A novel spectroscopy-deep learning approach for aqueous multi-heavy metal detection.

Addressing heavy metal contamination in water bodies is a critical concern for environmental scientists. Traditional detection methods are often complex and costly. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have shown significant potential in analytical chemistry.

Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods.

Microplastics (MPs) pose significant threats to ecosystems and human health due to their persistence and widespread distribution. This paper provides a comprehensive review of sampling methods for MPs in aquatic environments, soils, and biological samples, assessing pre-treatment procedures like digestion and separation. It examines the application and limitations of identification techniques, including microscopic observation, spectroscopic analysis, and thermal analysis.

An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments.

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs.

Regeneratable bioinspired nanochannels for highly sensitive electrochemical detection of glycated albumin.

Glycated albumin (GA) has been proposed as a reliable diabetes mellitus marker particularly useful in assessing intermediate glycemic control. Herein, we designed a bioinspired nanochannels for biochemical detection based on the host-guest interaction between β-cyclodextrin and azobenzene. Cyclodextrin was grafted on the inner surface of nanochannels of a nanoporous membrane and azobenzene was tagged to the terminal of GA aptamer, thereby facilitating the orientation of GA aptamer in the nanochannels.

A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models.

Self-Assembly of Heterogeneous Ferritin Nanocages for Tumor Uptake and Penetration.

Well-defined nanostructures are crucial for precisely understanding nano-bio interactions. However, nanoparticles (NPs) fabricated through conventional synthesis approaches often lack poor controllability and reproducibility. Herein, a synthetic biology-based strategy is introduced to fabricate uniformly reproducible protein-based NPs, achieving precise control over heterogeneous components of the NPs.

() regulates lemma development and affects rice grain yield.

Unlabelled: In grass, the lemma is a unique floral organ structure that directly determines grain size and yield. Despite a great deal of research on grain enlargement caused by changes in glume cells, the importance of normal development of the glume for normal grain development has been poorly studied. In this study, we investigated a rice spikelet mutant, (), which developed florets with a slightly degenerated or rod-like lemma.

Freshwater crustacean exposed to active pharmaceutical ingredients: ecotoxicological effects and mechanisms.

Concerns over the ecotoxicological effects of active pharmaceutical ingredients (APIs) on aquatic invertebrates have been raised in the last decade. While numerous studies have reported the toxicity of APIs in invertebrates, no attempt has been made to synthesize and interpret this dataset in terms of different exposure scenarios (acute, chronic, multigenerational), multiple crustacean species, and the toxic mechanisms. In this study, a thorough literature review was performed to summarize the ecotoxicological data of APIs tested on a range of invertebrates.

A spectral learning path for simultaneous multi-parameter detection of water quality.

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection.

The APC/CTAD1-WIDE LEAF 1-NARROW LEAF 1 pathway controls leaf width in rice.

Leaf morphology is one of the most important features of the ideal plant architecture. However, the genetic and molecular mechanisms controlling this feature in crops remain largely unknown. Here, we characterized the rice (Oryza sativa) wide leaf 1 (wl1) mutant, which has wider leaves than the wild-type due to more vascular bundles and greater distance between small vascular bundles.

Image learning to accurately identify complex mixture components.

The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniques have the ability to cope with this problem in a data-driven way. Herein, we report a 2D-spectral imaging method to collect matter information from mixture components, and the obtained feature images can be easily provided to deep convolutional neural networks (CNNs) for establishing a spectral network.

LF1 regulates the lateral organs polarity development in rice.

The patterning of adaxial-abaxial tissues plays a vital role in the morphology of lateral organs, which is maintained by antagonism between the genes that specify adaxial and abaxial tissue identity. The homeo-domain leucine zipper class III (HD-ZIP III) family genes regulate adaxial identity; however, little information is known about the physical interactions or transcriptionally regulated downstream genes of HD-ZIP III. In this study, we identified a dominant rice mutant, lateral floret 1 (lf1), which has defects in lateral organ polarity.

Machine learning based on holographic scattering spectrum for mixed pollutants analysis.

Determination of complex pollutants often involves many high-cost and laborious operations. Today's pop machine-learning (ML) technology has exhibited their amazing successes in image recognition, drug designing, disease detection, natural language understanding, etc. ML-driven samples testing will inevitably promote the development of related subjects and fields, but the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms.

Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors.

Due to the complexity of nonlinear reactions, the analysis of environmental samples often relies on expensive equipment as well as tedious and time-consuming experimental procedures. Currently, the efficient machine learning (ML) strategy based on big data offers some new insights for the analysis of complex components in the environmental field. In this study, ML was applied for the analysis of total organic carbon (TOC).

A color-spectral machine learning path for analysis of five mixed amino acids.

A machine learning (ML) strategy based on color-spectral images for mixed amino acid (AA) analysis is presented. The results showed that a well-trained ML model could accurately predict multiple AAs at the same time, suggesting its value for facilitating quantitative analysis of mixed AA systems.

Machine learning for mixture toxicity analysis based on high-throughput printing technology.

Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity prediction models aiming to a limited sampling dimension. The rapid development of machine learning (ML) algorithm will accelerate the exploration of many fields involving toxicity analysis.

A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment.

Soil heavy metal pollution has been becoming serious and widespread in China. To date, there are few studies assessing the nationwide soil heavy metal pollution induced by industrial and agricultural activities in China. This review obtained heavy metal concentrations in soils of 402 industrial sites and 1041 agricultural sites in China throughout the document retrieval.

Preparation and catalytic performance of copper-containing magnetic catalysts for degradation of azo dye (direct violet).

A novel magnetically separable magnetic activated carbon supporting-copper (MCAC) catalyst for catalytic wet peroxide oxidation (CWPO) was prepared by chemical impregnation. The prepared samples were characterized by X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) method, and scanning electron microscopy (SEM) equipped with energy dispersive spectrometry (EDS). The catalytic performance of the catalysts was evaluated by direct violet (D-BL) degradation in CWPO experiments.

Distribution of Heavy Metal Pollution in Surface Soil Samples in China: A Graphical Review.

Soil pollution in China is one of most wide and severe in the world. Although environmental researchers are well aware of the acuteness of soil pollution in China, a precise and comprehensive mapping system of soil pollution has never been released. By compiling, integrating and processing nearly a decade of soil pollution data, we have created cornerstone maps that illustrate the distribution and concentration of cadmium, lead, zinc, arsenic, copper and chromium in surficial soil across the nation.

Fluorescence imaging technology (FI) for high-throughput screening of selenide-modified nano-TiO2 catalysts.

A high-throughput screening (HTS) method based on fluorescence imaging (FI) was implemented to evaluate the catalytic performance of selenide-modified nano-TiO2. Chemical ink-jet printing (IJP) technology was reformed to fabricate a catalyst library comprising 1405 (Ni(a)Cu(b)Cd(c)Ce(d)In(e)Y(f))Se(x)/TiO2 (M6Se/Ti) composite photocatalysts. Nineteen M6Se/Tis were screened out from the 1405 candidates efficiently.