Experiments and simulations of MEMS thermal sensors for wall shear-stress measurements in aerodynamic control applications

MEMS thermal shear-stress sensors exploit heat-transfer effects to measure the shear stress exerted by an air flow on its solid boundary, and have promising applications in aerodynamic control. Classical theory for conventional, macroscale thermal shear-stress sensors states that the rate of heat removed by the flow from the sensor is proportional to the 1/3-power of the shear stress. However, we have observed that this theory is inconsistent with experimental data from MEMS sensors. This paper seeks to develop an understanding of MEMS thermal shear-stress sensors through a study including both experimental and theoretical investigations. We first obtain experimental data that confirm the inadequacy of the classical theory by wind-tunnel testing of prototype MEMS shear-stress sensors with different dimensions and materials. A theoretical analysis is performed to identify that this inadequacy is due to the lack of a thin thermal boundary layer in the fluid flow at the sensor surface, and then a two-dimensional MEMS shear-stress sensor theory is presented. This theory incorporates important heat-transfer effects that are ignored by the classical theory, and consistently explains the experimental data obtained from prototype MEMS sensors. Moreover, the prototype MEMS sensors are studied with three-dimensional simulations, yielding results that quantitatively agree with experimental data. This work demonstrates that classical assumptions made for conventional thermal devices should be carefully examined for miniature MEMS devices

Advanced Learning Methodologies for Biomedical Applications

University of Minnesota Ph.D. dissertation. October 2017. Major: Electrical/Computer Engineering. Advisor: Vladimir Cherkassky. 1 computer file (PDF); ix, 109 pages.There has been a dramatic increase in application of statistical and machine learning methods for predictive data-analytic modeling of biomedical data. Most existing work in this area involves application of standard supervised learning techniques. Typical methods include standard classification or regression techniques, where the goal is to estimate an indicator function (classification decision rule) or real-valued function of input variables, from finite training sample. However, real-world data often contain additional information besides labeled training samples. Incorporating this additional information into learning (model estimation) leads to nonstandard/advanced learning formalizations that represent extensions of standard supervised learning. Recent examples of such advanced methodologies include semi-supervised learning (or transduction) and learning through contradiction (or Universum learning). This thesis investigates two new advanced learning methodologies along with their biomedical applications. The first one is motivated by modeling complex survival data which can incorporate future, censored, or unknown data, in addition to (traditional) labeled training data. Here we propose original formalization for predictive modeling of survival data, under the framework of Learning Using Privileged Information (LUPI) proposed by Vapnik. Survival data represents a collection of time observations about events. Our modeling goal is to predict the state (alive/dead) of a subject at a pre-determined future time point. We explore modeling of survival data as binary classification problem that incorporates additional information (such as time of death, censored/uncensored status, etc.) under LUPI framework. Then we propose two advanced constructive Support Vector Machine (SVM)-based formulations: SVM+ and Loss-Order SVM (LO-SVM). Empirical results using simulated and real-life survival data indicate that the proposed LUPI-based methods are very effective (versus classical Cox regression) when the survival time does not follow classical probabilistic assumptions. Second advanced methodology investigates a new learning paradigm for classification called Group Learning. This approach is motivated by modeling high-dimensional data when the number of input features is much larger than the number of training samples. There are two main approaches to solving such ill-posed problems: (a) selecting a small number of informative features via feature selection; (b) using all features but imposing additional complexity constraints, e.g., ridge regression, SVM, LASSO, etc. The proposed Group Learning method takes a different approach, by splitting all features into many (t) groups, and then estimating a classifier in reduced space (of dimensionality d/t). This approach effectively uses all features, but implements training in a lower-dimensional input space. Note that the formation of groups reflects application-domain knowledge. For example, in classifying of two-dimensional images represented as a set of pixels (original high-dimensional input space), appropriate groups can be formed by grouping adjacent pixels or “local patches” because adjacent pixels are known to be highly correlated. We provide empirical validation of this new methodology for two real-life applications: (a) handwritten digit recognition, and (b) predictive classification of univariate signals, e.g., prediction of epileptic seizures from intracranial electroencephalogram (iEEG) signal. Prediction of epileptic seizures is particularly challenging, due to highly unbalanced data (just 4–5 observed seizures) and patient-specific modeling. In a joint project with Mayo Clinic, we have incorporated the Group Learning approach into an SVM-based system for seizure prediction. This system performs subject-specific modeling and achieves robust prediction performance

Clinical Characteristics of Recurrent Nasopharyngeal Carcinoma in High-Incidence Area

Background. To describe the clinical characteristics of the patients who suffered from relapse after conventional irradiation for nasopharyngeal carcinoma (NPC). Methods. Three hundred and fifty-one consecutive patients with first-time recurrent NPC between January 1999 and July 2005 were included. The patients' clinical data were reviewed, including recurrent interval time, symptoms, signs, imaging characteristics, pathologic features, and restaging. Results. The median interval of relapse was 26.0 months. The most common symptoms in symptomatic patients were nasal bloody discharge (37.9%) and headache (31.1%). Local recurrence alone accounted for 73.5%. Most patients were restaged as stage III (23.1%) and stage IV (51.1%). Subgroup analysis suggested a significantly higher proportion of the long-latent relapses originated from early primary. A series of postreirradiation complications were more frequent in patients with longer latency at reception. Conclusions. Most recurrent nasopharyngeal carcinoma is advanced disease. Patients with different recurrent interval time show different nature behavior

25-Hydroxyvitamin D-3 induces osteogenic differentiation of human mesenchymal stem cells

25-Hydroxyvitamin D-3 [25(OH)D-3] has recently been found to be an active hormone. Its biological actions are demonstrated in various cell types. 25(OH)D-3 deficiency results in failure in bone formation and skeletal deformation. Here, we investigated the effect of 25(OH)D-3 on osteogenic differentiation of human mesenchymal stem cells (hMSCs). We also studied the effect of 1 alpha, 25-dihydroxyvitamin D-3[1 alpha,25-(OH)(2)D-3], a metabolite of 25(OH)D-3. One of the vitamin D responsive genes, 25(OH)D-3-24-hydroxylase (cytochrome P450 family 24 subfamily A member 1) mRNA expression is up-regulated by 25(OH)D-3 at 250-500 nM and by 1 alpha, 25-(OH)(2)D-3 at 1-10 nM. 25(OH)D-3 and 1 alpha, 25-(OH)(2)D-3 at a time-dependent manner alter cell morphology towards osteoblast-associated characteristics. The osteogenic markers, alkaline phosphatase, secreted phosphoprotein 1 (osteopontin), and bone gamma-carboxyglutamate protein (osteocalcin) are increased by 25(OH)D-3 and 1 alpha,25-(OH)(2)D-3 in a dose-dependent manner. Finally, mineralisation is significantly increased by 25(OH)D-3 but not by 1 alpha, 25-(OH)(2)D-3. Moreover, we found that hMSCs express very low level of 25(OH)D-3-1 alpha-hydroxylase (cytochrome P450 family 27 subfamily B member 1), and there is no detectable 1 alpha, 25-(OH)(2)D-3 product. Taken together, our findings provide evidence that 25(OH)D-3 at 250-500 nM can induce osteogenic differentiation and that 25(OH)D-3 has great potential for cell-based bone tissue engineering.Peer reviewe

A Community-Based Walk-In Screening of Depression in Taiwan

Depression is a crucial public health problem because of its relatively high association with suicidal attempts, prolonged social isolation, poor physical health, and dementia. However, the available data and study on the prevalence of depression in Taiwan were mostly completed within the previous 1 to 2 decades, and these studies were limited to certain areas or populations. Little is known regarding the current status of depression in Taiwan. We used a brief tool, the Center for Epidemiological Studies Depression Scale (CES-D), to screen depression in 4 areas among the general and aged population. The results showed a higher CES-D score in the southern area among general (mean ± SD: 7.8 ± 8.4) or aged participants (mean ± SD: 7.2 ± 8.0) compared with other areas. The ratio of suspected depression patients was 16.4% of all recruited participants and 13.3% of aged participants. These results may provide information for this public health issue

Inpaint4DNeRF: Promptable Spatio-Temporal NeRF Inpainting with Generative Diffusion Models

Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion models (e.g., ControlNet) for direct generation of the underlying completed background content, regardless of static or dynamic. The key advantages of this generative approach for NeRF inpainting are twofold. First, after rough mask propagation, to complete or fill in previously occluded content, we can individually generate a small subset of completed images with plausible content, called seed images, from which simple 3D geometry proxies can be derived. Second and the remaining problem is thus 3D multiview consistency among all completed images, now guided by the seed images and their 3D proxies. Without other bells and whistles, our generative Inpaint4DNeRF baseline framework is general which can be readily extended to 4D dynamic NeRFs, where temporal consistency can be naturally handled in a similar way as our multiview consistency