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Abstract Background Appropriate physical activity (PA) and good sleep are beneficial to maternal and fetal health. This paper sought to explore the associations of PA and sleep quality among healthy women at the first and second trimesters of pregnancy on mental health and pregnancy outcomes. Methods Totally 268 healthy pregnant women were retrospectively analyzed as study subjects, 134 each in the first trimester (FT) and second trimester (ST). Their baseline clinical data were obtained respectively at two stages of pregnancy. The PA/sleep quality of subjects were assessed through the Pregnancy Physical Activity Questionnaire-Chinese version (PPAQ-C)/Pittsburgh Sleep Quality Index (PSQI) scale. The mental health was assessed via the Hospital Anxiety and Depression Scale (HADS). The correlations of PA and sleep quality with mental health were analyzed using Spearman correlation analysis. Pregnancy outcomes of all subjects, associations of moderate intensity (MI) PA and sleep quality with adverse pregnancy outcomes, and independent influencing factors for adverse outcomes were analyzed. Results Pregnant women in the ST group exhibited higher levels of MI, worse sleep quality, and lower levels of anxiety and depression than those in the FT group. Anxiety and depression were negatively correlated with MI but positively linked with PSQI scores at the first and second trimesters. MI ≥ 7.5 MET-h/week and good sleep quality were associated with a reduced incidence of adverse pregnancy outcomes. Conclusion MI ≥ 7.5 MET-h/week and good sleep quality at the first and second trimesters of pregnancy benefit mental health and markedly reduce the occurrence of adverse pregnancy outcomes.

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings in feature selection, hyperparameter tuning, underlying surface heterogeneity, and explainability. Addressing these challenges, this study leveraged extensive FVC field data from the Qinghai-Tibet Plateau. Initially, a feature selection algorithm combining genetic algorithms and XGBoost was proposed. This algorithm was integrated with the Optuna tuning method, forming the GA-OP combination to optimize feature selection and hyperparameter tuning in machine learning. Furthermore, comparative analyses of various machine learning models for FVC inversion in alpine grassland were conducted, followed by an investigation into the impact of the underlying surface heterogeneity on inversion performance using the NDVI Coefficient of Variation (NDVI-CV). Lastly, the SHAP (Shapley Additive exPlanations) method was employed for both global and local interpretations of the optimal model. The results indicated that: (1) GA-OP combination exhibited favorable performance in terms of computational cost and inversion accuracy, with Optuna demonstrating significant potential in hyperparameter tuning. (2) Stacking model achieved optimal performance in FVC inversion for alpine grassland among the seven models (R2 = 0.867, RMSE = 0.12, RPD = 2.552, BIAS = −0.0005, VAR = 0.014), with the performance ranking as follows: Stacking > CatBoost > XGBoost > LightGBM > RFR > KNN > SVR. (3) NDVI-CV enhanced inversion performance and result reliability by excluding data from highly heterogeneous regions that tended to be either overestimated or underestimated. (4) SHAP revealed the decision-making processes of the Stacking and CatBoost models from both global and local perspectives. This allowed for a deeper exploration of the causality between features and targets. This study developed a high-precision FVC inversion scheme, successfully achieving accurate FVC inversion on the Qinghai-Tibet Plateau. The proposed approach provides valuable references for other ecological parameter inversions.

Background The Minqin Oasis, which is located in Wuwei City, Gansu Province, China, faces a very serious land desertification problem, with about 94.5% of its total area desertified. Accordingly, it is crucial to implement ecological restoration policies such as cropland abandonment in this region. In abandoned croplands, abiotic factors such as soil properties may become more important than biotic factors in driving vegetation succession. However, the connections between soil properties and vegetation succession remain unclear. To fill this knowledge gap, this study investigated these connections to explore major factors that affected vegetation succession, which is meaningful to designing management measures to restore these degraded ecosystems. Methods This study investigated seven 1–29-year-old abandoned croplands using the “space for time” method in Minqin Oasis. Vegetation succession was classified into different stages using a canonical correlation analysis (CCA) and two-way indicator species analysis (Twinspan). The link between soil properties and vegetation succession was analyzed using CCA. The primary factors shaping community patterns of vegetation succession were chosen by the “Forward selection” in CCA. The responses of dominant species to soil properties were analyzed using generalized additive models (GAMs). Results Dominant species turnover occurred obviously after cropland abandonment. Vegetation succession can be classified into three stages (i.e., early, intermediate, and late successional stages) with markedly different community composition and diversity. The main drivers of vegetation succession among soil properties were soil salinity and saturated soil water content and they had led to different responses of the dominant species in early and late successional stages. During the development of vegetation succession, community composition became simpler, and species diversity decreased significantly, which was a type of regressive succession. Therefore, measures should be adopted to manage these degraded, abandoned croplands.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high-precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of high-precision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking-DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen’s slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking-DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high-precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.

Background Approximately two-thirds of patients with relapsed or refractory large B-cell lymphoma (R/R LBCL) do not respond to or relapse after anti-CD19 chimeric antigen receptor T (CAR T)-cell therapy, leading to poor outcomes. Previous studies have suggested that intensified lymphodepletion and hematological stem cell infusion can promote adoptively transferred T-cell expansion, enhancing antitumor effects. Therefore, we conducted a phase I/II clinical trial in which CNCT19 (an anti-CD19 CAR T-cell) was administered after myeloablative high-dose chemotherapy and autologous stem cell transplantation (HDT/ASCT) in patients with R/R LBCL.Methods Transplant-eligible patients with LBCL who were refractory to first-line immunochemotherapy or experiencing R/R status after salvage chemotherapy were enrolled. The study aimed to evaluate the safety and efficacy of this combinational therapy. Additionally, frozen peripheral blood mononuclear cell samples from this trial and CNCT19 monotherapy studies for R/R LBCL were used to evaluate the impact of the combination therapy on the in vivo behavior of CNCT19 cells.Results A total of 25 patients with R/R LBCL were enrolled in this study. The overall response and complete response rates were 92.0% and 72.0%, respectively. The 2-year progression-free survival rate was 62.3%, and the overall survival was 68.5% after a median follow-up of 27.0 months. No unexpected toxicities were observed. All cases of cytokine release syndrome were of low grade. Two cases (8%) experienced grade 3 or higher CAR T-cell-related encephalopathy syndrome. The comparison of CNCT19 in vivo behavior showed that patients in the combinational therapy group exhibited enhanced in vivo expansion of CNCT19 cells and reduced long-term exhaustion formation, as opposed to those receiving CNCT19 monotherapy.Conclusions The combinational therapy of HDT/ASCT and CNCT19 demonstrates impressive efficacy, improved CNCT19 behavior, and a favorable safety profile.Trial registration numbers ChiCTR1900025419 and NCT04690192.

Background Over 50% of patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) receiving CD19-targeted chimeric antigen receptor (CAR19) T-cell therapy fail to achieve durable remission. Early identification of relapse or progression remains a significant challenge. In this study, we prospectively investigate the prognostic value of dynamic circulating tumor DNA (ctDNA) and track genetic evolution non-invasively, for the first time in an Asian population of r/r patients undergoing CAR19 T-cell therapy.Methods Longitudinal plasma samples were prospectively collected both before lymphodepletion and at multiple timepoints after CAR19 T-cell infusion. ctDNA was detected using a capture-based next-generation sequencing which has been validated in untreated LBCL.Results The study enrolled 23 patients with r/r LBCL and collected a total of 101 ctDNA samples. Higher pretreatment ctDNA levels were associated with inferior progression-free survival (PFS) (p=0.031) and overall survival (OS) (p=0.023). Patients with undetectable ctDNA negative (ctDNA–) at day 14 (D14) achieved an impressive 3-month complete response rate of 77.8% vs 22.2% (p=0.015) in patients with detectable ctDNA positive (ctDNA+), similar results observed for D28. CtDNA– at D28 predicted significantly longer 1-year PFS (90.9% vs 27.3%; p=0.004) and OS (90.9% vs 49.1%; p=0.003) compared with patients who remained ctDNA+. Notably, it is the first time to report that shorter ctDNA fragments (

Waldenstrom macroglobulinemia (WM) is a type of incurable, indolent B-cell lymphoma that is prone to relapse. Over time, treatment strategies have progressed from cytotoxic drugs to rituximab (R)- or bortezomib (V)-based regimens, and have now entered into an era of Bruton tyrosine kinase inhibitor (BTKi)-based regimens. However, the optimal treatment for the relapsed patients is still unclear. Herein, we analyzed the outcomes of the first- and second-line therapies in 377 patients with WM to illustrate the optimal choices for second-line therapy. After a median follow-up of 45.4 months, 89 patients received second-line therapy, and 53 patients were evaluated for response. The major response rates (MRR) of first- and second-line treatment were 65.1% and 67.9% (P = 0.678). The median progression-free survival (PFS) for the second-line therapy (PFS2) was shorter than that for the first-line therapy (PFS1) (56.3 vs 40.7 months, P = 0.03). However, PFS2 in targeted drugs group (R-/V-/BTKi-based regimens) was comparable to PFS1 (60.7 months vs 44.7 months, respectively, P = 0.21). Regarding second-line therapy, patients who underwent sequential treatment escalation—such as transitioning from cytotoxic drugs to R-/V-/BTKi-based regimens or from R-/V-based to BTKi-based regimens (escalation group) —had higher MRR (80.6% vs 47.1%, respectively, P = 0.023) and longer PFS2 (50.4 vs 23.5 months, respectively, P < 0.001) compared to the non-escalation group. Patients in the escalation group also had longer post-relapse overall survival compared with the non-escalation group (median, not reached vs 50.7 months, respectively, P = 0.039). Our findings indicate that sequential treatment escalation may improve the survival of patients with WM.

MicroRNAs (MiRNAs) carried by exosomes play pivotal roles in the crosstalk between cell components in the tumor microenvironment. Our study aimed at identifying the expression profile of exosomal miRNAs (exo-miRNAs) in the serum of multiple myeloma (MM) patients and investigating the regulation networks and their potential functions by integrated bioinformatics analysis. Exosomes in serum from 19 newly diagnosed MM patients and 9 healthy donors were isolated and the miRNA profile was investigated by small RNA sequencing. Differential expression of exo-miRNAs was calculated and target genes of miRNAs were predicted. CytoHubba was applied to identify the hub miRNAs and core target genes. The LASSO Cox regression model was used to develop the prognostic model, and the ESTIMATE immune score was calculated to investigate the correlation between the model and immune status in MM patients. The top six hub differentially expressed serum exo-miRNAs were identified. 513 target genes of the six hub exo-miRNAs were confirmed to be differentially expressed in MM cells in the Zhan Myeloma microarray dataset. Functional enrichment analysis indicated that these target genes were mainly involved in mRNA splicing, cellular response to stress, and deubiquitination. 13 core exo-miRNA target genes were applied to create a novel prognostic signature to provide risk stratification for MM patients, which is associated with the immune microenvironment of MM patients. Our study comprehensively investigated the exo-miRNA profiles in MM patients. A novel prognostic signature was constructed to facilitate the risk stratification of MM patients with distinct outcomes.

Rituximab maintenance (RM) prolongs the progression-free survival (PFS) of responding patients with follicular lymphoma (FL), but the maintenance efficacy in different Follicular Lymphoma International Prognostic Index (FLIPI) risk group is still confusing. We performed a retrospective analysis of the effect of RM treatments in patients with FL responding to induction therapy based on their FLIPI risk assessment carried out prior to treatment. We identified 93 patients between 2013 and 2019 who received RM every 3 months for ≥4 doses (RM group), and 60 patients who did not accept RM or received rituximab less than 4 doses (control group). After a median follow-up of 39 months, neither median overall survival (OS) nor PFS was reached for the entire population. The PFS was significantly prolonged in the RM group compared to the control group (median PFS NA vs 83.1 months, P = .00027). When the population was divided into the 3 FLIPI risk groups, the PFS differed significantly (4-year PFS rates, 97.5% vs 88.8% vs 72.3%, P = .01) according to group. There was no significant difference in PFS for FLIPI low-risk patients with RM compared to the control group (4-year PFS rates, 100% vs 93.8%, P = .23). However, the PFS of the RM group was significantly prolonged for FLIPI intermediate-risk (4-year PFS rates, 100% vs 70.3%, P = .00077) and high-risk patients (4-year PFS rates, 86.7% vs 57.1%, P = .023). These data suggest that standard RM significantly prolongs the PFS of patients assigned to intermediate- and high-risk FLIPI groups but not to low-risk FLIPI group, and pending larger-scale studies to validate.

ABSTRACTObjective Multiple myeloma is a highly heterogenous plasma cell malignancy, commonly seen in older patients. Age is one of the important prognostic factors. However, nearly all the prognostic staging systems are based on clinical trials, where patients were relatively fit and young. It is unknown how the presence of biochemical or cytogenetic prognostic factors and their risk weights changes with older age. To further investigate this question, we retrospectively analyzed the data from a consecutive cohort of patients treated with either bortezomib or thalidomide-based therapy.Methods This retrospective study was carried out on a cohort of 1125 newly diagnosed multiple myeloma patients, from January 2008 to December 2019. Patients received bortezomib or thalidomide-based induction and maintenance therapy. Patients accepted hematopoietic stem cell transplantation if eligible. Statistical analysis was conducted by Stata/MP 16.0 and SPSS 26.0.Results With age increasing, the proportion of patients with ISS 3, performance status score ≥2, and the incidence rate of gain(1q) significantly increased. We also found that ISS became less important in older patients. However, cytogenetic abnormalities exerted a consistently adverse impact on survival, both in young and old patients. Older patients had an inferior outcome than their young counterparts. All patients in our cohort benefitted more from bortezomib than thalidomide-based induction therapy, except for patients ≥71 years old.Conclusions ISS may lose prognostic value in patients ≥71 years old. Older patients had an inferior outcome and needed more effective and less toxic treatment.Plain Language SummaryMultiple myeloma is a type of blood cancer commonly seen in older people. To treat this disease, genetic abnormality, the poor physical status of patients and the abundance of tumor cells are the main difficulties. We often draw these conclusions from clinical trials. However, clinical trials always enrolled relatively younger patients, so the presence and significance of these factors may vary from clinical trials to the real world. We conducted the study to find out the real risk in both young and old patients. We found that older patients were more likely to have anemia, poor nutritional status and renal function. We also found older patients had more risk of relapse, progression or death than young patients. Frail physical status is the key obstacle to treating older patients, and tumor burden no longer impacts the outcome of these people. Bortezomib is a powerful drug to treat this disease, but patients ≥71 years old had less benefit than younger ones. More studies should focus on older or frail patients as these patients need more effective and less toxic treatment.

ABSTRACTObjectives: There may be a shift in risk stratification at progression compared to that at diagnosis in patients with multiple myeloma (MM). We aimed to evaluate whether re-staging and stage migration is of prognostic impact.Methods: Real-world data from the National Longitudinal Cohort of Hematologic Diseases-multiple myeloma were collected; 263 consecutive patients demonstrating disease progression were finally included. Staging at diagnosis and re-staging at progression were performed using the International Staging System (ISS) and Revised International Staging System (RISS).Results: Based on ISS re-staging, the median post-progression survival (mPPS) of patients with stage I, II, and III was 44.2, 21.7, and 11.6 months, respectively (P

Alpine grasslands are vital in regulating carbon balance on the Qinghai-Tibetan Plateau (QTP) because of the large soil organic carbon (SOC) stocks, while persistent disturbance from the endemic small semifossorial herbivore, plateau pika (Ochotona curzoniae, hereafter pika), may break this balance. Pika affect the soil microclimate by creating a heterogeneous underlying surface, which is expected to alter soil microbial communities and eventually SOC stocks. However, our knowledge regarding the potential influence mechanism is still limited. Here, we investigated vegetation biomass, soil properties and soil microbes among 4 different surfaces (i.e., original vegetation, new pika pile, old pika pile and bare patch) of typical alpine grasslands to reveal soil microbial communities and the associated effect on SOC in response to pika bioturbation. Our results showed that pika bioturbation increased both bacterial and fungal diversity and their phyla abundance for SOC decomposition. Vegetation biomass, electrical conductivity and NH4+-N accounted for the variation in both bacterial and fungal community compositions and diversity. SOC stocks were 15–30% lower in pika piles and bare patches than in the original vegetation, which was mainly attributed to the reduced soil organic matter input from vegetation and the enhanced SOC consumption by soil microbial communities. Overall, we conclude that pika bioturbation altered the diversity and composition of soil microbial communities, which was associated with SOC loss and positive carbon feedback in alpine grasslands. Our findings provide insights into the role of small semifossorial herbivores in the carbon cycle of global grasslands.

Abstract Background The difference between clinical characteristics and outcomes between follicular lymphoma grade 1–2 (FL1-2) and FL3a defined pathologically remains unclear, resulting in uncertainty how to treat FL3a. However, it may be crucial for clinicians to discriminate grade 3a and grade 1–2 for predicting prognosis and thus making treatment decisions. Methods We compared 1403 patients with FL1-2 and 765 patients with FL3a diagnosed between January 2000 and December 2020 from fifteen centers nationwide in China to describe differences in clinical characteristics and outcomes. Results Compared with FL1-2 patients, FL3a subgroup had a higher percentage of elderly patients (P = 0.003), and relatively more FL3a patients presented with increased levels of LDH (P

Grassland health assessment (GHA) is a bridge of study and management of grassland ecosystem. However, there is no standardized quantitative indicators and long-term monitor methods for GHA at a large scale, which may hinder theoretical study and practical application of GHA. In this study, along with previous concept and practices (i.e., CVOR, the integrated indexes of condition, vigor, organization and resilience), we proposed an assessment system based on the indicators monitored by unmanned aerial vehicles (UAVs)-UAVCVOR, and tested the feasibility of UAVCVOR at typical household pastures on the Qinghai-Tibetan Plateau, China. Our findings show that: (1) the key indicators of GHA could be measured directly or represented by the relative counterpart indicators that monitored by UAVs, (2) there was a significantly linear relationship between CVOR estimated by field- and UAV-based data, and (3) the CVOR decreased along with the increasing grazing intensity nonlinearly, and there are similar tendencies of CVOR that estimated by the two methods. These findings suggest that UAVs is suitable for GHA efficiently and correctly, which will be useful for the protection and sustainable management of grasslands.

The deletion of chromosome 17p (del(17p)) is considered a crucial prognostic factor at the time of diagnosis in patients with multiple myeloma (MM). However, the impact of del(17p) on survival at different clonal sizes at relapse, as well as the patterns of clonal evolution between diagnosis and relapse and their prognostic value, has not been well described. To address these issues, we analyzed the interphase fluorescence in situ hybridization (iFISH) results of 995 newly diagnosed MM (NDMM) patients and 293 patients with MM at their first relapse. Among these patients, 197 had paired iFISH data at diagnosis and first relapse. Our analysis of paired iFISH revealed that a minor clone of del(17p) at relapse but not at diagnosis was associated with poor prognosis in MM (hazard ratio for median overall survival 1.64 vs. 1.44). Fifty-six and 12 patients developed one or more new cytogenetic abnormalities at relapse, mainly del(17p) and gain/amp(1q), respectively. We classified the patients into six groups based on the change patterns in the clonal size of del(17p) between the two time points. Patients who did not have del(17p) during follow-up showed the best outcomes, whereas those who acquired del(17p) during their disease course, experienced compromised survival (median overall survival: 61.3 vs. 49.4 months; hazard ratio =1.64; 95% confidence interval: 1.06-2.56; P

IntroductionBruton tyrosine kinase inhibitor (BTKi) has demonstrated substantial efficacy in treating B-cell lymphoproliferative diseases (BLPD). Nonetheless, the significant discontinuation rates due to toxicity or financial reasons cannot be overlooked. In China, empirical evidence on the usage of BTKi remains scarce.MethodsTo address this, a retrospective cohort study was conducted focused on 673 Chinese patients with BLPD who underwent at least one month of BTKi therapy.ResultsMedian age at BTKi initiation was 60 years. The median duration on BTKi treatment of the whole cohort was 36.4 months. The median post-BTK survival was not reach. BTKi-based treatment was permanently discontinued in 288 (43.8%) patients during follow-up, mostly attributed to progressive disease. Within the first 6 months of BTKi treatment, 76 patients (26.3%) had early treatment discontinuation. Patients with early discontinuation had extreme worse outcome with a median post-discontinuation survival of only 6.9 months. On multivariate analysis, withdrawal BTKi by toxicity and withdrawal BTKi within 6 months retained to be independent predictors of post-BTK survival, after taking account of the response depth, lines of therapy and baseline cytogenetics including 17p deletion. The decision between BTKi monotherapy and combination therapy, along with the preference for first or second-generation BTKi, exerted no significant impact on survival.DiscussionsThese observations contribute valuable real-world insights into the utilization of BTKi in China. We concluded that BTKi is an effective and well-tolerated treatment for long-term use in Chinese patient population. However, it is imperative to stress that a proportion of patients discontinue BTKi early, leading to suboptimal outcomes. This study underscores the importance of adherence to BTKi therapy for improved clinical outcomes in real-world patients.

Abstract Background Waldenström macroglobulinemia (WM) is a rare and incurable indolent B-cell malignancy. The molecular pathogenesis and the role of immunosuppressive microenvironment in WM development are still incompletely understood. Methods The multicellular ecosystem in bone marrow (BM) of WM were delineated by single-cell RNA-sequencing (scRNA-seq) and investigated the underlying molecular characteristics. Results Our data uncovered the heterogeneity of malignant cells in WM, and investigated the kinetic co-evolution of WM and immune cells, which played pivotal roles in disease development and progression. Two novel subpopulations of malignant cells, CD19+CD3+ and CD138+CD3+, co-expressing T-cell marker genes were identified at single-cell resolution. Pseudotime-ordered analysis elucidated that CD19+CD3+ malignant cells presented at an early stage of WM-B cell differentiation. Colony formation assay further identified that CD19+CD3+ malignant cells acted as potential WM precursors. Based on the findings of T cell marker aberrant expressed on WM tumor cells, we speculate the long-time activation of tumor antigen-induced immunosuppressive microenvironment that is involved in the pathogenesis of WM. Therefore, our study further investigated the possible molecular mechanism of immune cell dysfunction. A precursor exhausted CD8-T cells and functional deletion of NK cells were identified in WM, and CD47 would be a potential therapeutic target to reverse the dysfunction of immune cells. Conclusions Our study facilitates further understanding of the biological heterogeneity of tumor cells and immunosuppressive microenvironment in WM. These data may have implications for the development of novel immunotherapies, such as targeting pre-exhausted CD8-T cells in WM.

IntroductionMinimal residual disease (MRD) has been recognized as an important prognostic factor of survival in patients with hematological malignancies. However, the prognostic value of MRD in Waldenström macroglobulinemia (WM) remains unexplored.MethodsWe analyzed 108 newly diagnosed WM patients receiving systematic therapy and assessed for MRD by multiparameter flow cytometry (MFC) using bone marrow samples.ResultsOf the total patients, 34 (31.5%) achieved undetectable MRD (uMRD). A hemoglobin level of >115 g/L (P=0.03), a serum albumin level of >35 g/L (P=0.01), a β2-MG level of ≤3 mg/L (P=0.03), and a low-risk International Prognostic Scoring System for WM (IPSSWM) stage (P

Kobresia pygmaea is the endemic and one of the most important species in the alpine meadow in the Qinghai-Tibet Plateau. It is the key stage in the management of degraded grassland, and irreversible degradation will take place after the degradation succession phases of the Kobresia pygmaea community. However, knowledge about the spatial distribution and driving factors were still unknown. In this study, the potential distribution of the Kobresia pygmaea community was determined using the BIOMOD niche model. Combine with the reality distribution based on remote sensing classification, the driving factors of climate and human activities were identified. The findings revealed that: (1) among all environmental factors, the maximum radiation, monthly temperature difference, driest period precipitation were the main climate influencing factors for the Kobresia pygmaea community distribution, and random forest model achieved the highest prediction accuracy and best stability of any niche model. (2) The potential distribution area of Kobresia pygmaea community was 653.25 km2 (account for 3.28% of the study area), and mostly located in northern and central of Zeku County, northeast of Henan County, and northeast, central, and eastern parts of Maqu County. (3) Climate factors driven 21.12% of Kobresia pygmaea community reality distribution, while human activities driven for 79.98%. Our results revealed that human activities dominant the reality distribution of Kobresia pygmaea community in alpine meadow grassland in the east source region of Yellow River, China.

Grazing management is one of the most widely practiced land uses globally. Quantifying the spatiotemporal distribution of livestock is critical for effective management of livestock-grassland grazing ecosystem. However, to date, there are few convincing solutions for livestock dynamic monitor and key parameters quantification under actual grazing situations. In this study, we proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles (UAVs). We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. We found that: (1) yak herds grazing followed a rotational grazing pattern spontaneously within the pastures, (2) Dispersion Index of yak herds varied as an M-shaped curve within one day, and it was the lowest in July and August, and (3) the average distance between the yak herd and the campsites in the cold season was significantly shorter than that in the warm season. In this study, we developed a method to characterize the dynamic GD and HP of yak herds precisely and effectively. This method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability, delivering critical information for the development of grassland ecosystem and the implementation of sustainable grassland management.

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

IntroductionMultiple myeloma (MM) is still an incurable plasma cell malignancy. The efficacy of immunotherapy on MM remains unsatisfactory, and the underlying molecular mechanisms still are not fully understood.MethodsIn this study, we delineated the dynamic features of immune cell in MM bone marrow (BM) along with elevated tumor cell infiltration by single-cell RNA sequencing (scRNA-seq), and investigated the underlying mechanisms on dysfunction of immune cells associated with myelomagenesis.ResultsWe found that immune cells were activated in those patients with low infiltration of tumor cells, meanwhile suppressed with elevated infiltration of MM cells, which facilitated MM escaping from immune surveillance. Besides PD-1, abnormal expression of PIM kinases, KLRB1 and KLRC1 were involved in the defect of immune cells in MM patients. Importantly, we found aberrant metabolic processes were associated with the immunosuppressive microenvironment in MM patients. Disordered amino acid metabolism promoted the dysfunction of cytotoxicity CD8 T cells as well as lipid metabolism disorder was associated with the dysregulation of NK and DCs in MM. As metabolic checkpoints, PIM kinases would be potential effective strategies for MM immunotherapy.DiscussionIn summary, redressing the disordered metabolism should be the key points to get promising effects in immune-based therapies.

Abstract Background Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products. Methods This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated. Results (1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm). Conclusion This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Abstract Clinical features and outcomes of FL patients in Chinese population are limited, thus promoting us to perform this analysis on a large cohort of 1845 patients with FL enrolled from nine medical centers nationwide in China. In this cohort, the median age of patients at diagnosis was 53 years, which was comparable to that reported previously for Chinese FL patients (49–51 years) but younger than that for Western FL patients (60–65 years). In contrast with Western patients, Chinese FL patients more likely involved extranodal sites but less frequently infiltrated bone marrow. Other clinical characteristics were comparable between two populations. In this study, 91% of patients were managed with chemotherapy, yielding 72% and 46% of overall-response rate and complete remission. After median 55-month follow-up, 5-year progressive-free and overall survival were 61% and 89%, respectively. Both were analogous to those reported in prior Chinese and Western studies. Consistent with published data, addition of rituximab into both induction (R i) and maintenance (R m) treatment led to the most favorable outcomes. Interestingly, R i only had better outcomes than R m only. Notably, 7% of patients experienced histologic transformation (HT) and correlated with poor survival. Of the transformed FL cases, 3% and 4% of HT events occurred prior to or post-treatment, respectively. Importantly, the latter displayed worse outcomes than the former. Altogether, this study provides real-world information of the largest cohort of FL patients so far in China, which might lay a foundation for clinical investigation of Chinese FL in future.

Vegetation photosynthesis is a critical component of terrestrial carbon cycles, and its peak value (peak photosynthetic rate) dominates plant CO2 uptake. Urbanization has significantly influenced vegetation photosynthesis by changing environmental conditions directly (replacement of vegetated surfaces with impervious surfaces) and indirectly (e.g., atmospheric and climatic conditions). Although some studies have investigated the effects of urbanization on vegetation productivity across the whole study area, how the peak photosynthetic rate responds to urbanization for different vegetation types has received scant scholarly attention. Here, using the urban-rural gradient approach, we explored the effects of urbanization on the peak photosynthetic rate (surrogated by GPPmax and EVImax) of different vegetation types across urbanization intensity (UI) gradients in the Yangtze River Delta Urban Agglomeration (YRDUA). The results showed that (1) the GPPmax and EVImax of the forest, grassland, and wetland decreased with urban intensity. GPPmax and EVImax in rural areas (UI ≦ 1%) were 36.62 and 30.47% higher than those of urban areas (UI > 1%) overall vegetation types. (2) The indirect impacts can offset or exacerbate the direct loss of vegetation photosynthesis caused by urbanization in YRDUA. Especially for forest ecosystems in YRDUA, the negative direct urbanization effect is exacerbated by the indirect reduction in vegetation growth. However, the indirect effects of urbanization on grassland and wetland offset 47.08 and 87.23% loss of GPPmax, and 34.17 and 79.73% loss of EVImax due to the reduction of vegetated surface area, respectively. (3) The indirect impact of urbanization on vegetation photosynthesis was dominated by the elevated daytime land surface temperature (LSTday). Our study highlights the necessity of distinguishing the various effects of urbanization on vegetation photosynthesis, which may help stakeholders formulate more reasonable urban environmental planning.

Plant species diversity (PSD) is essential in evaluating the function and developing the management and conservation strategies of grassland. However, over a large region, an efficient and high precision method to monitor multiscale PSD (α-, β-, and γ-diversity) is lacking. In this study, we proposed and improved an unmanned aerial vehicle (UAV)-based PSD monitoring method (UAVB) and tested the feasibility, and meanwhile, explored the potential relationship between multiscale PSD and precipitation on the alpine grassland of the source region of the Yellow River (SRYR), China. Our findings showed that: (1) UAVB was more representative (larger monitoring areas and more species identified with higher α- and γ-diversity) than the traditional ground-based monitoring method, though a few specific species (small in size) were difficult to identify; (2) UAVB is suitable for monitoring the multiscale PSD over a large region (the SRYR in this study), and the improvement by weighing the dominance of species improved the precision of α-diversity (higher R2 and lower P values of the linear regressions); and (3) the species diversity indices (α- and β-diversity) increased first and then they tended to be stable with the increase of precipitation in SRYR. These findings conclude that UAVB is suitable for monitoring multiscale PSD of an alpine grassland community over a large region, which will be useful for revealing the relationship of diversity–function, and helpful for conservation and sustainable management of the alpine grassland.

Gynaephora alpherakii (G. alpherakii) is one of the dominant pests in the alpine meadow; its outbreak has substantial impacts on grass vegetation production and carrying capacity. The increasing frequency of G. alpherakii outbreaks will take place with global warming and human activities. Therefore, it is crucial to identify inhabitable areas to apply prevention measures efficiently and facilitate sustainable grassland management. In this study, field measurements of G. alpherakii (from 2010 to 2021) were used to explore the relationships between its density and environmental factors in the National Park of Qilian Mountain, China. Then, the inhabitability area was mapped. The results showed that (1) eight of the twenty-five factors studied (average and maximum value of solar radiation; minimum, standard deviation, and average value of normalized difference vegetation index (NDVI); maximum precipitation; and digital elevation model) had a greater influence on G. alpherakii density. (2) Among all of the estimation models, models based on the average of solar radiation, the minimum of NDVI, and the maximum precipitation were better than other factors, with a higher determination coefficient (R2) of 0.53–0.66 and a lower root mean square error (RMSE) of 40.54–47.32 head/m2. Models based on rest factors had a lower accuracy, with R2 within 0.38–0.49 and an RMSE of 50.91–58.68 head/m2. (3) The inhabitable area which was most suited for G. alpherakii growth, development, and frequent outbreaks was located in the northeast of the research area (24.72%), with inhabitability decreasing from southeast to northwest. This method is helpful for clarifying the distribution regions and occurrence dynamics of the G. alpherakii in the alpine meadow. The spatial distribution of G. alpherakii in the National Park of Qilian Mountain can also be clearly defined by using this method, which can provide data support for its prevention and control.

Mantle cell lymphoma (MCL) is a phenotypically and genetically heterogeneous malignancy in which the genetic alterations determining clinical indications are not fully understood. Here, we performed a comprehensive whole-exome sequencing analysis of 152 primary samples derived from 134 MCL patients, including longitudinal samples from 16 patients and matched RNA-Seq data from 48 samples. We classified MCL into 4 robust clusters (C1–C4). C1 featured mutated immunoglobulin heavy variable (IGHV), CCND1 mutation, amp(11q13), and active B cell receptor (BCR) signaling. C2 was enriched with del(11q)/ATM mutations and upregulation of NF-κB and DNA repair pathways. C3 was characterized by mutations in SP140, NOTCH1, and NSD2, with downregulation of BCR signaling and MYC targets. C4 harbored del(17p)/TP53 mutations, del(13q), and del(9p), and active MYC pathway and hyperproliferation signatures. Patients in these 4 clusters had distinct outcomes (5-year overall survival [OS] rates for C1–C4 were 100%, 56.7%, 48.7%, and 14.2%, respectively). We also inferred the temporal order of genetic events and studied clonal evolution of 16 patients before treatment and at progression/relapse. Eleven of these samples showed drastic clonal evolution that was associated with inferior survival, while the other samples showed modest or no evolution. Our study thus identifies genetic subsets that clinically define this malignancy and delineates clonal evolution patterns and their impact on clinical outcomes.

Remote sensing has become an indispensable method for estimating the regional-scale collection of grassland aboveground biomass (AGB). However, the lack of ground verification samples often reduces the inversion accuracy. This paper aimed to find a non-destructive method to quickly obtain grassland AGB at quadrat-scale through unmanned aerial vehicles (UAVs) in a large area. Thus, we proposed and assessed the vertical and horizontal indices from UAV RGB images as predictors of grassland AGB using the random forest (RF) machine learning technique. By comparing the performance of different indices combinations, we found that the model combing the horizontal and vertical indices (RFVH) performed best (R2 = 0.78; RMSE = 24.80 g/m2), followed by the model using only horizontal indices (the RFH model; R2 =0.73; RMSE =26.54 g/m2), and the last was the model using only the vertical index. However, the RFVH model was unsuitable for collecting AGB samples in a large area because the UAVs with RGB cameras failed to obtain vegetation height information in areas with high vegetation coverage. In conclusion, the RFH model can be used to replace the traditional destructive method for collecting ground data over large regions for AGB satellite inversion.

Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that 1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R2 0.04-0.39, and RMSE 859.68-1075.09 kg/ha, respectively; 2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate nonparameter models; 3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45%-44.27%. Meanwhile, RF models can explain 89.41% variation in AGB during grass growing season. This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy based on MODIS data was greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

Abstract: Rapid remission by induction therapy has long been recognized as an important predictor for long-time survival in acute leukemia. However, the impact of response kinetics on multiple myeloma (MM) seems to be different and remains unexplored. The relationship between response kinetics and outcome were assessed in 626 patients with newly diagnosed MM who were included in a prospective, nonrandomized clinical trial (BDH 2008/02). Patients were assigned to either immunomodulatory drug- or proteasome inhibitor–based therapy. The response depth, time to best response (TBR) and duration of best response (DBR) were collected. Depth of response was associated with superior outcomes, consistent with findings from other studies. However, the early responders (defined as TBR ≤3 months) showed significantly worse survival compared with late responders. We found that patients with rapid complete remission experienced inferior survivals comparable to those attaining a gradual partial remission. Moreover, 4 distinct response kinetics patterns were identified. Patients with gradual and sustained remission (“U-valley” pattern) experienced superior outcomes, whereas poor outcomes were observed in rapid and transient responders (“roller coaster” pattern) (median overall survival, 126 vs 30 months). The effects of response patterns on survival were confirmed in patients at different stages of disease and cytogenetic risk, including transplant-eligible patients and those attaining different extents of response depth. Collectively, our data indicated that slow and gradual response is a favorable prognostic factor in MM. In addition to response depth, the kinetic pattern of response is a simple and powerful predictor for survival even in the era of novel agents.

The burrowing activity of plateau pikas (Ochotona curzoniae; hereafter, pikas) may profoundly influence vegetation species composition on the Qinghai-Tibetan Plateau (QTP). Although significant efforts have been made to examine the relationship between vegetation species composition and pikas disturbance, our knowledge regarding the direct influence of pikas activity on vegetation species diversity is still limited. We conducted field observations on pikas burrows and surrounding vegetation patches at 23 alpine grassland sites to investigate this effect. When compared to vegetation patches, pikas burrowing activity decreased soil hardness, thus improving water infiltration, while caused the less reduction of soil nutrition and soil moisture when compared to adjacent vegetation patches. Vegetation species composition on pikas burrows significantly differed from that on vegetation patches. Common plant species between pikas burrows and vegetation patches were fewer than three in all types of grasslands, and ten species were found exclusively on pikas burrows. The total species richness, including both pikas burrows and vegetation patches, was approximately 1.3–2.5 times higher than that on each single patch type (pikas burrows or vegetation patches). A conceptual framework was proposed to synthesize the evolution of vegetation species composition under a disturbance regime resulting from pika’s burrowing. Overall, we concluded that pika’s burrowing activity enhanced vegetation species richness by loosening the soil, creating safe sites for seed settling and germination, which provided a novel habitat for vegetation invasion.

To alleviate grassland degradation and improve ecosystem functions, the Chinese government has implemented some ecological projects since 2004 across the Tibetan Plateau, such as ‘Return Grazing Land to Grassland Project’ and the ‘Compensation and Rewards to Herders for Natural Grassland Conservation’. Meanwhile, the frequency and intensity of drought events have strengthened in this plateau. However, how grasslands respond to these projects and drought remains controversial. Here, taking the northern Tibetan Plateau (NTP) as the study area, we first quantified the spatiotemporal changes in the annual maximum grassland NDVI (NDVImax) after the implementation of the ecological projects (2005–2015). Then, we assessed the effects of drought (Standardized Precipitation-Evapotranspiration Index, SPEI) and ecological projects on the dynamics of NDVImax. The results showed that (1) the grazing intensity decreased significantly across the NTP after the start of ecological projects, but grassland NDVImax did not increase as expected. Statistically, 53.7% of grasslands showed a decreasing NDVImax, of which 5.7% had a significant decrease (P

Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC products (GEOV3 and MuSyQ) in alpine grassland ecosystems were validated through direct validation and multi-scale validation. Based on the heterogeneity of the underlying surface (HUS) of the monitoring plots, the impact of the HUS of the monitoring plots on the product validation was analyzed. The results showed that: (1) the measured data directly validated that the GEOV3 FVC product performed better than the MuSyQ FVC product; (2) the multi-scale validation method based on high-resolution reference FVC map of Sentienl-2 satellite images validated the accuracy of these two FVC products, which was higher than the accuracy directly validated by FVC measured data, leading to overestimation of the validation results; and (3) the HUS of the monitored plots has a significant impact on the FVC product validation. By quantifying the HUS of the monitored plots and removing the heterogeneous monitoring plots, the uncertainty of the validation results can be reduced. It is necessary to consider the impact of validation methods and the HUS on the validation results in future product validation.

Vegetation coverage information is an important indicator of desert ecological environments. Accurately grasping vegetation coverage changes in desert areas can help in assessing the quality of ecosystems and maintaining their functions. Improving remote sensing methods to detect the vegetation coverage in areas of low vegetation coverage is an important challenge for the remote sensing of vegetation in deserts. In this study, based on the fusion of MOD09GA and MOD09GQ data, 2019–2021 low-altitude unmanned aerial vehicle (UAV) remote sensing data, and other factors (such as geographical, topographic, and meteorological factors), three types of inversion models for vegetation coverage were constructed: a multivariate parametric regression model, a support vector machine (SVM) regression model, and a back-propagation neural network (BPNN) regression model. The optimal model was then used to map the spatial distribution of vegetation coverage and its dynamic change in the Junggar Basin of Xinjiang, China, over 22 years (from 2000 to 2021). The results show that: (1) The correlation between enhanced vegetation index (EVI) obtained from image fusion and vegetation coverage in desert areas is the highest (r = 0.72). (2) Among the geographical and topographic factors, only longitude and latitude were significantly correlated with vegetation coverage (p < 0.05). The average monthly temperature and precipitation from the previous six months were correlated with the vegetation coverage (p < 0.05), but the vegetation coverage of the current month had the highest correlation with the average temperature (r = −0.27) and precipitation (r = 0.33) of the previous month. (3) Among the multivariate parametric models established by selecting the five aforementioned factors, the multiple linear regression model performed the best (R2 = 0.64). (4) The SVM regression model was superior to the other regression models (R2 = 0.80, mean squared error = 8.35%). (5) The average vegetation coverage in the desert area of the Junggar Basin was 7.36%, and from 2000–2021, the vegetation coverage in 54.59% of the desert area increased.

Vegetation patch patterns, which are used as indicators of state, functionality, and catastrophic changes in the arid ecosystem, have received much attention. However, little is known about the controlling factors and indicators that underlie vegetation patch patterns in the alpine grassland ecosystem. Here, we firstly studied characteristics of vegetation patch patterns with aerial photography by using an unmanned aerial vehicle and evaluated the vegetation patch-size distribution with power law (PL) and truncated power law (TPL) models on the central part of the Qinghai-Tibetan Plateau (QTP). We then investigated the effects of environmental factors and biotic disturbances on vegetation patch patterns. The results showed that (1) there were four typical vegetation patch patterns, i.e. spot, stripe, sheet, and uniform patterns; (2) soil water content and air temperature were major environmental factors affecting vegetation patch patterns; (3) biotic disturbance of plateau pika (Ochotona curzoniae) affected vegetation patch patterns by changing the number, area, and connectivity of vegetation patches; and (4) vegetation patch-size distribution parameters were significantly related to soil hydrothermal variables (P

Degradation of the alpine meadow has received increasing attention recently, and plateau pika (Ochotona curzoniae, hereafter pika) disturbance is considered as an important cause. Bare patches are the most obvious features indicating the degree of degradation. Previous studies mainly focus on bare patches’ overall status, i.e., the fractional bare patches (or fractional vegetation cover). However, our understanding of individual bare patches’ change rates and their relationship with pika disturbances is insufficient. In this study, aerial photos were taken by an unmanned aerial vehicle (UAV) within a typical alpine meadow ecosystem, over four monitoring sites with different area fraction of bare patches: almost no bare patches (NBP, < 5%), low bare patches (LBP, 5% ~ 20%), moderate bare patches (MBP, 20% ~ 60%) and high bare patches (HBP, > 60%) during 2015 and 2018. The relationship between the changes of pika disturbances and bare patches were analyzed based on these aerial photos. Results showed that: 1) changes of pika holes and bare patches were not significant (P > 0.05) at MBP and HBP, while they increased significantly (P

Grassland classification is crucial for grassland management. One commonly used method utilizes remote sensing vegetation indices (VIs) to map grassland classes at various scales. However, most grassland classifications were conducted as case studies in a small area due to lack of field data sources. At a small scale, classification is reliable; however, great uncertainty emerges when extended to other areas. In this study, large amounts of field observations (more than 30,000 aerial photos) were obtained using unmanned aerial vehicle photography in Inner Mongolia, China, during the peak period of grassland growth in 2018 and 2019. Then, four machine learning classification algorithms were constructed based on characteristic indices of MODIS NDVI in the growing season to map grassland classes of Inner Mongolia. Finally, the spatial distribution and temporal variation of temperate grassland classes were analyzed. Results showed that: (1) Among all characteristic indices, the maximum, average, and sum of MODIS NDVI from July to September during 2015 to 2019 greatly affected grassland classification. (2) The random forest method exhibited the best performance with overall accuracy and kappa coefficient being 72.17% and 0.62, respectively. (3) Compared with the grassland class mapped in the 1980s, 30.98% of grassland classes have been transformed. Our study provides a technological basis for effective and accurate classification of the temperate steppe class and a theoretical foundation for sustainable development and restoration of the temperate steppe ecosystem.

Abstract Objectives T cells play an essential role in controlling the development of B‐cell lymphoproliferative disorders (BLPDs), but the dysfunction of T cells in BLPDs largely remains elusive. Methods Using multiplexed flow cytometry, we quantified all major subsets of CD4+ helper T cells (Th) and CD8+ cytotoxic T cells (Tc) in 94 BLPD patients and 66 healthy controls. Statistics was utilised to rank T‐cell signatures that distinguished BLPDs from healthy controls and differentially presented between indolent and aggressive categories. Results By comparing with healthy controls, we found that the indolent but not aggressive type of BLPDs demonstrated a high degree of T‐cell activation, showing the increase in type I helper T (Th1) cells and follicular B‐helper T (Tfh) cells, both of which strongly associated with the enhanced differentiation of exhaustion‐like effector cytotoxic CD8+ T cells expressing PD‐1 (Tc exhaustion‐like) in indolent BLPDs. Random forest modelling selected a module of T‐cell immune signatures best performing binary classification of all BLPD patients. This signature module was composed of low naïve Th cells and high Th1, Tfh and Tc exhaustion‐like cells which efficiently identified > 85% indolent cases and was, therefore, assigned as the Indolent Dominant Module of T‐cell immune signature. In indolent BLPD patients, a strong bias towards such signatures was found to associate with clinical characteristics of worse prognosis. Conclusion Our study identified a prominent signature of T‐cell dysregulation specifically for indolent BLPDs, suggesting Th1, Tfh and Tc exhaustion‐like cells represent potential prognostic biomarkers and targets for immunotherapies.

Oxytropis ochrocephala Bunge is an herbaceous perennial poisonous weed. It severely affects the production of local animal husbandry and ecosystem stability in the source region of Yellow River (SRYR), China. To date, however, the spatiotemporal distribution of O. ochrocephala is still unclear, mainly due to lack of high-precision observation data and effective methods at a regional scale. In this study, an efficient sampling method, based on unmanned aerial vehicle (UAV), was proposed to supply basic sampling data for species distribution models (SDMs, BIOMOD in this study). A total of 3232 aerial photographs were obtained, from 2018 to 2020, in SRYR, and the potential and future distribution of O. ochrocephala were predicted by an ensemble model, consisting of six basic models of BIOMOD. The results showed that: (1) O. ochrocephala mainly distributed in the southwest, middle, and northeast of the SRYR, and the high suitable habitat of O. ochrocephala accounted for 3.19%; (2) annual precipitation and annual mean temperature were the two most important factors that affect the distribution of O. ochrocephala, with a cumulative importance of 60.45%; and (3) the distribution probability of O. ochrocephala tends to increase from now to the 2070s, while spatial distribution ranges will remain in the southwest, middle, and northeast of the SRYR. This study shows that UAVs can potentially be used to obtain the basic data for species distribution modeling; the results are both beneficial to establishing reasonable management practices and animal husbandry in alpine grassland systems.

The asymmetric response of productivity to precipitation was recently proposed as an early warning signal for the shifts in temperate grassland function in China. It was hypothesized that the asymmetry was influenced by the increased growth of the newly emerged seedlings from the soil seed bank. Therefore, the seed density in the soil seed bank and the similarity between species composition of the vegetation and the soil seed bank should be maximized where asymmetry was maximized. However, this knowledge was still limited and unconfirmed. In this study, the desert steppe, typical steppe and the transition zone between them (with the highest asymmetry) were selected for studying the similarity index in both 2018 (dry year) and 2019 (wet year). Plant species composition was monitored in situ using an unmanned aerial vehicle. Soil seed bank samples were collected, and the seed bank density and species composition were then examined and identified in the laboratory. Results showed that: (1) The variation in vegetation species richness between the two years was the highest (41%) in the transition zone (p < 0.05), while it was only 7% and 13% for the desert steppe and typical steppe, respectively. The presence of herbaceous species mainly caused the differences in variation among three grassland types. (2) Seed density was the highest in the transition zone (114 seeds/m2 and 68 seeds/m2 in the transient and persistent soil seed bank, respectively) (p < 0.05). Additionally, herbaceous species were the main components of the soil seed bank. (3) The similarity index was the highest in the transition zone (p < 0.05), with 38%/44% and 33%/44% for the transient/persistent soil seed bank in 2018 and 2019, respectively. Our study demonstrated that variation in vegetation species composition was very similar to the composition of the seeds accumulated in the soil seed bank. These results warrant further investigation for the mechanism of asymmetric response of productivity to precipitation.

Net primary productivity (NPP) and precipitation-use efficiency (PUE) are crucial indicators in understanding the responses of vegetation to global change. However, the relative contributions of climate change and human interference to the dynamics of NPP and PUE remain unclear. During the past few decades, the impacts of climate change and human activities on alpine grasslands on the Qinghai-Tibet Plateau (QTP) have been intensifying. The aims of the study were to investigate the spatiotemporal patterns of grassland NPP and PUE on the QTP during 2000–2017 and quantify how much of the variance in NPP and PUE can be attributed to the climatic factors (precipitation and temperature) and grazing intensity. The results showed that: (1) grassland NPP significantly increased with a rate of 0.6 g C m−2 year−1 over the past 18 years, mainly induced by the increased temperature and the enhanced precipitation. The temperature was the dominant factor for NPP interannual variation in mid-eastern QTP, and precipitation restrained vegetation growth most in the southwest and northeast. (2) The PUE was higher on the eastern and western parts of the plateau, but lower at the center. Regarding grassland types, the PUE of alpine steppe (0.19 g C m−2 mm−1) was significantly lower than those of alpine meadow (0.31 g C m−2 mm−1) and desert steppe (0.32 g C m−2 mm−1). (3) Precipitation was significantly and negatively correlated with PUE and contributed the most to the temporal variation of grassland PUE on the QTP (52.7%). (4) Furthermore, we found that the grazing activities had the lowest contributions to both NPP and PUE interannual variation, compared to temperature and precipitation. Thus, it is suggested that climate variability rather than grazing activities dominated vegetation changes on the QTP.