Advances in Application of Nanomaterials in Life Science

Nanomaterials had attracted much attention since their discovery with their unique structure, peculiar physical,chemical, mechanical properties and potential application prospects. In the past few years, the theoretical andexperimental research on biological nanomaterials has become the focus of attention, especially the biochemistry,biophysics, biomechanics, thermodynamics and electromagnetism of nucleic acid and protein, while its intelligentcomposites have become the forefront of life science and materials science. At present, nano-bio-chip materials,biomimetic materials, nano-motors, nanocomposites, interface biomaterials, nano-sensors and drug delivery systemshave made great progress. In this paper, the characteristics of these materials, research and development of theapplication were reviewed, a brief overview of the nano-materials in the life sciences of the main applications, and toexplore the development prospects of biological nano-materials

Rare microbial taxa as the major drivers of nutrient acquisition under moss biocrusts in karst area

Karst rocky desertification refers to the process of land degradation caused by various factors such as climate change and human activities including deforestation and agriculture on a fragile karst substrate. Nutrient limitation is common in karst areas. Moss crust grows widely in karst areas. The microorganisms associated with bryophytes are vital to maintaining ecological functions, including climate regulation and nutrient circulation. The synergistic effect of moss crusts and microorganisms may hold great potential for restoring degraded karst ecosystems. However, our understanding of the responses of microbial communities, especially abundant and rare taxa, to nutrient limitations and acquisition in the presence of moss crusts is limited. Different moss habitats exhibit varying patterns of nutrient availability, which also affect microbial diversity and composition. Therefore, in this study, we investigated three habitats of mosses: autochthonal bryophytes under forest, lithophytic bryophytes under forest and on cliff rock. We measured soil physicochemical properties and enzymatic activities. We conducted high-throughput sequencing and analysis of soil microorganisms. Our finding revealed that autochthonal moss crusts under forest had higher nutrient availability and a higher proportion of copiotrophic microbial communities compared to lithophytic moss crusts under forest or on cliff rock. However, enzyme activities were lower in autochthonal moss crusts under forest. Additionally, rare taxa exhibited distinct structures in all three habitats. Analysis of co-occurrence network showed that rare taxa had a relatively high proportion in the main modules. Furthermore, we found that both abundant and rare taxa were primarily assembled by stochastic processes. Soil properties significantly affected the community assembly of the rare taxa, indirectly affecting microbial diversity and complexity and finally nutrient acquisition. These findings highlight the importance of rare taxa under moss crusts for nutrient acquisition. Addressing this knowledge gap is essential for guiding ongoing ecological restoration projects in karst rocky desertification regions

Exploring Model Dynamics for Accumulative Poisoning Discovery

Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples based on their distinct dynamics from the clean samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks. Extensive experiments comprehensively characterized Memorization Discrepancy and verified its effectiveness. The code is publicly available at: https://github.com/tmlr-group/Memorization-Discrepancy.Comment: accepted by ICML 202

Combating Bilateral Edge Noise for Robust Link Prediction

Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less investigated. To close this gap, we first conduct an empirical study to disclose that the edge noise bilaterally perturbs both input topology and target label, yielding severe performance degradation and representation collapse. To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse. Different from the basic information bottleneck, RGIB further decouples and balances the mutual dependence among graph topology, target labels, and representation, building new learning objectives for robust representation against the bilateral noise. Two instantiations, RGIB-SSL and RGIB-REP, are explored to leverage the merits of different methodologies, i.e., self-supervised learning and data reparameterization, for implicit and explicit data denoising, respectively. Extensive experiments on six datasets and three GNNs with diverse noisy scenarios verify the effectiveness of our RGIB instantiations. The code is publicly available at: https://github.com/tmlr-group/RGIB.Comment: Accepted by NeurIPS 202

Iodine speciation in aerosol particle samples collected over the sea between offshore China and the Arctic Ocean

Iodine species collected by an onboard PM10 particle sampling system during the Second Chinese National Arctic Research Expedition (July–September 2003) were measured using inductively coupled plasma mass spectrometry and ion chromatography-inductively coupled plasma mass spectrometry. Iodine (I−) was detected in all samples over the Arctic Ocean, whereas additional iodine species including insoluble iodine, soluble organic iodine plus I− were detected over the northwestern Pacific Ocean. The results suggest that the main form of iodine is different within the Arctic Ocean than it is outside. Enrichment factor values showed moderate enrichment of iodine in the northwestern Pacific, whereas a high enrichment factor was found in polar regions, implying sources other than sea salt. A potential explanation was ascribed to the role of sea ice melt in the Arctic and rapid growth of algae in seawater, which enhances the production of iodocarbon and air sea exchange. This was confirmed by the larger values of total iodine in 2008 than in 2003, with greater sea ice melt in the former year. In comparison with earlier reports, ratios of iodate to iodide (IO3−/I−) were much smaller than 1.0. These ratios were also different from modeling results, implying more complicated cycles of atmospheric iodine than are presently understood

Gut microbiota and risk of lower respiratory tract infections: a bidirectional two-sample Mendelian randomization study

IntroductionObservational studies have reported the association between gut microbiota and the risk of lower respiratory tract infections (LRTIs). However, whether the association reflects a causal relationship remains obscure.MethodsA bidirectional twosample Mendelian randomization (MR) analysis was conducted by assessing genome-wide association study (GWAS) summary statistics for gut microbiota taxa and five common LRTIs. MR methods including inverse-variance-weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode were used to analyze the causality. Gene pleiotropy was tested using MR-Egger regression and MR-PRESSO methods. Cochran’s Q test was used to check for heterogeneity. Leave-one-out analysis was used to assess the stability of effect sizes. Detected significant associations were validated by using an independent LRTI GWAS summary statistics dataset. An optional MR method of causal analysis using summary effect estimates (CAUSE) was further performed as a validation to avoid potential false-positive results.ResultsAccording to the MR-Egger estimates in forward MR analysis, a causal effect of gut Blautia on increased odds of bronchiectasis and pneumonia was suggested. MR-Egger regression pleiotropy intercept methods detected no significant horizontal pleiotropy between the instrumental variables of these associations. MR-PRESSO global test examined no potential horizontal pleiotropy. Cochran’s Q test showed that no heterogeneity biased the results. The leave-one-out sensitivity analyses suggested robust causality results. These associations with consistent effect direction were successfully replicated in IVW analysis by using the validation GWAS dataset. However, these evidence of causality did not survive after applying strict Bonferroni correction or CAUSE analysis. The reverse MR analysis failed to achieve consistent results in the effect of LRTIs on gut microbiota through comprehensive discovery and validation processes.DiscussionThis study established no strong causality between genetically predicted gut microbiome and the risk of lower respiratory tract infections. However, specific subtypes of microbial genera, such as Blautia, were identified as potential influencers and require further investigation, particularly at the species or strain levels

Global food trade alleviates transgressions of planetary boundaries at the national scale

Summary: Food systems are among the leading causes for transgression of planetary boundaries globally, which define the safe operating space for humanity. We quantify unsustainable environmental impacts of food systems, indicated by the transgression of national-scale planetary boundaries (i.e., the safe operating space for food production in each country), from both production and consumption perspectives of 189 countries/regions around the world. A multi-regional input-output model is used to map the global transfers of the national-scale transgression of planetary boundaries, including freshwater use, land change, and biogeochemical flows (nitrogen and phosphorus). Our results show that China is a major global unsustainable water and nitrogen exporter and an unstable land and phosphorus importer. This means that water and nitrogen uses in China are used to support food demands in other countries, and food consumption in China requires unsustainable land and phosphorus uses elsewhere. In contrast, the US is a major exporter of unsustainable water, land, and nitrogen uses but only an importer of unsustainable phosphorus for food consumption. Globally, compared to a counterfactual scenario where there is no food trade among any countries, food trade saves massive transgressions of planetary boundaries (270 km3 of water, 18 million tons of nitrogen, 7 million tons of phosphorus, and 5,431 million km2 of land). Alleviation of national-scale planetary boundary transgression has been achieved primarily in the US, China, Saudi Arabia, etc., while aggravation was incurred in Pakistan, Australia, Argentina, and so forth

Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints

Variation of Petrophysical Properties and Adsorption Capacity in Different Rank Coals: An Experimental Study of Coals from the Junggar, Ordos and Qinshui Basins in China

The petrophysical properties of coal will vary during coalification, and thus affect the methane adsorption capacity. In order to clarify the variation rule and its controlling effect on methane adsorption, various petrophysical tests including proximate analysis, moisture measurement, methane isothermal adsorption, mercury injection, etc. were carried out on 60 coal samples collected from the Junggar, Ordos and Qinshui basins in China. In this work, the boundary values of maximum vitrinite reflectance (Ro,m) for dividing low rank, medium rank and high rank coals are set as 0.65% and 2.0%. The results show that vitrinite is the most abundant maceral, but the maceral contents are controlled by sedimentation without any relation to coal rank. Both the moisture content and porosity results show higher values in the low ranks and stabilized with Ro,m beyond 1%. Ro,m and VL (daf) show quadratic correlation with the peak located in Ro,m = 4.5–5%, with the coefficient (R2) reaching 0.86. PL decrease rapidly before Ro,m = 1.5%, then increase slowly. DAP is established to quantify the inhibitory effect of moisture on methane adsorption capacity, which shows periodic relationship with Ro,m: the inhibitory effect in lignite is the weakest and increases during coalification, then remains constant at Ro,m = 1.8% to 3.5%, and finally increases again. In the high metamorphic stage, clay minerals are more moisture-absorbent than coal, and the inherent moisture negatively correlates with the ratio of vitrinite to inertinite (V/I). During coalification, micro gas pores gradually become dominant, fractures tends to be well oriented and extended, and clay filling becomes more common. These findings can help us better understand the variation of petrophysical properties and adsorption capacity in different rank coals