome (PCOS). However, the causal relationship between these two factors remains unclear. Methods: A review of observational studies was conducted to compare changes in gut microbiota between PCOS patients and controls. The analysis focused on four levels of classification, namely, phylum, family, genus, and species/genus subgroups. To further investigate the causal relationship, Mendelian randomization (MR) was employed using genome-wide association study (GWAS) data on gut microbiota from the MiBioGen consortium, as well as GWAS data from a large meta-analysis of PCOS. Additionally, a reverse MR was performed, and the results were verified through sensitivity analyses. Results: The present review included 18 observational studies that met the inclusion and exclusion criteria. The abundance of 64 gut microbiota taxa significantly differed between PCOS patients and controls. Using the MR method, eight bacteria were identified as causally associated with PCOS. The protective effects of the genus Sellimonas on PCOS remained significant after applying Bonferroni correction. No significant heterogeneity or horizontal pleiotropy was found in the instrumental variables (IVs). Reverse MR analyses did not reveal a significant causal effect of PCOS on gut microbiota. Conclusion: The differences in gut microbiota between PCOS patients and controls vary across observational studies. However, MR analyses identified specific gut microbiota taxa that are causally related to PCOS. Future studies should investigate the gut microbiota that showed significant results in the MR analyses, as well as the underlying mechanisms of this causal relationship and its potential clinical significance.

Memristor-Based Neural Network Circuit of Associative Memory With Overshadowing and Emotion Congruent Effect.

Most memristor-based neural network circuits consider only a single pattern of overshadowing or emotion, but the relationship between overshadowing and emotion is ignored. In this article, a memristor-based neural network circuit of associative memory with overshadowing and emotion congruent effect is designed, and overshadowing under multiple emotions is taken into account. The designed circuit mainly consists of an emotion module, a memory module, an inhibition module, and a feedback module. The generation and recovery of different emotions are realized by the emotion module. The functions of overshadowing under different emotions and recovery from overshadowing are achieved by the inhibition module and the memory module. Finally, the blocking caused by long-term overshadowing is implemented by the feedback module. The proposed circuit can be applied to bionic emotional robots and offers some references for brain-like systems.

Diagnostic and prognostic risk factors analysis for distant metastasis in melanoma: a population-based study.

BACKGROUND: We aimed to develop tools that could predict the occurrence of distant metastases in melanoma and its prognosis based on clinical and pathological characteristics. MATERIALS AND METHODS: We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database of melanoma patients diagnosed between 2010 and 2019. Logistic analyses were performed to identify independent risk factors associated with distant metastasis. Additionally, multivariate Cox analyses were conducted to determine independent prognostic factors for patients with distant metastasis. Two nomograms were established and evaluated with the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Furthermore, we performed a retrospective analysis of melanoma with distant metastasis from our institute between March 2018 and June 2022. RESULTS: Of the total 19 396 melanoma patients, 352 (1.8%) had distant metastases at the time of diagnosis. The following clinical and pathological characteristics were identified as independent risk factors for distant metastasis in melanoma: N stage, tumor size, ulceration, mitosis, primary tumor site, and pathological subtype. Furthermore, tumor size, pathological subtype, and radiotherapy were identified as independent prognostic factors. The results of the training and validation cohorts' ROC curves, calibration, DCA, and Kaplan-Meier survival curves demonstrate the effectiveness of the two nomograms. The retrospective study results from our center supported the results from the SEER database. CONCLUSION: The clinical and pathological characteristics of melanoma can predict a patient's risk of metastasis and prognosis, and the two nomograms are expected to be effective tools to guide therapy decisions.

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.

Identification of anoikis-related subtypes and immune landscape in kidney renal clear cell carcinoma.

Anoikis is a specific form of programmed cell death induced by the loss of cell contact with the extracellular matrix and other cells, and plays an important role in organism development, tissue homeostasis, disease development and tumor metastasis. We comprehensively investigated the expression patterns of anoikis-related genes (ARGs) in kidney renal clear cell carcinoma (KIRC) from public databases. Anoikis-related prognostic signatures were established based on four ARGs expression, in which KIRC patients were assigned different risk scores and divided into two different risk groups. In addition, four ARGs expression was validated by qRT-PCR. A better prognosis was observed in the low-risk group, but with lower immune activity (including immune cells and immune-related functions) in the tumor microenvironment. Combined with the relevant clinical characteristics, a nomogram for clinical application was established. Receiver operating characteristics (ROC) and calibration curves were constructed to demonstrate the predictive power of this risk signature. In addition, higher risk scores were significantly and positively correlated with higher gene expression of tumor mutation load (TMB), immune checkpoints (ICPs) and mismatch repair (MMR)-related proteins in general. The results also suggested that the high-risk group was more sensitive to immunotherapy and certain chemotherapeutic agents. Anoikis-related prognostic signatures may provide a better understanding of the roles of ARGs and offer new perspectives for clinical prognosis and individualized treatment.

Integrative analyses of the mRNA expression profile reveal the involvement of STC1 in chicken folliculogenesis.

Efficient ovarian follicle development, maturation, and ovulation are critical for egg production performance. Previous research has underscored the importance of messenger RNAs (mRNAs) in regulating development and folliculogenesis in chicken ovarians. However, the molecular mechanism is not fully understood, especially in the late period of the laying cycle. In the present study, ovarian tissues from 80-week-old Hy-Line Brown layers (three with high and three with low rates of egg laying) were collected for transcriptome sequencing. A total of 306 differentially expressed genes (DEGs) were identified in this study, at a false discovery rate (FDR)-corrected P-value < 0.05 and a log2|fold change| (log2|FC|) ≥1.5. Among these DEGs, stanniocalcin 1 (STC1) was mainly related to cellular processes, single-organism processes, biological regulation, metabolic processes, developmental processes, and reproductive processes. Then, we further investigated the regulation of STC1 during chicken follicle development and found that STC1 inhibited the proliferation and stimulated the apoptosis of follicular granulosa cells (GCs), and decreased the expression of progesterone (P4) and estradiol (E2). Collectively, these results suggest that STC1 plays an important role in chicken follicle development by decreasing GC proliferation and steroidogenesis and stimulating GC apoptosis. This study contributes to the understanding of the reproductive biology of laying hens in the late period of the laying cycle and further lays a foundation for the improvement of egg production in poultry breeding.

The egg production performance of chickens is an essential economic trait that differs significantly between high- and low-egg-laying breeds. In addition to external factors such as feeding, light, and environment, the periodic recruitment of pre-hierarchical follicles and the normal development of hierarchical follicles affect this difference. Thus, we used high-throughput sequencing technology to perform transcriptome analysis of ovarian tissues from 80-wk-old Hy-Line Brown layers with high- and low-egg-laying rates (HH and HL), and an association with the laying performance gene stanniocalcin 1 (STC1) was found. The proliferation and apoptosis of granulosa cells (GCs), as the basic functional cells of ovarian follicles, are highly correlated with the normal development and regression of follicles. Therefore, this study used ovarian follicular GCs cultured in vitro to study the effects of the STC1 gene on the proliferation, apoptosis, and secretion function of GCs and to explore its mechanism of action, laying a foundation for the study of the regulation of the STC1 gene on follicular development.

Adsorption properties of hollow carbon spheres to gaseous cyclohexane using DFT simulation and experiments.

The cyclohexane is the common toxic volatiles emitted from the various industry in worldwide leading to environmental degradation and human illnesses. Hence, there is a requirement for an efficient and stable adsorbent for adsorbing these toxic molecules to safeguard human health and the air atmosphere. Hollow carbon spheres (HCS) are a new type of carbon nanomaterial with large specific surface area, low density, and good chemical and thermal stability. In this study, DFT simulations and static-dynamic adsorption studies of cyclohexane were carried out using HCS as the adsorbent material. Among them, static adsorption focuses on adsorption/desorption isotherm, adsorption isotherm model fitting and isosteric heat of adsorption. Dynamic adsorption was mainly studied the effect of initial concentrations, gas flow rate, and ambient temperature on adsorption performance. The results showed that HCS exhibited very good performance in cyclohexane adsorption.

Zeolite-templated carbon-supported Ru-based catalysts for efficient alkaline hydrogen evolution reaction.

Herein, a series of Ru/ZTCs samples were prepared using LaY zeolite-templated carbon as a support. Characterizations showed that the unique structure of the ZTCs and the chemical state of Ru facilitated superior HER performance compared to other carbon-supported samples. This work offers a new strategy for designing excellent electrocatalysts.

Robust Self-Supported SnO2-Mn2O3@CC Electrode for Efficient Electrochemical Degradation of Cationic Blue X-GRRL Dye.

Exploration of highly efficient and robust catalyst is pivotal for electrocatalytic degradation of dye wastewater, but it still is a challenge. Here, we develop a three-dimensional self-supported SnO2-Mn2O3 hybrid nanosheets grown on carbon cloth (noted by SnO2-Mn2O3@CC) electrode via a simple hydrothermal method and annealing treatment. Benefitting from the interlaced nanosheets architecture that enlarges the surface area and the synergetic component effect that accelerates the interfacial electronic transfer, SnO2-Mn2O3@CC electrode exhibits a superior electrocatalytic degradation efficiency for cationic blue X-GRRL dye in comparison with the single metal oxide electrode containing SnO2@CC and Mn2O3@CC. The degradation efficiency of cationic blue X-GRRL on SnO2-Mn2O3@CC electrode can reach up to 97.55% within 50 min. Furthermore, self-supported architecture of nanosheets on carbon cloth framework contributes to a robust stability compared with the traditional electrode via the multiple dip/brush coating accompanied by the thermal decomposition method. SnO2-Mn2O3@CC electrode exhibits excellent recyclability, which can still retain a degradation efficiency of 94.12% after six cycles. This work may provide a new pathway for the design and exploration of highly efficient and robust electrooxidation catalysts for dye degradation.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC.

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Interaction mechanism and spatial effect of cross-regional haze pollution based on a multisectoral economy-energy-environment (3E) model and the evidence from China.

The transboundary characteristics and multisectoral factor interaction mechanism of haze pollution have aroused widespread attention but remain understudied. This article proposes a comprehensive conceptual model that clarifies regional haze pollution, further establishes a theoretical framework on a cross-regional, multisectoral economy-energy-environment (3E) system, and attempts to empirically investigate the spatial effect and interaction mechanism employing a spatial-econometrics model based on China's province-level regions. The results demonstrate that (1) regional haze pollution is a transboundary atmospheric state formed by the accumulation and agglomeration of various emission pollutants; moreover, there is a "snowball" effect and a spatial spillover effect. (2) The formation and evolution of haze pollution are driven by the multisectoral factors of 3E system interaction, and the findings still hold after theoretical and empirical analysis and robustness tests. (3) Significant spatial autocorrelation exists for the 3E factors, presenting different clustering modes with a dynamic spatiotemporal evolution, particularly in the high-high (H-H) mode and low-low (L-L) mode. (4) Significant heterogeneous impacts of economic and energy factors on haze pollution are identified, namely, an inverted "U-shaped" relationship and a positive linear association, respectively. Further spatial analysis demonstrates a strong spatial spillover and obvious path dependence among local and neighboring regions. Policymakers are advised to consider multisectoral 3E system interaction and cross-regional collaboration. Integr Environ Assess Manag 2023;19:1525-1543. © 2023 SETAC.

A novel magnetic AgVO3/rGO/CuFe2O4 hybrid catalyst for efficient hydrogen evolution and photocatalytic degradation.

A superior semiconductor material with efficient charge separation and easy reuse could be a promising route for efficient photocatalytic hydrogen evolution and pollutant degradation. AgVO3 is one of the best visible light active materials which has attracted much attention for several biological and environmental applications. In the aim of enhancing its stability and recyclability a novel AgVO3/rGO/CuFe2O4 heterojunction was prepared by hydrothermal method for hydrogen generation (H2) and 4-nitrophenol (4-NP) degradation as well. The composite was well characterized by XRD, SEM, HR-TEM, XPS and VSM. The morphological images suggested the rod shaped AgVO3 and irregular shaped CuFe2O4 are unevenly distributed on reduced graphene oxide (rGO) layers. The hydrogen evolution results indicated that the composite showed around 8.937 mmol g-1h-1 of H2 generation which was ∼2.3 times and ∼9.2 times higher than pure AgVO3 (3.895 mmol g-1h-1) and CuFe2O4 (0.96 mmol g-1h-1) respectively. The 4-NP degradation efficiency of the prepared composite was observed as 94.7% (k = 0.01841 min-1) which is much higher than the AgVO3 (66.3%) and CuFe2O4 (38.2%) after 4 h of irradiation. The higher efficiency could be attributed to the heterojunction formation and faster charge separation. The radical trapping results indicated that the •OH, O2•- and photogenerated h+ are the main species responsible for efficient activity. The AgVO3/rGO/CuFe2O4 heterojunction showed 49.6 emu/g of saturation magnetization and confirms that it could be easily separated with an external magnet, and showed 85.3% of degradation efficiency even after 6 recycles which indicated its higher stability and recyclability. Thus, our study provides new insight into hydrogen generation and phenol degradation using AgVO3 based recyclable composites.

Relationship between food-derived antioxidant vitamin intake and breast cancer risk: a mendelian randomized study.

BACKGROUND: In previous observational studies, food-derived antioxidant vitamins have been suggested to be associated with breast cancer. However, the findings were inconsistent and the causal relationship could not be clearly elucidated. To confirm the potential causal relationship between food-derived antioxidants (retinol, carotene, vitamin C and vitamin E) and the risk of breast cancer, we conducted a two-sample Mendelian randomization (MR) study. METHODS: The instrumental variables (IVs) as proxies of genetic liability to food-derived antioxidant vitamins were obtained from the UK Biobank Database. We extracted breast cancer data (122,977 cases and 105,974 controls) from the Breast Cancer Consortium (BCAC). In addition, we studied estrogen expression status categorically, including estrogen receptor positive (ER+) breast cancer (69,501 cases and 105,974 controls) and versus estrogen receptor (ER-) negative breast cancer (21,468 cases and 105,974 controls). We performed two-sample Mendelian randomization study, and inverse variance-weighted (IVW) test was regarded as main analysis. Sensitivity analyses were further conducted to assess heterogeneity and horizontal pleiotropy. RESULTS: The results of IVW showed that among the four food-derived antioxidants, only vitamin E had protective effect on the risk of overall breast cancer (OR = 0.837, 95% CI 0.757-0.926, P = 0.001) and ER+ breast cancer (OR = 0.823, 95% CI 0.693-0.977, P = 0.026). However, we found no association between food-derived vitamin E and ER- breast cancer. CONCLUSIONS: Our study suggested food-derived vitamin E can decrease the risk of breast cancer overall and ER+ breast cancer, and the robustness of our results was confirmed by sensitivity analyses.

Introducing non-bridging ligand in metal-organic framework-based electrocatalyst enabling reinforced oxygen evolution in seawater.

Using seawater as the replacement of freshwater for electrolysis, with the integration of renewable energy, is deemed as an attractive manner to harvest green hydrogen. However, the complexity of seawater puts forward stricter requirement to the electrocatalyst to alleviate the chlorine electrochemistry and corrosion. Herein, a nanosheet array of NiFe-MOF@Ni2P/Ni(OH)2 is devised by partially substituting terephthalic acid (H2BDC) ligand by ferrocenecarboxylic acid (FcCA). Tailoring the active site into an under-coordinated fashion affords NiFe-MOF@Ni2P/Ni(OH)2 excellent performance towards oxygen evolution reaction (OER), only requiring the overpotentials of 302 mV and 394 mV in alkaline seawater to drive the current densities of 100 and 1000 mA cm-2, respectively. Moreover, the as-obtained electrocatalyst showed robust durability for operating more than 120 h at 500 mA cm-2 under harsh condition (6 M KOH + 1.5 M NaCl, 60 ℃). Density functional theory (DFT) calculations confirmed that tuning the coordination environment of Ni in NiFe-MOF by incorporating the non-bridging FcCA ligands could boost the formation of more active catalytic sites, which can simultaneously enhance the electronic conductivity and accelerate OER kinetics. This work provides beneficial enlightenment of combining MOF-based electrocatalyst with direct electrolysis of seawater.

Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks.

With the rapid progress of deep neural network (DNN) applications on memristive platforms, there has been a growing interest in the acceleration and compression of memristive networks. As an emerging model optimization technique for memristive platforms, bit-level sparsity training (with the fixed-point quantization) can significantly reduce the demand for analog-to-digital converters (ADCs) resolution, which is critical for energy and area consumption. However, the bit sparsity and the fixed-point quantization will inevitably lead to a large performance loss. Different from the existing training and optimization techniques, this work attempts to explore more sparsity-tolerant architectures to compensate for performance degradation. We first empirically demonstrate that in a certain search space (e.g., 4-bit quantized DARTS space), network architectures differ in bit-level sparsity tolerance. It is reasonable and necessary to search the architectures for efficient deployment on memristive platforms by the neural architecture search (NAS) technology. We further introduce bit-level sparsity-tolerant NAS (BST-NAS), which encapsulates low-precision quantization and bit-level sparsity training into the differentiable NAS, to explore the optimal bit-level sparsity-tolerant architectures. Experimentally, with the same degree of sparsity and experiment settings, our searched architectures obtain a promising performance, which outperform the normal NAS-based DARTS-series architectures (about 5.8% higher than that of DARTS-V2 and 2.7% higher than that of PC-DARTS) on CIFAR10.

Projection Synchronization of Three-Dimensional Chaotic Systems With Active Control Based on DNA Strand Displacement.

The emergence of biological computing based on DNA strand displacement has allowed chaotic systems to have more abundant dynamic behaviors. So far, the synchronization of chaotic systems based on DNA strand displacement has been mainly realized by coupling control and PID control. In this paper, the projection synchronization of chaotic systems based on DNA strand displacement is achieved using an active control method. First, some basic catalytic reaction modules and annihilation reaction modules are constructed based on the theoretical knowledge of DNA strand displacement. Second, the chaotic system and the controller are designed according to the above mentioned modules. On the basis of chaotic dynamics, the complex dynamic behavior of the system is verified by the lyapunov exponents spectrum and the bifurcation diagram. Third, the active controller based on DNA strand displacement is used to realize the projection synchronization between the drive system and the response system, where the projection can be adjusted within a certain range by changing the value of the scale factor. The result of projection synchronization of chaotic system is more flexible, which is realized by active controller. Our control method provides an efficient way to achieve synchronization of chaotic systems based on DNA strand displacement. The designed projection synchronization is verified to have excellent timeliness and robustness by the results Visual DSD simulation.