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Abstract Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction—achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength. We also designed a wavelength-multiplexed P-D2NN, where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions, at two distinct illumination wavelengths. Furthermore, we demonstrate that by cascading multiple unidirectional P-D2NN modules, we can achieve higher magnification factors. The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination, successfully matching our numerical simulations. P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

Abstract Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

Abstract Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

Abstract Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

Abstract Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

Abstract Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans

High-performance lithium-ion batteries (LIB) are important in powering emerging technologies. Cathodes are regarded as the bottleneck of increasing battery energy density, among which layered oxides are the most promising candidates for LIB. However, a limitation with layered oxides cathodes is the transition metal and Li site mixing, which significantly impacts battery capacity and cycling stability. Despite recent research on Li/Ni mixing, there is a lack of comprehensive understanding of the origin of cation mixing between the transition metal and Li; therefore, practical means to address it. Here, a critical review of cation mixing in layered cathodes has been provided, emphasising the understanding of cation mixing mechanisms and their impact on cathode material design. We list and compare advanced characterisation techniques to detect cation mixing in the material structure; examine methods to regulate the degree of cation mixing in layered oxides to boost battery capacity and cycling performance, and critically assess how these can be applied practically. An appraisal of future research directions, including superexchange interaction to stabilise structures and boost capacity retention has also been concluded. Findings will be of immediate benefit in the design of layered cathodes for high-performance rechargeable LIB and, therefore, of interest to researchers and manufacturers.

Abstract Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control.

Metasurfaces have emerged as a unique group of two-dimensional ultra-compact subwavelength devices for perfect wave absorption due to their exceptional capabilities of light modulation. Nonetheless, achieving high absorption, particularly with multi-band broadband scalability for specialized scenarios, remains a challenge. As an example, the presence of atmospheric windows, as dictated by special gas molecules in different infrared regions, highly demands such scalable modulation abilities for multi-band absorption and filtration. Herein, by leveraging the hybrid effect of Fabry–Perot resonance, magnetic dipole resonance and electric dipole resonance, we achieved multi-broadband absorptivity in three prominent infrared atmospheric windows concurrently, with an average absorptivity of 87.6% in the short-wave infrared region (1.4–1.7 μm), 92.7% in the mid-wave infrared region (3.2–5 μm) and 92.4% in the long-wave infrared region (8–13 μm), respectively. The well-confirmed absorption spectra along with its adaptation to varied incident angles and polarization angles of radiations reveal great potential for fields like infrared imaging, photodetection and communication.

The title compound, C13H10FNO2, was obtained by the reaction of 2-bromo-4-fluorobenzoic acid with aniline. There are two independent molecules, A and B, in the asymmetric unit, with slight conformational differences: the dihedral angles between the aromatic rings are 55.63 (5) and 52.65 (5)°. Both molecules feature an intramolecular N—H...O hydrogen bond. In the crystal, the molecules are linked by pairwise O—H...O hydrogen bonds to form A–B acid–acid dimers and weak C—H...F interactions further connect the dimers.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Abstract Many exciting terahertz imaging applications, such as non-destructive evaluation, biomedical diagnosis, and security screening, have been historically limited in practical usage due to the raster-scanning requirement of imaging systems, which impose very low imaging speeds. However, recent advancements in terahertz imaging systems have greatly increased the imaging throughput and brought the promising potential of terahertz radiation from research laboratories closer to real-world applications. Here, we review the development of terahertz imaging technologies from both hardware and computational imaging perspectives. We introduce and compare different types of hardware enabling frequency-domain and time-domain imaging using various thermal, photon, and field image sensor arrays. We discuss how different imaging hardware and computational imaging algorithms provide opportunities for capturing time-of-flight, spectroscopic, phase, and intensity image data at high throughputs. Furthermore, the new prospects and challenges for the development of future high-throughput terahertz imaging systems are briefly introduced.

Abstract Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is ≥~2N i N o , where N i and N o refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function (H) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H(m, n; m′, n′) = |h(m, n; m′, n′)|2, where h is the spatially coherent point spread function of the same diffractive network, and (m, n) and (m′, n′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2N i N o . We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

Abstract Lithium metal batteries (LMBs) have attracted considerable interest for use in electric vehicles and as next‐generation energy storage devices because of their high energy density. However, a significant practical drawback with LMBs is the instability of the Li metal/electrolyte interface, with concurrent parasitic reactions and dendrite growth, that leads to low Coulombic efficiency and poor cycle life. Owing to the significant role of electrolytes in batteries, rationally designed electrolytes can improve the electrochemical performance of LMBs and possibly achieve fast charge and a wide range of working temperatures to meet various requirements of the market in the future. Although there are some review papers about electrolytes for LMBs, the focus has been on a single parameter or single performance separately and, therefore, not sufficient for the design of electrolytes for advanced LMBs for a wide range of working environments. This review presents a systematic summary of recent progress made in terms of electrolytes, covering the fundamental understanding of the mechanism, scientific challenges, and strategies to address drawbacks of electrolytes for high‐performance LMBs. The advantages and disadvantages of various electrolyte strategies are also analyzed, yielding suggestions for optimum properties of electrolytes for advanced LMBs applications. Finally, the most promising research directions for electrolytes are discussed briefly.

Microplastics (MPs) have been shown to be a new type of pollutant in the oceans, with complex biofilms attached to their surfaces. Bacteria with quorum sensing (QS) systems are important participants in biofilms. Such bacteria can secrete and detect signal molecules. When a signal molecule reaches its threshold level, bacteria with QS systems can perform several biological functions, such as biofilm formation and antibiotic metabolite production. However, the ecological effects of QS bacteria in biofilm as MPs distribute globally with ocean currents are not to be elucidate yet. In this study, polypropylene and polyvinyl chloride were selected for on-site enrichment to acquire microplastics with biofilms. Eight culturable QS bacteria in the resulting biofilm were isolated by using biosensor assays, and their biodiversity was analyzed. The profiles of the N-acyl-homoserine lactones (AHLs) produced by these bacteria were analyzed by using thin-layer chromatography (TLC)–bioautography and gas chromatography and mass spectrometry (GC–MS). Biofilm-forming properties and several biological characteristics, such as bacteriostasis, algal inhibition, and dimethylsulfoniopropionate (DMSP) degradation, were explored along with QS quenching. Results showed that QS bacteria were mainly affiliated with class Alphaproteobacteria, particularly Rhodobacteraceae, followed by class Gammaproteobacteria. TLC–bioautography and GC–MS analyses revealed that seven AHLs, namely, C6-HSL, C8-HSL, 3-oxo-C6-HSL, 3-oxo-C8-HSL, 3-oxo-C10-HSL, and two unidentified AHLs were produced. The QS system equipped bacteria with strong biofilm-forming capacity and may contribute to the keystone roles of Rhodobacteraceae. In addition, QS bacteria may exacerbate the adverse environmental effects of MPs, such as inducing the misfeeding of planktons on MPs. This study elucidated the diversity of QS bacteria in MP-associated biofilms and provided a new perspective of the effect of key membrane-forming bacteria on the marine ecological environment.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the crucial pathogenesis for intra-hepatic and extra-hepatic diseases, especially in elderly adults. Lifestyle management may be a modifiable cost-effective measure for MAFLD prevention, but the evidence is limited. A total of 23,408 middle-aged and elderly individuals were included in a longitudinal study from 2008 to 2018. Combined lifestyle scores (range 0–6) were evaluated by BMI, smoking, drinking, diet, physical activity, and sleep. Logistic regression models were used to calculate ORs for the risks of MAFLD and specific subtypes. The mean age of participants was 61.7 years, and 44.5% were men. Compared with poor lifestyle (scores 0–2), ORs (95% CIs) of the ideal lifestyle (scores 5–6) were 0.62 (0.57–0.68) for MAFLD, 0.31 (0.28–0.34) for MAFLD with excess weight and obesity, 0.97 (0.75–1.26) for MAFLD with diabetes, and 0.56 (0.51–0.62) for MAFLD with metabolic dysregulation. Additionally, lifestyle improvement was associated with lower risks of MAFLD (OR, 0.76; 95% CI, 0.68–0.86), MAFLD with excess weight and obesity (OR, 0.72; 95% CI, 0.63–0.81), MAFLD with diabetes (OR, 0.74; 95% CI, 0.54–1.02) and MAFLD with metabolic dysregulation (OR, 0.49; 95% CI, 0.43–0.55), respectively. Our findings suggest that adherence to a combined healthy lifestyle was associated with lower risks of MAFLD, particularly in excess weight/obese individuals or those with metabolic dysregulation.

All-optical computation of a group of linear transforms is achieved using a polarization-encoded diffractive optical processor.

Abstract An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

Microbes play an essential role in the restoration of degraded coastal wetlands. However, few studies have focused on the role of key bacteria in the restoration process. Here, Suaeda salsa was planted to recover the biodiversity in the degraded Beidaihe coastal wetland. We monitored omics and soil environmental factors to understand the complex relationship between the bacterial community and wetland health during the restoration period. After planting S. salsa in the degraded area, the soil quality was improved in the later stage of restoration (LPR). Bacterial α-diversity increased with restoration and was positively correlated with TOC. Proteobacteria is the dominant bacterial phylum during the restoration period, and Bacteroidetes, Planctomycetes, Gemmatimonadetes, and Acidobacteria were sensitive to the planting restoration. Random forest analysis picked 30 key OTUs, showing the key bacterial variation of successful restoration. The result indicated that the sum of the relative abundances of key bacterial OTUs was more than 2% in the health wetland. The β-diversity showed that the growth of S. salsa reshaped the soil bacterial community structure and function in the LPR, which recovered to the level in the control area. Putative biogeochemical functions showed that symbionts and aromatic compound degradation were dominant bacterial functions in the growth period of S. salsa. Our study proposed a new indicator to assess wetland health and the planting restoration of S. salsa increased bacteria groups with the ability of symbionts and aromatic compound degradation in the Beidaihe coastal wetland. This study expanded our knowledge of coastal wetland restoration and its ecological contributions.

Diffractive networks have recently been discussed as an all-optical analogue for performing neural network operations. The authors present a method using deep learning-designed 3D-printed diffractive surfaces to engineer temporal waveforms and perform pulse shaping in the terahertz regime.

Diffractive networks light the way for better optical image classification Scientists in USA have demonstrated significant improvements in the performance of diffractive optical networks, marking a major step forward for their use in optics-based computation and machine learning. There is renewed interest in optical computing hardware due to its potential advantages, including parallelization, power efficiency, and computation speed. Diffractive optical networks utilize deep learning-based design of successive diffractive layers to all-optically process information as the light is transmitted from the input to the output plane. Led by Aydogan Ozcan, a team of researchers from University of California, Los Angeles has significantly improved the statistical inference performance of diffractive optical networks using feature engineering and ensemble learning. Using a pruning algorithm, they searched through 1,252 unique diffractive networks to design ensembles of desired size that substantially improve the overall system’s all-optical image classification accuracy.

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

As a new type of pollutant, microplastics widely exist in the marine environment and have attracted a lot of attention from the international community. In order to study the distribution of microplastics and the influence of ocean current, microplastic samples in seawater of the southern Indian Ocean were collected using a peristaltic pump equipped on-board and concentrated on site. Qualitative and quantitative analyses of microplastics were performed using a stereo-microscope and a micro-Fourier transform infrared spectroscope attenuated total reflection. The results showed that the average abundance of microplastics in seawater of the southern Indian Ocean was 2.3 ± 2.1 items/m3, which was consistent with that in other oceans. Polyethylene terephthalate (PET), polyethylene (PE), Rayon, polyamide (PA), and polyvinylidene chloride (PVDC) were the main polymers of microplastics in the southern Indian Ocean. The size range of all detected microplastics was 108.2–4703.0 µm. All microplastics had different colors, such as black, red, yellow, gray, blue, green, purple, and transparent. Fiber was the dominant shape of microplastics. The abundance distribution of microplastics fluctuated in the latitudinal direction. The abundance of microplastics from the present study area was higher in the coastal region of the South Africa continent and the Indian Ocean garbage patch, with an average abundance of 4.0 items/m3. The average abundance of microplastics was relatively high in the convergence area of the circulation, which revealed that the ocean current facilitated the agglomeration and transportation of microplastics.

The ubiquitous presence of microplastics in bivalve mollusks and related risks have raised particular concerns. In this study, the available data on the abundance and polymer type of microplastics in bivalves from twenty-two countries were extracted to comprehensively understand the risks of microplastics in bivalves. Following the data from 52 peer-reviewed papers, the abundance, chemical composition, and human exposure risks of microplastics of bivalves among countries were initially assessed. Abundance risk results indicated that bivalves from 22 countries presented a low pollution load index, showing a lower risk level (level I). The polymer risk index (H) of bivalves from Portugal (Hcountry = 1335, level IV) and India (Hcountry = 1187, level IV) were higher than the other countries due to the occurrence of hazardous microplastics, such as polyvinyl chloride. For the human exposure risks, the global mean value of microplastic exposure to humans via mollusk consumption is estimated to be 751 microplastics/capita/year, with the maximum intake by the Chinese. This study suggests that abundance risk may be a fundamental indicator for assessing the potential hazard to humans until the chemical composition risks are confirmed. This study is the first attempt to assess the potential risks of microplastics in bivalves using three evaluation models based on microplastic abundances and polymer types, which will contribute to establishing future human health risk assessment frameworks. These findings will also assist efforts in policy-making to minimize microplastic risks in seafood.

As a new type of pollutant, microplastics have attracted widespread attention in recent years. Studies have shown that microplastics can affect the growth of microalgae, but little study has been done on macroalgae. In this paper, the enrichment and the characteristics of microplastics on Ulva prolifera, a green tide species, from the Yellow Sea of China were studied. Our results showed that U. prolifera enriched microplastic with an average abundance of 4603.3 ​± ​1167.2 items kg−1 d. w., which was more than 10,000 times of the microplastics in the ambient water (448.9 ​± ​145.68 items m−3). U. prolifera showed different enrichment capacity for microplastics with different shapes, with foam and film being the dominant shapes. The microplastic trapped by U. prolifera were usually in the size range of 2–5 ​mm and the main polymer types were polystyrenepe (PS) and polyethylene terephthalate (PET). The adsorption mechanism of microplastics on U. prolifera showed that its binding to microplastics was mainly through physical interaction. By salvaging U. prolifera at an amount of roughly 239,000 tons/year, a large amount of microplastics (approximately 35.6 ​tons/year.) can be removed from the Yellow Sea, which obviously plays a role in reducing the pollution of microplastics in the marine environment.

A Correction to this paper has been published: https://doi.org/10.1038/s41377-021-00473-1

Quantification of the association between breastfeeding and risk of endometrial cancer is still conflicting. We therefore conducted a meta-analysis to assess the association between breastfeeding and endometrial cancer risk. Pertinent studies were identified by a search of PubMed and Web of Knowledge through April 2015. A random effect model was used to combine the data for analysis. Sensitivity analysis and publication bias were conducted. Dose-response relationships were assessed by restricted cubic spline and variance-weighted least squares regression analysis. Fourteen articles involving 5158 endometrial cancer cases and 706,946 participants were included in this meta-analysis. Pooled results suggested that breastfeeding significantly reduced the risk of endometrial cancer (summary relative risk (RR): 0.77, 95% CI: 0.62–0.96, I2: 63.0%), especially in North America (summary RR: 0.87, 95% CI: 0.79–0.95). A linear dose-response relationship was found, with the risk of endometrial cancer decreased by 2% for every one-month increase in the duration of breastfeeding (summary RR: 0.98, 95% CI: 0.97–0.99). Our analysis suggested that breastfeeding, particularly a longer duration of breastfeeding, was inversely associated with the risk of endometrial cancer, especially in North America, but not in Europe and Asia, probably due to the small number of cases included. Due to this limitation, further studies originating in other countries are required to assess the association between breastfeeding and endometrial cancer risk.

Chemical investigation of a Chinese collection of marine red alga Symphyocladia latiuscula yielded two new highly brominated phenols. The structures of the new compounds were elucidated by detailed spectroscopic analysis, including HRMS, 1D and 2D NMR and MS methods. Compounds 1 and 2 were evaluated for radical scavenging capability by 1,1-diphenyl-2-picrylhydrazuyl (DPPH) radical with the IC50 value of 14.5 and 20.5 μg/mL, respectively.