Influence of climate and land use on watershed anthropogenic phosphorus inputs and riverine phosphorus export dynamics: A global analysis.

Many studies have found predictive relationships between riverine phosphorus (P) export and net anthropogenic P inputs (NAPI) at the watershed scale, but the global or regional extent of these relationships has not been empirically quantified. Herein, we present a data-driven global assessment of the response of riverine total P (TP) fluxes to NAPI based on 358 watersheds. NAPI exhibited high spatial heterogeneity (2-12,085 kg P km-2 yr-1) and was well correlated with riverine TP fluxes. Riverine TP export fractions of NAPI were primarily regulated by NAPI components, hydroclimate factors, and land-use as determined through a random-forest meta-analysis. In watersheds dominated by disturbed land-use (e.g., agricultural and developed lands), runoff emerged as pivotal climate-related factors influencing riverine export fractions of NAPI. In watersheds dominated by natural land-use, runoff, precipitation and temperature were identified as the most critical factors. We developed a mixed-effects meta-regression model (R2 = 0.63-0.70, RMSE = 19-78 %, n = 87-202) to examine the quantitative relationship between riverine TP fluxes and NAPI, which avoids subjectivity in selecting influencing factors and regression forms. The model estimated that legacy P contributed 14-17 % of annual riverine TP fluxes in Chinese watersheds, 25 % in North American watersheds and 11-27 % in European watersheds. Annual NAPI contributions to annual riverine TP flux were 83-86 % in China, 75 % in North America and 73-89 % in Europe. The model forecasted 52-67 %, 69-71 % and 74-77 % reductions in riverine TP fluxes across Chinese, North American, and European watersheds by 2050 under five shared socio-economic pathway scenarios compared to 2010 baseline conditions, respectively. This study provides a straightforward and reliable method for quantifying anthropogenic P input and riverine P export dynamics within an acceptable error range. It provides guidance for developing phosphorus pollution control strategies to counter potential increases in phosphorus inputs due to expected changes in climate and land use.

Evaluation of three prevalent global riverine nutrient transport models.

Global riverine nitrogen (N) and phosphorus (P) transport models offer important insights into basin nutrient cycling. However, appropriate model selection for a given research objective remains ambiguous. This study conducted a meta-analysis to evaluate the performance and applicability of three prevalent global riverine nutrient transport models: Global NEWS, IMAGE-GNM, and WorldQual. According to performance criteria (satisfactory: R2 > 0.50 and NSE > 0.50), the Global NEWS model performs satisfactorily in simulating dissolved organic nitrogen (DON; n = 101, R2 = 0.58, NSE = 0.57) and dissolved organic phosphorus loads (DOP; n = 80, R2 = 0.59, NSE = 0.59). The model falls short in simulating dissolved inorganic nitrogen (DIN; n = 644, R2 = 0.56, NSE = - 0.80) and dissolved inorganic phosphorus loads (DIP; n = 450, R2 = 0.33, NSE = - 0.12). The IMAGE-GNM model shows satisfactory accuracies in simulating riverine total nitrogen (TN; n = 831, R2 = 0.56, NSE = 0.53) and total phosphorus (TP; n = 902, R2 = 0.59, NSE = 0.48) concentrations, particularly in European basins. The WorldQual model presented unsatisfactory performance in simulating riverine TN (n = 11, R2 = 0.76, NSE = 0.34) and TP (n = 13, R2 = 0.71, NSE = - 0.25) concentrations. Using a two-segment linear model, we recommend the Global NEWS model for basins larger than 2.2 × 104 km2 for DIN and 3.2 × 104 km2 for DIP. The IMAGE-GNM model is best suited for basins with long-term datasets and high latitudes (TN > 21 years and > 53.8 °N; TP > 22 years and > 54.5 °N). For model improvements, both the Global NEWS and WorldQual models could benefit from enhanced in-stream nutrient retention/release modules. The Global NEWS model could be further improved with a better chemical weathering module. For the IMAGE-GNM model, refining the soil erosion module is warranted to enhance model performance. Addressing legacy nutrient effects is crucial for all three models. This study provides valuable guidance for selecting and improving nutrient transport models based on specific research needs.

Tuberculostearic acid incorporated predictive model contributes to the clinical diagnosis of tuberculous meningitis.

The conventional confirmation tests of tuberculous meningitis (TBM) are usually low in sensitivity, leading to high TBM mortality. Hence, sensitive methods for indicating the presence of bacilli are required. Tuberculostearic acid (TBSA), a constituent from Mycobacterium tuberculosis had been evaluated as a promising marker, but fails to demonstrate consistent results for definite TBM. This study retrospectively reviewed medical records of 113 TBM suspects, constructing a TBSA-combined scoring system based on multiple factors, which show sensitivity and specificity of 0.8148 and 0.8814, respectively, and the area under the receiver operating characteristic curve of 0.9010. Multivariate analyses revealed four co-predictive factors strongly associated with TBSA: extra-neural tuberculosis, basal meningeal enhancement, CSF glucose/Serum glucose <0.595, and coinfection in CNS (Total). The subsequent machine learning-based validation showed correspondent importance to factors in the TBSA model. This study demonstrates a simple scoring system to facilitate TBM prediction, yield reliable diagnoses and allow timely treatment initiation.

Distinct circuits in anterior cingulate cortex encode safety assessment and mediate flexibility of fear reactions.

Safety assessment and threat evaluation are crucial for animals to live and survive in the wilderness. However, neural circuits underlying safety assessment and their transformation to mediate flexibility of fear-induced defensive behaviors remain largely unknown. Here, we report that distinct neuronal populations in mouse anterior cingulate cortex (ACC) encode safety status by selectively responding under different contexts of auditory threats, with one preferably activated when an animal staysing in a self-deemed safe zone and another specifically activated in more dangerous environmental settings that led to escape behavior. The safety-responding neurons preferentially target the zona incerta (ZI), which suppresses the superior colliculus (SC) via its GABAergic projection, while the danger-responding neurons preferentially target and excite SC. These distinct corticofugal pathways antagonistically modulate SC responses to threat, resulting in context-dependent expression of fear reactions. Thus, ACC serves as a critical node to encode safety/danger assessment and mediate behavioral flexibility through differential top-down circuits.

Quantifying groundwater phosphorus flux to rivers in a typical agricultural watershed in eastern China.

Increasing evidence indicates that groundwater can contain high dissolved phosphorus (P) concentrations, thereby contributing as a potential pollution source for surface waters. However, limited quantitative knowledge is available concerning groundwater P fluxes to rivers. Based on monthly hydrochemical monitoring data for rivers and groundwater in 2017-2020, this study combined baseflow separation methods and a load apportionment model (LAM) to quantify contributions from point sources, surface runoff, and groundwater/subsurface runoff to riverine P pollution in a typical agricultural watershed of eastern China. In the studied Shuanggang River, most total P (TP) and dissolved P (DP) concentrations exceeded targeted water quality standards (i.e., TP ≤ 0.2 mg P L-1, DP ≤ 0.05 mg P L-1), with DP (76 ± 20%) being the major riverine P form. Observed DP concentrations in groundwater were generally higher than those of river waters. There was a strong correlation between river and groundwater P concentrations, implying that groundwater might be a considerable P pollution source to rivers. The nonlinear reservoir algorithm estimated that baseflow/groundwater contributed 66-68% of monthly riverine water discharge on average, which was consistent with results estimated by an isotope-based sine-wave fitting method. The LAM incorporating point sources, surface runoff, and groundwater effectively predicted daily riverine TP [calibration: coefficient of determination (R2) = 0.76-0.82, Nash-Sutcliffe Efficiency (NSE) = 0.61-0.77; validation: R2 = 0.88-0.98, NSE = 0.54-0.64] and DP loads (calibration: R2 = 0.73-0.84, NSE = 0.67-0.72; validation: R2 = 0.88-0.97, NSE = 0.56-0.83). The LAM estimated point source, surface runoff, and groundwater contributions to riverine loads were 15-18%, 14-35%, and 46-70% for TP loads and 7-9%, 10-32%, and 59-82% for DP loads, respectively. Groundwater was the dominant riverine P source due to long-term accumulation of P from excess fertilizer and farmyard manure applications. The developed methodology provides an alternative method for quantifying P pollution loads from point sources, surface runoff, and groundwater to rivers. This study highlights the importance of controlling groundwater P pollution from agricultural lands to address riverine water quality objectives and further implies that decreasing fertilizer P application rates and utilizing legacy soil P for crop uptake are required to reduce groundwater P loads to rivers.

Soft Template-Assisted Fabrication of Mesoporous Graphenes for High-Performance Energy Storage Systems.

Graphene is a promising active material for electric double layer supercapacitors (EDLCs) due to its high electric conductivity and lightweight nature. However, for practical uses as a power source of electronic devices, a porous structure is advantageous to maximize specific energy density. Here, we propose a facile fabrication approach of mesoporous graphene (m-G), in which self-assembled mesoporous structures of poly(styrene)-block-poly(2-vinylpyridine) copolymer (PS-b-P2VP) are exploited as both mesostructured catalytic template and a carbon source. Notably, the mesostructured catalytic template is sufficient to act as a rigid support without structural collapse, while PS-b-P2VP converts to graphene, generating m-G with a pore diameter of ca. 3.5 nm and high specific surface area of 186 m2/g. When the EDLCs were prepared using the obtained m-G and ionic liquids, excellent electrochemical behaviors were achieved even at high operation voltages (0 ∼ 3.5 V), including a large specific capacitance (130.2 F/g at 0.2 A/g), high-energy density of 55.4 W h/kg at power density of 350 W/kg, and excellent cycle stability (>10,000 cycles). This study demonstrates that m-G is a promising material for high-performance energy storage devices.

From Mouth to Brain: Distinct Supragingival Plaque Microbiota Composition in Cerebral Palsy Children With Caries.

Children with cerebral palsy (CP) present a higher prevalence and severity of caries. Although researchers have studied multiple risk factors for caries in CP, the role of microorganisms in caries remains one of the critical factors worth exploring. In order to explore the differences in the supragingival plaque microbiota (SPM), supragingival plaque samples were collected from 55 CP children and 23 non-CP children for 16S rRNA sequencing. Distinct SPM composition was found between CP children with severe caries (CPCS) and non-CP children with severe caries (NCPCS). Further subanalysis was also done to identify if there were any differences in SPM among CP children with different degrees of caries, namely, caries-free (CPCF), mild to moderate caries (CPCM), and severe caries (CPCS). After selecting the top 15 most abundant species in all groups, we found that CPCS was significantly enriched for Fusobacterium nucleatum, Prevotella intermedia, Campylobacter rectus, Porphyromonas endodontalis, Catonella morbi, Alloprevotella tannerae, Parvimonas micra, Streptobacillus moniliformis, and Porphyromonas canoris compared to NCPCS. By comparing CPCF, CPCM, and CPCS, we found that the core caries-associated microbiota in CP children included Prevotella, Alloprevotella, Actinomyces, Catonella, and Streptobacillus, while Capnocytophaga and Campylobacter were dental health-associated microbiota in CP children. Alpha diversity analysis showed no significant difference between NCPCS and CPCS, but the latter had a much simpler core correlation network than that of NCPCS. Among CP children, CPCM and CPCF displayed lower bacterial diversity and simpler correlation networks than those of CPCS. In summary, the study showed the specific SPM characteristics of CPCS compared to NCPCS and revealed the core SPM in CP children with different severities of caries (CPCF, CPCM, and CPCS) and their correlation network. Hopefully, the study would shed light on better caries prevention and therapies for CP children. Findings from the current study offer exciting insights that warrant larger cohort studies inclusive of saliva and feces samples to investigate the potential pathogenic role of oral microbiota through the oral-gut-brain axis in CP children with caries.

[Estimation of Nitrous Oxide Emission from River System Based on Water Discharge and Dissolved Nitrous Oxide Concentration].

Due to increasing active nitrogen pollution loads, river systems have become an important source of nitrous oxide (N2O) in many areas. Due to the lack of monitoring data in many studies as well as the difficulty in estimating intermediate parameters and expressing temporal-spatial variability in current methods, a high level of uncertainty remains in the estimates of riverine N2O emission quantity. Based on the monthly monitoring efforts conducted for 10 sampling sites across the Yonganxi River system in Zhejiang Province from June 2016 to July 2019, the temporal and spatial dynamics of riverine N2O dissolved concentrations ρ(N2O), N2O fluxes, and their influencing factors were addressed. A multiple regression model was then developed for predicating riverine N2O emission flux to estimate annual N2O emission quantity for the entire river system. The results indicated that observed riverine ρ(N2O) (0.03-2.14 µg·L-1) and the N2O fluxes[1.32-82.79 µg·(m2·h)-1] varied by 1-2 orders of magnitude of temporal-spatial variability. The temporal and spatial variability of ρ(N2O) were mainly influenced by the concentrations of nitrate, ammonia, and dissolved organic carbon, whereas the N2O emission fluxes were mainly affected by river water discharges and ρ(N2O). A multiple regression model that incorporates variables of river water discharge and ρ(N2O) could explain 90% of the variability in riverine N2O emission fluxes and has high accuracy. The model estimated N2O emission quantity from the entire Yonganxi River system of 3.67 t·a-1, with 29% from the main stream and 71% from the tributaries. The IPCC default emission factor method might greatly overestimate and underestimate N2O emission quantities for rivers impacted by low and high pressures of human activities, respectively. This study advances our quantitative understanding of N2O emission for the entire river system and provides a reference method for estimating riverine N2O emission with more accuracy.

Electrohydrodynamic-Jet-Printed Cinnamate-Fluorinated Cross-Linked Polymeric Dielectrics for Flexible and Electrically Stable Operating Organic Thin-Film Transistors and Integrated Devices.

Herein, printable polymer series containing different portions of cinnamate and perfluorinated phenyl functionalities, namely, polyperfluorostyrene-co-poly(vinylbenzyl cinnamates) (PFS-co-PVBCi (x:y)) copolymers, were synthesized and applied as gate dielectrics for organic thin-film transistors (OTFTs). The polymeric dielectrics were successfully printed via electrostatic force-assisted dispensing mode of electrohydrodynamic jet printing. The dielectric characteristics of the printed polymers, such as surface energy, dielectric constant, leakage current, atomic depth profiles, and deposited semiconducting layer characteristics, were clearly identified. In particular, the difference in driving stability of OTFTs according to the type of polymer was analyzed in detail and a possible mechanism was proposed. Results suggested that PFS-co-PVBCi (3:7) led to optimized consequences, yielding an almost negligible Vth shift under continuous bias stress. Through this, we successfully implemented flexible OTFT and logic devices using printed PFS-co-PVBCi (3:7) dielectrics with stable operation properties. Therefore, we believe that this study will facilitate the printing and synthesis of polymer dielectrics to produce printed and flexible OTFTs.

Modeling Riverine N2O Sources, Fates, and Emission Factors in a Typical River Network of Eastern China.

Estimates of riverine N2O emission contain great uncertainty because of the lack of quantitative knowledge concerning riverine N2O sources and fates. Using a 3.5-year record of monthly N2O measurements from the Yongan River network of eastern China, we developed a mass-balance model to address the riverine N2O source and sink processes. We achieved reasonable model efficacies (R2 = 0.44-0.84, Nash-Sutcliffe coefficients = 0.40-0.80) across three tributaries and the entire river system. Estimated riverine N2O loads originated from groundwater (38-88%), surface runoff (3-26%), and in-stream production (4-48%). Estimated in-stream losses via atmospheric release + complete denitrification accounted for 76, 95, 25, and 89% of riverine N2O fate for the agricultural, residential, forest, and entire river system, respectively. Considering limited complete denitrification, the model estimated an upper-bound riverine N2O emission rate of 2.65 ton N2O-N km-2 year-1 for the entire river system. Riverine N2O emission estimates were of comparable magnitude to those estimated with a power-law scaling model. Riverine N2O emissions using the IPCC default emission factor (0.26%) overestimated emissions by 3-15 times, whereas the dissolved N2O concentration-based emission factor overestimated or underestimated emissions. This study highlights the importance of combining comprehensive information on N2O sources and fates to achieve accurate riverine N2O emission estimates.

National estimates of environmental thresholds for upland soil phosphorus in China based on a meta-analysis.

The environmental threshold for upland soil phosphorus (P) content (ETSP, i.e., inflection point of soil P content leading to enhanced P loss) provides an important metric for guiding agricultural nonpoint source P pollution control. This study achieved the first meta-analysis to determine ETSP values for upland soils in China. The estimated national-level ETSP based on 472 field experimental observations of Olsen-P content and P loss rate was 30.1 ± 4.0 mg P kg-1, which was lower than the average ETSP value (52.1 ± 5.0 mg P kg-1) but higher than the average agronomic threshold values (16.0 ± 6.4 mg P kg-1) previously reported. Lower upland ETSP values occurred in acidic soils and soils having higher organic matter content (SOM), precipitation and slope (ETSP: 30.5 for pH < 7.0 versus 46.1 for pH ≥ 7.0; >56.4 for SOM < 2%, 49.9 for SOM = 2%-3%, and <3 for SOM > 3%; 33 for precipitation < 1000 mm yr-1, 27.5 for precipitation = 1000-1200 mm yr-1 and <5 for precipitation > 1200 mm yr-1; and 39.8 for slopes < 5° versus <9 for slopes ≥ 5°). A multiple regression model that incorporates SOM, pH, precipitation and slope was developed to predict upland ETSP values (R2 = 0.73, p < 0.01). The model estimated national upland ETSP values ranging from ~0 to 100 mg P kg-1 with an areal-weighted average of 60.6 mg P kg-1 and 15% of national upland soils having ETSP values <30 mg P kg-1. Upland soil P contents in Guangdong, Fujian and Zhejiang provinces largely exceeded their corresponding ETSP values by 1-22 mg P kg-1, indicating high P loss risks. Controlling upland P loss requires integrated management of soil P content, SOM, pH and erosion control. This study provides the first national estimate of upland soil ETSP, providing critical quantitative information for designing management practices to attenuate agricultural nonpoint source P pollution.

Estimation of nitrogen runoff loss from croplands in the Yangtze River Basin: A meta-analysis.

Nitrogen (N) runoff loss from croplands due to excessive anthropogenic N additions is a principal cause of non-point source water pollution worldwide. Quantitative knowledge of regional-scale N runoff loss from croplands is essential for developing sustainable agricultural N management and efficient water N pollution control strategies. This meta-analysis quantifies N runoff loss rates and identifies the primary factors regulating N runoff loss from uplands (n = 570) and paddy (n = 434) fields in the Yangtze River Basin (YRB). Results indicated that total N (TN) runoff loss rates from uplands and paddy fields consistently increased from upstream to downstream regions. Runoff depth, soil N content and fertilizer addition rate (chemical fertilizer + manure) were the major factors regulating variability of TN runoff loss from uplands, while runoff depth and fertilizer addition rate were the main controls for paddy fields. Multiple regression models incorporating these influencing factors effectively predicted TN runoff loss rates from uplands (calibration: R2 = 0.60, n = 242; validation: R2 = 0.55, n = 104) and paddy fields (calibration: R2 = 0.70, n = 189; validation: R2 = 0.85, n = 82). Models estimated total cropland TN runoff loss load in YRB of 0.54 (95% Cl: 0.23-1.33) Tg, with 0.30 (95% Cl: 0.15-0.56) Tg from uplands and 0.24 (95% Cl: 0.08-0.77) Tg from paddy fields in 2017. Guangxi, Jiangxi, Fujian, Hunan and Henan provinces within the YRB were identified as cropland TN runoff loss hotspots. Models predicted that TN runoff loss loads from croplands in YRB would decrease by 0.8-13.7% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. Reducing upland TN runoff loss should focus primarily on soil N utilization and runoff management, while reducing N fertilizer addition and runoff provided the most sensitive strategies for paddy fields. Integrated management of water, soil and fertilizer is required to effectively reduce cropland N runoff loss.

ACE2 and Furin Expressions in Oral Epithelial Cells Possibly Facilitate COVID-19 Infection via Respiratory and Fecal-Oral Routes.

Background: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that mainly transfers from human to human via respiratory and gastrointestinal routes. The S-glycoprotein in the virus is the key factor for the entry of SARS-CoV-2 into the cell, which contains two functional domains: S1 is an angiotensin-converting enzyme 2 (ACE2) receptor binding domain, and S2 is necessary for fusion of the coronavirus and cell membranes. Moreover, it has been reported that ACE2 is likely to be the receptor for SARS-CoV-2. In addition, mRNA level expression of Furin enzyme and ACE2 receptor had been reported in airway epithelia, cardiac tissue, and enteric canals. However, the expression patterns of ACE2 and Furin in different cell types of oral tissues are still unclear. Methods: In order to investigate the potential infective channel of the new coronavirus via the oropharyngeal cavity, we analyze the expression of ACE2 and Furin in human oral mucosa using the public single-cell sequence datasets. Furthermore, immunohistochemistry was performed in mucosal tissue from different oral anatomical sites to confirm the expression of ACE2 and Furin at the protein level. Results: The bioinformatics results indicated the differential expression of ACE2 and Furin on epithelial cells from different oral anatomical sites. Immunohistochemistry results revealed that both the ACE2-positive and Furin-positive cells in the target tissues were mainly positioned in the epithelial layers, partly expressed in fibroblasts, further confirming the bioinformatics results. Conclusions: Based on these findings, we speculated that SARS-CoV-2 could invade oral mucosal cells through two possible routes: binding to the ACE2 receptor and fusion with cell membrane activated by Furin protease. Our results indicated that oral mucosa tissues are susceptible to SARS-CoV-2 that could facilitate COVID-19 infection via respiratory and fecal-oral routes.