Use of power doppler for the assessment of vascularization in equine tendinitis treated with mesenchymal stem cells

Tendinous lesions are very common in athlete horses. The process of tendon healing is slow and the quality of the new tissue is often inferior to the original, leading in many cases to recurrence of the lesion. One of the main reasons for the limited healing capacity of tendons is its poor vascularization. At present, cell therapy is used in equine practice for the treatment of several disorders including tendinitis, desmitis and joint disease. However, there is little information regarding the mechanisms of action of these cells during tissue repair. It is known that Mesenchymal Stem Cells (MSCs) release several growth factors at the site of implantation, some of which promote angiogenesis. Comparison of blood flow using power Doppler ultrasonography was performed after the induction superficial digital flexor tendon tendinitis and implantation of adipose tissue-derived MSCs in order to analyze the effect of cell therapy on tendon neovascularization. For quantification of blood vessel histopathological examinations were conducted. Increased blood flow and number of vessels was observed in treated tendons up to 30 days after cell implantation, suggesting promotion of angiogenesis by the cell therapy.

Development and in-vitro characterization of an implantable flow sensing transducer for hydrocephalus

An implantable transducer for monitoring the flow of Cerebrospinal fluid (CSF) for the treatment of hydrocephalus has been developed which is based on measuring the heat dissipation of a local thermal source. The transducer uses passive telemetry at 13.56 MHz for power supply and read out of the measured flow rate. The in vitro performance of the transducer has been characterized using artificial Cerebrospinal Fluid (CSF) with increased protein concentration and artificial CSF with 10\% fresh blood. After fresh blood was added to the artificial CSF a reduction of flow rate has been observed in case that the sensitive surface of the flow sensor is close to the sedimented erythrocytes. An increase of flow rate has been observed in case that the sensitive surface is in contact with the remaining plasma/artificial CSF mix above the sediment which can be explained by an asymmetric flow profile caused by the sedimentation of erythrocythes having increased viscosity compared to artificial CSF. After removal of blood from artificial CSF, no drift could be observed in the transducer measurement which could be associated to a deposition of proteins at the sensitive surface walls of the packaged flow transducer. The flow sensor specification requirement of +-10\% for a flow range between 2 ml/h and 40 ml/h. could be confirmed at test conditions of 37 degrees C.

A Method for Detecting Population Genetic Structure in Diverse, High Gene-Flow Species

Detecting small amounts of genetic subdivision across geographic space remains a persistent challenge. Often a failure to detect genetic structure is mistaken for evidence of panmixia, when more powerful statistical tests may uncover evidence for subtle geographic differentiation. Such slight subdivision can be demographically and evolutionarily important as well as being critical for management decisions. We introduce here a method, called spatial analysis of shared alleles (SAShA), that detects geographically restricted alleles by comparing the spatial arrangement of allelic co-occurrences with the expectation under panmixia. The approach is allele-based and spatially explicit, eliminating the loss of statistical power that can occur with user-defined populations and statistical averaging within populations. Using simulated data sets generated under a stepping-stone model of gene flow, we show that this method outperforms spatial autocorrelation (SA) and UST under common real-world conditions: at relatively high migration rates when diversity is moderate or high, especially when sampling is poor. We then use this method to show clear differences in the genetic patterns of 2 nearshore Pacific mollusks, Tegula funebralis (5 Chlorostoma funebralis) and Katharina tunicata, whose overall patterns of within-species differentiation are similar according to traditional population genetics analyses. SAShA meaningfully complements UST/FST, SA, and other existing geographic genetic analyses and is especially appropriate for evaluating species with high gene flow and subtle genetic differentiation.

Probabilistic-based hurricane risk assessment and mitigation considering the potential impacts of climate change

Studies are suggesting that hurricane hazard patterns (e.g. intensity and frequency) may change as a consequence of the changing global climate. As hurricane patterns change, it can be expected that hurricane damage risks and costs may change as a result. This indicates the necessity to develop hurricane risk assessment models that are capable of accounting for changing hurricane hazard patterns, and develop hurricane mitigation and climatic adaptation strategies. This thesis proposes a comprehensive hurricane risk assessment and mitigation strategies that account for a changing global climate and that has the ability of being adapted to various types of infrastructure including residential buildings and power distribution poles. The framework includes hurricane wind field models, hurricane surge height models and hurricane vulnerability models to estimate damage risks due to hurricane wind speed, hurricane frequency, and hurricane-induced storm surge and accounts for the timedependant properties of these parameters as a result of climate change. The research then implements median insured house values, discount rates, housing inventory, etc. to estimate hurricane damage costs to residential construction. The framework was also adapted to timber distribution poles to assess the impacts climate change may have on timber distribution pole failure. This research finds that climate change may have a significant impact on the hurricane damage risks and damage costs of residential construction and timber distribution poles. In an effort to reduce damage costs, this research develops mitigation/adaptation strategies for residential construction and timber distribution poles. The costeffectiveness of these adaptation/mitigation strategies are evaluated through the use of a Life-Cycle Cost (LCC) analysis. In addition, a scenario-based analysis of mitigation strategies for timber distribution poles is included. For both residential construction and timber distribution poles, adaptation/mitigation measures were found to reduce damage costs. Finally, the research develops the Coastal Community Social Vulnerability Index (CCSVI) to include the social vulnerability of a region to hurricane hazards within this hurricane risk assessment. This index quantifies the social vulnerability of a region, by combining various social characteristics of a region with time-dependant parameters of hurricanes (i.e. hurricane wind and hurricane-induced storm surge). Climate change was found to have an impact on the CCSVI (i.e. climate change may have an impact on the social vulnerability of hurricane-prone regions).

Three dimensional finite element simulation of polymer melting and flow in a single-screw extruder : optimization of screw channel geometry

Single-screw extrusion is one of the widely used processing methods in plastics industry, which was the third largest manufacturing industry in the United States in 2007 [5]. In order to optimize the single-screw extrusion process, tremendous efforts have been devoted for development of accurate models in the last fifty years, especially for polymer melting in screw extruders. This has led to a good qualitative understanding of the melting process; however, quantitative predictions of melting from various models often have a large error in comparison to the experimental data. Thus, even nowadays, process parameters and the geometry of the extruder channel for the single-screw extrusion are determined by trial and error. Since new polymers are developed frequently, finding the optimum parameters to extrude these polymers by trial and error is costly and time consuming. In order to reduce the time and experimental work required for optimizing the process parameters and the geometry of the extruder channel for a given polymer, the main goal of this research was to perform a coordinated experimental and numerical investigation of melting in screw extrusion. In this work, a full three-dimensional finite element simulation of the two-phase flow in the melting and metering zones of a single-screw extruder was performed by solving the conservation equations for mass, momentum, and energy. The only attempt for such a three-dimensional simulation of melting in screw extruder was more than twenty years back. However, that work had only a limited success because of the capability of computers and mathematical algorithms available at that time. The dramatic improvement of computational power and mathematical knowledge now make it possible to run full 3-D simulations of two-phase flow in single-screw extruders on a desktop PC. In order to verify the numerical predictions from the full 3-D simulations of two-phase flow in single-screw extruders, a detailed experimental study was performed. This experimental study included Maddock screw-freezing experiments, Screw Simulator experiments and material characterization experiments. Maddock screw-freezing experiments were performed in order to visualize the melting profile along the single-screw extruder channel with different screw geometry configurations. These melting profiles were compared with the simulation results. Screw Simulator experiments were performed to collect the shear stress and melting flux data for various polymers. Cone and plate viscometer experiments were performed to obtain the shear viscosity data which is needed in the simulations. An optimization code was developed to optimize two screw geometry parameters, namely, screw lead (pitch) and depth in the metering section of a single-screw extruder, such that the output rate of the extruder was maximized without exceeding the maximum temperature value specified at the exit of the extruder. This optimization code used a mesh partitioning technique in order to obtain the flow domain. The simulations in this flow domain was performed using the code developed to simulate the two-phase flow in single-screw extruders.

Implementation of porous silicon technology for a fluidic flow-through optical sensor for pH measurements

This work presents an innovative integration of sensing and nano-scaled fluidic actuation in the combination of pH sensitive optical dye immobilization with the electro-osmotic phenomena in polar solvents like water for flow-through pH measurements. These flow-through measurements are performed in a flow-through sensing device (FTSD) configuration that is designed and fabricated at MTU. A relatively novel and interesting material, through-wafer mesoporous silica substrates with pore diameters of 20 -200 nm and pore depths of 500 µm are fabricated and implemented for electro-osmotic pumping and flow-through fluorescence sensing for the first time. Performance characteristics of macroporous silicon (> 500 µm) implemented for electro-osmotic pumping include, a very large flow effciency of 19.8 µLmin-1V-1 cm-2 and maximum pressure effciency of 86.6 Pa/V in comparison to mesoporous silica membranes with 2.8 µLmin-1V-1cm-2 flow effciency and a 92 Pa/V pressure effciency. The electrical current (I) of the EOP system for 60 V applied voltage utilizing macroporous silicon membranes is 1.02 x 10-6A with a power consumption of 61.74 x 10-6 watts. Optical measurements on mesoporous silica are performed spectroscopically from 300 nm to 1000 nm using ellipsometry, which includes, angularly resolved transmission and angularly resolved reflection measurements that extend into the infrared regime. Refractive index (n) values for oxidized and un-oxidized mesoporous silicon sample at 1000 nm are found to be 1.36 and 1.66. Fluorescence results and characterization confirm the successful pH measurement from ratiometric techniques. The sensitivity measured for fluorescein in buffer solution is 0.51 a.u./pH compared to sensitivity of ~ 0.2 a.u./pH in the case of fluorescein in porous silica template. Porous silica membranes are efficient templates for immobilization of optical dyes and represent a promising method to increase sensitivity for small variations in chemical properties. The FTSD represents a device topology suitable for application to long term monitoring of lakes and reservoirs. Unique and important contributions from this work include fabrication of a through-wafer mesoporous silica membrane that has been thoroughly characterized optically using ellipsometry. Mesoporous silica membranes are tested as a porous media in an electro-osmotic pump for generating high pressure capacities due to the nanometer pore sizes of the porous media. Further, dye immobilized mesoporous silica membranes along with macroporous silicon substrates are implemented for continuous pH measurements using fluorescence changes in a flow-through sensing device configuration. This novel integration and demonstration is completely based on silicon and implemented for the first time and can lead to miniaturized flow-through sensing systems based on MEMS technologies.

Experimental investigation of regular fluids and nanofluids during flow boiling in a single microchannel at different heat fluxes and mass fluxes

The dissipation of high heat flux from integrated circuit chips and the maintenance of acceptable junction temperatures in high powered electronics require advanced cooling technologies. One such technology is two-phase cooling in microchannels under confined flow boiling conditions. In macroscale flow boiling bubbles will nucleate on the channel walls, grow, and depart from the surface. In microscale flow boiling bubbles can fill the channel diameter before the liquid drag force has a chance to sweep them off the channel wall. As a confined bubble elongates in a microchannel, it traps thin liquid films between the heated wall and the vapor core that are subject to large temperature gradients. The thin films evaporate rapidly, sometimes faster than the incoming mass flux can replenish bulk fluid in the microchannel. When the local vapor pressure spike exceeds the inlet pressure, it forces the upstream interface to travel back into the inlet plenum and create flow boiling instabilities. Flow boiling instabilities reduce the temperature at which critical heat flux occurs and create channel dryout. Dryout causes high surface temperatures that can destroy the electronic circuits that use two-phase micro heat exchangers for cooling. Flow boiling instability is characterized by periodic oscillation of flow regimes which induce oscillations in fluid temperature, wall temperatures, pressure drop, and mass flux. When nanofluids are used in flow boiling, the nanoparticles become deposited on the heated surface and change its thermal conductivity, roughness, capillarity, wettability, and nucleation site density. It also affects heat transfer by changing bubble departure diameter, bubble departure frequency, and the evaporation of the micro and macrolayer beneath the growing bubbles. Flow boiling was investigated in this study using degassed, deionized water, and 0.001 vol% aluminum oxide nanofluids in a single rectangular brass microchannel with a hydraulic diameter of 229 µm for one inlet fluid temperature of 63°C and two constant flow rates of 0.41 ml/min and 0.82 ml/min. The power input was adjusted for two average surface temperatures of 103°C and 119°C at each flow rate. High speed images were taken periodically for water and nanofluid flow boiling after durations of 25, 75, and 125 minutes from the start of flow. The change in regime timing revealed the effect of nanoparticle suspension and deposition on the Onset of Nucelate Boiling (ONB) and the Onset of Bubble Elongation (OBE). Cycle duration and bubble frequencies are reported for different nanofluid flow boiling durations. The addition of nanoparticles was found to stabilize bubble nucleation and growth and limit the recession rate of the upstream and downstream interfaces, mitigating the spreading of dry spots and elongating the thin film regions to increase thin film evaporation.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Central venous catheter integrity during mechanical power injection of iodinated contrast medium

PURPOSE: To evaluate a widely used nontunneled triple-lumen central venous catheter in order to determine whether the largest of the three lumina (16 gauge) can tolerate high flow rates, such as those required for computed tomographic angiography. MATERIALS AND METHODS: Forty-two catheters were tested in vitro, including 10 new and 32 used catheters (median indwelling time, 5 days). Injection pressures were continuously monitored at the site of the 16-gauge central venous catheter hub. Catheters were injected with 300 and 370 mg of iodine per milliliter of iopamidol by using a mechanical injector at increasing flow rates until the catheter failed. The infusion rate, hub pressure, and location were documented for each failure event. The catheter pressures generated during hand injection by five operators were also analyzed. Mean flow rates and pressures at failure were compared by means of two-tailed Student t test, with differences considered significant at P < .05. RESULTS: Injections of iopamidol with 370 mg of iodine per milliliter generate more pressure than injections of iopamidol with 300 mg of iodine per milliliter at the same injection rate. All catheters failed in the tubing external to the patient. The lowest flow rate at which catheter failure occurred was 9 mL/sec. The lowest hub pressure at failure was 262 pounds per square inch gauge (psig) for new and 213 psig for used catheters. Hand injection of iopamidol with 300 mg of iodine per milliliter generated peak hub pressures ranging from 35 to 72 psig, corresponding to flow rates ranging from 2.5 to 5.0 mL/sec. CONCLUSION: Indwelling use has an effect on catheter material property, but even for used catheters there is a substantial safety margin for power injection with the particular triple-lumen central venous catheter tested in this study, as the manufacturer's recommendation for maximum pressure is 15 psig.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

Interhemispheric cerebral blood flow balance during recovery of motor hand function after ischemic stroke - a longitudinal MRI study using arterial spin labeling perfusion

BACKGROUND Unilateral ischemic stroke disrupts the well balanced interactions within bilateral cortical networks. Restitution of interhemispheric balance is thought to contribute to post-stroke recovery. Longitudinal measurements of cerebral blood flow (CBF) changes might act as surrogate marker for this process. OBJECTIVE To quantify longitudinal CBF changes using arterial spin labeling MRI (ASL) and interhemispheric balance within the cortical sensorimotor network and to assess their relationship with motor hand function recovery. METHODS Longitudinal CBF data were acquired in 23 patients at 3 and 9 months after cortical sensorimotor stroke and in 20 healthy controls using pulsed ASL. Recovery of grip force and manual dexterity was assessed with tasks requiring power and precision grips. Voxel-based analysis was performed to identify areas of significant CBF change. Region-of-interest analyses were used to quantify the interhemispheric balance across nodes of the cortical sensorimotor network. RESULTS Dexterity was more affected, and recovered at a slower pace than grip force. In patients with successful recovery of dexterous hand function, CBF decreased over time in the contralesional supplementary motor area, paralimbic anterior cingulate cortex and superior precuneus, and interhemispheric balance returned to healthy control levels. In contrast, patients with poor recovery presented with sustained hypoperfusion in the sensorimotor cortices encompassing the ischemic tissue, and CBF remained lateralized to the contralesional hemisphere. CONCLUSIONS Sustained perfusion imbalance within the cortical sensorimotor network, as measured with task-unrelated ASL, is associated with poor recovery of dexterous hand function after stroke. CBF at rest might be used to monitor recovery and gain prognostic information.

EEG marker of inhibitory brain activity correlates with resting-state cerebral blood flow in the reward system in major depression

Frontal alpha band asymmetry (FAA) is a marker of altered reward processing in major depressive disorder (MDD), associated with reduced approach behavior and withdrawal. However, its association with brain metabolism remains unclear. The aim of this study is to investigate FAA and its correlation with resting – state cerebral blood flow (rCBF). We hypothesized an association of FAA with regional rCBF in brain regions relevant for reward processing and motivated behavior, such as the striatum. We enrolled 20 patients and 19 healthy subjects. FAA scores and rCBF were quantified with the use of EEG and arterial spin labeling. Correlations of the two were evaluated, as well as the association with FAA and psychometric assessments of motivated behavior and anhedonia. Patients showed a left – lateralized pattern of frontal alpha activity and a correlation of FAA lateralization with subscores of Hamilton Depression Rating Scale linked to motivated behavior. An association of rCBF and FAA scores was found in clusters in the dorsolateral prefrontal cortex bilaterally (patients) and in the left medial frontal gyrus, in the right caudate head and in the right inferior parietal lobule (whole group). No correlations were found in healthy controls. Higher inhibitory right – lateralized alpha power was associated with lower rCBF values in prefrontal and striatal regions, predominantly in the right hemisphere, which are involved in the processing of motivated behavior and reward. Inhibitory brain activity in the reward system may contribute to some of the motivational problems observed in MDD.

Thrombosis during pregnancy: Risks, prevention, and treatment for mother and fetus--harvesting the power of omic technology, biomarkers and in vitro or in vivo models to facilitate the treatment of thrombosis

Maternal thromboembolism and a spectrum of placenta-mediated complications including the pre-eclampsia syndromes, fetal growth restriction, fetal loss, and abruption manifest a shared etiopathogenesis and predisposing risk factors. Furthermore, these maternal and fetal complications are often linked to subsequent maternal health consequences that comprise the metabolic syndrome, namely, thromboembolism, chronic hypertension, and type II diabetes. Traditionally, several lines of evidence have linked vasoconstriction, excessive thrombosis and inflammation, and impaired trophoblast invasion at the uteroplacental interface as hallmark features of the placental complications. "Omic" technologies and biomarker development have been largely based upon advances in vascular biology, improved understanding of the molecular basis and biochemical pathways responsible for the clinically relevant diseases, and increasingly robust large cohort and/or registry based studies. Advances in understanding of innate and adaptive immunity appear to play an important role in several pregnancy complications. Strategies aimed at improving prediction of these pregnancy complications are often incorporating hemodynamic blood flow data using non-invasive imaging technologies of the utero-placental and maternal circulations early in pregnancy. Some evidence suggests that a multiple marker approach will yield the best performing prediction tools, which may then in turn offer the possibility of early intervention to prevent or ameliorate these pregnancy complications. Prediction of maternal cardiovascular and non-cardiovascular consequences following pregnancy represents an important area of future research, which may have significant public health consequences not only for cardiovascular disease, but also for a variety of other disorders, such as autoimmune and neurodegenerative diseases.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

Debris flow magnitudes: a global catalog

The episodic occurrence of debris flow events in response to stochastic precipitation and wildfire events makes hazard prediction challenging. Previous work has shown that frequency-magnitude distributions of non-fire-related debris flows follow a power law, but less is known about the distribution of post-fire debris flows. As a first step in parameterizing hazard models, we use frequency-magnitude distributions and cumulative distribution functions to compare volumes of post-fire debris flows to non-fire-related debris flows. Due to the large number of events required to parameterize frequency-magnitude distributions, and the relatively small number of post-fire event magnitudes recorded in the literature, we collected data on 73 recent post-fire events in the field. The resulting catalog of 988 debris flow events is presented as an appendix to this article. We found that the empirical cumulative distribution function of post-fire debris flow volumes is composed of smaller events than that of non-fire-related debris flows. In addition, the slope of the frequency-magnitude distribution of post-fire debris flows is steeper than that of non-fire-related debris flows, evidence that differences in the post-fire environment tend to produce a higher proportion of small events. We propose two possible explanations: 1) post-fire events occur on shorter return intervals than debris flows in similar basins that do not experience fire, causing their distribution to shift toward smaller events due to limitations in sediment supply, or 2) fire causes changes in resisting and driving forces on a package of sediment, such that a smaller perturbation of the system is required in order for a debris flow to occur, resulting in smaller event volumes.