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Deep marine carbonate paleokarst reservoirs have large hydrocarbon reserves in Tarim Basin, NW China. Geological modeling and characterization are critical to the development of such reservoirs. However, reliable 3D modeling of paleokarst is a challenging task due to their multi-scale, multi-type, highly discrete, and complex spatial distribution characteristics. Here we show the detailed workflow of paleokarst classification, modeling, and characterization methods according to multi-disciplinary data and karst genesis. First, the paleokarst is classified into large-scale karst caves, small-scale dissolved pores, and multi-scale fractures, and the large-scale caves are divided into weathered crust caves, subsurface river system, and fault-controlled caves according to their genesis. Then, in order to obtain an accurate representation of the 3D external shape and internal architecture of such reservoirs, a classified modeling approach for converting seismic attributes to geological objects based on geostatistics and karst theory is proposed. Combined seismic wave impedance inversion with frequency-divided attribute can quantitatively characterize large-scale caves. Collaborative sequential simulation method is used for small-scale dissolved pores simulation. Multi-scale discrete fracture networks are characterized by several methods, including manual interpretation, ant tracing, and stochastic simulation. Finally, a fusion principle and technique for different type of caves, pores, and fractures based on the karst genesis is developed. The proposed technology has been applied to the 3D geological modeling and quantitative characterization of TH80 Block in Tahe Oilfield, and the reliability of model is verified by 3 new drilling wells. The results show that the strong heterogeneity of study area is characterized effectively, and the 3D model finely describes the spatial distribution and petro-physical properties of different reservoir groups. The reserve size and composition are calculated and refined based on the model. The remaining oil distribution is clarified after model applies to reservoir numerical simulation, which lays reliable geological foundation for further adjustment of development plan. [ABSTRACT FROM AUTHOR]

Background: Catheter ablation has been recommended as the first-line treatment option for selected patients with atrial fibrillation (AF). However, a widely accepted ablation strategy for persistent AF (perAF) has not yet been established. The benefits of ablation strategies are not conclusive for perAF. There is an urgent need to systematically analyze the results of previous studies and rank these treatment strategies to guide clinical practice. Methods: Randomized controlled trials (RCTs) on ablation for perAF were included. The primary outcome was recurrence of atrial tachyarrhythmia (AT) after a single ablation procedure. A Bayesian random-effects network meta-analysis model was fitted. Results: Twenty-three studies were included in the analysis. A total of 3394 patients and 22 ablation strategies were found in the involved studies. The ablation strategy of pulmonary vein isolation (PVI) + electrical box isolation of the left atrial posterior wall (PBOX) + non-PV trigger ablation (NPV) showed the best treatment effect in terms of the primary outcome. The individualized ablation strategies of mapping and ablation combined with PVI, such as PVI + rotors, PVI + dispersion areas, and PVI + low voltage zone (LVZ) also showed a better ablation effect in perAF. Conclusions: PVI ablation is a widely used strategy in perAF and is recognized as a cornerstone procedure for perAF. The PVI + PBOX + NPV strategy showed the highest rank in our analysis. Mapping and ablation strategies that could provide individualized substrate modification also showed a better rank in our analysis and are believed to be a promising direction for the treatment of perAF. [ABSTRACT FROM AUTHOR]

Peatlands are some of the largest carbon reservoirs in terrestrial ecosystems and play a key role in the global carbon cycle. Understanding peatland development, carbon accumulation processes, and the peatland response to varying forcing factors over different temporal and spatial scales helps reveal the underlying processes and general patterns of these ecosystems. To assess the role of climate and local conditions in peatland development, the basal samples from 23 peat cores and three well dated long peat cores were used to explore peatland initiation, lateral expansion, and carbon accumulation rate in the Baijianghe peatland located in the Changbai Mountains, Northeast China. Our results reveal that the Baijianghe peatland was initiated from forest conditions at 7.9 cal. kyr BP and then expanded laterally by paludification. The rapid expansion between 5 and 4 cal. kyr BP likely resulted from high precipitation and gentle topography. The mean carbon accumulation rates of the three long peat cores were 36.3, 39.1 and 48.4 g C m−2 yr−1, respectively, which are higher than rates from the northern peatlands. Both climate and local conditions have exerted an important influence on carbon accumulation rates in the Baijianghe peatland since the middle Holocene. The carbon accumulation patterns between 5 and 1.5 cal. kyr BP were probably linked to local conditions rather than climatic settings, including topography, hydrological conditions, and plant composition. The consistently decreasing carbon accumulation rate values at all locations within the BJH peatland over the last 1.5 cal. kyr BP suggests that climate is the primary control. This study highlights the varying primary controls on the process of peatland development and reveals the important role of local conditions in carbon accumulation. [ABSTRACT FROM AUTHOR]

Background: Genetic correlations, causalities and pathways between large-scale complex exposures and ovarian and breast cancers need systematic exploration. Methods: Mendelian randomisation (MR) and genetic correlation (GC) were used to identify causal biomarkers from 95 cancer-related exposures for risk of breast cancer [BC: oestrogen receptor-positive (ER + BC) and oestrogen receptor-negative (ER − BC) subtypes] and ovarian cancer [OC: high-grade serous (HGSOC), low-grade serous, invasive mucinous (IMOC), endometrioid (EOC) and clear cell (CCOC) subtypes]. Results: Of 31 identified robust risk factors, 16 were new causal biomarkers for BC and OC. Body mass index (BMI), body fat mass (BFM), comparative body size at age 10 (CBS-10), waist circumference (WC) and education attainment were shared risk factors for overall BC and OC. Childhood obesity, BMI, CBS-10, WC, schizophrenia and age at menopause were significantly associated with ER + BC and ER − BC. Omega-6:omega-3 fatty acids, body fat-free mass and basal metabolic rate were positively associated with CCOC and EOC; BFM, linoleic acid, omega-6 fatty acids, CBS-10 and birth weight were significantly associated with IMOC; and body fat percentage, BFM and adiponectin were significantly associated with HGSOC. Both GC and MR identified 13 shared factors. Factors were stratified into five priority levels, and visual causal networks were constructed for future interventions. Conclusions: With analysis of large-scale exposures for breast and ovarian cancers, causalities, genetic correlations, shared or specific factors, risk factor priority and causal pathways and networks were identified. [ABSTRACT FROM AUTHOR]