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Abstract Recent studies have reported worldwide vegetation suppression in response to increasing atmospheric vapor pressure deficit (VPD). Here, we integrate multisource datasets to show that increasing VPD caused by warming alone does not suppress vegetation growth in northern peatlands. A site-level manipulation experiment and a multiple-site synthesis find a neutral impact of rising VPD on vegetation growth; regional analysis manifests a strong declining gradient of VPD suppression impacts from sparsely distributed peatland to densely distributed peatland. The major mechanism adopted by plants in response to rising VPD is the “open” water-use strategy, where stomatal regulation is relaxed to maximize carbon uptake. These unique surface characteristics evolve in the wet soil‒air environment in the northern peatlands. The neutral VPD impacts observed in northern peatlands contrast with the vegetation suppression reported in global nonpeatland areas under rising VPD caused by concurrent warming and decreasing relative humidity, suggesting model improvement for representing VPD impacts in northern peatlands remains necessary.

The swift integration of advanced technologies in the construction sector has significantly propelled the adoption of smart construction sites (SCSs). Quality management (QM), a critical endeavor within the construction domain, is central to the operational success of construction projects. The establishment of quality management in smart construction sites (SCS-QM) specifically seeks to delineate the principal factors influencing quality management in the context of SCS, with the objective of enhancing overall project quality. This study has identified 19 pivotal factors impacting SCS-QM by drawing upon the 4M1E quality management framework and an extensive review of the literature. Utilizing the hybrid DEMATEL-ISM-MICMAC analytical framework, the research evaluates these factors in terms of significance, hierarchical structure, and interdependencies, thereby formulating targeted strategies for the advancement of SCS-QM. Through a systematic evaluation by nine experts, this study categorizes the influencing factors into nine levels, three layers, and four areas, further classifying them into four distinct impact typologies. The results underscore that those technologies, such as automation and intelligence, along with regulatory frameworks, comprehensive quality management standards, transparency of critical technologies, training of construction personnel, and effective process management, constitute the foundational elements crucial for enhancing SCS-QM.

At present, the importance and commercial value of wireless fidelity (Wi-Fi) technology have been widely recognized.And people are also pursuing better quality of service (QoS).Transmission control protocol (TCP) as a connection-oriented communication protocol that provides end-to-end reliability services, its importance is also increasing, and it is the dominant transmission protocol in the Internet.Besides, since the coverage of a single access point (AP) is limited, it is often necessary to extend the coverage through cascading in many scenarios.However, the current research on cascading scenarios is based on the user datagram protocol (UDP), which means that only downlink traffic exists in the scenario.Actually, uplink and downlink traffic often exist at the same time in many scenarios, which means TCP traffic is running.How to calculate the TCP throughput obtained by users in the cascading scenario has always been an unresolved problem.To solve this problem, firstly, a Wi-Fi throughput model under the condition of saturated TCP traffic was established in the cascading scenario, and the calculation of user throughput was realized through two Markov chains.Secondly, a cascading system was built and actual tests were conducted.The validity of the model is verified by comparing the theoretical simulation results with the actual test results.

The integration of RNA metabolic labelling by nucleoside analogues with high-throughput RNA sequencing has been harnessed to study RNA dynamics. The immunoprecipitation purification or chemical pulldown technique is generally required to enrich the analogue-labelled RNAs. Here we developed an a6A-seq method, which takes advantage of N6-allyladenosine (a6A) metabolic labelling on cellular mRNAs and profiles them in an immunoprecipitation-free and mutation-based manner. a6A plays a role as a chemical sequencing tag in that the iodination of a6A in mRNAs results in 1,N6-cyclized adenosine (cyc-A), which induces base misincorporation during RNA reverse transcription, thus making a6A-labelled mRNAs detectable by sequencing. A nucleic acid melting assay was utilized to investigate why cyc-A prefers to be paired with guanine. a6A-seq was utilized to study cellular gene expression changes under a methionine-free stress condition. Compared with regular RNA-seq, a6A-seq could more sensitively detect the change of mRNA production over a time scale. The experiment of a6A-containing mRNA immunoprecipitation followed by qPCR successfully validated the high-throughput a6A-seq data. Together, our results show a6A-seq is an effective tool to study RNA dynamics.

The burgeoning application of advanced information technology in the construction industry has led to the widespread adoption of smart construction sites (SCSs) in recent years. As a novel concept, smart construction site application maturity (SCS-AM) aims to identify the pivotal factors impeding the current progression of SCSs and foster the metamorphosis of the construction sector. Through a meticulous review of the existing literature, this study delineates 14 fundamental factors influencing SCS-AM. Employing both Exploratory Factor Analysis (EFA) and confirmatory factor analysis (CFA), alongside the acquisition of 217 valid questionnaires, practitioners’ perceptions regarding these factors within the smart construction domain were examined. This study initially categorized the 14 factors into four dimensions by utilizing the EFA method: technological innovation and integration (TII), project management and implementation (PMI), collaboration mechanism and information sharing (CMIS), and standardization and compliance (SC). Subsequently, a first-order CFA was employed to elucidate the correlations between the observed variables and latent factors, while a second-order CFA was employed to delve into the interplay among the first-order factors and their collective influence on SCS-AM. The results underscore the paramount impact of standardization and compliance (SC) and technological innovation and integration (TII) on SCS-AM. By meticulously analyzing the key influencing factors, this study offers theoretical underpinnings for bolstering SCS-AM, thereby providing stakeholders such as governments and construction enterprises with strategic insights for future development endeavors.

Because of the flanges on T-shaped shear walls (TSSWs), the shear force acting on such walls results in a shear lag effect, making it impossible to forecast with accuracy the normal stresses of the flanges using the Bernoulli–Euler assumption. Shear lag (SL) in flanged walls has, however, received less attention from researchers, particularly in experimental studies. Understanding the SL in T-shaped reinforced concrete shear walls under shear and axial force is the main goal of this work. First, a SL model is suggested for TSSWs. In this model, the SL deflection is considered to be the generalized displacement and the SL warping deformation, and it is assumed to be a quadratic nonlinear function. Then, experimental and numerical simulation studies are, respectively, conducted to investigate SL effect of TSSWs, and also to evaluate the accuracy of the SL method. Finally, the parameter analysis is conducted to investigate the influence of axial load, shear force, and flange length on the SL effect of TSSWs. The results show that the SL of the TSSW is significant, the normal stress distribution (NSD) of the flange is uneven, and the normal stresses near the web are higher, according to the results of the analytical, simulated, and experimental results. The SL model can accurately predict the normal stresses of the flange of TSSWs, and the quadratic parabola assumption of the SL warp displacement of TSSWs is reasonable. Parameter analysis shows that axial force has little effect on the SL effect of TSSWs. The TSSWs under larger shear force have the more obvious SL effect. A more obvious SL effect occurs in the TSSWs with longer flanges.

The positive Arctic–methane (CH4) feedback forms when more CH4 is released from the Arctic tundra to warm the climate, further stimulating the Arctic to emit CH4. This study utilized the CLM-Microbe model to project CH4 emissions across five distinct Arctic tundra ecosystems on the Alaska North Slope, considering three Shared Socioeconomic Pathway (SSP) scenarios using climate data from three climate models from 2016 to 2100. Employing a hyper-resolution of 5 m × 5 m within 40,000 m2 domains accounted for the Arctic tundra’s high spatial heterogeneity; three sites were near Utqiaġvik (US-Beo, US-Bes, and US-Brw), with one each in Atqasuk (US-Atq) and Ivotuk (US-Ivo). Simulated CH4 emissions substantially increased by a factor of 5.3 to 7.5 under the SSP5–8.5 scenario compared to the SSP1–2.6 and SSP2–4.5 scenarios. The projected CH4 emissions exhibited a stronger response to rising temperature under the SSP5–8.5 scenario than under the SSP1–2.6 and SSP2–4.5 scenarios, primarily due to strong temperature dependence and the enhanced precipitation-induced expansion of anoxic conditions that promoted methanogenesis. The CH4 transport via ebullition and plant-mediated transport is projected to increase under all three SSP scenarios, and ebullition dominated CH4 transport by 2100 across five sites. Projected CH4 emissions varied in temperature sensitivity, with a Q10 range of 2.7 to 60.9 under SSP1–2.6, 3.8 to 17.6 under SSP2–4.5, and 5.7 to 17.2 under SSP5–8.5. Compared with the other three sites, US-Atq and US-Ivo were estimated to have greater increases in CH4 emissions due to warmer temperatures and higher precipitation. The fact that warmer sites and warmer climate scenarios had higher CH4 emissions suggests an intensified positive Arctic–CH4 feedback in the 21st century. Microbial physiology and substrate availability dominated the enhanced CH4 production. The simulated intensified positive feedback underscores the urgent need for a more mechanistic understanding of CH4 dynamics and the development of strategies to mitigate CH4 across the Arctic.

Acinetobacter baumannii is an important nosocomial pathogen. Upon colonizing a host, A. baumannii are subjected to selective pressure by immune defenses as they adapt to the host environment. However, the mechanism of this pathoadaptation is unknown. Here, we established an in vitro system to evolve A. baumannii driven by the continuous selective pressure exerted by epithelial cells, and we used a combination of experimental evolution, phenotypic characterization and multi-omics analysis to address the underlying mechanism. When continuously exposed to selective pressure by pulmonary epithelial cells, A. baumannii showed ptk mutation-mediated mucoid conversion (reduced adhesion and increased anti-phagocytic ability) by enhancement of capsular exopolysaccharide chain length; rsmG mutation-mediated deficiency of 7-methylguanosine modification in the 524th nucleotide of 16S rRNA, which increased ribosome translation efficiency; and rnaseI mutation-mediated changes in outer membrane permeability and efflux pump expression. Together, these mutations altered susceptibility to a variety of antimicrobial agents, including the novel antibiotic cefiderocol, by regulating siderophore and siderophore-receptor biosynthesis. In conclusion, pulmonary epithelial cells modulate A. baumannii pathoadaptation, implicating the host–microbe interaction in the survival and persistence of A. baumannii.

Returning crop residues to the field after harvesting is a proven effective strategy for improving soil fertility, carbon sequestration, and crop productivity. However, the relationships between crop residue return modes, SOC and nutrient contents, and crop yields are still unclear. In this study, a field trial was conducted to investigate the effects of different maize straw return modes, i.e., straw mulching (SMU), straw deep ploughing (SDP), and control without straw return (CK), on soil organic carbon (SOC) and nutrient contents in soil layers of 0–40 cm in a Mollisol. The relationships between straw return modes, SOC and nutrient contents, and maize yield were evaluated. Compared with CK, SMU and SDP significantly increased SOC, total nitrogen (N), available N, total phosphorus (P), and available P contents in all soil layers. Relative to SMU, SOC, total N, available N, total P, and available P contents were significantly lower in soil layers of 0–10 cm, but they were significantly higher in soil layers of 20–40 cm in SDP. Redundancy analysis indicated that total N, available N, and SOC were major factors controlling maize yield. Structural equation modeling further showed that straw return modes indirectly affected maize yield by directly and preferentially affecting total N and available N contents. The results indicated that SMU and SDP were beneficial for increasing SOC and nutrient contents at the surface and subsurface soils, respectively. Optimizing a nitrogen management strategy is important to achieve high maize yield with straw return.

Ma et al. profile the dynamic landscape of m6A during pancreatic differentiation, and identify ALKBH5 as an essential m6A regulator supporting pancreatic differentiation, indicating a role for m6A-mediated mRNA metabolism in cell-fate specification.

Abstract Primary cilia are antenna-like subcellular structures to act as signaling platforms to regulate many cellular processes and embryonic development. m1A RNA modification plays key roles in RNA metabolism and gene expression; however, the physiological function of m1A modification remains largely unknown. Here we find that the m1A demethylase ALKBH3 significantly inhibits ciliogenesis in mammalian cells by its demethylation activity. Mechanistically, ALKBH3 removes m1A sites on mRNA of Aurora A, a master suppressor of ciliogenesis. Depletion of ALKBH3 enhances Aurora A mRNA decay and inhibits its translation. Moreover, alkbh3 morphants exhibit ciliary defects, including curved body, pericardial edema, abnormal otoliths, and dilation in pronephric ducts in zebrafish embryos, which are significantly rescued by wild-type alkbh3, but not by its catalytically inactive mutant. The ciliary defects caused by ALKBH3 depletion in both vertebrate cells and embryos are also significantly reversed by ectopic expression of Aurora A mRNA. Together, our data indicate that ALKBH3-dependent m1A demethylation has a crucial role in the regulation of Aurora A mRNA, which is essential for ciliogenesis and cilia-associated developmental events in vertebrates.

Terrestrial ecosystems are a critical carbon dioxide (CO2) sink for achieving carbon (C) neutrality before 2060 in China. Here, we used the Coupled Model Intercomparison Project phase 6 (CMIP6) model outputs to quantify the spatiotemporal patterns of net ecosystem productivity (NEP) and its major environmental controls, as well as the dominant C pools for NEP during 1900–2100. We found that (a) according to CMIP6 NEP spatiotemporal characteristics, terrestrial ecosystems in China sequestered 0.310 ± 0.058 Pg C·year–1 during 1980–2014 and will act as a strong C sink [from 0.515 ± 0.075 Pg C·year–1 under Shared Socioeconomic Pathway 126 (SSP126) to 0.631 ± 0.089 Pg C·year–1 under SSP585] in the future (2015–2100). Pronounced turning points were found for the temporal trends of NEP during the historical (1984) and under 4 future emissions scenarios (2057 for SSP126, 2053 for SSP245, 2038 for SSP370, and 2044 for SSP585). (b) The positive effect of temperature on NEP appears to weaken after each turning point of future scenarios. (c) The enlarged vegetation C pool size dominates the growing terrestrial ecosystem C storage. The CMIP6 projection shows that the total C storage in Chinese terrestrial ecosystems increases continuously and peaks in the 2040s to 2050s under each scenario. Future afforestation in the northeast, southeast, and southwestern regions, as well as soil C pool management in the northwest and middle north regions, will greatly contribute to achieving C neutrality in China, particularly under low emission scenario (SSP126).

Policy quantitative analysis can effectively evaluate the government's response to COVID-19 emergency management effect, and provide reference for the government to formulate follow-up policies. The content mining method is used to explore the 301 COVID-19 policies issued by the Central government of China since the outbreak of the epidemic in a multi-dimensional manner and comprehensively analyze the characteristics of epidemic prevention policies. Then, based on policy evaluation theory and data fusion theory, a COVID-19 policy evaluation model based on PMC-AE is established to evaluate quantitatively eight representative COVID-19 policy texts. The results show that: Firstly, China's COVID-19 policies are mainly aimed at providing economic support to enterprises and individuals affected by the epidemic, issued by 49 departments, and include 32.7 percent supply-level and 28.5 percent demand-level, and 25.8 percent environment-level. In addition, strategy-level policies accounted for at least 13 percent. Secondly, according to the principle of openness, authority, relevance and normative principle, eight COVID-19 policies are evaluated by PMC-AE model. Four policies are level Ⅰ policies, three policies are level Ⅱ policies and one policy is level Ⅲ policies. The reason for its low score is mainly affected by four indexes: policy evaluation, incentive measures, policy emphasis and policy receptor. To sum up, China has taken both non-structural and structural measures to prevent and control the epidemic. The introduction of specific epidemic prevention and control policy has realized complex intervention in the whole process of epidemic prevention and control.

Whether the place-based industrial policy promotes regional economic growth is a hot issue in the field of regional industrial economic practice. As a major national strategy in China, the Beijing-Tianjin-Hebei industrial coordinated development policy has been implemented more than 8 years. Verifying its effect on regional economic growth and revealing the policy action path will help to further optimize the policy implementation process through feedback. In this paper, the policy effect and its differentiation are empirically studied from 'quality' and 'quantity' respectively by establishing a growth model using the Dual Differences method. The results show that the Beijing-Tianjin-Hebei industrial coordinated development policy improves total factor productivity by 2.26% in terms of 'quality', and reduces GDP growth rate by 4.65% in terms of 'quantity'. For the different region, the GDP growth rate increased by 1.28%, while total factor productivity decreased by 2.63% in Beijing, the GDP growth rate decreased by 3.17% and total factor productivity increased by 0.87% in Tianjin, and the GDP growth rate increased by 2.56% and total factor productivity increased by 1.58% in Hebei. The policy is mainly realized by fixed asset investment, capital deepening degree and enterprise scale expansion, while the effect of labor input, R&D investment and enterprise number is not significant. The policy is to give full play to the driving role of fixed asset investment such as "new infrastructure", increase investment in labor and research and development in the region, strengthen the construction of a competitive market environment, and stabilize the 'quality' and 'quantity' to further release policy dividends.

Since the 1990s, the climate in the Amur River Basin (ARB) has changed, and large-scale wetlands in the region have been reclaimed for paddy fields. The study of the influence of climate change and agricultural expansion on groundwater storage is of great significance to the evaluation of regional water resource balance and the promotion of ecological protection and agricultural development. In this work, the groundwater storage anomaly (GWSA) in the ARB and its drivers were analyzed for the period 2003–2016 using Gravity Recovery and Climate Experiment (GRACE) satellite data, a Global Land Data Assimilation System model, and in situ observations of groundwater levels. Results indicated that 1) the GWSA in the ARB increased at a rate of 2.0–2.4 mm/yr from 2003 to 2016; the GWSA in the upper reaches of the ARB increased, whereas the GWSA in the middle and lower reaches decreased during the study period. 2) The GWSA in the middle and lower reaches of the ARB was greatly influenced by temperature (Tmp) and evapotranspiration (ET). Tmp was positively correlated with GWSA, whereas ET was negatively correlated with GWSA (p < 0.05). 3) Extreme rainfall had a delayed effect on groundwater recharge. Wetland degradation and agricultural development were the main factors causing the decrease of the GWSA in the middle and lower reaches of the ARB. In summary, temperature and evapotranspiration affect groundwater storage by regulating the water–heat balance, wetland reclamation reduces the regional storage capacity, and the irrigation required for reclaimed farmland is the main source of groundwater loss.

Wetlands account for up to 70% of the natural source of methane (CH4) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH4 in wetlands. Therefore, simulating microbial mechanisms of CH4 production and consumptionwould improve the predictability of CH4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH4 flux across sites. Marsh wetlands had higher CH4 flux than mountain peatlands. Ebullition dominated the CH4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH4 flux, making all wetlands a CH4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH4 dynamics under the changing environment.

Wetland conversion to cropland substantially suppresses methane (CH4) emissions due to the strong suppression of methanogenesis, which consists of various pathways. In this study, we evaluated the cultivation impacts on four predominant CH4 production pathways, including acetate, carbon dioxide (CO2), methylamines, and methanol, in a wetland and cultivated cropland in northeastern China. The results showed significant suppression of CH4 production potential and the abundance of genes for all four methanogenic pathways in cropland. The consistency between CH4 production and methanogenesis genes indicates the robustness of genomic genes in analyzing methanogenesis. The suppression effects varied across seasons and along soil profiles, most evident in spring and 0 to 30 cm layers. The acetate pathway accounted for 55% in wetland vs. 70% in the cropland of all functional genes for CH4 production; while the other three pathways were stronger in response to cultivation, which presented as stronger suppressions in both abundance of functional genes (declines are 52% of CO2 pathway, 68% of methanol pathway, and 62% of methylamines pathway, vs. 19% of acetate pathway) and their percentages in four pathways (from 20 to 15% for CO2, 15 to 9% for methylamines, and 10 to 6% for methanol pathway vs. 55 to 70% for acetate pathway). The structural equation models showed that substrate availability was most correlated with CH4 production potential in the wetland, while the positive correlations of acetate, CO2, and methylamine pathways with CH4 production potential were significant in the cropland. The quantitative responses of four CH4 production pathways to land conversion reported in this study provide benchmark information for validating the CH4 model in simulating CH4 cycling under land use and land cover change.

Human RBM42 associates with the spliceosome complex. Here the authors show that the fungus counterpart of RBM42, FgRbp1 regulates splicing by interacting with FgU2AF23.

Spatial heterogeneity in methane (CH4) flux requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH4 flux in early growing seasons. The simulated monthly CH4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m−2 s−1 and 0.782 using the DF algorithm, but 0.007 μmol m−2 s−1 and 0.758 using HF and 0.007 μmol m−2 s−1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH4 flux for all five tower domains despite relatively weak differences in simulated CH4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH4 flux in terrestrial ecosystems.

The natural chemical modifications of messenger RNA (mRNA) in living organisms have shown essential roles in both physiology and pathology. The mapping of mRNA modifications is critical for interpreting their biological functions. In another dimension, the synthesized nucleoside analogs can enable chemical labeling of cellular mRNA through a metabolic pathway, which facilitates the study of RNA dynamics in a pulse-chase manner. In this regard, the sequencing tools for mapping both natural modifications and nucleoside tags on mRNA at single base resolution are highly necessary. In this work, we review the progress of chemical sequencing technology for determining both a variety of naturally occurring base modifications mainly on mRNA and a few on transfer RNA and metabolically incorporated artificial base analogs on mRNA, and further discuss the problems and prospects in the field.

Summary: We describe here a metabolic labeling protocol for detecting cellular transcriptome-wide mRNA N6-methyladenosine (m6A) at base resolution. By feeding cells an analog of methionine, potential mRNA m6A forming sites are replaced with the N6-allyladenosine (a6A). A mild chemical iodination of a6A in RNA results in its opposite base misincorporation during RNA reverse transcription, and thus m6A locations could be precisely identified in the high-throughput sequencing. m6A-label-seq provides a strategy to label and identify cellular epitranscriptomic modification sites.For complete details on the use and execution of this profile, please refer to Shu et al., 2020.

Abstract N6-Methyladenosine (m6A) modification has been implicated in the progression of several cancers. We reveal that during epithelial-mesenchymal transition (EMT), one important step for cancer cell metastasis, m6A modification of mRNAs increases in cancer cells. Deletion of methyltransferase-like 3 (METTL3) down-regulates m6A, impairs the migration, invasion and EMT of cancer cells both in vitro and in vivo. m6A-sequencing and functional studies confirm that Snail, a key transcription factor of EMT, is involved in m6A-regulated EMT. m6A in Snail CDS, but not 3’UTR, triggers polysome-mediated translation of Snail mRNA in cancer cells. Loss and gain functional studies confirm that YTHDF1 mediates m6A-increased translation of Snail mRNA. Moreover, the upregulation of METTL3 and YTHDF1 act as adverse prognosis factors for overall survival (OS) rate of liver cancer patients. Our study highlights the critical roles of m6A on regulation of EMT in cancer cells and translation of Snail during this process.

Permafrost soils represent an enormous carbon (C) pool that is highly vulnerable to climate warming. We used the model output ensemble of the Coupled Model Intercomparison Project Phase 6 to estimate the C storage in soil, litter, and vegetation in the current extent of northern Asian permafrost during 1900–2100. The contemporary (1995–2014) C storage was estimated to be 368.1 ± 82.5 Pg C for the full column depth of the soil, 13.3 ± 4.6 Pg C in litter, and 22.2 ± 3.2 Pg C in vegetation biomass, while these C storage levels are projected to decline by 3.9 Pg C (1.1%) in soils, increase of 0.03 Pg C (0.2%) in litter, and increase by 21.1 Pg C (95.3%) in vegetation biomass by the end of the 21st century under SSP585. The total C storage was dominated by warming-induced vegetation growth. Partial correlation analysis showed that surface air temperature (TAS), soil liquid water, and soil mineral nitrogen (SMN) dominated the soil and vegetation C pools, while SMN controlled litter C during the historical period. Under future scenarios, TAS and SMN dominated the changes of soil and litter C, while TAS determined the vegetation C increase. The growing soil C loss with warming indicates positive C-climate feedback in soils; this warming-induced acceleration of soil C loss was canceled out by the enhanced vegetation C accumulation, leading to a strong C sink in the 21st century.

The net ecosystem CO2 exchange (NEE) is a critical parameter for quantifying terrestrial ecosystems and their contributions to the ongoing climate change. The accumulation of ecological data is calling for more advanced quantitative approaches for assisting NEE prediction. In this study, we applied two widely used machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to build models for simulating NEE in major biomes based on the FLUXNET dataset. Both models accurately predicted NEE in all biomes, while XGBoost had higher computational efficiency (6~62 times faster than RF). Among environmental variables, net solar radiation, soil water content, and soil temperature are the most important variables, while precipitation and wind speed are less important variables in simulating temporal variations of site-level NEE as shown by both models. Both models perform consistently well for extreme climate conditions. Extreme heat and dryness led to much worse model performance in grassland (extreme heat: R2 = 0.66~0.71, normal: R2 = 0.78~0.81; extreme dryness: R2 = 0.14~0.30, normal: R2 = 0.54~0.55), but the impact on forest is less (extreme heat: R2 = 0.50~0.78, normal: R2 = 0.59~0.87; extreme dryness: R2 = 0.86~0.90, normal: R2 = 0.81~0.85). Extreme wet condition did not change model performance in forest ecosystems (with R2 changing −0.03~0.03 compared with normal) but led to substantial reduction in model performance in cropland (with R2 decreasing 0.20~0.27 compared with normal). Extreme cold condition did not lead to much changes in model performance in forest and woody savannas (with R2 decreasing 0.01~0.08 and 0.09 compared with normal, respectively). Our study showed that both models need training samples at daily timesteps of >2.5 years to reach a good model performance and >5.4 years of daily samples to reach an optimal model performance. In summary, both RF and XGBoost are applicable machine learning algorithms for predicting ecosystem NEE, and XGBoost algorithm is more feasible than RF in terms of accuracy and efficiency.

Summary: Transcriptional regulation of circadian rhythms is essential for lipid metabolic homeostasis, disruptions of which can lead to metabolic diseases. Whether N6-methyladenosine (m6A) mRNA methylation impacts circadian regulation of lipid metabolism is unclear. Here, we show m6A mRNA methylation oscillations in murine liver depend upon a functional circadian clock. Hepatic deletion of Bmal1 increases m6A mRNA methylation, particularly of PPaRα. Inhibition of m6A methylation via knockdown of m6A methyltransferase METTL3 decreases PPaRα m6A abundance and increases PPaRα mRNA lifetime and expression, reducing lipid accumulation in cells in vitro. Mechanistically, YTHDF2 binds to PPaRα to mediate its mRNA stability to regulate lipid metabolism. Induction of reactive oxygen species both in vitro and in vivo increases PPaRα transcript m6A levels, revealing a possible mechanism for circadian disruption on m6A mRNA methylation. These data show that m6A RNA methylation is important for circadian regulation of downstream genes and lipid metabolism, impacting metabolic outcomes. : Zhong et al. reveal that hepatic Bmal1 deletion changes m6A mRNA methylation, particularly of PPaRα. METTL3 or YTHDF2 knockdown affects PPaRα transcription and translation, impacting downstream lipid metabolism. These findings further reveal the overlap between circadian gene network disruption, mRNA m6A modifications, and metabolic state. Keywords: circadian clock, Bmal1, hepatic, lipid metabolism, ROS, m6A RNA methylation, post-transcriptional regulation, METTL3, YTHDF2, PPaRα

DNA 6mA is a poorly understood epigenetic mark present at a low abundance in eukaryotic genomes. Here the authors observe high levels in zebrafish and pig during early embryogenesis enriched to repetitive regions of the genome and followed by attenuation during development.

DNA N6-methyladenine is prevalent in prokaryotes, and is recently also detected in eukaryotes such as roundworm and fly. Here, Luo et al. report a DpnI-assisted base resolution method that detects 6mA genome-wide with nanograms of input DNA and lower sequencing depth than the previous restriction enzyme-based approach.

A four-year field experiment was conducted to investigate the effect of subsoiling depth on root morphology, nitrogen (N), phosphorus (P), and potassium (K) uptake, and grain yield of spring maize. The results indicated that subsoil tillage promoted root development, increased nutrient accumulation, and increased yield. Compared with conventional soil management (CK), root length, root surface area, and root dry weight at 0–80 cm soil depth under subsoil tillage to 30 cm (T1) and subsoil tillage to 50 cm (T2) were significantly increased, especially the proportions of roots in deeper soil. Root length, surface area, and dry weight differed significantly among three treatments in the order of T2 > T1 > CK at the 12-leaf and early filling stages. The range of variation of root diameter in different soil layers in T2 treatment was the smallest, suggesting that roots were more likely to grow downwards with deeper subsoil tillage in soil. The accumulation of N, P, and K in subsoil tillage treatment was significantly increased, but the proportions of kernel and straw were different. In a comparison of T1 with T2, the grain accumulated more N and P, while K accumulation in kernel and straw varied in different years. Grain yield and biomass were increased by 12.8% and 14.6% on average in subsoil tillage treatments compared to conventional soil treatment. Although no significant differences between different subsoil tillage depths were observed for nutrient accumulation and grain yield, lodging resistance of plants was significantly improved in subsoil tillage to 50 cm, a characteristic that favors a high and stable yield under extreme environments.

Soil water shortage is a critical issue for the Southwest US (SWUS), the typical arid region that has experienced severe droughts over the past decades, primarily caused by climate change. However, it is still not quantitatively understood how soil water storage in the SWUS is affected by climate change. We integrated the time-series data of water storage and evapotranspiration derived from satellite data, societal water consumption, and meteorological data to quantify soil water storage changes and their climate change impacts across the SWUS from 2003 to 2014. The water storage decline was found across the entire SWUS, with a significant reduction in 98.5% of the study area during the study period. The largest water storage decline occurred in the southeastern portion, while only a slight decline occurred in the western and southwestern portions of the SWUS. Net atmospheric water input could explain 38% of the interannual variation of water storage variation. The climate-change-induced decreases in net atmospheric water input predominately controlled the water storage decline in 60% of the SWUS (primarily in Texas, Eastern New Mexico, Eastern Arizona, and Oklahoma) and made a partial contribution in approximately 17% of the region (Central and Western SWUS). Climate change, primarily as precipitation reduction, made major contributions to the soil water storage decline in the SWUS. This study infers that water resource management must consider the climate change impacts over time and across space in the SWUS.

The integration of RNA metabolic labelling by nucleoside analogues with high-throughput RNA sequencing has been harnessed to study RNA dynamics. The immunoprecipitation purification or chemical pulldown technique is generally required to enrich the analogue-labelled RNAs. Here we developed an a6A-seq method, which takes advantage of N6-allyladenosine (a6A) metabolic labelling on cellular mRNAs and profiles them in an immunoprecipitation-free and mutation-based manner. a6A plays a role as a chemical sequencing tag in that the iodination of a6A in mRNAs results in 1, N6-cyclized adenosine (cyc-A), which induces base misincorporation during RNA reverse transcription, thus making a6A-labelled mRNAs detectable by sequencing. A nucleic acid melting assay was utilized to investigate why cyc-A prefers to be paired with guanine. a6A-seq was utilized to study cellular gene expression changes under a methionine-free stress condition. Compared with regular RNA-seq, a6A-seq could more sensitively detect the change of mRNA production over a time scale. The experiment of a6A-containing mRNA immunoprecipitation followed by qPCR successfully validated the high-throughput a6A-seq data. Together, our results show a6A-seq is an effective tool to study RNA dynamics. [ABSTRACT FROM AUTHOR]