Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity.

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3' end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3' processing are deployed across human tissues, with nearly half of multi-transcript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

Pollution source identification and abatement for water quality sections in Huangshui River basin, China.

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3-N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3-N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3-N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3-N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

A novel regional-scale human health risk assessment model for soil heavy metal(loid) pollution based on empirical Bayesian kriging.

Soil heavy metal(loid)s contamination caused by rapid urbanization and industrialization seriously affects human health and hinders the global sustainable development goals (SDGs). Currently, there is a lack of comprehensive human health risk assessment (HHRA) studies for multiple land use types at the regional scale. We propose a practical risk assessment framework that integrates empirical Bayesian kriging (EBK), pollution level analyses, and modified HHRA modeling. The concentrations of copper industry-related metals (Cu, Ni, Cd, As, and Hg) in 332 topsoil samples from the south bank of the Yangtze River in Tongling were investigated. Obvious enrichment of Cu, Cd, As, and Hg was detected, and the average concentration of Cu was 5.24 times higher than the background values. The distribution of heavy metal(loid) pollution was typically high in the south and east, and low in the north and west. The mean errors of interpolation for Cu, Ni, and Hg were 0.84, 1.29, and 0, respectively, and the root mean square errors of interpolation for Cd and As were 1.29 and 0.86, respectively. Non-carcinogenic risks of soil heavy metal(loid)s were assessed as acceptable throughout the studied area. The hazard index decreased in the order As (0.448) > Ni (0.0729) > Cd (0.0136) > Hg (9.04 ×10-4) > Cu (6.41 ×10-4). Nevertheless, the carcinogenic risks of Ni, Cd, and As in 70-80% of the administrative units (AUs) were between 10-6 to 10-4, considered an unacceptable level. Exposure through the oral ingestion route accounted for 88.0-99.2% of the total three exposure routes. It is worth noting that four AUs were considered to be the priority control units, and Ni and As were identified as the priority control soil heavy metal(loid)s. This case demonstrates the feasibility and scientific validity of the new EBK-HHRA framework, which confirms that EBK can effectively predict the spatial distribution patterns of soil heavy metal(loid)s and that modified HHRA models are conducive to risk integration at the regional scale. The EBK-HHRA approach is generic and provides substantial support for risk source identification and risk management of soil heavy metal(loid)s contamination at the regional scale.

Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer.

Background: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology. Methods: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters. Results: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively). Conclusions: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. PURPOSE: To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). MATERIAL AND METHODS: In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. RESULTS: The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). CONCLUSION: Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

Survival benefit after neoadjuvant or adjuvant radiotherapy for stage II-III gastroesophageal junction adenocarcinoma: A large population-based cohort study.

Objective: The standard treatment for stage II-III gastroesophageal junction adenocarcinoma (GEJA) remains controversial, and the role of radiotherapy (RT) in stage II-III GEJA is unclear. Herein, we aimed to evaluate the prognosis of different RT sequences and identify potential candidates to undergo neoadjuvant RT (NART) or adjuvant RT (ART). Materials and methods: In total, we enrolled 3,492 patients with resectable stage II-III GEJA from the Surveillance, Epidemiology, and End Results (SEER) database, subsequently assigned to three categories: T1-2N+, T3-4N-, and T3-4N+. Survival curves were evaluated using the Kaplan-Meier method along with the log-rank test. We compared survival curves for NART, ART, and non-RT in the three categories. To further determine histological types impacting RT-associated survival, we proposed new categories by combining the tumor, node, and metastasis (TNM) stage with Lauren's classification. Results: ART afforded a significant survival benefit in patients with T1-2N+ and T3-4N+ tumors. In addition, NART conferred a survival advantage in patients with T3-4N+ and T3-4 exhibiting the intestinal type. Notably, ART and NART were both valuable in patients with T3-4N+, although no significant differences between treatment regimens were noted. Conclusions: Both NART and ART can prolong the survival of patients with stage II-III GEJA. Nevertheless, the selection of NART or ART requires a concrete analysis based on the patient's condition.

Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer.

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.

Development of a portable TDCR system at NIM, China.

In order to meet the demand of on-site measurement for radionuclides, a portable liquid scintillation TDCR system was developed at National Institute of Metrology (NIM), China. The system consists small size TDCR counter for the measurement of liquid scintillation sources, and digital electronics for pulse signal processing. The optical chamber adopts Teflon material with high diffuse reflection efficiency. Two independent signal processing solutions were used here for TDCR counting. One employed the on-line TDCR solution based on FPGA counting module named TDCR-DMCA, and the other adopted the off-line TDCR solution based on a stand-alone desktop digitizer of CAEN. Two solutions are applied to perform coincidence, dead-time and counting operations follow by MAC3 logic. The performance of the TDCR counting system was tested in benchmark comparison with the traditional custom-built TDCR counting system at NIM through activity measurements of 3H, 14C. Good agreement between these two systems was observed.

Boundary delineation and grading functional zoning of Sanjiangyuan National Park based on biodiversity importance evaluations.

Recently, China advanced a policy of establishing a protected area system with national parks as the main body, and so the development of protected areas has entered a transitional period. However, to promote the coordinated development of economic construction and ecological protection of protected areas, their management needs to be more comprehensive and refined. Therefore, a more quantitative and refined spatial planning and management method for protected areas is urgently needed. This study took Sanjiangyuan National Park as the research object and considered the three biodiversity elements of species, ecosystems and landscape. The maximum entropy (MaxEnt) model, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and Fragstats software were used to determine the areas with highly suitable species habitat, areas of high ecosystem service value and areas of high landscape diversity. Based on these areas, Marxan software was used to calculate the irreplaceability value, identify the boundary of Sanjiangyuan National Park and clarify the gaps of the existing Sanjiangyuan National Park in the space. We suggest that at least 18,569 km2 of the eastern part of the Sanjiangyuan Region with a high irreplaceability value should be included in the existing Sanjiangyuan National Park. Moreover, the first-level zones were also classified based on irreplaceability, and the second-level functional zones were classified by K-means clustering based on the evaluation of ecological vulnerability and economic construction suitability. The first-level zones include "core conservation zones" and "general control zones", which had areas of 71,758.20 km2 (57.99%) and 51,980.13 km2 (42.01%), respectively. The core conservation zones were further zoned into primary vulnerable zones and secondary vulnerable zones. The subzones of the general control zones are multifunctional and they are tourism-grazing-living zones, grazing-living zones, tourism-living zones, tourism zones and other areas.

cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution.

PURPOSE: Cell-free DNA (cfDNA) offers a noninvasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including minimal residual disease (MRD), recurrence, evolution, and second primary cancers. EXPERIMENTAL DESIGN: Three simulation datasets were generated from 26 patients with cancer to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n = 76) from patients with cancer (n = 35) with six different cancer types were used for performance validation during various treatments. RESULTS: We present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fractions ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fractions ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods. CONCLUSIONS: Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.

Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis.

Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this challenge. Furthermore, by leveraging cfDNA's comprehensive coverage of tumor clonal landscape, cfSNV can profile mutations in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide an effective biomarker to predict immunotherapy outcomes.

An integrated assessment system for the carrying capacity of the water environment based on system dynamics.

Implementing an integrated assessment system for the carrying capacity of water environments should include recognizing and eliminating warning signs based on future predictions. However, existing methodologies tend to ignore the warning methods already in place, and current studies fail to quantify water ecology issues adequately. To help solve these problems, the ecological footprint that involves water uses a procedural indicator system, which adopts an early warning methodology system approach. This reconstruction has devised definitions, recognizes hazards, states forecasts status, analyses signs, judges situations, distinguishes levels, and eliminates risks. Based on these procedures, a dynamic system model has been developed, comprising five subsystems with an overarching parent system. These subsystems are population, ecology, water resources, water environment, and water ecology. The simulation involves carrying rates for the water environment, water resource, water ecology, and the level of harmony between society and the environment. All these serve to describe the water environment carrying capacity, i.e., the upper limit of the capacity to supply resources, remove pollutants, and offer sustainable ecological services. To properly quantify the carrying capacity, the water environment carrying rate was assessed by a comprehensive analysis of the water environment, water resources, and water ecological carrying rate. The carrying rates were calculated as the ratios of currently existing pressure to the maximum pressure that can be born. When values are greater than 1, they indicate overload because the actual pressure is greater than the pressure they can bear. The degree of coordination between economy and environment was standardized to range between 0 and 1. The larger the value, the more harmonious the relationship. For this research, the North Canal basin, a basin consisting of several rivers flowing through Beijing, Tianjin, and Hebei in northern China and its surrounding areas, was chosen. The results showed that water environment and resource carrying rates would decline to 2.60 and 0.94, respectively, while the water ecology carrying rate would remain high at 10.98 by the year 2025. In addition, the degree of coordination would increase from 0.65 to 0.79. These statistics mean that the overload statuses will be high for a long time, although they are expected to ease gradually. Besides, the relationship between society and the environment would become more stable. Considering both the overload statuses and the relationship between society and the environment, the warning signs would not vanish. Based on predictions, the measures used were explained from three perspectives, i.e., alleviating pressures, enhancing carrying capacities, and finding a balance between society and the environment. Finally, the effects of the measures were estimated quantitatively.

Spatial-temporal variation, sources and driving factors of organic carbon burial in rift lakes on Yunnan-Guizhou plateau since 1850.

Burial of organic carbon (OC) in rift lakes on plateau is an important part of the global cycle. It is affected by natural and anthropogenic factors. In this study, we selected the sediment records of 7 rift lakes on the Yunnan-Guizhou Plateau to study spatial-temporal variation, sources and driving factors of organic carbon burial since 1850. The analysis of the temporal and spatial trend of carbon burial shows that the TOC concentration, TOC flux, C: N and mass accumulate rate have increased significantly since 1850. Co-occurrence network analysis indicated that a strong correlation between the TOC concentration and silty. TOC concentration were identified as core genera due to their high concentration. Carbon isotope tracing results show that before 1950, endogenous OC input played a dominant role, and after 1950, the proportion of exogenous OC increased. Canonical correlation analysis indicated that after 1950, agriculture intensification and population increase become one of the factors affecting the carbon burial of lakes in this area. The result of this study indicate that anthropogenic factors have become the main factors promoting carbon burial in rift lakes on the plateau.

Ecological network analysis of an urban water metabolic system: Integrated metabolic processes of physical and virtual water.

The contradiction between social economy and water environment has become increasingly prominent, and the analysis of urban water metabolism system (UWMS) represents a problem-solving approach from the perspective of the entire flow process. However, a comprehensive UWMS model that considers both physical and virtual water flows is currently lacking. This paper presents an innovative application of an ecological network model of the UWMS-integrated metabolic process of physical and virtual water in Xining during the 2002-2018 period. By analysing and screening the metabolic characteristics, metabolic structure and metabolic relationships, the sustainability of the UWMS is evaluated in depth, and the main causes and critical compartments of the unhealthy metabolic process are identified. The findings show that the UWMS in Xining maintains a moderate level of robustness (the average R = 0.48) with limited metabolic efficiency. Since 2012, the water management policies in Xining have been significantly strengthened, contributing to a slight increase in robustness by improving the water use efficiency and metabolic structure. The integrated metabolic process is unhealthy because the metabolic structure is not reasonable, and the systematic metabolic relationship tends to be antagonistic due to the network mutualism index dropping to 1.0 during 2016-2018. We conclude that efficient irrigation management, more infrastructure projects for the sewer network, wastewater treatment and recycling could provide effective support to enhance the coordinate development of the social economy and water environment in Xining.

Using stable nitrogen isotopes to reproduce the process of the impact of human activities on the lakes in the Yunnan Guizhou Plateau in the past 150-200 years.

Nitrogen deposition in lake sediment is an important factor reflecting the evolution of lake environments. Over the past 150-200 years, lakes in China have been affected by natural factors and anthropogenic factors, and nitrogen deposition has increased. As a result, it is critical to reconstruct the spatiotemporal variation trend of nitrogen deposition and analyse the nitrogen source and driving factors. On a regional scale, based on the sediment TN, δ15N and C: N ratio variation trends, this study analysed the buried nitrogen variation trend in Yunnan-Guizhou Plateau lakes over the past 150-200 years. The effects of lake morphology on nitrogen deposition were also analysed by using natural lake parameters. At the watershed scale, the δ15N isotope in the sediment was used to distinguish the sediment sources. On this basis, this study analysed the relationship between nitrogen deposition in nine lakes and the socioeconomic conditions during 1949-2010. The results show that (1) during the last 150-200 years, the TN, δ15N and the C: N ratio in the sediments increased. (2) Lake depth and area are the main natural factors affecting the extent of nitrogen deposition. (3) Before 1950, the nitrogen in the lake sediments in the region was sourced mainly from natural sources such as precipitation, woodland, grassland and aquatic plants. After 1950, man-made sources such as sewage and farmland became the main sources of nitrogen. (4) Human social and economic activities have an increasingly significant influence on the lake water environment in the Yunnan-Guizhou Plateau and are also the main factors leading to the deterioration of the aquatic environment.

Exploring the influencing paths of natives' conservation behavior and policy incentives in protected areas: Evidence from China.

To enhance ecosystem conservation in protected areas, natives' behavior, mainly the use of natural resources or direct human disturbance to ecosystems, must be well intervened and regulated. To explore the determinants and incentives of the conservation behavior of natives, this paper establishes a theoretical behavior model emphasizing livelihood assets and conservation policy impacts, and specifies it with structural equation modeling. The protected areas of Chishui county in southwest China and the Three-River-Source National Park in the Qinghai-Tibet Plateau were selected for empirical analyses based on household surveys. Results show that conservation intentions that shape the natives' conservation behavior are collectively decided by conservation attitude, subjective norm, and perceived behavioral control. Livelihood assets exert indirect positive effects on conservation behavior by influencing the aforementioned psychological variables. The impacts on conservation behavior of the governmental policy instruments, Grain to Green Program, Grassland Ecological Protection Subsidy and Incentive Policy, and Ecological Ranger Post Setting are evaluated. The command & control functions of the first two instruments are verified to be effective. The propaganda & education functions of all three policy instruments affect conservation intention and behavior by influencing conservation attitude, subjective norm, and perceived behavioral control of the natives. The ecological compensation functions of the policies prevent the loss of the natives' livelihood assets but fail to enhance it. The findings demonstrate that besides the direct impacts on conservation behavior of the protection policy instruments, the indirect policy impacts affecting the protection consciousness of natives should not be underestimated. In the long run, a suggestion is to strengthen eco-compensation to improve livelihood assets, to better promote the protection behavior of the local residents and obtain sustainable ecosystem conservation in the protected areas. This research demonstrates applicability and feasibility of the behavioral theory and causal model method in natural conservation study and shows strong policy implication.

Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei.

FSHD is characterized by the misexpression of DUX4 in skeletal muscle. Although DUX4 upregulation is thought to be the pathogenic cause of FSHD, DUX4 is lowly expressed in patient samples, and analysis of the consequences of DUX4 expression has largely relied on artificial overexpression. To better understand the native expression profile of DUX4 and its targets, we performed bulk RNA-seq on a 6-day differentiation time-course in primary FSHD2 patient myoblasts. We identify a set of 54 genes upregulated in FSHD2 cells, termed FSHD-induced genes. Using single-cell and single-nucleus RNA-seq on myoblasts and differentiated myotubes, respectively, we captured, for the first time, DUX4 expressed at the single-nucleus level in a native state. We identified two populations of FSHD myotube nuclei based on low or high enrichment of DUX4 and FSHD-induced genes ("FSHD-Lo" and "FSHD Hi", respectively). FSHD-Hi myotube nuclei coexpress multiple DUX4 target genes including DUXA, LEUTX and ZSCAN4, and also upregulate cell cycle-related genes with significant enrichment of E2F target genes and p53 signaling activation. We found more FSHD-Hi nuclei than DUX4-positive nuclei, and confirmed with in situ RNA/protein detection that DUX4 transcribed in only one or two nuclei is sufficient for DUX4 protein to activate target genes across multiple nuclei within the same myotube. DUXA (the DUX4 paralog) is more widely expressed than DUX4, and depletion of DUXA suppressed the expression of LEUTX and ZSCAN4 in late, but not early, differentiation. The results suggest that the DUXA can take over the role of DUX4 to maintain target gene expression. These results provide a possible explanation as to why it is easier to detect DUX4 target genes than DUX4 itself in patient cells and raise the possibility of a self-sustaining network of gene dysregulation triggered by the limited DUX4 expression.