Metagenomics reveals the potential transmission risk of resistomes from urban park environment to human.

Urban parks play a significant role in urban ecosystems and are strongly associated with human health. Nevertheless, the biological contamination of urban parks - opportunistic pathogens and antibiotic resistance genes (ARGs) - has been poorly reported. Here, metagenomic and 16 S rRNA sequencing methods were used to study the distribution and assembly of opportunistic pathogens and ARGs in soil and water from nine parks in Lanzhou city, and further compared them with local human gut microbiomes to investigate the potential transmission risk. Our results revealed that the most important type of drug resistance in urban parks was multidrug resistance, with various resistance mechanisms. Approximately half of ARGs were shared between human gut and park environment, and it was noteworthy that cross-species transmission might exist among some high-risk ARGs, such as mepA and mdtE, with a significant enrichment in human gut. Metagenomic binning uncovered several bacterial genomes carrying adjacent ARGs, MGEs, and virulence genes, indicating a possibility that these genes may jointly transfer among different environments, particularly from park environment to human. Our results provided a reference point for the management of environmental pollutants in urban parks.

Cavity structure-based active controllable thermal switch for battery thermal management.

Batteries may degrade fast at extreme temperatures, posing a challenge in meeting the dual requirements of heat preservation at low temperatures and efficient cooling at high temperatures. To address this issue, we propose a cavity structure-based active controllable thermal switch. It has a potential switch ratio (SR) of approximately 300, with an experimental SR of 15.4. Furthermore, the thermal resistance can be actively controlled. The "OFF State" of the thermal switch increases energy discharge at low temperatures. Pre-heating with the "OFF State" consumes only 60% of the energy required in the "ON State". By employing the "ON State" at an ambient temperature of 20°C, the battery temperature can be maintained below 35°C. And the "ON + State" keeps the maximum battery temperature remaining below 42°C under extreme conditions. These findings demonstrate that the implementation of the proposed thermal switch enhances the usability of batteries in extreme environments.

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

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.

Microplastic ingestion from atmospheric deposition during dining/drinking activities.

Human-health risks from microplastics have attracted considerable attention, but little is known about human-exposure pathways and intensities. Recent studies posited that inhalation of atmospheric microplastics was the dominant human-exposure pathway. Herein, our study identified that atmospheric microplastics ingested from deposition during routine dining/drinking activities represent another important exposure pathway. We measured abundances of atmospheric-deposited microplastics of up to 105 items m-2 d-1 in dining/drinking venues, with 90% smaller than 100 µm and a dominance of amorphous fragments rather than fibers. Typical work-life scenarios projected an annual ingestion of 1.9 × 105 to 1.3 × 106 microplastics through atmospheric deposition on diet, with higher exposure rates for indoor versus outdoor dining/drinking settings. Ingestion of atmospheric-deposited microplastics through diet was similar in magnitude to presumed inhalation exposure, but 2-3 orders of magnitude greater than direct ingestion from food sources. Simple mitigation strategies (e.g., covering and rinsing dishware) can substantially reduce the exposure of atmospheric deposition microplastics through diet.

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

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.

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

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.