Zhijie Wang

Bio: Zhijie Wang is an academic researcher from Colorado State University. The author has contributed to research in topics: Pulmonary hypertension & Pulmonary artery. The author has an hindex of 31, co-authored 89 publications receiving 3432 citations. Previous affiliations of Zhijie Wang include Chinese Academy of Sciences & University of Wisconsin-Madison.

Complex Hemodynamics at the Apex of an Arterial Bifurcation Induces Vascular Remodeling Resembling Cerebral Aneurysm Initiation

Abstract: Background and Purpose— Arterial bifurcation apices are common sites for cerebral aneurysms, raising the possibility that the unique hemodynamic conditions associated with flow dividers predispose the apical vessel wall to aneurysm formation. This study sought to identify the specific hemodynamic insults that lead to maladaptive vascular remodeling associated with aneurysm development and to identify early remodeling events at the tissue and cellular levels. Methods— We surgically created new branch points in the carotid vasculature of 6 female adult dogs. In vivo angiographic imaging and computational fluid dynamics simulations revealed the detailed hemodynamic microenvironment for each bifurcation, which were then spatially correlated with histologic features showing specific tissue responses. Results— We observed 2 distinct patterns of vessel wall remodeling: (1) hyperplasia that formed an intimal pad at the bifurcation apex and (2) destructive remodeling in the adjacent region of flow acceleration that resembled the initiation of an intracranial aneurysm, characterized by disruption of the internal elastic lamina, loss of medial smooth muscle cells, reduced proliferation of smooth muscle cells, and loss of fibronectin. Conclusions— Strong localization of aneurysm-type remodeling to the region of accelerating flow suggests that a combination of high wall shear stress and a high gradient in wall shear stress represents a “dangerous” hemodynamic condition that predisposes the apical vessel wall to aneurysm formation.

Incremental learning for ν -Support Vector Regression

Abstract: The ? -Support Vector Regression ( ? -SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ? on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ? -Support Vector Classification ( ? -SVC) (Scholkopf et?al., 2000), ? -SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ? -SVC algorithm (AONSVM) to ? -SVR will not generate an effective initial solution. It is the main challenge to design an incremental ? -SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ? -SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ? -SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ? -SVR algorithms with both cold and warm starts.

Sensing crop nitrogen status with fluorescence indicators. A review

Abstract: The optimization of nitrogen (N) fertilization is the object of intense research efforts around the world. Overfertilization is commonly used as a form of insurance against uncertain soil fertility level. However, this practice results in lower nitrogen use efficiency, high levels of residual N after harvest, and losses in the environment. Determining an N recommendation that would preserve actual crop requirements, profitability of the farm, and quality of the environment has been subjected to a number of research initiatives with a variable degree of success. On one hand, soil tests are capable of estimating the intensity of N release at any point in time, but rarely the capacity factor over a longer period. On the other hand, in the context of in-season N applications, crops are often considered good integrators of factors such as the presence of mineral N, climatic conditions, soil properties, and crop management. Strategies have been proposed with plant sensor-based diagnostic information for N recommendations, but the sensitivity of reflectance-based parameters alone do not provide complete satisfaction (delayed sensitivity, need of specific chlorophyll, biomass or cover fraction ranges, lack of specificity to the N stress). Fluorescence sensing methods have been used to monitor crop physiology for years, and they may offer solutions for N status diagnosis over reflectance-based methods. In this paper, we review three plant fluorescence components related to four sensing approaches—variable chlorophyll fluorescence, leaf chlorophyll content-related fluorescence emission ratio, blue-green fluorescence, and epidermal screening of chlorophyll fluorescence by phenolic compounds—from the perspective of their relevance to N fertilization management of agricultural crops. We examine the existence of N-induced changes in each case, together with applications and limitations of the approach. Among these approaches, the fluorescence emission ratio method is the most important and the most widely used to date. However, blue-green fluorescence and epidermal screening of chlorophyll fluorescence by phenolic compounds has also received a great deal of attention particularly with the recent commercial release of instruments which can measure in real time and in vivo both the leaf chlorophyll content and several phenolic compounds (anthocyanins, flavonoids, hydroxycinnamic acids). Overall, our conclusion is that fluorescence-based technologies allow for highly sensitive plant N status information, independently from soil interference, leaf area, or biomass status. They also allow for probing not only the chlorophyll status but also other physiological parameters known to react to N fertility conditions. These new parameters have the potential to provide new N status indicators that can be assessed remotely in a precision agriculture context.

Pulmonary Vascular Wall Stiffness: An Important Contributor to the Increased Right Ventricular Afterload with Pulmonary Hypertension:

Abstract: Pulmonary hypertension (PH) is associated with structural and mechanical changes in the pulmonary vascular bed that increase right ventricular (RV) afterload. These changes, characterized by narrowing and stiffening, occur in both proximal and distal pulmonary arteries (PAs). An important consequence of arterial narrowing is increased pulmonary vascular resistance (PVR). Arterial stiffening, which can occur in both the proximal and distal pulmonary arteries, is an important index of disease progression and is a significant contributor to increased RV afterload in PH. In particular, arterial narrowing and stiffening increase the RV afterload by increasing steady and oscillatory RV work, respectively. Here we review the current state of knowledge of the causes and consequences of pulmonary arterial stiffening in PH and its impact on RV function. We review direct and indirect techniques for measuring proximal and distal pulmonary arterial stiffness, measures of arterial stiffness including elastic mo...

Saccular Aneurysms on Straight and Curved Vessels Are Subject to Different Hemodynamics: Implications of Intravascular Stenting

Abstract: Our aim was to examine hemodynamic implications of intravascular stenting in the canine venous pouch (sidewall or straight-vessel) and rabbit elastase (curved-vessel) aneurysm models. Flow dynamics in stented (Wallstent) and nonstented versions were studied by using computational fluid dynamics simulations and in vitro flow visualization, with a focus on stent placement effects on aneurysmal flow stagnancy and flow impingement. Results show that sidewall and curved aneurysm models have fundamentally different hemodynamics (shear-driven versus inertia-driven) and thus stent placement outcomes.

人唾液天疱疮抗原的纯化

Abstract: 1. Place animal in induction chamber and anesthetize the mouse and ensure sedation. 2. Once the animal is sedated, move it to a nose cone for hair removal using cream. Only apply cream to the area of the chest that will be utilized for imaging. Once the hair is removed, wipe area with wet gauze to ensure all hair is removed. 3. Move the animal to the imaging platform and tape its paws to the ECG lead plates and insert rectal probe. Body temperature should be maintained at 36-37°C. During imaging, reduce anesthesia to maintain proper heart rate. If the animal shows signs of being awake, use a higher concentration of anesthetic.

Deep Learning for Health Informatics.

Abstract: With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Abstract: A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Impact of several common tree species of European temperate forests on soil fertility

Abstract: The aim of the present work was to provide a synopsis of the scientific literature concerning the effects of different tree spe- cies on soil and to quantify the effect of common European temperate forest species on soil fertility. The scientific literature dealing with the tree species effect on soil has been reviewed. The composition of forest overstory has an impact on the chemical, physical and biolo- gical characteristics of soil. This impact was highest in the topsoil. Different tree species had significantly different effects on water ba- lance and microclimate. The physical characteristics of soils also were modified depending on the overstory species, probably through modifications of the soil fauna. The rates of organic matter mineralization and nitrification seem to be dependent on tree species. A coni- ferous species, Picea abies, had negative input-output budgets for some nutrients, such as Ca and Mg. This species promoted a higher soil acidification and a decrease in pH. Thus, it should not be planted in very poor soils in areas affected by acidic atmospheric deposi- tions. Nevertheless, the effect of the canopy species on soil fertility was rarely significant enough to promote forest decline. The impact of a tree species on soil fertility varied depending on the type of bedrock, climate and forest management. forest soils / tree species / fertility / sustainability / resiliency

Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

Abstract: Accurate yield estimation and optimised nitrogen management is essential in agriculture. Remote sensing (RS) systems are being more widely used in building decision support tools for contemporary farming systems to improve yield production and nitrogen management while reducing operating costs and environmental impact. However, RS based approaches require processing of enormous amounts of remotely sensed data from different platforms and, therefore, greater attention is currently being devoted to machine learning (ML) methods. This is due to the capability of machine learning based systems to process a large number of inputs and handle non-linear tasks. This paper discusses research developments conducted within the last 15 years on machine learning based techniques for accurate crop yield prediction and nitrogen status estimation. The paper concludes that the rapid advances in sensing technologies and ML techniques will provide cost-effective and comprehensive solutions for better crop and environment state estimation and decision making. More targeted application of the sensor platforms and ML techniques, the fusion of different sensor modalities and expert knowledge, and the development of hybrid systems combining different ML and signal processing techniques are all likely to be part of precision agriculture (PA) in the near future.