Microbial evolution-An under-appreciated driver of soil carbon cycling.

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Greenhouse gas emissions, carbon stocks and wheat productivity following biochar, compost and vermicompost amendments: comparison of non-saline and salt-affected soils.

Understanding the impact of greenhouse gas (GHG) emissions and carbon stock is crucial for effective climate change assessment and agroecosystem management. However, little is known about the effects of organic amendments on GHG emissions and dynamic changes in carbon stocks in salt-affected soils. We conducted a pot experiment with four treatments including control (only fertilizers addition), biochar, vermicompost, and compost on non-saline and salt-affected soils, with the application on a carbon equivalent basis under wheat crop production. Our results revealed that the addition of vermicompost significantly increased soil organic carbon content by 18% in non-saline soil and 52% in salt-affected soil compared to the control leading to improvements in crop productivity i.e., plant dry biomass production by 57% in non-saline soil with vermicompost, while 56% with the same treatment in salt-affected soil. The grain yield was also noted 44 and 50% more with vermicompost treatment in non-saline and salt-affected soil, respectively. Chlorophyll contents were observed maximum with vermicompost in non-saline (24%), and salt-affected soils (22%) with same treatments. Photosynthetic rate (47% and 53%), stomatal conductance (60% and 12%), and relative water contents (38% and 27%) were also noted maximum with the same treatment in non-saline and salt-affected soils, respectively. However, the highest carbon dioxide emissions were observed in vermicompost- and compost-treated soils, leading to an increase in emissions of 46% in non-saline soil and 74% in salt-affected soil compared to the control. The compost treatment resulted in the highest nitrous oxide emissions, with an increase of 57% in non-saline soil and 62% in salt-affected soil compared to the control. In saline and non-saline soils treated with vermicompost, the global warming potential was recorded as 267% and 81% more than the control, respectively. All treatments, except biochar in non-saline soil, showed increased net GHG emissions due to organic amendment application. However, biochar reduced net emissions by 12% in non-saline soil. The application of organic amendments increased soil organic carbon content and crop yield in both non-saline and salt-affected soils. In conclusion, biochar is most effective among all tested organic amendments at increasing soil organic carbon content in both non-saline and salt-affected soils, which could have potential benefits for soil health and crop production.

Transcriptomic profiling of an evolved Yarrowia lipolytica strain: tackling hexanoic acid fermentation to increase lipid production from short-chain fatty acids.

BACKGROUND: Short-chain fatty acids (SCFAs) are cost-effective carbon sources for an affordable production of lipids. Hexanoic acid, the acid with the longest carbon chain in the SCFAs pool, is produced in anaerobic fermentation of organic residues and its use is very challenging, even inhibiting oleaginous yeasts growth. RESULTS: In this investigation, an adaptive laboratory evolution (ALE) was performed to improve Yarrowia lipolytica ACA DC 50109 tolerance to high hexanoic acid concentrations. Following ALE, the transcriptomic analysis revealed several genetic adaptations that improved the assimilation of this carbon source in the evolved strain compared to the wild type (WT). Indeed, the evolved strain presented a high expression of the up-regulated gene YALI0 E16016g, which codes for FAT1 and is related to lipid droplets formation and responsible for mobilizing long-chain acids within the cell. Strikingly, acetic acid and other carbohydrate transporters were over-expressed in the WT strain. CONCLUSIONS: A more tolerant yeast strain able to attain higher lipid content under the presence of high concentrations of hexanoic acid has been obtained. Results provided novel information regarding the assimilation of hexanoic acid in yeasts.

Comparative molecular dynamics simulations provided insights into the mechanisms of cold-adaption of alginate lyases from the PL7 family.

Alginate is an important polysaccharide that is abundant in the marine environments, including the Polar Regions, and bacterial alginate lyases play key roles in its degradation. Many reported alginate lyases show characteristics of cold-adapted enzymes, including relatively low temperature optimum of activities (Topt) and low thermal stabilities. However, the cold-adaption mechanisms of alginate lyases remain unclear. Here, we studied the cold-adaptation mechanisms of alginate lyases by comparing four members of the PL7 family from different environments: AlyC3 from the Arctic ocean (Psychromonas sp. C-3), AlyA1 from the temperate ocean (Zobellia galactanivorans), PA1167 from the human pathogen (Pseudomonas aeruginosa PAO1), and AlyQ from the tropic ocean (Persicobacter sp. CCB-QB2). Sequence comparison and comparative molecular dynamics (MD) simulations revealed two main strategies of cold adaptation. First, the Arctic AlyC3 and temperate AlyA1 increased the flexibility of the loops close to the catalytic center by introducing insertions at these loops. Second, the Arctic AlyC3 increased the electrostatic attractions with the negatively charged substrate by introducing a high portion of positively charged lysine at three of the insertions mentioned above. Furthermore, our study also revealed that the root mean square fluctuation (RMSF) increased greatly when the temperature was increased to Topt or higher, suggesting the RMSF increase temperature as a potential indicator of the cold adaptation level of the PL7 family. This study provided new insights into the cold-adaptation mechanisms of bacterial alginate lyases and the marine carbon cycling at low temperatures.

Longitudinal stream synoptic (LSS) monitoring to evaluate water quality in restored streams.

Impervious surface cover increases peak flows and degrades stream health, contributing to a variety of hydrologic, water quality, and ecological symptoms, collectively known as the urban stream syndrome. Strategies to combat the urban stream syndrome often employ engineering approaches to enhance stream-floodplain reconnection, dissipate erosive forces from urban runoff, and enhance contaminant retention, but it is not always clear how effective such practices are or how to monitor for their effectiveness. In this study, we explore applications of longitudinal stream synoptic (LSS) monitoring (an approach where multiple samples are collected along stream flowpaths across both space and time) to narrow this knowledge gap. Specifically, we investigate (1) whether LSS monitoring can be used to detect changes in water chemistry along longitudinal flowpaths in response to stream-floodplain reconnection and (2) what is the scale over which restoration efforts improve stream quality. We present results for four different classes of water quality constituents (carbon, nutrients, salt ions, and metals) across five watersheds with varying degrees of stream-floodplain reconnection. Our work suggests that LSS monitoring can be used to evaluate stream restoration strategies when implemented at meter to kilometer scales. As streams flow through restoration features, concentrations of nutrients, salts, and metals significantly decline (p < 0.05) or remain unchanged. This same pattern is not evident in unrestored streams, where salt ion concentrations (e.g., Na+, Ca2+, K+) significantly increase with increasing impervious cover. When used in concert with statistical approaches like principal component analysis, we find that LSS monitoring reveals changes in entire chemical mixtures (e.g., salts, metals, and nutrients), not just individual water quality constituents. These chemical mixtures are locally responsive to restoration projects, but can be obscured at the watershed scale and overwhelmed during storm events.

[Safety Analysis Using Event Tree Analysis for Multi-Ion Therapy].

At the National Institutes for Quantum Science and Technology (QST), a multi-ion therapy using helium, carbon, oxygen, and neon ions has been studied for charged particle therapy with more optimal biological effects. To make multi-ion therapy clinically feasible, a new treatment system was developed to realize the changes of the ion species in each irradiation using the Heavy Ion Medial Accelerator in Chiba (HIMAC). Since radiation therapy is safety-critical, it is necessary to construct a safety system that includes multiple safety barriers in the new treatment system for multi-ion therapy and to perform a safety analysis for the prevention of serious accidents. In this study, we conducted a safety analysis using event tree analysis (ETA) for newly introduced processes in the treatment planning, accelerator, and irradiation system of the multi-ion therapy. ETA is an optimal method to verify multiple safety barriers that are essential for medical safety and to shorten the time for safety analysis by focusing only on the new processes. Through ETA, we clarified the types of malfunctions and human errors that may lead to serious accidents in the new system for multi-ion therapy, and verified whether safety barriers such as interlock systems and human check procedures are sufficient to prevent such malfunctions and human errors. As a result, 6 initial events which may lead to serious accidents were listed in the treatment planning process, 16 initial events were listed in the accelerator system, and 13 initial events were listed in the irradiation system. Among these 35 initial events, 5 cautionary initial events were identified that could lead to serious final events and they had a probability of occurrence higher than 10-4. Meanwhile, the others were all initial events that do not lead to serious accidents, or the initial events that can lead to serious accidents but were considered to have sufficient safety barriers. The safety analysis using ETA successfully identified the system malfunctions and the human errors that can lead to serious accidents, and the multiple safety barriers against them were systematically analyzed. It became clear that the multiple safety barriers were not sufficient for some initial events. We plan to improve the safety barriers for the five cautionary initial events before the start of the clinical trial. Based on these findings, we achieved our objective to conduct a safety analysis for a new treatment system for multi-ion therapy. The safety analysis procedure using ETA proposed by this study will be effective when new systems for radiotherapy are established at QST and other facilities in the future as well.

Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach.

Forest biomass plays a crucial role in the global carbon cycle as a significant contributor derived from both soil and trees. This study focuses on investigating tree carbon stock (TCS) and estimating aboveground biomass (AGB) based on elevation within the Srivilliputhur Wildlife Sanctuary forest, while also exploring the various factors that influence their contribution. Utilizing a non-destructive approach for carbon estimation, we found that the total tree biomass in this region ranged from 220.9 Mg/ha (in Z6) to 720.6 Mg/ha (Z2), while tree carbon stock ranged from 103.8 to 338.7 Mg/ha. While Kruskal-Wallis tests did not reveal a significant relationship (p = 0.09) between TCS and elevation, linear regression showed a weak correlation (R2 = 0.002, p < 0.05) with elevation. To delve deeper into the factors influencing TCS and biomass distribution, we employed a random forest (RF) machine learning algorithm, demonstrating that stand structural attributes, such as basal area (BA), diameter at breast height (DBH), and density, held a more prominent role than climatic variables, including temperature, precipitation, and slope. Generalized linear models (GLM) were also utilized, confirming that BA, mean DBH, and elevation significantly influenced AGB (p ≤ 0.001), with species richness, precipitation, and temperature having lower significance (p ≤ 0.01) comparatively. Overall, the RF model exhibited superior performance (R2 = 0.92, RMSE = 0.12) in terms of root mean square error (RMSE) compared to GLM (R2 = 0.88, RMSE = 0.35). These findings shed light on the intricate dynamics of biomass distribution and the importance of both stand structural and climatic factors in shaping forest ecosystems.

Warming Leads to Changes in Soil Organic Carbon Molecules Due to Decreased Mineral Protection.

Climate change affects the content and composition of soil organic carbon (SOC). However, warming-induced changes in the SOC compounds remain unknown. Using nuclear magnetic resonance spectroscopy, molecular mixing models, and Fourier transform ion cyclotron resonance mass spectrometry, we analyzed the variations and relationships in molecular compounds in Mollisol with 10-56 g C kg-1 soil-1 by translocating soils under six climate regimes. We found that increased temperature and precipitation were negatively correlated with carbohydrate versus lipid and lignin versus protein. The former was consistent across soils with varying SOC contents, but the latter decreased as the SOC content increased. The carbohydrate-lipid correlations were related to dithionite-citrate-extractable Fe, while the lignin-protein correlations were linked to changes in moisture and pyrophosphate-extractable Fe/Al. Our findings indicate that the reduction in the mineral protection of SOC is associated with molecular alterations in SOC under warming conditions.

Fungal Prenyltransferase AnaPT and Its F265 Mutants Catalyze the Dimethylallylation at the Nonaromatic Carbon of Phloretin.

Phloretin is widely found in fruit and shows various biological activities. Here, we demonstrate the dimethylallylation, geranylation, and farnesylation, particularly the first dimethylallylation at the nonaromatic carbon of phloretin (1) by the fungal prenyltransferase AnaPT and its mutants. F265 was identified as a key amino acid residue related to dimethylallylation at the nonaromatic carbon of phloretin. Mutants AnaPT_F265D, AnaPT_F265G, AnaPT_F265P, AnaPT_F265C, and AnaPT_F265Y were discovered to generally increase prenylation activity toward 1. AnaPT_F265G catalyzes the O-geranylation selectively at the C-2' hydroxyl group, which involves an intramolecular hydrogen bond with the carbonyl group of 1. Seven products, 1D5, 1D7-1D9, 1G2, 1G4, and 1F2, have not been reported prior to this study. Twelve compounds, 1D3-1D9, 1G1-1G3, and 1F1-1F2, exhibited potential inhibitory effects on α-glucosidase with IC50 values ranging from 11.45 ± 0.87 to 193.80 ± 6.52 µg/mL. Among them, 1G1 with an IC50 value of 11.45 ± 0.87 µg/mL was the most potential α-glucosidase inhibitor, which is about 30 times stronger than the positive control acarbose with an IC50 value of 346.63 ± 15.65 µg/mL.

The roles of non-structural carbohydrates in fruiting: a review focusing on mango (Mangifera indica).

Reproductive development of fruiting trees, including mango (Mangifera indica L.), is limited by non-structural carbohydrates. Competition for sugars increases with cropping, and consequently, vegetative growth and replenishment of starch reserves may reduce with high yields, resulting in interannual production variability. While the effect of crop load on photosynthesis and the distribution of starch within the mango tree has been studied, the contribution of starch and sugars to different phases of reproductive development requires attention. This review focuses on mango and examines the roles of non-structural carbohydrates in fruiting trees to clarify the repercussions of crop load on reproductive development. Starch buffers the plant's carbon availability to regulate supply with demand, while sugars provide a direct resource for carbon translocation. Sugar signalling and interactions with phytohormones play a crucial role in flowering, fruit set, growth, ripening and retention, as well as regulating starch, sugar and secondary metabolites in fruit. The balance between the leaf and fruit biomass affects the availability and contributions of starch and sugars to fruiting. Crop load impacts photosynthesis and interactions between sources and sinks. As a result, the onset and rate of reproductive processes are affected, with repercussions for fruit size, composition, and the inter-annual bearing pattern.

Metagenomics reveals the variations in functional metabolism associated with greenhouse gas emissions during legume-vegetable rotation process.

Legume-based rotation is commonly recognized for its mitigation efficiency of greenhouse gas (GHG) emissions. However, variations in GHG emission-associated metabolic functions during the legume-vegetable rotation process remain largely uncharacterized. Accordingly, a soybean-radish rotation field experiment was designed to clarify the responses of microbial communities and their GHG emission-associated functional metabolism through metagenomics. The results showed that the contents of soil organic carbon and total phosphorus significantly decreased during the soybean-radish process (P < 0.05), while soil total potassium content and bacterial richness and diversity significantly increased (P < 0.05). Moreover, the predominant bacterial phyla varied, with a decrease in the relative abundance of Proteobacteria and an increase in the relative abundance of Acidobacteria, Gemmatimonadetes, and Chloroflexi. Metagenomics clarified that bacterial carbohydrate metabolism substantially increased during the rotation process, whereas formaldehyde assimilation, methanogenesis, nitrification, and dissimilatory nitrate reduction decreased (P < 0.05). Specifically, the expression of phosphate acetyltransferase (functional methanogenesis gene, pta) and nitrate reductase gamma subunit (functional dissimilatory nitrate reduction gene, narI) was inhibited, indicating of low methane production and nitrogen metabolism. Additionally, the partial least squares path model revealed that the Shannon diversity index was negatively correlated with methane and nitrogen metabolism (P < 0.01), further demonstrating that the response of the soil bacterial microbiome responses are closely linked with GHG-associated metabolism during the soybean-radish rotation process. Collectively, our findings shed light on the responses of soil microbial communities to functional metabolism associated with GHG emissions and provide important insights to mitigate GHG emissions during the rotational cropping of legumes and vegetables.

Accumulation of Ambient Black Carbon Particles Within Key Memory-Related Brain Regions.

Importance: Ambient air pollution is a worldwide problem, not only related to respiratory and cardiovascular diseases but also to neurodegenerative disorders. Different pathways on how air pollutants could affect the brain are already known, but direct evidence of the presence of ambient particles (or nanoparticles) in the human adult brain is limited. Objective: To examine whether ambient black carbon particles can translocate to the brain and observe their biodistribution within the different brain regions. Design, Setting, and Participants: In this case series a label-free and biocompatible detection technique of nonincandescence-related white light generation was used to screen different regions of biobanked brains of 4 individuals from Belgium with neuropathologically confirmed Alzheimer disease for the presence of black carbon particles. The selected biological specimens were acquired and subsequently stored in a biorepository between April 2013 and April 2017. Black carbon measurements and data analysis were conducted between June 2020 and December 2022. Main Outcomes and Measures: The black carbon load was measured in various human brain regions. A Kruskal-Wallis test was used to compare black carbon loads across these regions, followed by Dunn multiple comparison tests. Results: Black carbon particles were directly visualized in the human brain of 4 individuals (3 women [75%]; mean [SD] age, 86 [13] years). Screening of the postmortem brain regions showed a significantly higher median (IQR) number of black carbon particles present in the thalamus (433.6 [289.5-540.2] particles per mm3), the prefrontal cortex including the olfactory bulb (420.8 [306.6-486.8] particles per mm3), and the hippocampus (364.7 [342.0-448.7] particles per mm3) compared with the cingulate cortex (192.3 [164.2-277.5] particles per mm3), amygdala (217.5 [147.3-244.5] particles per mm3), and the superior temporal gyrus (204.9 [167.9-236.8] particles per mm3). Conclusions and Relevance: This case series provides evidence that ambient air pollution particles are able to translocate to the human brain and accumulate in multiple brain regions involved in cognitive functioning. This phenomenon may contribute to the onset and development of neurodegenerative disorders.

The agricultural carbon reduction effect of digital rural construction under the dual carbon target.

The exploration of the agricultural carbon emission reduction effect of digital rural construction offers a promising path towards achieving dual carbon goals. This study establishes an evaluation system for digital rural construction and analyzes its impact on agricultural carbon emissions using various creative techniques including panel fixed effects, mediation effects, threshold effects, and spatial Durbin models based on provincial panel data from 2011 to 2021.It is found that: (1) The impact of digital rural construction on agricultural carbon emissions exhibits a "inverted U-shaped" pattern, with a nonlinear effect on emissions through promoting agricultural green total factor productivity and adjusting agricultural structure.(2) Digital rural construction has both promoting and inhibiting effects on agricultural carbon emissions, both locally and in adjacent areas. It also demonstrates a threshold effect, with rural human capital as the sole threshold. Once the threshold value 8.830 is surpassed, the agricultural carbon emission reduction effect becomes prominent.(3)Digital rural construction has a dual effect on local agricultural carbon emissions in terms of both promoting and then restraining the emissions, which has a spatial spill-over effect in the neighboring areas. This study contributes to our understanding of carbon reduction pathways by highlighting the comprehensive utilization of digital rural construction and expanding research on the dynamic context of its impact on carbon emissions.

From 13C-lignin to 13C-mycelium: Agaricus bisporus uses polymeric lignin as a carbon source.

Plant biomass conversion by saprotrophic fungi plays a pivotal role in terrestrial carbon (C) cycling. The general consensus is that fungi metabolize carbohydrates, while lignin is only degraded and mineralized to CO2. Recent research, however, demonstrated fungal conversion of 13C-monoaromatic compounds into proteinogenic amino acids. To unambiguously prove that polymeric lignin is not merely degraded, but also metabolized, carefully isolated 13C-labeled lignin served as substrate for Agaricus bisporus, the world's most consumed mushroom. The fungus formed a dense mycelial network, secreted lignin-active enzymes, depolymerized, and removed lignin. With a lignin carbon use efficiency of 0.14 (g/g) and fungal biomass enrichment in 13C, we demonstrate that A. bisporus assimilated and further metabolized lignin when offered as C-source. Amino acids were high in 13C-enrichment, while fungal-derived carbohydrates, fatty acids, and ergosterol showed traces of 13C. These results hint at lignin conversion via aromatic ring-cleaved intermediates to central metabolites, underlining lignin's metabolic value for fungi.

Decoding the role of oxidative stress resistance and alternative carbon substrate assimilation in the mature biofilm growth mode of Candida glabrata.

BACKGROUND: Biofilm formation is viewed as a vital mechanism in C. glabrata pathogenesis. Although, it plays a significant role in virulence but transcriptomic architecture and metabolic pathways governing the biofilm growth mode of C. glabrata remain elusive. The present study intended to investigate the genes implicated in biofilm growth phase of C. glabrata through global transcriptomic approach. RESULTS: Functional analysis of Differentially expressed genes (DEGs) using gene ontology and pathways analysis revealed that upregulated genes are involved in the glyoxylate cycle, carbon-carbon lyase activity, pre-autophagosomal structure membrane and vacuolar parts whereas, down- regulated genes appear to be associated with glycolysis, ribonucleoside biosynthetic process, ribosomal and translation process in the biofilm growth condition. The RNA-Seq expression of eight selected DEGs (CgICL1, CgMLS1, CgPEP1, and CgNTH1, CgERG9, CgERG11, CgTEF3, and CgCOF1) was performed with quantitative real-time PCR (RT-qPCR). The gene expression profile of selected DEGs with RT-qPCR displayed a similar pattern of expression as observed in RNA-Seq. Phenotype screening of mutant strains generated for genes CgPCK1 and CgPEP1, showed that Cgpck1∆ failed to grow on alternative carbon substrate (Glycerol, Ethanol, Oleic acid) and similarly, Cgpep1∆ unable to grow on YPD medium supplemented with hydrogen peroxide. Our results suggest that in the absence of glucose, C. glabrata assimilate glycerol, oleic acid and generate acetyl coenzyme-A (acetyl-CoA) which is a central and connecting metabolite between catabolic and anabolic pathways (glyoxylate and gluconeogenesis) to produce glucose and fulfil energy requirements. CONCLUSIONS: The study was executed using various approaches (transcriptomics, functional genomics and gene deletion) and it revealed that metabolic plasticity of C. glabrata (NCCPF-100,037) in biofilm stage modulates its virulence and survival ability to counter the stress and may promote its transition from commensal to opportunistic pathogen. The observations deduced from the present study along with future work on characterization of the proteins involved in this intricate process may prove to be beneficial for designing novel antifungal strategies.

Impact of regenerative farming practices on soil quality and yield of cotton-sorghum system in semi arid Indian conditions.

Regenerative agricultural practices, i.e. organic and natural farming, are rooted in India since ancient times. However, the high cost of production, lack of organic pest control measures and premium price of organic produces in chemical agriculture encourage natural farming. In the present study, the quality improvement of calcareous soils under organic (OGF) and natural (NTF) management was compared with integrated conventional (ICF) and non-invasive (NIF) farming practices with cotton-sorghum crops over three consecutive years. A total of 23 soil attributes were analyzed at the end of the third cropping cycle and subjected to principal component analysis (PCA) to select a minimum data set (MDS) and obtain a soil quality index (SQI). The attributes soil organic carbon (SOC), available Fe, pH, bulk density (BD) and alkaline phosphatase (APA) were selected as indicators based on correlations and expert opinions on the lime content of the experimental soil. The SQI was improved in the order of OGF (0.89) > NTF(0.69) > ICF(0.48) > NIF(0.05). The contribution of the indicators to SQI was in the order of available Fe (17-44%) > SOC (21-28%), APA (11-36%) > pH (0-22%), and BD (0-20%) regardless of the farming practices. These indicators contribute equally to soil quality under natural (17-22%) and organic (18-22%) farming. The benefit:cost ratio was calculated to show the advantage of natural farming and was in the order of NTF(1.95-2.29), ICF (1.34-1.47), OGF (1.13-1.20) and NIF (0.84-1.47). In overall, the natural farming significantly sustained the soil quality and cost benefit compared to integrated conventional farming practices.

Green Synthesis of Ceria Nanoparticles from Cassava Tubers for Electrochemical Aptasensor Detection of SARS-CoV-2 on a Screen-Printed Carbon Electrode.

Green synthesis approaches for making nanosized ceria using starch from cassava as template molecules to control the particle size are reported. The results of the green synthesis of ceria with an optimum calcination temperature of 800 °C shows a size distribution of each particle of less than 30 nm with an average size of 9.68 nm, while the ratio of Ce3+ to Ce4+ was 25.6%. The green-synthesized nanoceria are applied to increase the sensitivity and attach biomolecules to the electrode surface of the electrochemical aptasensor system for coronavirus disease (COVID-19). The response of the aptasensor to the receptor binding domain of the virus was determined with the potassium ferricyanide redox system. The screen-printed carbon electrode that has been modified with green-synthesized nanoceria shows 1.43 times higher conductivity than the bare electrode, while those modified with commercial ceria increase only 1.18 times. Using an optimized parameter for preparing the aptasensors, the detection and quantification limits were 1.94 and 5.87 ng·mL-1, and the accuracy and precision values were 98.5 and 89.1%. These results show that green-synthesized ceria could be a promising approach for fabricating an electrochemical aptasensor.

Optimizing hydrothermal treatment for sustainable valorization and fatty acid recovery from food waste.

This study employs response surface methodology and a central composite design (CCD) to optimize hydrothermal treatment (HTT) conditions for the valorization of food waste (FW). Lab-scale pressure reactor-based HTT processes are investigated to detect the effects of temperature (220-340 °C) and resident time (90-260 min) on elemental composition and fatty acid recovery in the hydrothermal liquid. Central to the study is the identification of temperature as the primary factor influencing food waste conversion during the HTT process, showcasing its impact on HTT product yields. The liquid fraction, rich in saturated fatty acids (SFA), demonstrates a temperature-dependent trend, with higher temperatures favoring SFA recovery. Specifically, HTT at 340 °C in 180 min exhibits the highest SFA percentages, reaching up to 52.5 wt%. The study establishes HTT as a promising avenue for nutrient recovery, with the liquid fraction yielding approximately 95% at optimized conditions. Furthermore, statistical analysis using response surface methodology predicts the optimal achievable yields for hydrochar and hydrothermal liquid at 6.15% and 93.85%, respectively, obtained at 320 °C for 200 min.

Ultrahigh peroxidase-like catalytic performance of Cu-N4 and Cu-N4S active sites-containing reduced graphene oxide for sensitive electrochemical biosensing.

Carbon-based nanozymes possessing peroxidase-like activity have attracted significant interest because of their potential to replace native peroxidases in biotechnology. Although various carbon-based nanozymes have been developed, their relatively low catalytic efficiency needs to be overcome to realize their practical utilization. Here, inspired by the elemental uniqueness of Cu and the doped elements N and S, as well as the active site structure of Cu-centered oxidoreductases, we developed a new carbon-based peroxidase-mimicking nanozyme, single-atom Cu-centered N- and S-codoped reduced graphene oxide (Cu-NS-rGO), which preserved many Cu-N4 and Cu-N4S active sites and showed dramatically high peroxidase-like activity without any oxidase-like activity, yielding up to 2500-fold higher catalytic efficiency (kcat/Km) than that of pristine rGO. The high catalytic activity of Cu-NS-rGO might be attributed to the acceleration of electron transfer from Cu single atom as well as synergistic effects from both Cu-N4 and Cu-N4S active sites, which was theoretically confirmed by Gibbs free energy calculations using density functional theory. The prepared Cu-NS-rGO was then used to construct an electrochemical bioassay system for detecting choline and acetylcholine by coupling with the corresponding oxidases. Using this system, both target molecules were selectively determined with high sensitivity that was sufficient to clinically determine their levels in physiological fluids. Overall, this study will facilitate the development of nanocarbon-based nanozymes and their electrochemical biosensing applications, which can be extended to the development of miniaturized devices in point-of-care testing environments.

Research on the spatial effect of digital economy development on urban carbon reduction.

Cities are one of the main sources of regional carbon emissions, and reducing urban carbon emission is the key to reducing emissions. The digital economy has transformed the economic operation mode, and it is a significant approach to support the "dual carbon goals" (carbon peaking and carbon neutrality). This article considers the externalities of the digital economy and carbon emissions. And we use spatial econometric models to analyze the effectiveness of digital economy in empowering carbon emissions reduction. Besides, we explore the static and dynamic spillover effects, and use spatial Durbin panel quantile model to analyze the digital economy's heterogeneity on carbon emissions. Research has shown that the digital economy has a remarkable carbon reduction effect, and the conclusion remains valid after considering robustness tests such as replacing the weight matrices, calculation methods, and proxy variables. The analysis of static and dynamic spillover effects indicates that the degree of the digital economy's impact on carbon emissions are significantly different. Heterogeneity analysis shows that as the digital economy develops from a low level to a high level, its impact on carbon emissions also shifts from positive promotion to negative suppression. This paper proposes a policy reference to help the development of digital economy and promote carbon neutrality in the face of severe environmental challenges.