"六经为川,肠胃为海"内涵探析

基于中医学整体观念,运用取象比类的方法,借自然界"川""海"之象进一步阐释"六经"之经与腑的双重含义,以及"肠胃为海"在气血化生、津液输布、糟粕传化、扶正培本方面的内涵.指出"六经"与"肠胃"在生理上彼此依存、病理上相互影响.在现代肠道研究中,肠道消化吸收营养物质、排泄代谢废物,体现了肠胃为"气血生化之海""津液输布之海""糟粕传化之海".肠道菌群促进肠黏膜免疫系统的发育并参与调节机体免疫,体现了肠胃为"扶正培本之海".肠神经系统与内分泌系统通过脑-肠轴的双向调控,如同六经与肠胃之间的海川循环,彼此依存、生生不息.通过探析"六经为川,肠胃为海"在肠道各功能网络中的内涵,揭示象思维在中西医整合医学发展中的重要作用,为挖掘经典及科研创新开辟思路.     

2009—2018年安徽省淮河两岸地区恶性肿瘤死亡率变化趋势

[目的]分析安徽省淮河两岸地区恶性肿瘤死亡率时间变化趋势。[方法]收集安徽省淮河两岸地区5个县区2009—2018年恶性肿瘤死亡数据,标化死亡率采用2000年中国人口普查标准人口进行标化,采用年度变化百分比(APC)分析死亡率时间变化趋势,并用年度变化贡献率分析各个癌种对恶性肿瘤死亡率趋势的贡献大小。[结果]无论性别,2009—2018年安徽省淮河两岸地区恶性肿瘤标化死亡率均呈下降趋势,但年均降幅较小。各癌种变化趋势不尽相同,其中食管癌、胃癌、肝癌下降较为明显,女性乳腺癌和宫颈癌上升明显。女性乳腺癌和宫颈癌APC分别为4.60%和12.45%,对女性恶性肿瘤上升趋势的贡献达85.21%。[结论]去除人口老龄化因素,自2009年以来安徽省淮河两岸地区恶性肿瘤死亡率有所下降,但降幅较小。消化系统恶性肿瘤死亡率呈下降趋势或可归功于癌症早诊早治项目工作的开展。妇女"两癌"防治值得重视。     

塔里木盆地西部鸟山—古董山地区断裂构造特征分析

鸟山—古董山地区位于塔里木盆地西部,巴楚隆起与麦盖提斜坡之间,鸟山—玛南、玛扎塔格、古董山和罗斯塔格构造带在此交汇,附近还发育与之密切相关的沙陇断裂。鸟山—古董山地区的主干断裂形成于白垩纪末—古近纪初,包括鸟山、罗斯塔格和玛扎塔格白垩纪末—古近纪初冲断构造带和玛南白垩纪末—古近纪初走滑断裂带,玛南断裂是玛扎塔格构造带与鸟山和罗斯塔格构造带之间的调节断层。该期构造变形受控于拉萨地块与亚洲大陆之间的碰撞造山作用。鸟山—古董山地区的断裂构造于中新世末基本定型。因帕米尔突刺楔入于塔里木地块和卡拉库姆地块之间,在塔西南地区形成一系列走滑断裂,包括玛扎塔格—罗斯塔格中新世末走滑断裂,古董山断裂是其派生断层。白垩纪末—古近纪初是研究区构造和圈闭的关键形成期,上新世晚期—全新世早期以古近系底部膏盐层为主滑脱面的滑脱—冲断构造保护早期形成的圈闭和油气藏。鸟山和玛扎塔格构造带是研究区最有利的油气勘探区带,玛南构造带是重要的油气运移通道。     

全国人体寄生虫分布调查——人体蠕虫感染的地理分布特点和规律

我国人体蠕虫感染有明显的地理分布特点和规律，如吸虫感染呈现随水系流域分布的规律，绦虫感染随地势三级阶梯而异，土源性线虫感染随温度带、干湿区域不同而呈不同的分布。     

健康产业作为长三角区域战略性新兴产业的研究

健康产业是极具发展潜力的新兴产业。长三角地区作为我国经济率先发展的地区,大力发展健康产业正当其时,不仅有助于改造提升长三角地区的传统工业、促进地区战略新兴产业的发展、推进地区经济发展方式转变,也是促进长三角地区经济增长与保障民生有机结合的重要措施。     

Detection and source tracking of Escherichia coli,harboring intimin and Shiga toxin genes,isolated from the Little Bighorn River,Montana

The Little Bighorn River flows through the Crow Indian Reservation in Montana. In 2008, Escherichia coli concentrations as high as 7179 MPN/100 ml were detected in the river at the Crow Agency Water Treatment Plant intake site. During 2008, 2009, and 2012, 10 different serotypes of E. coli, including O157:H7, harboring both intimin and Shiga toxin genes were isolated from a popular swim site of the Little Bighorn River in Crow Agency. As part of a microbial source tracking study, E. coli strains were isolated from river samples as well as from manure collected from a large cattle feeding operation in the upper Little Bighorn River watershed; 23% of 167 isolates of E. coli obtained from the manure tested positive for the intimin gene. Among these manure isolates, 19 were identified as O156:H8, matching the serotype of an isolate collected from a river sampling site close to the cattle feeding area.     

Indirect nitrous oxide emissions from streams within the US Corn Belt scale with stream order

N₂O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N₂O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N₂O emissions, implying that EFs may be biased low. Here, we measured N₂O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N₂O fluxes from headwater streams often exceeded 29 nmol N₂O-N m⁻²⋅s⁻¹ and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF_5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N₂O emissions by 40%. We show that accounting for zero-order streams as potential N₂O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF_5r underestimation explains the large differences observed between top-down and bottom-up emission estimates.N₂O is projected to remain the dominant stratospheric ozone-depleting substance of the 21st century (1) and is a powerful greenhouse gas (GHG) that currently accounts for about 6% of the net radiative forcing associated with long-lived anthropogenic GHGs (2). The detrimental environmental impacts of N₂O have stimulated appeals to regulate emissions from agricultural lands (1, 3), which account for nearly 80% of the global anthropogenic N₂O budget (4, 5). The successful regulation and mitigation of N₂O emissions requires a sound understanding of the direct and indirect emission processes and reduced uncertainty regarding the emission factors (EFs) (6).The Intergovernmental Panel on Climate Change (IPCC) tier 1 approach uses EFs to provide first-order approximations of annual N₂O emissions based on mechanistic and empirical information that have been constrained by field studies. These EFs are widely used in bottom-up inventories such as the Emission Database for Global Atmospheric Research (EDGAR) (7) and the Global Emissions Initiative (GEIA) (8). These inventories are essential tools for tracking country specific emission trends, assessing thresholds for international treaties, and evaluating the impacts of mitigation policies. Recent independent top-down estimates note large discrepancies with these bottom-up inventories.Tall-tower and aircraft-based top-down studies use atmospheric concentration data to estimate landscape N₂O fluxes. Several studies using these approaches demonstrate that bottom-up inventories underestimate N₂O emissions by up to ninefold in the Midwest US Corn Belt (9–12), implying that some EFs are too small. An important problem, therefore, is determining which EFs are biased low and how to reduce their uncertainty.Bottom-up N₂O emission inventories include direct and indirect emission pathways. Direct emissions describe the loss of N₂O produced in soils by microbial processes (e.g., nitrification and denitrification). This source is arguably well constrained as a consequence of more than 1,000 chamber-based emission studies (6, 13). Plant N₂O fluxes, although neglected in direct emissions inventories, appear to be negligible (10). The relatively low direct emission uncertainty range (0.4–3.8 Tg N⋅y⁻¹) (14) suggests that this EF (0.003–0.03) (15) is well constrained.Indirect emissions represent the aggregate of N₂O production from leaching and runoff, human sewage, and atmospheric deposition of reactive nitrogen. Global indirect emission estimates range from 0.23 to 11.9 Tg N⋅y⁻¹ (16) and represent nearly two thirds of the uncertainty in the total global N₂O budget (14). In fact, the EF for leaching and runoff (IPCC emission factor: EF₅), which includes emissions from groundwater (EF_5g), rivers (EF_5r), and estuaries (EF_5e), is the single largest source of uncertainty in the bottom-up inventory (14). In the 2006 IPCC Emission Guidelines report, the EF₅ value was reduced from 0.025 to 0.0075 by reducing both the EF_5g and EF_5r to 0.0025 in response to two studies from New Zealand and the United Kingdom (15). However, recent studies (17–20) suggesting riverine N₂O loss is underestimated by up to threefold notably contradict the EF_5r reduction.Uncertainty in the EF_5r can be attributed to a scarcity of studies (21, 22), poorly constrained water-air gaseous exchange relationships (23, 24), and high variability in river morphology (25, 26). Further, the EF_5r assumes a linear relation between nitrate in water and N₂O emissions (14), the validity of which is the subject of considerable debate (27–30). Finally, N₂O fluxes derived from simple gas exchange models have been shown to underestimate the flux if stream channel hydraulics (i.e., stream flow velocity) are ignored (31), highlighting that stream chemistry alone is not an accurate predictor of N₂O fluxes.We posit that the indirect N₂O fluxes in agricultural landscapes are highly dependent on stream hierarchy, which is semiquantitatively represented with the Strahler stream order (S), a numerical classification system. Here, we demonstrate that with detailed knowledge of S, N₂O fluxes can be scaled up to the region and help to resolve the discrepancy between top-down and bottom-up N₂O emission estimates in the US Corn Belt.     

青海"三江源"地区鼠疫血清流行病学调查分析

目的对青海省"三江源"地区开展鼠疫血清流行病学调查,了解分析流行态势,为该地区鼠疫防治措施的制定与落实提供依据。方法对历年的鼠疫细菌学资料进行总结分析,对"三江源"部分地区人及各种动物采血做血清抗体调查。结果该地区发现15种动物可自然感染鼠疫,10种动物血清存在鼠疫抗体,人群血清鼠疫抗体阳性率达2.47%。结论"三江源"地区动物鼠疫持续流行,参与染疫的动物种类多,时常波及人间造成人间鼠疫流行,人群存在部分隐性感染鼠疫者。尚有部分地区需进行深入的自然疫源地调查。     

Global phosphorus retention by river damming

More than 70,000 large dams have been built worldwide. With growing water stress and demand for energy, this number will continue to increase in the foreseeable future. Damming greatly modifies the ecological functioning of river systems. In particular, dam reservoirs sequester nutrient elements and, hence, reduce downstream transfer of nutrients to floodplains, lakes, wetlands, and coastal marine environments. Here, we quantify the global impact of dams on the riverine fluxes and speciation of the limiting nutrient phosphorus (P), using a mechanistic modeling approach that accounts for the in-reservoir biogeochemical transformations of P. According to the model calculations, the mass of total P (TP) trapped in reservoirs nearly doubled between 1970 and 2000, reaching 42 Gmol y⁻¹, or 12% of the global river TP load in 2000. Because of the current surge in dam building, we project that by 2030, about 17% of the global river TP load will be sequestered in reservoir sediments. The largest projected increases in TP and reactive P (RP) retention by damming will take place in Asia and South America, especially in the Yangtze, Mekong, and Amazon drainage basins. Despite the large P retention capacity of reservoirs, the export of RP from watersheds will continue to grow unless additional measures are taken to curb anthropogenic P emissions.The systematic damming of rivers began with the onset of the Industrial Revolution and peaked in the period from 1950 to 1980 (1, 2). After slowing down during the 1990s, the pace of dam building has recently risen again sharply (3). As a consequence, the number of hydroelectric dams with generating capacity >1 MW is expected to nearly double over the next two decades (2). The current surge in dam construction will increase the proportion of rivers that are moderately to severely impacted by flow regulation from about 50% at the end of the 20th century to over 90% by 2030 (3). Homogenization of river flow regimes resulting from damming is a growing, worldwide phenomenon and has been invoked as one of the reasons for the decline in freshwater biodiversity (4).Another major global driver of environmental change of river systems is enrichment by anthropogenic nutrients, in particular phosphorus (P) (5, 6). Fertilizer use, soil erosion, and the discharge of wastewater have more than doubled the global P load to watersheds compared with the inferred natural baseline (7–10). Because P limits or colimits primary productivity of many aquatic ecosystems, increased river fluxes of P have been identified as a main cause of eutrophication of surface water bodies, including lakes and coastal marine environments (6, 11, 12). River damming and P enrichment are interacting anthropogenic forcings, because sediments accumulating in reservoirs trap P and, thus, reduce the downstream transfer of P along the river continuum (13–15). This raises the question to what extent P retention by dams may offset anthropogenic P enrichment of rivers.The number of published studies from which P retention efficiencies in dam reservoirs can be obtained is small: an extensive literature search only yields useable data for 155 reservoirs (Dataset S1), that is, less than 0.2% of the ∼75,000 dam reservoirs larger than 0.1 km² (16). The existing data nonetheless clearly show that even a single dam can significantly alter the flow of P along a river. For example, dam-impounded Lake Kariba (Zambezi River), Lake Diefenbaker (South Saskatchewan River), and Lac d’Orient (Seine River) sequester ∼87%, 94%, and 71% of their total P inflows, respectively (17–19). For the 1 million km² Lake Winnipeg watershed, 28 reservoirs and lakes accumulate over 90% of the total P load (18). The global retention of P by dams, however, remains poorly constrained (20, 21). Previous estimations have simply applied a correction factor to river P loads to represent retention by dams (22–24). This approach does not distinguish between the various chemical forms of P, nor does it account for differences in reservoir hydraulics or provide information about uncertainties on retention estimates.Here, we follow a mass balance modeling approach developed previously to calculate the global retention of nutrient silicon by dams (25). The mass balance model represents the key biogeochemical processes controlling P cycling in reservoirs (Fig. 1). The model separates total P (TP) into the following pools: total dissolved P (TDP); particulate organic P (POP); exchangeable P (EP); and unreactive particulate P (UPP). UPP consists mostly of crystalline phosphate minerals that are inert on reservoir-relevant timescales (≤100 y); TDP comprises inorganic and organic forms of P, whereas EP includes orthophosphate and organic P molecules sorbed to or coprecipitated with oxides, clay minerals, and organic matter. Reactive P (RP) is defined as the sum of TDP, EP, and POP; RP represents the potentially bioavailable fraction of TP.Open in a separate windowFig. 1.Mass balance model used to estimate retention of P in reservoirs. F_in,i is the influx of the ith P pool into the reservoir, F_i,out is the corresponding efflux out of the reservoir, F₁₂ represents P fixation by primary productivity, F₂₁ represents mineralization of POP, F₁₃ and F₃₁ are the sorption and desorption rates of dissolved P, and F_i,bur is the permanent burial flux of the ith particulate P pool in the reservoir’s sediments.Global predictive relationships for the retention of TP and RP in reservoirs are derived from a Monte Carlo analysis of the model, which accounts for parameter variability within expected ranges. The relationships are applied to the reservoirs in the Global Reservoirs and Dams (GRanD) database (16), to estimate the sequestration of TP and RP by dams in each of the major river basins of the world. Throughout, P retention efficiencies in a reservoir are defined asRX=Xin−XoutXin,[1]where R_X is the fractional retention of TP or RP, and X_in and X_out are the input and output fluxes of TP or RP in units of mass per unit time. Annual amounts of TP and RP retained in a reservoir are then calculated by multiplying the R_X values with the corresponding TP and RP input fluxes from the dam’s upstream watershed. The latter are obtained from the Global-NEWS-HD model, which estimates emission yields for dissolved inorganic P (DIP), dissolved organic P (DOP), and particulate P (PP), of which 20% is assumed to be reactive (7, 26). The Global-NEWS-HD yield estimates are based on the biogeophysical characteristics, population density, socioeconomic status, land use, and climatic conditions within the drainage basin (20).Because the biogeochemical mass balance model explicitly represents the in-reservoir transformations between the different forms of P, it allows us to estimate how dams modify both the total and reactive fluxes of P along rivers. With the proposed approach, we reconstruct global TP and RP retentions by dams in 1970 and 2000 and make projections for 2030. For the latter, we apply the nutrient P loading trends developed for the four Millennium Ecosystem Assessment (MEA) scenarios (27). The results illustrate the evolving role of damming in the continental P cycle and, in particular, the ongoing geographical shift in P retention resulting from the current boom in dam construction.     

From the Cover: Amazonian functional diversity from forest canopy chemical assembly

Patterns of tropical forest functional diversity express processes of ecological assembly at multiple geographic scales and aid in predicting ecological responses to environmental change. Tree canopy chemistry underpins forest functional diversity, but the interactive role of phylogeny and environment in determining the chemical traits of tropical trees is poorly known. Collecting and analyzing foliage in 2,420 canopy tree species across 19 forests in the western Amazon, we discovered (i) systematic, community-scale shifts in average canopy chemical traits along gradients of elevation and soil fertility; (ii) strong phylogenetic partitioning of structural and defense chemicals within communities independent of variation in environmental conditions; and (iii) strong environmental control on foliar phosphorus and calcium, the two rock-derived elements limiting CO₂ uptake in tropical forests. These findings indicate that the chemical diversity of western Amazonian forests occurs in a regionally nested mosaic driven by long-term chemical trait adjustment of communities to large-scale environmental filters, particularly soils and climate, and is supported by phylogenetic divergence of traits essential to foliar survival under varying environmental conditions. Geographically nested patterns of forest canopy chemical traits will play a role in determining the response and functional rearrangement of western Amazonian ecosystems to changing land use and climate.Foliage is a locus of chemical investment undertaken by plants to capture and use sunlight for carbon gain under changing environmental conditions and compete with coexisting individuals and species. Plants acquire essential chemical elements from soils, and they synthesize a wide variety of compounds in their leaves to support multiple interdependent physiological processes. Uptake of nitrogen and phosphorus plus the internal production of photosynthetic pigments, including chlorophyll and carotenoids, are required for light capture and carbon fixation in foliage (1). Soluble carbon, primarily comprised of sugars, starch, pectins, and lipids, is then synthesized to meet the energy requirements of the entire plant (2). Other macro- and micronutrients (e.g., calcium) underpin critical leaf functions, such as stomatal conductance and cell wall development. To support the carbon capture process, foliar structural compounds, such as lignin and cellulose, are synthesized to provide strength and longevity (3), and polyphenols are generated for chemical defense (4). Variation in this leaf chemical portfolio expresses multiple strategies evolved in plants to maximize fitness through growth and longevity in any given environment.Despite our understanding of plant chemical and physiological processes, the way that environment and evolution interact to determine geographic variation in plant canopy chemistry remains a mystery. In turn, this shortfall sets a fundamental limit on our knowledge of the core determinants of functional diversity in and across ecosystems, with cascading limits on our understanding of biogeographic and biogeochemical processes. Although much research has either focused on plant functional trait differentiation among coexisting species in communities (5) or emphasized trait convergence in response to environmental filters, such as climate and soils (6), few studies have examined the interconnections between phylogeny and environment in determining functional diversity by way of canopy chemistry (7). This gap is particularly true in the tropics, where our understanding of the interplay between evolution and environmental factors is perhaps weakest because of high plant diversity and a poor understanding of plant community assembly (8). Today, we know very little about canopy chemical traits at community to biome scales in the tropics (9).Western Amazonian forests are a case in point. The forested corridor stretching from Colombia to Bolivia and from the Andean tree line to the Amazon lowlands harbors thousands of plant species arranged in communities distributed across widely varying elevation, geologic, soil, and hydrologic conditions (10, 11). Although the general biological diversity of the region is coming into focus (12, 13), the functional diversity of the forest remains unknown. To understand the regional assembly of forest functional traits and their underlying controls in Amazonia, we must determine the degree to which canopy chemistry is environmentally filtered and phylogenetically partitioned as well as how chemical traits are organized within and among communities. If chemical traits are plastic among coexisting taxa, then biological diversity may be decoupled from functional diversity. Alternatively, if there exists strong phylogenetic organization of canopy chemical traits, then biological diversity may express functional trait diversity and vice versa. Determining the connection between functional and biological diversity may help to explain how so many species coexist within communities and how communities differ throughout the region (14).Here, we are interested in chemical diversity among coexisting tropical canopy tree species and their evolved responses to regional environmental filters thought to limit functional trait divergence. Thus, we developed chemical trait portfolios for tree canopies spread along a 3,500-m elevation gradient stretching from lowland Amazonia to the Andean tree line in Peru (SI Methods and Tables S1 and S2). We assessed the role of taxonomy as well as within- (intraspecific) and between-species (interspecific) variations in determining community and regional chemical assembly. Our study incorporated 2,420 canopy tree species in 19 forests along the elevation gradient, and our sampling included the majority of canopy tree species known to occur in the western Amazon (11, 12). Because submontane to montane Andean forests exist primarily on younger geologic surfaces, whereas lowland forests occur on a mosaic of young to old substrates, we also considered the role of soils in mediating canopy chemical trait distributions. We asked two questions. (i) How does the canopy chemistry of western Amazonian forests vary with elevation? (ii) How much of the variation is explained by taxonomy compared with plasticity within taxa? We focused on light capture and growth traits (including N, P, and photosynthetic pigments) as well as structure and defense traits (total C, lignin, cellulose, and phenols). We also considered Ca as a key element regulating foliar metabolism and nutrient cycling in humid tropical ecosystems (15, 16), and we measured δ¹³C and soluble carbon as indicators of performance (17). Finally, we assessed sources of variation in leaf mass per area (LMA), a foliar structural property expressing plant investment strategies based on multiple chemical and physiological traits (18).