基于Adaptive Elastic Net与加速失效时间模型的亚组识别方法的应用拓展

目的 针对适应性设计下的Adaptive Elastic Net与加速失效时间模型亚组识别方法进行更多适用条件下的研究,以获得该方法最佳应用效果所对应的参数。方法 基于前期所提出的亚组识别方法,进一步探讨协变量间相关性、二阶段显著性水准([α1]和[α2])、协变量与样本量比例对该方法的影响。通过模拟研究,探讨含/不含协变量主效应的惩罚模型在不同情形下的亚组识别效果。结果 协变量间的相关性r=0、0.3、0.5时,检验效能（power）表现稳定;在二阶段自适应设计中,当[α1]和[α2]分别为0.035和0.015时,模型的power最高;固定样本量n的情况下,power随着待选协变量个数与n比例的上升而下降,比例升到1之后power呈现平稳趋势;对于不同生存时间的参数分布,单变量模型表现出不同的模式,而惩罚AFT模型相对稳定。结论 协变量间的相关性不影响检验power;（0.035,0.015）可作为自适应设计显著性水准的参考设置;获益亚组与非获益亚组间的治疗效果差异较小时,含协变量主效应的惩罚性AFT模型（Penalized,Eq_in）优于不含协变量主效应的单变量AFT模型（Univariate,Eq_ex）;当协变量数量与样本量的比值小于1时,“Univariate,Eq_ex”的power更高;否则,“Penalized,Eq_in”的效果会更好;生存数据的参数分布对单变量模型的影响较大,但对惩罚模型的影响较小。     

基孔肯雅病毒的分子变异及其流行趋势

由基孔肯雅病毒（Chikungunya virus,CHIKV）引起的基孔肯雅热已从仅在非洲南部、东南亚和印度部分热带地区流行的蚊媒传染病传播至亚热带地区的多个国家。既往CHIKV的传播主要以埃及伊蚊作为传播媒介,2004年基孔肯雅热疫情在南亚地区的暴发流行中,出现了能够以地区分布更为广泛的白纹伊蚊为媒介传播病毒进化株,这也成为是基孔肯雅热流行区域不断扩大的重要原因。研究表明CHIKV主要是通过其基因组中编码的包膜糖蛋白（envelope glycoproteins）的多次突变以不断增强其对白纹伊蚊的适应性。对CHIKV进化规律和基因突变特征的研究不仅可以为研究病毒对蚊媒适应性的分子机制提供线索,还可以对可能的基孔肯雅热暴发流行提供预警。     

Adaptive optics imaging of the retinal microvasculature

The eye has long been recognised as the window to pathological processes occurring in the brain and other organs. By imaging the vasculature of the retina we have improved the scientific understanding and clinical best practice for a diverse range of conditions, ranging from diabetes, to stroke, to dementia. Mounting evidence suggests that damage to the smallest and most delicate vessels in the body, the capillaries, is the first sign in many vasculopathies. These are the most critical vessels involved in the exchange of metabolites with tissue. Accurate assessment of retinal capillary structure and function would therefore be of great benefit across a broad range of disciplines in medical science; however, their small size does not make this an easy task. This has led to the development of high-resolution adaptive optics imaging methods to non-invasively explore retinal microvascular networks in living human eyes. This review describes the present state of the art in the field, the scientific breakthroughs that have been made possible in the understanding of vessel structure and function in health and disease, and future directions for this emerging technology.     

Dynamic neural network-based robust observers for uncertain nonlinear systems

A dynamic neural network (DNN) based robust observer for uncertain nonlinear systems is developed. The observer structure consists of a DNN to estimate the system dynamics on-line, a dynamic filter to estimate the unmeasurable state and a sliding mode feedback term to account for modeling errors and exogenous disturbances. The observed states are proven to asymptotically converge to the system states of high-order uncertain nonlinear systems through Lyapunov-based analysis. Simulations and experiments on a two-link robot manipulator are performed to show the effectiveness of the proposed method in comparison to several other state estimation methods.     

Projective synchronization for fractional neural networks

In this paper, the global projective synchronization of fractional-order neural networks is investigated. First, a sufficient condition in the sense of Caputo’s fractional derivation to ensure the monotonicity of the continuous and differential functions and a new fractional-order differential inequality are derived, which play central roles in the investigation of the fractional adaptive control. Based on the preparation and some analysis techniques, some novel criteria are obtained to realize projective synchronization of fractional-order neural networks via combining open loop control and adaptive control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the addressed neural networks. Finally, an example with numerical simulations is given to show the effectiveness of the obtained results.     

Time dependent integration of matrix metalloproteinases and their targeted substrates directs axonal sprouting and synaptogenesis following central nervous system injury

Over the past two decades, many investigators have reported how extracellular matrix molecules act to regulate neuroplasticity. The majority of these studies involve proteins which are targets of matrix metalloproteinases. Importantly, these enzyme/substrate interactions can regulate degenerative and regenerative phases of synaptic plasticity, directing axonal and dendritic reorganization after brain insult. The present review first summarizes literature support for the prominent role of matrix metalloproteinases during neuroregeneration, followed by a discussion of data contrasting adaptive and maladaptive neuroplasticity that reveals time-dependent metalloproteinase/substrate regulation of postinjury synaptic recovery. The potential for these enzymes to serve as therapeutic targets for enhanced neuroplasticity after brain injury is illustrated with experiments demonstrating that metalloproteinase inhibitors can alter adaptive and maladaptive outcome. Finally, the complexity of metalloproteinase role in reactive synaptogenesis is revealed in new studies showing how these enzymes interact with immune molecules to mediate cellular response in the local regenerative environment, and are regulated by novel binding partners in the brain extracellular matrix. Together, these different examples show the complexity with which metalloproteinases are integrated into the process of neuroregeneration, and point to a promising new angle for future studies exploring how to facilitate brain plasticity.     

Spatially robust estimates of biological nitrogen (N) fixation imply substantial human alteration of the tropical N cycle

Biological nitrogen fixation (BNF) is the largest natural source of exogenous nitrogen (N) to unmanaged ecosystems and also the primary baseline against which anthropogenic changes to the N cycle are measured. Rates of BNF in tropical rainforest are thought to be among the highest on Earth, but they are notoriously difficult to quantify and are based on little empirical data. We adapted a sampling strategy from community ecology to generate spatial estimates of symbiotic and free-living BNF in secondary and primary forest sites that span a typical range of tropical forest legume abundance. Although total BNF was higher in secondary than primary forest, overall rates were roughly five times lower than previous estimates for the tropical forest biome. We found strong correlations between symbiotic BNF and legume abundance, but we also show that spatially free-living BNF often exceeds symbiotic inputs. Our results suggest that BNF in tropical forest has been overestimated, and our data are consistent with a recent top-down estimate of global BNF that implied but did not measure low tropical BNF rates. Finally, comparing tropical BNF within the historical area of tropical rainforest with current anthropogenic N inputs indicates that humans have already at least doubled reactive N inputs to the tropical forest biome, a far greater change than previously thought. Because N inputs are increasing faster in the tropics than anywhere on Earth, both the proportion and the effects of human N enrichment are likely to grow in the future.Over the last few decades, humans have dramatically altered the global nitrogen (N) cycle (1–3). Three main processes—Haber–Bosch fixation of atmospheric N₂, widespread cultivation of leguminous N-fixing crops, and incidental N fixation during fossil fuel combustion—collectively add more reactive N to the biosphere each year than all natural processes combined (2). Although human perturbation of the N cycle has brought substantial benefits to society (most notably, an increase in crop production) (4), it has also had a number of negative effects on both ecosystems (5, 6) and people (7).Although humanity’s large imprint on the global N cycle is clear, quantifying the extent of anthropogenic changes depends, in large part, on establishing baseline estimates of nonanthropogenic N inputs (1, 8, 9). Before recent human activities, biological N fixation (BNF) was the largest source of new N to the biosphere (9). Terrestrial BNF has been particularly challenging to quantify, because it displays high spatial and temporal heterogeneity at local scales, it arises from both symbiotic associations between bacteria and plants as well as free-living microorganisms (e.g., in leaf litter and soil) (10), and high atmospheric concentrations of N₂ make direct flux measurements unfeasible. Consequently, spatial estimates of BNF have always been highly uncertain (11), and global rate estimates have fallen precipitously in the last 15 y (from 100–290 to ∼44 Tg N y⁻¹) (9). This decline in BNF implies an increase in the relative magnitude of anthropogenic N inputs from 100–150% to 190–470% of BNF (9).Historically, the largest anthropogenic changes to the N cycle have occurred in the northern temperate zone: first throughout the United States and western Europe and more recently, in China (12, 13). Large-scale estimates of BNF in natural ecosystems in these regions are consistently low (11), leading some to conclude that anthropogenic N inputs in the northern temperate zone exceed naturally occurring BNF and preindustrial atmospheric N deposition by an order of magnitude or more (1, 14). By contrast, the highest rates of naturally occurring BNF have been thought to occur in the evergreen lowland tropical rainforest biome (11), implying that, on a regional basis, human alteration of the tropical N cycle has been comparatively modest. However, in recent years, the tropics have seen some of the most dramatic increases in anthropogenic N inputs of any region on Earth—a trend that is likely to continue (2, 6, 13). Anthropogenic N inputs are increasing in tropical regions, primarily because of increasing fossil fuel combustion (13) and expanding high-N-input agriculture for both food and biofuels (6). These anthropogenic N inputs are having a measurable effect on tropical ecosystems (15). However, understanding and forecasting the effects of anthropogenic N depend, in part, on accurate estimates of BNF in lowland tropical rainforest.Unfortunately, the paradigm that the tropics have high rates of BNF is based on a paucity of evidence and several tenuous assumptions. For example, an early global synthesis of terrestrial BNF (11)—which included contributions from both symbiotic and free-living sources—included only one measured estimate of symbiotic BNF from tropical forest (16 kg N ha⁻¹ y⁻¹) (16). That single estimate, scaled over thousands of square kilometers, represented the only direct evidence of high tropical BNF rates available at that time (Fig. 1). Subsequent modeled estimates (17) that indirectly estimated BNF have reinforced the notion that tropical BNF rates are high and dominated by the symbiotic form of fixation (Fig. 1). Such high estimates of symbiotic BNF are consistent with the large number of leguminous trees in tropical forest (18–20). However, many legume species do not form N-fixing nodules (21), and of those species that do, nodulation in individuals varies with soil nutrient status, N demand, and tree age (22). Several recent analyses (10, 22–24) indicate lower tropical forest BNF and suggest that symbiotic BNF may not be as important to total BNF as previously thought (Fig. 1), although few studies have simultaneously measured symbiotic and free-living BNF.Open in a separate windowFig. 1.Previous estimates of BNF in tropical rainforest and BNF measured in this study. Percentages indicate the proportion of total BNF from symbiotic BNF. Cleveland et al. 1999 A (11) is a literature database-derived estimate of tropical forest BNF; Cleveland et al. 1999 B (11) is a modeled estimate of BNF based on the correlation between net primary productivity (NPP) and BNF derived with remotely sensed NPP and evergreen broadleaved forest (EBF) land cover classification. Central estimates and variance for Cleveland et al., 1999 A (11) and Reed et al. 2011 (10) represent the low, central, and high data-based estimates of BNF assuming 5%, 15%, and 15% legume cover, respectively. Central estimates and variance for Wang and Houlton 2009 (17) represent the modeled mean and SD of BNF predicted for the EBF biome. Central estimates and variance for Cleveland et al. 2010 (23) represent the low, central, and high estimates of symbiotic BNF plus free-living BNF or modeled BNF plus free-living BNF. Central estimates and variance for BNF in the four forest ages measured here (primary, 5–15 y, 15–30 y, and 30–50 y) represent means ± 1 SD (n = 3). Our estimate of BNF in a dynamic primary forest (gap dynamics) lacks SD, because it consisted of only two measurements: low and high estimates of forest turnover times equal to 150 and 75 y, respectively.There is also a sound theoretical basis for questioning high estimates of BNF in tropical forest. Namely, high concentrations of soil N in the legume-rich tropics create something of a paradox. Although BNF could create N-rich conditions, the substantial energetic cost of BNF means—and some data show—that BNF should be suppressed under high N availability in primary forests (25). Because of high rates of net primary productivity and high N demand in secondary forests (26, 27), regenerating canopy gaps or abandoned agricultural land may have higher rates of BNF than late-successional forest ecosystems (26).Resolving the uncertainty in the tropical (and global) N cycle requires that we overcome the enduring challenge of quantifying BNF in any ecosystem. How do we estimate large-scale rates of a process that displays extreme spatial heterogeneity at local scales? Whether using acetylene reduction assays, ¹⁵N tracer incubations, or the ¹⁵N natural abundance method, most past approaches to empirically estimate symbiotic BNF have relied on spatial extrapolations of BNF rates measured at the level of individual trees. Typically, such extrapolations are based on legume abundance (e.g., percent cover) and make species- or genera-level assumptions about nodulation status of putative N fixers. Here, we applied a method commonly used by community ecologists to measure rare species abundances—stratified adaptive cluster sampling (SACS) (28)—to measure symbiotic BNF. This approach could be used in any ecosystem, and in contrast to other methods, SACS generates unbiased estimates of mean symbiotic BNF (independent of legume abundance) and can more robustly capture the irregular distribution of nodules on the landscape. We simultaneously measured symbiotic and free-living BNF multiple times over the course of 1 y to generate spatially explicit rates of BNF inputs in primary and secondary (5–50 y old) lowland tropical forest in Costa Rica and then used the understanding gained from those estimates to revisit estimates of BNF and anthropogenic N inputs in the tropical forest biome.     

醒脑静注射液治疗缺血性中风适应症状群的真实世界研究

[目的] 基于真实世界医疗数据,分析醒脑静注射液治疗缺血性中风的适应症状群。[方法] 以电子住院记录为资料开展回顾性分析,根据治疗前后病情变化将病例分为有效组与无效组,通过χ²检验、二元Logistic回归分析确定适应症状群相关指标,并将结果可视化为列线图。[结果] 红舌、滑脉对醒脑静疗效起正向影响,具备该特征的患者有效性较好;神疲、面色萎黄、少苔对醒脑静疗效起负向影响,具备该特征的患者有效性较低。[结论] 明确中成药临床适应症状群可以指导医师合理用药,利用真实世界医疗数据可以成为研究中成药临床适应症状群的一种模式。     

Adaptive rejection of unmatched input disturbances for output tracking using a control separation LQ method

In this paper, an adaptive disturbance rejection scheme is developed for output tracking of multivariable nonminimum‐phase systems in the presence of uncertain unmatched input disturbances. Using a new cost function, a control separation–based LQ control framework is established for desired disturbance rejection and output tracking. The finite‐time and infinite‐time control separation–based LQ solutions are derived in an explicit composite controller structure, which has enhanced disturbance compensation and output tracking properties, as compared with a traditional LQ solution. An adaptive parameter update algorithm is used for estimation of uncertain disturbances, based on which an adaptive control separation–based LQ control solution is developed for plants in the presence of uncertain disturbances. Simulation results are presented to verify the desired adaptive disturbance rejection control system performance.     

Helper T‐cell responses and pulmonary fungal infections

The mucosal surface of the respiratory tract encounters microbes, such as fungal particles, with every inhaled breath. When pathogenic fungi breach the physical barrier and innate immune system within the lung to establish an infection, adaptive immunity is engaged, often in the form of helper CD4 T‐cell responses. Type 1 responses, characterized by interferon‐γ production from CD4 cells, promote clearance of Histoplasma capsulatum and Cryptococcus neoformans infection. Likewise, interleukin‐17A (IL‐17A) production from Th17 cells promotes immunity to Blastomyces dermatitidis and Coccidioides species infection by recruiting neutrophils. In contrast the development of T helper type 2 responses, characterized by IL‐5 production from T cells and eosinophil influx into the lungs, drives allergic bronchopulmonary aspergillosis and poor outcomes during C. neoformans infection. Experimental vaccines against several endemic mycoses, including Histoplasma capsulatum, Coccidioides, Cryptococcus and Blastomyces dermatitidis, induce protective T‐cell responses and foreshadow the development of vaccines against pulmonary fungal infections for use in humans. Additionally, recent work using antifungal T cells as immunotherapy to protect immune‐compromised patients from opportunist fungal infections also shows great promise. This review covers the role of T‐cell responses in driving protection and pathology in response to pulmonary fungal infections, and highlights promising therapeutic applications of antifungal T cells.