Search

Background Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change. In the agreements of the United Nations Framework Convention on Climate Change, involved countries have committed to reduction targets. However, carbon (C) sink and its involving processes by natural ecosystems remain difficult to quantify. Methods Using a transient traceability framework, we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under RCP8.5. Results The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total, among which, Russia, Canada, USA, China, and Brazil sequester the most. This C sink consists of four components: production-driven change, turnover-driven change, change in instantaneous C storage potential, and interaction between production-driven change and turnover-driven change. The four components account for 49.5%, 28.1%, 14.5%, and 7.9% of the land C sink, respectively. Conclusion The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions, especially for countries where net primary production (NPP) likely increases substantially and inherent residence time elongates.

Bowenoid papulosis (BP) is a rare infectious skin disease that occurs in sexually active young people. BP is associated with human papillomavirus (HPV)16 and 18 infections, spreads through sexual contact, and tends to become cancerous. Currently, there are different BP treatment methods, and few reports on the efficacy of photodynamic therapy (PDT) with 5-aminolevulinic acid (ALA) (ALA-PDT). We compared the differences in the clearance and recurrence rates of BP between the ALA-PDT group and other groups, and explored methods to improve clinical effects and reduce lesion recurrence by combining clinical retrospective data.We enrolled patients who were diagnosed with bowel papulosis" between January 1, 2009, and December 31, 2020. Patient information, such as ID number, hospitalization number, admission time, name, age, gender, telephone number, admission record, discharge record, outpatient medical history, HPV type analysis, histopathological examination, and treatment plan were recorded. Outcomes were obtained and recorded through outpatient visits, telephone, and online follow-ups from patients who had reached the treatment endpoint. We compared and evaluated the differences in lesion clearance rate, lesion recurrence rate, and patient satisfaction between the ALA-PDT group and other groups using statistical analysis.The lesion clearance rate of the ALA-PDT group was significantly higher than that of the control group, the lesion recurrence rate was lower than that of the control group, and patient satisfaction was higher than that of the control group. The lesion clearance rate of the ALA-PDT group was higher than that of the microwave and radiofrequency groups, the lesion recurrence rate of the ALA-PDT group was significantly lower than that of the microwave and radiofrequency groups, and patient satisfaction was significantly higher than that of the microwave and radiofrequency groups. In the lesions of patients with BP, in addition to HPV16 and HPV18 types, other types were also detected, and most of them were mixed types of HPV infection. Age, sex, and duration of disease were not risk factors that affected BP recurrence.ALA-PDT for BP has the advantages of high lesion clearance rate, low lesion recurrence rate, and high patient satisfaction. ALA-PDT is worthy of recommendation as a preferred treatment plan for the treatment of BP.

The facial angiofbromas due to tuberous sclerosis complex produced significant social and emotional distress for affected individuals, but there is no specific therapeutic strategy up to now. Herein, we report a case of facial angiofibromas successfully treated by 5-aminolaevulinic acid-mediated photodynamic therapy (ALA-PDT) with no recurrence for 6 years, thus providing a promising therapeutic option.

The impacts of coastal reclamation on carbon (C) and nitrogen (N) sinks of coastal wetlands remain unclearly understood. This study was conducted to investigate the alterations of soil organic C and N (SOC and SON) pools following conversion of Phragmites australis salt marsh into fishpond, wheat and rapeseed fields and town construction land through reclamation along Jiangsu coast in eastern China. Coastal reclamation significantly increased stocks of soil total, labile and recalcitrant organic C and N (SLOC, SLON, SROC, and SRON), and concentrations of water-soluble organic C (WSOC), microbial biomass C and N (SMBC and SMBN), cumulative CO2-C mineralization (MINC) following conversion of P. australis salt marsh into fishpond, wheat and rapeseed fields. However, coastal reclamation reduced SOC, SLOC, SROC, SRON, WSOC, SMBC, SMBN, and MINC following conversion of P. australis salt marsh into town construction land. Our results suggest that coastal reclamation affects C and N sinks of coastal wetlands by changing SOC and SON pools size, stability and dynamics changes following conversion of P. australis salt marsh into other land use types. This finding were primarily attributed to alterations in quantity and quality of exogenous materials returning the soil, and soil physiochemical properties as affected by coastal reclamation.

Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in the literature, including a commonly used turnover time. However, we know little about how different transit time and turnover time are in representing carbon cycling through multiple compartments under a non-steady state. In this study, we estimate both C turnover time as defined by the conventional stock over flux and mean C transit time as defined by the mean age of C mass leaving the system. We incorporate them into the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate C turnover time and transit time in response to climate warming and rising atmospheric [CO2]. Modelling analysis shows that both C turnover time and transit time increase with climate warming but decrease with rising atmospheric [CO2]. Warming increases C turnover time by 2.4 years and transit time by 11.8 years in 2100 relative to that at steady state in 1901. During the same period, rising atmospheric [CO2] decreases C turnover time by 3.8 years and transit time by 5.5 years. Our analysis shows that 65 % of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35 % increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO2] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the transit time, the turnover time, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of warming and rising atmospheric [CO2] on C diagnostic time and potentially led to deviations in estimating land C sequestration in multi-compartmental ecosystems.

Ubiquitous thermal acclimation of leaf respiration could mitigate the respiration increase. However, whether species of different plant functional groups showing distinct or similar acclimation justifies the simple prediction of respiratory carbon (C) loss to a warming climate. In this study, leaf dark respiration (Rd) of illinois bundleflower (IB, legume), stiff goldenrod (GR, C₃ forbs), indian grass, little bluestem and king ranch bluestem (IG, LB and KB, C₄ grass) were measured with detached leaves sampled in a 17-year warming experiment. The results showed that Rd at 20°C and 22°C (R₂₀ and R₂₂) were significantly lower in the warming treatment for all the five species. Lower R₂₂ in warmed than R₂₀ in control in GR, KB, LB and IG imply acclimation homeostasis, but not in IB. The significant decline in temperature sensitivity of respiration (Q₁₀) of GR resulted in the marginal reduction of Q₁₀ across species. No significant changes in Q₁₀ of C₄ grasses suggest different acclimation types for C₃ forbs and C₄ grass. The magnitude of acclimation positively correlated with leaf C/N. Our results suggest that non-legume species had a relative high acclimation, although the acclimation type was different between C₃ forbs and C₄ grasses, and the legume species displayed no acclimation in Rd. Thus, the plant functional types should be taken into account in the grassland ecosystem C models.

Phosphorus limitation on terrestrial plant growth is being incorporated into Earth system models. The global pattern of terrestrial phosphorus limitation, however, remains unstudied. Here, we examined the global-scale latitudinal pattern of terrestrial phosphorus limitation by analysing a total of 1068 observations of aboveground plant production response to phosphorus additions at 351 forest, grassland or tundra sites that are distributed globally. The observed phosphorus-addition effect varied greatly (either positive or negative), depending significantly upon fertilisation regime and production measure, but did not change significantly with latitude. In contrast, phosphorus-addition effect standardised by fertilisation regime and production measure was consistently positive and decreased significantly with latitude. Latitudinal gradient in the standardised phosphorus-addition effect was explained by several mechanisms involving substrate age, climate, vegetation type, edaphic properties and biochemical machinery. This study suggests that latitudinal pattern of terrestrial phosphorus limitation is jointly shaped by macro-scale driving forces and the fundamental structure of life.

Nitrogen immobilization usually leads to nitrogen retention in soil and, thus, influences soil nitrogen supply for plant growth. Understanding soil nitrogen immobilization is important for predicting soil nitrogen cycling under anthropogenic activities and climate changes. However, the global patterns and drivers of soil nitrogen immobilization remain unclear. We synthesized 1350 observations of gross soil nitrogen immobilization rate (NIR) from 97 articles to identify patterns and drivers of NIR. The global mean NIR was 8.77 ± 1.01 mg N kg-1 soil day-1 . It was 5.55 ± 0.41 mg N kg-1 soil day-1 in croplands, 15.74 ± 3.02 mg N kg-1 soil day-1 in wetlands, and 15.26 ± 2.98 mg N kg-1 soil day-1 in forests. The NIR increased with mean annual temperature, precipitation, soil moisture, soil organic carbon, total nitrogen, dissolved organic nitrogen, ammonium, nitrate, phosphorus, and microbial biomass carbon. But it decreased with soil pH. The results of structural equation models showed that soil microbial biomass carbon was a pivotal driver of NIR, because temperature, total soil nitrogen, and soil pH mostly indirectly influenced NIR via changing soil microbial biomass. Moreover, microbial biomass carbon accounted for most of the variations in NIR among all direct relationships. Furthermore, the efficiency of transforming the immobilized nitrogen to microbial biomass nitrogen was lower in croplands than in natural ecosystems (i.e., forests, grasslands, and wetlands). These findings suggested that soil nitrogen retention may decrease under the land use change from forests or wetlands to croplands, but NIR was expected to increase due to increased microbial biomass under global warming. The identified patterns and drivers of soil nitrogen immobilization in this study are crucial to project the changes in soil nitrogen retention.

Soil respiration, the major pathway for ecosystem carbon (C) loss, has the potential to enter a positive feedback loop with the atmospheric CO

Accurate estimation of N2O emission is one of the primary objectives to project the warming potential. However, the global patterns and main controlling factors of soil N2O emission remain elusive. We compiled a dataset with 6016 field observations from 219 articles and found that the averaged soil N2O emission rate was 1111.8 ± 26.59 µg N m-2 day-1. Soil N2O emission rates were significantly influenced by climatic factors (i.e. mean annual temperature), soil physical and chemical properties (e.g. pH, nitrate, ammonium, and total nitrogen), and microbial traits (microbial biomass nitrogen) at a global scale. The combined direct effects of soil nitrate, ammonium, and total nitrogen (combined standard coefficient = 0.45) accounted for the most variance of global soil N2O emissions (total standard coefficient = 0.84). This study highlights the critical roles of soil nitrogen substrates on N2O emission, which will be helpful to optimize the process-models on soil N2O emissions.

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. Despite intensive studies for two decades, the magnitude, direction, and duration of such feedbacks are uncertain, and their underlying microbial mechanisms are still poorly understood. Here we examined the responses of soil respiration and microbial community structure to long-term experimental warming in a temperate grassland ecosystem. Our results indicated that the temperature sensitivity of soil microbial respiration (i.e.,Q10) persistently decreased by 12.0±3.7% across 7 years of warming. Integrated metagenomic and functional analyses showed that microbial community adaptation played critical roles in regulating respiratory acclimation. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improved the modeling performance of soil microbial respiration by 5–19%, compared to the traditional non-microbial model. Model parametric uncertainty was also reduced by 55–71% when gene abundances were used. In addition, our modeling analyses suggested that decreased temperature sensitivity could lead to considerably less heterotrophic respiration (11.6±7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

With the development and popularization of online shopping, a large number of commodities on the online retail platform should be managed through the multilevel category system, in which there are thousands of categories. Automatic commodity classification has become an issue. Classifying commodities according to their text titles can improve the efficiency of online retail platforms. The text titles of commodities contain the information of commodities, but the text titles seldom follow the grammar rules and the text length varies greatly. We propose a classification model, Temporal Multiple-Convolutional Network (TMN), which combines Temporal Convolutional Network(TCN) model and Multiple-Convolutional Neural Network(MCNN) model. The TCN model is firstly used to model sequence data based on both word embedding and character embedding respectively, and generate two output vectors. Then, the MCNN models are used to extract features from the abstract vectors. Finally, output layer employs softmax function to classify commodities. We conduct experiment on the online retail platform dataset provided by Inspur Group Co., Ltd. The results show that the TMN has a commodity classification accuracy of 84.2%, which is superior to that of state-of-the-art models.

Phosphorus (P) limitation of aboveground plant production is usually assumed to occur in tropical regions but rarely elsewhere. Here we report that such P limitation is more widespread and much stronger than previously estimated. In our global meta-analysis, almost half (46.2%) of 652 P-addition field experiments reveal a significant P limitation on aboveground plant production. Globally, P additions increase aboveground plant production by 34.9% in natural terrestrial ecosystems, which is 7.0–15.9% higher than previously suggested. In croplands, by contrast, P additions increase aboveground plant production by only 13.9%, probably because of historical fertilizations. The magnitude of P limitation also differs among climate zones and regions, and is driven by climate, ecosystem properties, and fertilization regimes. In addition to confirming that P limitation is widespread in tropical regions, our study demonstrates that P limitation often occurs in other regions. This suggests that previous studies have underestimated the importance of altered P supply on aboveground plant production in natural terrestrial ecosystems., Plants are thought to be limited by phosphorus (P) especially in tropical regions. Here, Hou et al. report a meta-analysis of P fertilization experiments to show widespread P limitation on plant growth across terrestrial ecosystems modulated by climate, ecosystem properties, and fertilization regimes

Many animals exhibit an astonishing ability to form groups of large numbers of individuals. The dynamic properties of such groups have been the subject of intensive investigation. The actual grouping processes and underlying neural mechanisms, however, remain elusive. Here, we established a social clustering paradigm in Drosophila to investigate the principles governing social group formation. Fruit flies spontaneously assembled into a stable cluster mimicking a distributed network. Social clustering was exhibited as a highly dynamic process including all individuals, which participated in stochastic pair-wise encounters mediated by appendage touches. Depriving sensory inputs resulted in abnormal encounter responses and a high failure rate of cluster formation. Furthermore, the social distance of the emergent network was regulated by ppk-specific neurons, which were activated by contact-dependent social grouping. Taken together, these findings revealed the development of an orderly social structure from initially unorganised individuals via collective actions.

Background Anal canal condyloma acuminata are common, sexually transmitted lesions, most often caused by the human papillomavirus. The relatively high recurrence rate of anal canal condyloma acuminata can be attributed to the unsuccessful elimination of viruses in areas of subclinical and latent infection. This study aimed to observe and evaluate the effectiveness of 5-aminolevulinic acid-photodynamic therapy combined with monitoring of human papillomavirus load changes in patients with anal canal condyloma acuminata. Methods A total of 19 patients with anal genital warts were recruited for this study. Firstly, visible warts around the anus were removed. Next, an anoscope examination was performed. Human papillomavirus detection, using real-time polymerase chain reaction assays, was performed before every cycle of treatment. Absorbent cotton rolls soaked with a concentration of 20% 5-aminolevulinic acid were inserted into the anus for 3 h. Finally, photodynamic therapy was applied to the lesions. Each patient required multiple PDT sessions to achieve complete response. Results All patients achieved complete clinical remission one week after the last session of treatment, and human papillomavirus loads decreased significantly. Six months follow-up after completion of therapy, none of the patients had recurrence. Conclusions 5-aminolevulinic acid-photodynamic therapy is an effective and safe approach for anal canal condyloma acuminata. Dynamic human papillomavirus viral quantitative monitoring can aid in the evaluation of therapeutic effects and lead to better treatment outcomes.

Bowenoid papulosis is a polymorphic papular disease that occurs on the external genital area. We investigated the efficacy of 5-aminolevulinic acid-mediated photodynamic therapyin the treatment of Bowenoid papulosis.We investigated 200 Bowenoid papulosis cases from the Department of Dermatology and Venereology of Nanfang Hospital in 2016-2018. Biopsies were performed from Bowenoid papulosis lesions before treatment. The patients were divided into two groups: 100 patients each in the 5-aminolevulinic acid-mediated photodynamic therapy and control groups(radiofrequency cauterisation, microwave ablation, and surgical resection groups). Differences in lesion clearance, recurrence rate, and patient satisfaction after treatment were evaluated.Photodynamic therapy sessions for multifocal Bowenoid papulosis were more frequent than those for monofocal lesions. All lesions in the 5-aminolevulinic acid-mediated photodynamic therapy group were cleared after photodynamic therapy, with no recurrence at the 1-year follow-up; however, 20 (20.0 %) patients in the control showed recurrence after 1 year. Only 5patients in the photodynamic group were unsatisfied with the treatment cost and 34 patients in the control group experienced short-term pain and scarring. The recurrence rate was significantly lower (P 0.05) and patient satisfaction was higher (P 0.05) in the 5-aminolevulinic acid-mediated photodynamic therapy group than those in the control. The recurrence rate was significantly lower (P 0.05) and patient satisfaction was higher (P 0.05) in the 5-aminolevulinic acid-mediated photodynamic therapy group than those in the surgical resection group. The recurrence rate of lesions was significantly lower in the surgical resection group than that in the rest of the control group (P 0.05). There was no difference in recurrence rate and patient satisfaction between the radiofrequency cauterisation and microwave ablation groups.5-aminolevulinic acid-mediated photodynamic therapy for Bowenoid papulosis results in a low recurrence rate and high satisfaction.

Background Condyloma acuminata is a common sexually transmitted disease for which an underlying chronic human papillomavirus (HPV) infection is an important cause of recurrence. 5-aminolaevulinic acid-mediated photodynamic therapy (ALA-PDT) has become an effective method to treat condyloma acuminata. Urinary tract condyloma acuminata are not considered common, but warts in the bladder can be detected by cystoscopy. Objective To treat a urinary tract HPV infection in a 35 years old woman diagnosed with condyloma acuminata and to eliminate HPV infection. Method A total of six sessions of ALA-PDT to the cervix and seven sessions of ALA-PDT to the vagina were executed, and the patient subsequently had a cystoscope examination. The wart in the bladder was found by cystoscopy and cauterized with radiofrequency. Result Internal urethra, urethral orifice, cervical, vaginal, and vulvar HPV-DNA detection eventually all returned negative results, and there was no recurrence with two years of follow-up.

Background Photodynamic therapy (PDT) has demonstrated good clinical cure rates and low recurrence rates in the treatment of genital warts. Human papillomavirus (HPV) genotypes and viral load assays can reflect the status of persistent or latent infection and serve as a predictor of infection clearance. Methods Specimens from 41 patients with HPV infection were obtained, and the HPV genotypes and viral load were analyzed using real-time polymerase chain reaction (PCR) assays. Traditional treatment, such as radiofrequency, microwave, or surgical therapy, was used to remove the visible lesions, and then PDT treatment was performed every week. HPV DNA testing was performed at every patient visit and the frequency of PDT treatment was determined by changes in HPV viral loads. Results HPV viral loads decreased significantly after PDT treatment. There were significant differences in HPV viral loads between pretherapy and three or six rounds of PDT treatment. Significant differences were also observed between single and multiple type HPV infection after six rounds of PDT treatment. Patients with single type HPV infection had significantly higher rates of negative HPV DNA test results, as compared with patients with multiple infections after six rounds of PDT treatment; however, there was no difference in recurrence rates between the two groups. Conclusions Dynamic monitoring of HPV genotypes and viral loads can be used to guide PDT treatment and indicate PDT treatment efficacy in eliminating HPV.

Climate warming has been proposed to increase primary production of natural grasslands in cold regions. However, how climate warming affects the production of artificial pastures in cold regions remains unknown. To address this question, we used open-top chambers to simulate warming in a major artificial pasture (forage oat) on the cold Tibetan Plateau for three consecutive years. Surprisingly, climate warming decreased aboveground and belowground biomass production by 23.1%-44.8% and 35.0%-46.5%, respectively, without a significant impact on their ratio. The adverse effects on biomass production could be attributed to the adverse effects of high-temperatures on leaf photosynthesis through increases in water vapor pressure deficit (by 0.05-0.10 kPa), damages to the leaf oxidant system, as indicated by a 46.6% increase in leaf malondialdehyde content, as well as reductions in growth duration (by 4.7-6.7 days). The adverse effects were also related to exacerbated phosphorus limitation, as indicated by decreases in soil available phosphorus and plant phosphorus concentrations by 31.9%-40.7% and 14.3%-49.4%, respectively, and increases in the plant nitrogen: phosphorus ratio by 19.2%-108.3%. The decrease in soil available phosphorus concentration could be attributed to reductions in soil phosphatase activities (by 9.6%-18.5%). The findings of this study suggest an urgent need to advance agronomic techniques and cultivate more resilient forage genotypes to meet the increasing demand of forage for feeding livestock and to reduce grazing damage to natural grasslands on the warming-sensitive Tibetan Plateau.

Succession, a central theme throughout the history of ecology, has been well studied predominantly in plant communities, but the general trends in soil microbial communities during succession remain unclear. Here, we compiled a comprehensive data set of 85 age sequences worldwide with the aims to (1) examine the trends in soil microbial composition, bioenergetics, and activity during secondary succession, and (2) explore their coordinating changes with soil properties. The results showed that the fungi to bacteria ratio ( fungi:bacteria ) increased, while the microbial respiration per unit biomass carbon ( R/C mic ) decreased as the succession proceeds. Secondary succession had the rising trends in microbial biomass carbon to soil carbon ratio ( C mic / C soil ) and microbial biomass nitrogen to soil nitrogen ratio ( N mic / N soil ). These successional trends in microbes were coincident with the macro-ecological succession theory in plants and animals. Specifically, early successional stages tended to be dominated by r -strategists (bacteria) that had higher R/C mic and lower C mic / C soil and N mic / N soil , whereas late successional stages tended to be dominated by K -strategists (fungi) that behaved oppositely. The soil C to N ratio ( C:N soil ) increased significantly with the successional stage, with a fast increasing C:N soil ratio being accompanied by a fast increase of fungi:bacteria , a slow decrease of R/C mic , and a slow increase of C mic /C soil . This result suggests that the stoichiometry theory may provide a feasible approach to explain the divergent successional trends in microbial communities. In conclusion, our global synthesis highlights the application of the existing macro-ecological theory to soil microbial ecology studies.

Anthropogenic nitrogen (N) deposition is expected to increase substantially and continuously in the future. Soil N availability regulates microbial communities and the decomposition and formation of soil organic matter, which have great impacts on global carbon (C) cycling. We conducted a meta-analysis based on 454 N-addition experiments in order to synthesize the patterns and mechanisms of responses by soil microbial communities to N addition in various biomes (i.e., boreal forest, temperate forest, tropical/subtropical forest, grassland, and desert). Results showed that the effects of N addition on the total microbial biomass varied depending on biome types, methodologies (fumigation–extraction technique vs. total phospholipid fatty acid), and N-addition rates. Nitrogen addition consistently decreased the microbial C:N and fungi to bacteria ratio (F:B), but increased Gram positive bacteria to Gram negative bacteria ratio (GP:GN) among biome types and N-addition rates. Nitrogen addition increased soil N availability and thereby resulted in soil acidification. Regression technique and principal component analyses showed that the shifts in the F:B and GP:GN mainly resulted from enhanced N availability due to N addition rather than soil acidification. When the N addition rate is lower than 100 kg N ha−1 year−1, about ten times higher than of global normal rate, the positive response of microbial growth was found. Overall, these findings revised the previous notion that N addition inhibited the microbial growth. Microbial species shifts might accentuate or mitigate the effects of alterations in microbial biomass at the ecosystem level, highlighting the critical role of microbial community composition in soil ecosystem functions under N deposition scenarios.

Uptake of anthropogenically emitted carbon (C) dioxide by terrestrial ecosystem is critical for determining future climate. However, Earth system models project large uncertainties in future C storage. To help identify sources of uncertainties in model predictions, this study develops a transient traceability framework to trace components of C storage dynamics. Transient C storage (X) can be decomposed into two components, C storage capacity (Xc) and C storage potential (Xp). Xc is the maximum C amount that an ecosystem can potentially store and Xp represents the internal capacity of an ecosystem to equilibrate C input and output for a network of pools. Xc is co-determined by net primary production (NPP) and residence time (τN), with the latter being determined by allocation coefficients, transfer coefficients, environmental scalar, and exit rate. Xp is the product of redistribution matrix (τch) and net ecosystem exchange. We applied this framework to two contrasting ecosystems, Duke Forest and Harvard Forest with an ecosystem model. This framework helps identify the mechanisms underlying the responses of carbon cycling in the two forests to climate change. The temporal trajectories of X are similar between the two ecosystems. Using this framework, we found that different mechanisms leading to a similar trajectory between the two ecosystems. This framework has potential to reveal mechanisms behind transient C storage in response to various global change factors. It can also identify sources of uncertainties in predicted transient C storage across models and can therefore be useful for model intercomparison.

Ericoid mycorrhizal associations are a symbiotic relationship between soil fungi and ericaceous plants. Diversity of fungi associated with hair roots of ericaceous plants may vary as a result of frequent disturbances by human activities. The fungal diversity and communities associated with hair roots of Rhododendron ovatum were investigated along a human disturbance gradient in subtropical forests of China. Nine hundred fungal operational taxonomic units were determined by high-throughput sequencing, including different phyla such as Ascomycota, Basidiomycota, Zygomycota, Chytridiomycota, and Glomeromycota. The dominant phylum in Cunninghamia lanceolata plantations and old-growth forest was Ascomycota, while Basidiomycota was the dominant phylum in secondary forests. The indicator species analyses showed that more pathogenic indicator fungi appeared in the disturbed forests, whereas more putative ericoid mycorrhizal fungi existed in the old-growth forests. Principal component analysis also showed that the fungal communities in the hair roots of R. ovatum were distinct between natural forests and plantations, suggesting that the fungal communities associated with hair roots of R. ovatum after logging were resilient and could recover to predisturbance status. The results of envfit analysis showed that performance of host plants rather than accompanying plant community and soil parameters of plots was the key determinant of the root-associated fungal community of R. ovatum.

©2017. American Geophysical Union. All Rights Reserved. Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

Genital HPV infections are widely prevalent. HPV can persist and be transmitted to partners even after warts are gone. The HPV genotype and viral load assay can reveal whether infections are persistent or latent, and it can serve as a predictor of infection clearance. Although numerous studies have demonstrated that photodynamic therapy (PDT) is effective against condyloma acuminata, there is no data on its effects on latent, persistent infections.A total of 20 patients with latent or subclinical HPV infection were evaluated. At each patient visit, polymerase chain reaction was used to identify HPV genotypes and measure the viral loads, which reflect the status of HPV infection. We administered 5-aminolevulinic acid (ALA)-based PDT weekly for patients with active infections, while no treatment was administered to patients with regressing infections.PDT treatment can effectively eliminate HPV, significantly reducing viral loads after three rounds of treatment (p 0.001). The rate of negative HPV DNA test results was higher in patients with latent or subclinical infections than in patients with genital warts after one round of PDT treatment.ALA-PDT can effectively eliminate latent or subclinical HPV infections. Additionally, our assay for identifying HPV genotypes and viral loads, which reflect the status of HPV infection, can accurately guide ALA-PDT treatment.

Background Aminolaevulinic acid-mediated photodynamic therapy (ALA-PDT) is used to treat condyloma acuminata (CA), yielding a high clearance rate and low recurrence rate. Consecutive human papillomavirus (HPV) DNA detection can be used to dynamically monitor the therapeutic efficiency of PDT. Here, we evaluated the efficacy of ALA-PDT in the context of different HPV infection states. Methods One hundred thirty-eight patients with HPV infection and visible anogenital warts were enrolled. Microwave or radiofrequency was used to remove visible lesions before PDT. HPV DNA detection was performed using real-time polymerase chain reaction before each PDT session and at follow-up. Treatment was halted after the patient showed two negative results for HPV DNA detection in a row. Results Of the 138 patients enrolled in the study, 72 completed treatment. Multisite HPV-infected patients required more sessions of PDT than did single-site infected patients to reach the endpoint of treatment. Compared with patients with only external CA, individuals with internal CA required more sessions to eliminate HPV infection. The total number of PDT sessions performed in the multitype HPV-infected group was significantly higher than that in the single-type infected group. Patients with non-high-risk (HR)-HPV infection required fewer PDT sessions than did those with HR-HPV infection by the end of treatment. Sixty-nine patients were followed-up for at least 6 months, only 2.9% of whom showed recurrence. Conclusions Combined ALA-PDT and HPV DNA detection was an effective strategy for the treatment of CA. Patients with multisite and multitype HPV infection required more PDT sessions to eliminate the virus.

With the development and popularization of smart-phones, human activity recognition methods based on contact perception are proposed. The smartphones which are embedded with various sensors can be used as a platform of mobile sensing for human activity recognition. In this paper, we propose an automated human activity recognition network HDL with smartphone motion sensor units. The HDL network combines DBLSTM (Deep Bidirectional Long Short-Term Memory) model and CNN (Convolutional neural network) model. The DBLSTM model is first used to model long sequence data and ultimately generate a bidirectional output vector in a abstract way. The DBLSTM model is good at dealing with serialization tasks but poor in the ability to extract features. Hence, the CNN model is then used to extract features from the abstract vector. Finally, the output layer employs a softmax function to classify human activities. We conduct experiments on the Public domain UCI dataset. The experimental results show that the proposed HDL network achieves reliable results with accuracy and F1 score as high as 97.95% and 97.27%. Compared with other networks based on the same smartphone dataset, the accuracy of HDL is higher than S-LSTM and Dropout CNN network by 2.14% and 6.97% respectively.

Computer emotion recognition plays an important role in the field of artificial intelligence and is a key technology to realize human-machine interaction. Aiming at a cross-modal fusion problem of two nonlinear features of facial expression image and speech emotion, a bimodal fusion emotion recognition model (D-CNN) based on convolutional neural network is proposed. Firstly, a fine-grained feature extraction method based on convolutional neural network is proposed. Secondly, in order to obtain joint features representation, a feature fusion method based on the fine-grained features of bimodal is proposed. Finally, in order to verify the performance of the D-CNN model, experiments were conducted on the open source dataset eNTERFACE'05. The experimental results show that the multi-modal emotion recognition model D-CNN is more than 10% higher than the single emotion recognition model of speech and facial expression respectively. In addition, compared with the other commonly used bimodal emotion recognition methods(such as universal background model), the recognition rete of D-CNN is increased by 5%.

In the era of information, recommender systems are playing an indispensable role in our lives. A lot of deep learning based recommender systems have been created and proven to be good progress. However, users’ decisions are determined by both long-term and short-term preferences, and most of the existing efforts study these two requirements separately. In this paper, we seek to build a bridge between the long-term and short-term preferences. We propose a Long & Short-term Preference Model (LSPM), which incorporates LSTM and self-attention mechanism to learn the short-term preference and jointly model the long-term preference by a neural latent factor model. We conduct experiments to demonstrate the effectiveness of LSPM on three public datasets. Compared with the state-of-the-art methods, LSPM got a significant improvement in HR@10 and NDCG@10, which relatively increased by \(3.875\%\) and \(6.363\%\). We publish our code at https://github.com/chenjie04/LSPM/.

Grasslands cover about 40.5% of the world's terrestrial area ( Suttie et al., 2005 ) and provide important ecosystem services, such as supporting a variety of animals and plants. Climate change may have profound impacts on grasslands. Such impacts must be fully understood in order to mitigate climate change impacts and maintain grassland ecosystem functions and services throughout the world. There are dozens of manipulative experiments that have been set up in grassland ecosystems worldwide to explore how climate warming would affect them. In this chapter, we synthesize the current knowledge from these manipulative experiments. We focus on the responses of plants and microbes to experimental warming, carbon (C) and nitrogen (N) cycles as affected by experimental warming, and modeling studies on grassland ecosystems in response to warming.

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO 2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO 2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO 2 uptake since late 1990s. Here, based on in situ atmospheric CO 2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO 2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO 2 concentration ([CO 2 ] min ), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO 2 concentration.

In the smart cities, sharing electric vehicles have many advantages such as environmental protection, low carbon emission, and high efficiency, which will greatly facilitate human life. However, whether the shared electric vehicle can be charged in time directly affects the users' experience. Based on this fact, we study that how to deploy the charging piles in the parking station of the shared electric vehicle, and propose a charging pile deployment model. First, the number of shared electric vehicles that check out from parking stations is predicted based on the random forest algorithm. Then, a mobile model of the shared electric vehicle is established. Based on the mobile model, the number of shared electric vehicles that check in within the target period is calculated and whether they reach the starvation state are determined. After that, the number of charging piles to be deployed is determined. Finally, the effects of hunger rate, battery capacity and the distance between parking stations on the number of charging piles were simulated by experiments. The experimental results verify the validity and feasibility of the proposed model. The prediction model proposed in this paper can provide a certain decision basis for the sharing of electric vehicle charging pile planning.

5-aminolevulinic acid (ALA) mediated photodynamic therapy (PDT) is a highly effective, safe, and promising treatment for human papillomavirus (HPV)-induced anogenital condyloma acuminata (CA). Specific HPV subtypes are responsible for the pathogenesis of CA and dynamic changes in viral loads may reflect the efficiency of PDT. Here we present two cases of anogenital CA successfully treated by ALA-PDT under the guidance of HPV DNA detection including genotype identification and viral load measurement, which provided clues for unexpected intra-anal infection. Both of these male patients were free of detectable HPV at the end of the treatment and no recurrence was observed after 1 year. We conclude that the HPV DNA detection can accurately guide ALA-PDT, indicate unnoticed lesions, as well as improve and evaluate the efficiency.

Objectives: Condyloma acuminatum are the most common sexually transmitted diseases worldwide, and they are closely associated with human papillomavirus (HPV) infection. Urethral meatus is one of the places that warts occur. Many treatments for uretheral warts have limitations. In this study, we performed 5-aminolevulinic acid-photodynamic therapy (ALA-PDT) on patients and investigated the effectiveness of reducing HPV viral loads. Materials and Methods: In our study, 21 patients diagnosed with urethral condyloma acuminatum were included. After 4 h treatment of ALA, patients received PDT. Each patient received HPV test before every PDT cycle. The frequency of PDT was dependent on viral load changes. Results: All patients achieved complete clinical remission after the last session of ALA-PDT. There were significant differences in HPV viral loads between pretherapy and after one or three rounds of PDT treatment. Conclusions: ALA-PDT is a safe and effective method for treatment of condyloma acuminatum in urethra meatus. Dynamic monitoring of HPV viral loads can more objectively demonstrate the effectiveness and guide the treatment of PDT.

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87g/m2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.

The context-related semantic information of the text in the traditional short text classification algorithms are not fully captured, a text classification model based on bidirectional temporal convolutional network and attention mechanism (BTCA) was proposed. Multi-layer dilated convolution was used to increase the receptive field and better capture long distance dependent information. At the same time, attention mechanism was used to increase the attention to the local key feature in the text, and the bidirectional temporal convolution network was used to extract contextual multi-scale semantic information to enrich semantics, the problem of sparse short text features was solved to a certain extent, and text classification effect was improved. The public corpus of the THUCNews was used to conduct a comparative experiment. It is pointed out that effect of short text classification is improved by using BTCA model, with an accuracy rate of 91.47%, which is better than commonly used models.

A multi-task learning network traffic classification model (MTL-NTC) is experimentally studied. The identification task of the MTL-NTC model contains three tasks: identifying the type of traffic, predicting the bandwidth, and the duration of the flow. Identifying the type of traffic is the main task. It includes the classification of five types of traffic. The bandwidth prediction and the duration of the flow are auxiliary tasks, which are used to improve the performance of the main task. In the public network traffic data set QUIC, the MTL-NTC model and the common model are used for comparative experiments. The experimental results show that the recognition accuracy of this model is 97.33%, which is better than the common models.

The automatic detection technology of helmet wearing is of great significance to the safety monitoring of construction site. It is difficult to detect a helmet when the object is small, overlapped, or unclear. Therefore, we propose SPZ-Det model to improve the performance of detect small targets in helmet detection task. We redesign the backbone network layers and proposes a Single Pixel Zoom (SPZ) module. SPZ-Det introduces the lower-level feature map, while for the same scale feature map, we select the higher-level feature map. Then the important elements of each feature map layer are amplified by SPZ module to solve the problem of small object feature fading. In the attention module of SPZ, the residual mechanism is introduced to weaken the influence of the attention. The proposed model improves the detection accuracy and keeps the detection speed. On the Safety-Helmet-Wearing dataset, the mAP of SPZ-Det is 27.9% higher than Efficientdet-d0 and gets an 8.8% increase compares with YOLOv3.

Despite the importance of future carbon (C) pools for policy and land management decisions under various climate change scenarios, predictions of these pools under altered climate vary considerably. Chronic warming will likely impact both ecosystem C fluxes and the abundance and distribution of plant functional types (PFTs) within systems, potentially interacting to create novel patterns of C exchange. Here, we report results from a 3-year warming experiment using open top chambers (OTC) on the Tibetan Plateau meadow grassland. Warming significantly increased C uptake through gross primary productivity (GPP) but not ecosystem respiration (ER), resulting in a 31.0% reduction in net ecosystem exchange (NEE) in warmed plots. The OTC-induced changes in ecosystem C fluxes were not fully explained by the corresponding changes in soil temperature and moisture. Warming treatments significantly increased the biomass of graminoids and legumes by 12.9 and 27.6%. These functional shifts were correlated with enhanced local GPP, but not ER, resulting in more negative NEE in plots with larger increases in graminoid and legume biomass. This may be due to a link between greater legume abundance and higher levels of total inorganic nitrogen, which can potentially drive higher GPP, but not higher ER. Overall, our results indicate that C-climate feedbacks might be closely mediated by climate-induced changes in PFTs. This highlights the need to consider the impacts of changes in PFTs when predicting future responses of C pools under altered climate scenarios.