As plastic pollution continues to increase and plastic waste is shredded to form smaller plastic particles, there is growing concern about the potential impact of nanoplastics (NPs) on freshwater ...ecosystems. In this work, the effects of three surface-modified NPs, including polystyrene (PS), PS-NH2, and PS-COOH, on the growth, photosynthetic activity, oxidative damage, and microcystins (MCs) production/release of Microcystis aeruginosa (M. aeruginosa) were investigated. Results indicated that all three NPs significantly inhibited the growth of M. aeruginosa after a 96 h exposure, and the growth inhibition followed the order of PS-NH2 > PS > PS-COOH (p < 0.05). Meanwhile, all three NPs at the concentration of 100 mg/L significantly increased the content of intra-MCs (115 %, 147 %, and 121 % higher than the control, respectively) and extra-MCs (142 %, 175 %, and 151 % higher than the control, respectively) after a 96 h exposure (p < 0.05). Moreover, our findings also suggested that the potential mechanisms of surface-modified PS NPs on M. aeruginosa growth and MCs production/release were associated with physical constraints, photosynthetic activity obstruct, and oxidative damage. Our findings provided direct evidence for different kinds of surface modifications of PS NPs on freshwater algae and improve the understanding of the potential risk of NPs in aquatic ecosystems.
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•Acute effects of surface modified NPs on M. aeruginosa were studied.•The growth inhibition of M. aeruginosa followed the order: PS-NH2 > PS > PS-COOH.•All three PS NPs can significantly inhibit the growth of M. aeruginosa.•All three PS NPs significantly promoted the production and release of MCs in M. aeruginosa.•Three NPs affect the growth and MCs of M. aeruginosa mainly through three pathways.
The climate regime shift during the 1980s had a
substantial impact on the terrestrial ecosystems and vegetation at different
scales. However, the mechanisms driving vegetation changes, before and ...after
the shift, remain unclear. In this study, we used a biophysical dynamic
vegetation model to estimate large-scale trends in terms of carbon fixation,
vegetation growth, and expansion during the period 1958–2007, and to
attribute these changes to environmental drivers including elevated
atmospheric CO2 concentration (hereafter eCO2), global warming,
and climate variability (hereafter CV). Simulated leaf area index (LAI) and
gross primary production (GPP) were evaluated against observation-based data.
Significant spatial correlations are found (correlations > 0.87),
along with regionally varying temporal correlations of 0.34–0.80 for LAI and
0.45–0.83 for GPP. More than 40 % of the global land area shows
significant positive (increase) or negative (decrease) trends in LAI and GPP
during 1958–2007. Regions over the globe show different characteristics in terms
of ecosystem trends before and after the 1980s. While 11.7 % and 19.3 %
of land have had consistently positive LAI and GPP trends, respectively,
since 1958, 17.1 % and 20.1 % of land saw LAI and GPP trends, respectively,
reverse during the 1980s. Vegetation fraction cover (FRAC) trends,
representing vegetation expansion and/or shrinking, are found at the edges of
semi-arid areas and polar areas. Environmental drivers affect the change in
ecosystem trend over different regions. Overall, eCO2 consistently
contributes to positive LAI and GPP trends in the tropics. Global warming
mostly affects LAI, with positive effects in high latitudes and negative
effects in subtropical semi-arid areas. CV is found to dominate the
variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The
eCO2 and global warming effects increased after the 1980s, while the CV
effect reversed during the 1980s. In addition, plant competition is shown to
have played an important role in determining which driver dominated the
regional trends. This paper presents new insight into ecosystem
variability and changes in the varying climate since the 1950s.
This study represents an initial attempt to examine environmental justice on inequalities in access to ecological attractions that provide top cultural ecosystem services at a national scale. With ...big data from social media, billions of anonymized samples were analysed via an experimental procedure to capture the behavioral characteristics of visitors who accessed China’s Five-A ecological attractions. The results indicate that (1) social equity can be achieved in the context of ecosystem services in China, especially at the individual level; and (2) although China’s vulnerable groups do not appear to have been treated unfairly, market mechanisms may exacerbate the inequitable development of ecosystem services among different regions. To address the problem of inequality and uneven development on ecosystem services that may result from the capital, the government should take measures to reduce the accessibility threshold and consider providing appropriate green infrastructure for different social groups apart from Five-A ecological attractions.
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•A big data method to re-visit access of top ecological spaces at a national scale.•Vulnerable groups were not unequally treated on environment recreation in China.•The equality of accessibility frequency appears much greater than distance.•People-based measures of accessibility are more reliable than place-based measures.•Age, income, and hukou rate were unrelated to accessibility at a national scale.
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Nucleophilic amino acids play important roles in maintenance of protein structure and function, covalent modification of such amino acid residues by therapeutic agents is an efficient ...way to treat human diseases. Most of current clinical drugs are structurally limited to α,β-unsaturated amide as an electrophilic warhead. To alleviate this issue, many novel electrophiles have been developed in recent years that can covalently bind to different amino acid residues and provides a unique way to interrogate proteins, including “undruggable” targets. With an activity-based protein profiling (ABPP) approach, the activity and functionality of a protein and its binding sites can be assessed. This facilitates an understanding of protein function, and contributes to the discovery of new druggable targets and lead compounds. Meanwhile, many novel inhibitors bearing new reactive warhead were developed and displayed remarkable pharmaceutical properties. In this perspective, we have reviewed the recent remarkable progress of novel electrophiles and their applications in target identification and drug discovery.
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A highly efficient and stable hydrotalcite-derived Cu-MgAlO catalyst was developed for the partial oxidation of cyclohexane with molecular oxygen. The physical–chemical properties of ...Cu-MgAlO catalysts were studied, and the results indicated that the copper component had been successfully introduced into the hydrotalcite unit layer structure. The catalytic reaction results showed that copper as the active species could activate CH bond and effectively promote the decomposition of cyclohexyl hydroperoxide (CHHP) to the mixture of cyclohexanol and cyclohexanone (KA oil). 8.3% of cyclohexane conversion and 82.9% of selectivity for KA oil were obtained over 9%Cu-MgAlO catalyst at 150 °C with 0.6 MPa of oxygen pressure for 2 h. Especially, its catalytic performance was still stable after five runs.
•The deep reinforcement learning is firstly employed to solve source searching problem.•The belief state is maintained by the particle filter.•The feature of belief state is extracted by the DBSCAN ...algorithm.•Transfer learning is employed to reuse the trained Q-network in heterogeneous tasks.
The localization of hazardous sources (e.g. poisonous gas sources) is an important task regarding the security of human society. To find the unknown source in time, various autonomous source searching methods have mushroomed and been employed over the past decade. This paper designs a fresh source searching approach, namely particle clustering-deep Q-network, PC-DQN, which applies the deep reinforcement learning (DRL) techniques as a source searching approach for the first time. Specifically, the search process is formulated as the partially observable Markov decision process, then converted into the Markov decision process based on the belief state (represented by the particle filter). PC-DQN leverages the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the feature of belief state, and employ the deep Q-network (DQN) algorithm to find the optimal policy for the source searching task. Through the comparison with two baseline methods (i.e. RANDOM and Entrotaxis algorithm) under various experimental conditions, the viability of our proposed PC-DQN is testified. Results explicitly reveal that the success rate of the PC-DQN maintains at a high level (beyond 99.6%) in all scenarios in this paper, and the mean search step shows evident superiority over baseline methods in most scenarios. Significantly, we also introduce the transfer learning concept to reuse the well-trained Q-network into new scenarios. These findings show important implications of the DRL-based approach as an alternative and more effective source searching approach.
This paper investigated the static in vitro degradation behavior of poly(L-lactic acid) (PLLA)-oriented microcellular materials. The study compared the influence of different molecular chains and ...internal morphological structures on water penetration into the material. Also, the relationship between thse self-accelerated degradation caused by ester bond breakage and reduced material molecular weight and mechanical properties was explored. Furthermore, the mechanism behind the static in vitro degradation of oriented microcellular PLLA materials in simulated human body fluid was explored, laying the foundation for regulating the mechanical performance decline of oriented microcellular biomimetic bone repair materials to match specific bone healing periods.
•Oseltamivir offers a higher recovery rate than peramivir in children with influenza A.•Oseltamivir has shorter hospital stays than peramivir in children with influenza A.•Oseltamivir and peramivir ...exhibit comparable efficacy in children with influenza B.
The effectiveness of oseltamivir versus peramivir in children infected with influenza remains unclear. This study aimed to evaluate their effectiveness in young children (aged 0-5 years) infected with severe influenza A virus (IAV) or influenza B virus (IBV).
We analyzed a cohort of 1662 young children with either IAV (N = 1095) or IBV (N = 567) who received oseltamivir or peramivir treatment from January 1, 2018 to March 31, 2022. Propensity score matching methods were applied to match children who were oseltamivir-treated versus peramivir-treated.
Children who were IAV-infected and IBV-infected shared similar features, such as influenza-associated symptoms and comorbidities at baseline. Among children infected with IAV with bacterial coinfection, the recovery rate was significantly greater in children treated with oseltamivir than in children treated with peramivir (15.6% vs 4.4%, P = 0.01). The median duration of hospitalization was also shorter in children treated with oseltamivir. Among children infected with IAV without bacterial coinfection, the recovery rate was greater in children treated with oseltamivir than in children treated with peramivir (21.1% vs 3.7%, P = 0.002). However, oseltamivir and peramivir offered similar recovery rates and duration of hospitalization (P >0.05 for both) among children infected with IBV.
Oseltamivir and peramivir exhibit similar effectiveness in young children with severe influenza B, whereas oseltamivir demonstrated improved recovery and shorter hospitalization in the treatment of severe influenza A in hospitalized children.
The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely, ...cells), sparse mobile crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in the real world, as the cost of sensing in a subarea can be diverse due to many factors, e.g., the condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable biobjective optimization problem under certain assumptions and approximations. Then, we presented two selection strategies, i.e., the Pareto optimization selection (POS) and generalized cost-benefit greedy (GCB-GREEDY) selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring data sets (Parking, Flow, Traffic, and Humidity) and three cost maps (independent identically distributed with dynamic cost map, monotonic with dynamic cost map, and spatial-correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-query by committee (QBC) in a sensing cycle. The findings show important implications in sparse MCS for urban context properties.
Protein modification by chemical reagents has played an essential role in the treatment of human diseases. However, the reagents currently used are limited to the covalent modification of cysteine ...and lysine residues. It is thus desirable to develop novel methods that can covalently modify other residues. Despite the fact that the carboxyl residues are crucial for maintaining the protein function, few selective labeling reactions are currently available. Here, we describe a novel reactive probe, 3-phenyl-2
-azirine, that enables chemoselective modification of carboxyl groups in proteins under both in vitro and in situ conditions with excellent efficiency. Furthermore, proteome-wide profiling of reactive carboxyl residues was performed with a quantitative chemoproteomic platform.
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