Detection of various microplastics in placentas, meconium, infant feces, breastmilk and infant formula: A pilot prospective study.

There's something in the air: A review of sources, prevalence and behaviour of microplastics in the atmosphere.

It has been established that various anthropogenic contaminants have already reached all the world's pristine locations, including the polar regions. While some of those contaminants, such as lead and soot, are decreasing in the environment, thanks to international regulations, other novel contaminants emerge. Plastic pollution has been shown as a durable novel pollutant, and, since recently, smaller and smaller plastics particles have been identified in various environments (air, water and soil). Considerable research already exists measuring the plastics in the 5 mm to micrometre size range (microplastics). However, far less is known about the plastics debris that fragmented to the sub-micrometre size (nanoplastics). As these small particles are light, it is expected that they have already reached the most remote places on Earth, e.g. transported across the globe by air movement. In this work, we used a novel method based on Thermal Desorption - Proton Transfer Reaction - Mass Spectrometry (TD-PTR-MS) to detect and measure nanoplastics of different types in the water sampled from a Greenland firn core (T2015-A5) and a sea ice core from Antarctica. We identify polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), and Tire wear nanoparticles in the 14 m deep Greenland firn core and PE, PP and PET in sea ice from Antarctica. Nanoplastics mass concentrations were on average 13.2 ng/mL for Greenland firn samples and 52.3 ng/mL for Antarctic sea ice. We further discuss the possible sources of nanoplastics that we found at these remote locations, which likely involve complex processes of plastic circulation (emission from both land and sea surface, atmospheric and marine circulation). 

https://doi.org/10.1016/j.envres.2022.112741

Nanoplastics measurements in Northern and Southern polar ice.

From natural environment to animal tissues: A review of microplastics(nanoplastics) translocation and hazards studies.

Phytotoxicity of polystyrene, polyethylene and polypropylene microplastics on tomato (Lycopersicon esculentum L.).

Airborne microplastics (MPs) are receiving increasing attention due to their ubiquitous nature and the potential human health consequences resulting from inhalation. The limited data for airborne MP concentrations vary widely among studies (∼4 orders of magnitude), but comparisons are tenuous due to the inconsistent collection and detection/enumeration methodologies among studies. Herein, we used uniform methodologies to obtain comparable airborne MP concentration data to assess MP exposure intensity in five Chinese megacities. Airborne MP concentrations in northern cities (358 ± 132 items/m3) were higher than those in southeast cities (230 ± 94 items/m3) but of a similar order of magnitude, unlike previous studies. The majority (94.7%) of MPs found in air samples were smaller than 100 μm, and the main shape of airborne MPs was fragments (88.2%). Polyethylene, polyester, and polystyrene were the dominant polymers comprising airborne MPs. No consistent relationships were detected between airborne MP concentration and typical socioeconomic indices, and the spatial and diurnal patterns for airborne MPs were different from various components of air quality indices (PM2.5, PM10, etc.). These findings reflect the contrasting source/generation dynamics between airborne MPs and other airborne pollutants. Maximum annual exposure of humans to airborne MPs was estimated in the range of 1-2 million/year in these megacities, highlighting the need for additional research examining the human health risks from the inhalation of airborne MPs.

Airborne Microplastic Concentrations in Five Megacities of Northern and Southeast China.

/pdf/microplastic-ingestion-from-atmospheric-deposition-during-mc659xxg.pdf

Microplastic ingestion from atmospheric deposition during dining/drinking activities.

Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in‐depth interpretation of DNN channels. A four‐dimensional (4D) convolution operation was also included to extract temporo‐spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the seven different task benchmarks from the Human Connectome Project (HCP) dataset. The visualization analysis further illustrated the hierarchical emergence of task‐specific masks with depth. Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively. Transfer learning also achieves good performance. Further visualization analysis shows that, after transfer learning, low‐level attention masks remained similar to the source domain, whereas high‐level attention masks changed adaptively. In conclusion, the proposed 4D model with attention module performed well and facilitated interpretation of DNNs, which is helpful for subsequent research.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.25813

Attention module improves both performance and interpretability of four‐dimensional functional magnetic resonance imaging decoding neural network

Introduction Decoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data. Methods In this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair. Results We pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model’s performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks. Discussion Our results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks.

/pdf/self-supervised-pretraining-improves-the-performance-of-3ay4wmly.pdf

Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging

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Airborne Microplastic Concentrations in Five Megacities of Northern and Southeast China.

Microplastic ingestion from atmospheric deposition during dining/drinking activities.

Attention module improves both performance and interpretability of four‐dimensional functional magnetic resonance imaging decoding neural network

Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging