Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing.

In-sensor computing, which integrates sensing, memory and processing functions, has shown substantial potential in artificial vision systems. However, large-scale monolithic integration of in-sensor computing based on emerging devices with complementary metal-oxide-semiconductor (CMOS) circuits remains challenging, lacking functional demonstrations at the hardware level. Here we report a fully integrated 1-kb array with 128 × 8 one-transistor one-optoelectronic memristor (OEM) cells and silicon CMOS circuits, which features configurable multi-mode functionality encompassing three different modes of electronic memristor, dynamic OEM and non-volatile OEM (NV-OEM).

Environmental policy uncertainty and green transformation dilemma of Chinese enterprises.

With the intensification of environmental issues, environmental policies have played an increasingly important role in the Chinese economic system. The previous literature focuses on the impact of environmental policies on the green transformation of enterprises but pays little attention to policy-related environmental risks. In this way the impact of environmental policy uncertainty on enterprise green transformation remains a black box, which forms the initial motivation of this essay.

Room-Temperature Magneto-Photoresponse in All-2D Optoelectronic Devices for In-Sensor Vision Systems.

Interplay between magnetism and photoelectric properties introduces the effective control of photoresponse in optoelectronic devices via magnetic field, termed as magneto-photoresponse. It enriches the application scenarios and shows potential to construct in-sensor vision systems for artificial intelligence with gate-free architecture. However, achieving a simultaneous existence of room-temperature magnetism and notable photoelectric properties in semiconductors is a great challenge.

Comparative proteomics reveals that fatty acid metabolism is involved in myocardial adaptation to chronic hypoxic injury.

Congenital heart disease (CHD) is the most serious form of heart disease, and chronic hypoxia is the basic physiological process underlying CHD. Some patients with CHD do not undergo surgery, and thus, they remain susceptible to chronic hypoxia, suggesting that some protective mechanism might exist in CHD patients. However, the mechanism underlying myocardial adaptation to chronic hypoxia remains unclear.

Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology.

Cotton ( L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods.

Can network infrastructure construction improve the low-carbon innovation of Chinese enterprises?

This study considers the implementation of the "Broadband China" strategy as an exogenous policy shock and examines the impact of network infrastructure construction (NIC) on the low-carbon innovation (LCI) of enterprises and its underlying mechanisms by using a progressive difference-in-difference model based on the data of Chinese listed enterprises from 2009 to 2020. This study finds that NIC can improve the LCI of enterprises. After the elimination of the sample selection bias and selection of the urban slope as the exogenous instrumental variable, the conclusions remained robust.

Edge learning using a fully integrated neuro-inspired memristor chip.

Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost.

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning.

Background: The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits because of the advances in the point cloud deep learning method.

Can the green finance improve export green-sophistication? New insights from China.

The green financial policy is one of the important policy tools for China to achieve the national carbon peak goal and carbon neutrality through financial means. How financial development affects the growth of international trade has been an important research topic. This paper uses the Pilot Zones for Green Finance Reform and Innovations (PZGFRI) implemented in 2017 as a natural experiment drawing on the relevant data of Chinese provinces' panel data from 2010 to 2019.

An in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning.

The growing computational demand in artificial intelligence calls for hardware solutions that are capable of in situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing) but also memory hardware with tunable properties to simultaneously meet the demand for training and inference. Here we report a duplex device structure based on a ferroelectric field-effect transistor and an atomically thin MoS channel, and realize a universal in-memory computing architecture for in situ learning.

Green credit guideline and enterprise export green-sophistication.

Green credit is a major policy innovation to guide enterprises to participate in environmental governance actively. This study uses the data of Chinese A-share listed companies from 2007 to 2016, takes the green credit guideline (GCG) issued in 2012 as a quasi-natural experiment, and uses a difference in difference (DID) model to test the effect of GCG on the enterprises' export green-sophistication (EGS) and its internal and external mechanisms. The study finds that GCG improves enterprises' EGS and research and development (R&D) investment is the intermediation channel for GCG to affect EGS.

Rapid detection of HPV16/18 based on a CRISPR-Cas13a/Cas12a dual-channel system.

Purpose: cervical cancer is the leading cause of cancer deaths in women in the developing world, with high-risk HPV16 and HPV18 accounting for approximately 70% of all cervical cancers. Early detection of HPV, especially high-risk HPV types, is essential to prevent disease progression.

Methods: in this study, we established a highly sensitive and specific nucleic acid assay based on a CRISPR-Cas13a/Cas12a dual-channel system combined with multiplex RAA for rapid detection and typing of HPV16/18, which provides a new idea for cervical cancer screening.

Hydrothermal Aging Mechanisms of All-Steel Radial Tire Composites.

This work focused on the effects of the hydrothermal environment on the aging of all-steel radial tire (ASRT) composites. Composite specimens were conditioned by immersion in deionized water at 30, 60 and 90 °C. Its water absorption, thermal and mechanical properties (tensile strength, elasticity modulus, elongation at break and interfacial shear strength), morphological structure, as well as molecular cross-linking reaction were investigated before and after aging.

Incidence and mortality of ovarian cancer at the global, regional, and national levels, 1990-2017.

Background: Ovarian cancer (OC) is a commonly diagnosed gynecologic cancer. Knowing the incidence and mortality rates of OC is critical to understanding the disease burden and updating prevention strategies.

Methods: We retrieved the age-standardized incidence and mortality rates (ASIR and ASMR, respectively) of OC from the Global Burden of Disease study online database.

[ARTICLE WITHDRAWN] Long Noncoding RNA KCNQ1OT1 Accelerates the Progression of Ovarian Cancer via MicroRNA-212-3/LCN2 Axis.

ARTICLE WITHDRAWN: This article was withdrawn by the authors with the following Withdrawal Statement - The integrity of the current study is not acceptable. The authors intend to enrich the study to make it more valuable. Thus, the authors want to withdraw the current study.

SOX17, β-catenin and CyclinD1 expression in the endometrioid adenocarcinoma and influence of 5-AZA on expression.

The present study discusses the expression and effect of the SOX17 gene in endometrioid adenocarcinoma. MTT assay is performed to determine the growth inhibition ratio of the DNA methyltransferase inhibitor 5-AZA for endometrial carcinoma cells, and the real-time fluorescence quantification PCR (qRT-PCR) was used to detect the mRNA expression of SOX17, β-catenin, and CyclinD1 in endometrial carcinoma tissues before and after using 5-AZA to treat the endometrial carcinoma cell line. There were 30 cases on endometrioid adenocarcinoma tissues and 10 cases on normal endometrial tissues.

Characterizing the landscape of peritoneal exosomal microRNAs in patients with ovarian cancer by high-throughput sequencing.

In the present study, differentially expressed microRNAs (miRNAs) in peritoneal exosomes that were isolated from 10 patients with epithelial ovarian cancer (EOC) with metastasis in the abdominal cavity and 10 participants without cancer (NC) were identified. These differentially expressed miRNAs that were revealed by next-generation sequencing were categorized by Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of their target genes. Notably, two miRNAs that were associated with EOC-miR-149-3p and miR-222-5p-were identified.

Gap analysis and implications for seasonal management on a local scale.

Background: Identifying biodiversity hotspots on a local scale, using multiple data sources, and ecological niche modeling, has the potential to contribute to more effective nature reserve management.

Methods: In this study, we used infrared-triggered camera trapping, field surveys, and interviews to create a dataset on the distribution of species (mammals and birds) in Hebei Wulingshan Nature Reserve (Hebei Province, China).

Results: We identified 101 species (14 orders, 38 families), 64 of which (2,142 effective records) were selected for environmental niche modeling.

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking.

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion.

An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration.

In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking.