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The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. This work reports a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

In this article, we present a novel type of medium-grained reconfigurable architecture that we term the Field Programmable Operation Array (FPOA). This device has been designed specifically for the implementation of HLS-generated circuitry. At the core of the FPOA is the OP-block. Unlike a standard LUT, an OP-block performs multi-bit operations through gate-based logic structures, translating into greater speed and efficiency in digital circuit implementation. Our device is not optimized for a specific application domain. Rather, we have created a device that is optimized for a specific circuit structure, namely those generated by HLS. This gives the FPOA a significant advantage as it can be used across all application domains. In this work, we add support for both distributed and block memory to the FPOA architecture. Experimental results show up to a 13.5× reduction in logic area and a 9.5× reduction in critical path delay for circuit implementation using the FPOA compared to a standard FPGA.

Existing algorithms can be automatically translated from software to hardware using High-Level Synthesis (HLS), allowing for quick prototyping or deployment of embedded designs. High-level software is written with a single main memory in mind, whereas hardware designs can take advantage of many parallel memories. The translation and optimization of memory usage, and the generation of resulting architectures, is important for high-performance designs. Tools provide optimizations on memory structures targeting data reuse and partitioning, but generally these are applied separately for a given object in memory. Memory access that cannot be effectively optimized is serialized to the memory, hindering any further parallelization of the surrounding generated hardware. In this work, we present an automated optimization method for creating custom cache memory architectures for HLS generated designs. Our optimization uses runtime profiling data, and is performed at a localized scope. This method combines data reuse savings and memory partitioning to further increase the potential parallelism and alleviate the serialized memory access, increasing performance. Comparisons are made against architectures without this optimization, and against other HLS caching approaches. Results are presented showing this method requires 72% of the number of execution cycles compared to a single-cache design, and 31% compared to designs with no caches.

Two frequently used tools to acquire high-resolution images of cells are scanning electron microscopy (SEM) and atomic force microscopy (AFM). The former provides a nanometer resolution view of cellular features rapidly and with high throughput, while the latter enables visualizing hydrated and living cells. In current practice, these images are viewed by eye to determine cellular status, e.g. activated versus resting. Automatic and quantitative data analysis is lacking. This work develops an algorithm of pattern recognition that works very effectively for AFM and SEM images. Using rat basophilic leukemia (RBL) cells, our approach creates a support vector machine (SVM) to automatically classify resting and activated cells. 10-fold cross-validation with cells that are known to be activated or resting gives a good estimate of the generalized classification results. The pattern recognition of AFM images achieves 100% accuracy, while SEM reaches 95.4% for our images as well as images published in prior literature. This outcome suggests that our methodology could become an important and frequently used tool for researchers utilizing AFM and SEM for structural characterization as well as determining cellular signaling status and function.

Purpose: To eliminate CD44, a putative cancer stem cell (CSC) marker, overexpressing head and neck squamous cell carcinoma (HNSCC) cells by using hyaluronan-conjugated, dextran-coated super paramagnetic iron oxide nanoparticles (HA-DESPIONs), in conjunction with induced heat produced by exposure to an alternating magnetic field (AMF).Methods: An AMF generator was constructed by means of a solenoid coil and an impedance circuit driven by a power amplifier. A signal generator produced a small sinusoidal signal of 130 kHz that was then amplified to 9 A (peak to peak value) to generate an AMF of approximately 10 kA/m (12.6 mT) at the center of a coil. The heat generating effect of the AMF generator was tested via several kinetic and dose-dependent bulk heating experiments by exposing readily available magnetic nanoparticles to AMF. For elimination of CD44 population, UT-SCC-14 cells were exposed to either targeted HA-DESPIONs or non-targeted DESPIONs at a concentration 200 μg/ml and exposed to AMF for 30 minutes. Cells were processed after 24 hours for flow cytometry based analysis of apoptosis.Results: Magnetic nanoparticles caused a concentration-dependent bulk heating effect in response to AMF resulting in a significant temperature rise. Following the exposure to AMF, non-conjugated DESPIONs were unable to induce targeted hyperthermia and hence had no effect on CD44 cell death in HNSCC cells. However, there was a significant cell death in the CD44 population treated with HA-DESPIONs and exposed to AMF. This effect was only obeserved when the magnetic field was turned on.Conclusion: Bulk heating experiments concluded that a simple AMF generator was able to activate magnetic nanoparticles and flow cytometry demonstrated that HA- DESPIONs were able to cause apoptosis in UT-SCC-14 cells that express CD44.This may be a promising strategy to specifically target cancer stem cells (CSCs) for the treatment of HNSCC.

Particle Swarm Optimization (PSO), a population based optimization algorithm, has recently been attracting the attention of the embedded computing community. It is an efficient tool for many continuous multimodal and multidimensional problem classes. This paper first evaluates the performance of the PSO algorithm on embedded processor platforms with limited computational resources. The results on such platforms demonstrate the lack of sufficient execution speed for real-time applications. Thus, to address the shortcomings of the software PSO we developed a hardware architecture that significantly accelerates its execution performance. Besides improving the execution efficiency, the design is shown to be modular, flexible and reusable for solving different optimization problems. The accelerated execution performance of the proposed architecture is demonstrated on standard mathematical benchmark functions as well as on a real world problem scenario: emission source localization in distributed sensor networks. A parallelization scheme for further speed-up of the hardware PSO is also demonstrated.

A teamwork survey was conducted at Oakland University, Rochester, MI, in 533 engineering and computer science courses over a two-year period. Of the 6435 student respondents, 4349 (68%) reported working in teams. Relative to the students who only worked individually, the students who worked in teams were significantly more likely to agree that the course had achieved its stated learning objectives (p < 0.001). Regression analysis showed that roughly one-quarter of the variance in belief about whether the objectives were met could be explained by four factors: 1) student satisfaction with the team experience; 2) the presence of instructor guidance related to teamwork; 3) the presence of slackers on teams; and 4) team size. Pearson product-moment correlations revealed statistically significant associations between agreement that the course objectives had been fulfilled and the use of student teams and between satisfaction with teams and the occurrences of instructor guidance on teamwork skills. These and other results suggest that assigning work to student teams can lead to learning benefits and student satisfaction, provided that the instructor pays attention to how the teams and the assignments are set up.

This paper describes a new method of executing a software program on an FPGA for embedded systems. Rather than combine reconfigurable logic with a microprocessor core, this method uses a new technique to compile Java programs directly to special-purpose processors that are called ''flowpaths''. Flowpaths allow a software program to be executed in such a way that one low-capacity FPGA is executing a piece of the program while a second low-capacity FPGA is being dynamically reconfigured to take over execution after the first one has completed its task. In this fashion, the program is executed by continuously alternating between the two chips. This process allows large programs to be implemented on limited hardware resources and at higher clock frequencies on an FPGA. The sequencer and rules for partitioning a flowpath are presented and the method is illustrated using a genetic algorithm compiled from Java directly to flowpaths using a flowpath compiler designed in our lab. The genetic algorithm flowpath requires 68% of an expensive, high-density Virtex2 XC2V8000 with a maximum frequency of 68.9MHz (this genetic algorithm would have required 285% of the smaller, more reasonably priced Virtex 1000). However, by splitting flowpaths as presented in this paper, the algorithm can be implemented with an average maximum clock frequency of 154.8MHz using only two low-capacity, inexpensive Virtex XCV50 chips, the smallest capacity of the Virtex family. The split flowpaths require one-quarter of the clock cycles used by the JStamp microcontroller. Therefore, instead of using an expensive high-density FPGA, the proposed design illustrates how a genetic algorithm can be implemented using only a pair of inexpensive chips that have only 50,000 equivalent gates each; a low-cost sequencer; and 11.81 MB of memory to store bit files. The overall time performance depends on the speed of the FPGA reconfiguration process.

The performance of software executed on a microprocessor is adversely affected by the basic fetch–execute cycle. A further performance penalty results from the load–execute–store paradigm associated with the use of local variables in most high-level languages. Implementing the software algorithm directly in hardware such as on an FPGA can alleviate these performance penalties. Such implementations are normally developed in a hardware description language such as VHDL or Verilog. More recently, several methods for using C as a hardware description language and for compiling C programs to hardware have been researched. Several software-programming languages compile to an intermediate representation (IR) that is stack based such as Java to Java bytecodes. Forth is a programming language that minimizes the use of local variables by exchanging the load–execute–store paradigm for stack manipulation instructions. This paper introduces a new systems architecture for FPGAs, called flowpaths, which can implement Java bytecodes or software programs written in Forth directly in an FPGA without the need for a microprocessor core. In the flowpath implementation of Forth programs all stack manipulation instructions are represented as simple wire connections that take zero time to execute. In the flowpath implementation of Java bytecodes the normal load–execute–store paradigm is represented as a single sequential operation and stack-manipulation operations become combinational thus executing faster. This paper compares the use of flowpaths in an FPGA generated from Java bytecodes and a high-level Forth program for the Sieve of Eratosthenes with C, Java, and Forth executed on microprocessors and microprocessor cores on FPGAs. The results show that flowpaths perform within a factor of two of a minimal hand-crafted direct hardware implementation of the Sieve and orders of magnitude better than compiling the program to a microprocessor.

Making the non-terminal nodes of a binary tree classifier fuzzy can mitigate tree brittleness. Using a genetic algorithm, two optimization techniques are explored. In one case, each generation minimizes classification error by optimizing a common fuzzy percent, pT, used to determine parameters at every node. In the other case, each generation yields a sequence of minimized node-specific parameters. The output value is determined through defuzzification after input vectors, in general, take both paths at each node with a weighting factor determined by the node membership functions. Experiments conducted using this geno-fuzzy approach yield an improvement compared with other classical algorithms.

The Forth programming language is typically implemented to run on some particular microprocessor. Several Forth engines have been designed that execute Forth instructions directly, typically in a single clock cycle. With the advent of high density FPGAs it has become feasible to implement a high-performance Forth core in an FPGA. This paper describes the design of a Forth core using VHDL that has been implemented on a Xilinx Spartan II FPGA. Examples are presented of high-level Forth programs that are compiled to VHDL code that implements a ROM embedded in the FPGA. The use of a Forth core in an FPGA allows for rapid prototyping of digital systems. Experiments show that an identical Forth program for the Sieve of Eratosthenes executes nearly 30 times faster on the FPGA Forth core than on a 68HC12 microcontroller at the same clock speed. This same program executes over six times faster on the FPGA Forth core than an equivalent compiled C program run on the same 68HC12. The Forth core is available as an EDIF file at www.tigs.com/fc16 , which can be included in a VHDL project and uses approximately 30% of a Spartan II FPGA.

This paper provides an overview of nanobioscience and bioinformatics initiatives in North America. The paper's format consists of an introduction followed by two sections providing scientific as well as financial, business, and entrepreneurial perspective with regard to nanobioscience and bioinformatics, respectively, in the United States and Canada. These sections are then followed by thumbnail sketches of the broad range of North American university-level research in nanobioscience and bioinformatics.

Polynomial expressions for the speed of sound as a function of pressure for 68 different organic liquids are presented in tabular form. (The liquids form a subset of those discussed in the companion paper: Ultrasonic parameters as a function of absolute hydrostatic pressure. I. A review of the data for organic liquids.) The polynomial expressions are based upon the experimental results reported by many different researchers. For some common liquids, such as benzene, hexane, ethanol, and carbon tetrachloride, the results of as many as five different researchers are reported. These results sometimes vary widely—far more than would be expected from calculated experimental uncertainties. An analysis is presented of how well pressure-dependent polynomials fit the experimental data when the number of coefficients is increased. The error in the polynomial fit is also explored when both pressure and temperature dependencies are present. Finally, differences between ultrasonic and Brillouin scattering experimental...

This review provides an overview of experimental results involving ultrasonic parameters as a function of absolute hydrostatic pressure in organic liquids. Major topics of discussion include the pioneering work of Litovitz and Carnevale involving deduction of the chemical and structural properties of liquids from acoustical properties as a function of pressure; modern general ultrasonic studies of a broad range of organic liquids; work accomplished by Russians and others from the former Soviet block countries, particularly the work headed by Otpuschennikov at the Kursk Pedagogical Institute; the studies involving refrigerants published by Takagi at the Kyoto Institute of Technology; tribological and petroleum industry studies related to oils; Brillouin scattering experiments; and thermodynamic methods of B/A measurement. The importance of ultrasonic parameters as a function of pressure to the understanding of a variety of processes is highlighted. A table of 325 liquids and liquid mixtures with a total of...

The nature of the double-exchange (DE) interaction in lanthanum manganites is studied through chemical substitutions, Cs for La, and high-pressure measurements. Static and high-frequency magnetic measurements and high-pressure electrical transport studies were carried out on bulk polycrystalline and radio-frequency sputtered thin films of La0.7-xCsxCa0.3MnO3 for x=0-0.1. The samples are found to be cubic. Curie temperature Tc measurements provide evidence for bond-length-related weakening of DE as x is increased from 0 to 0.03. For higher x, the bond-angle-related changes lead to an increase in the strength of DE. High-pressure mangetoresistance data indicate both bond length and bond-angle-related increase of 10–20 K/GPa in Tc with pressure, with the largest increase measured for x=0.03. The rate of increase in the Curie temperature with pressure decreases with increasing Tc. Anomalies are observed in the magnetic parameters for x=0.03. The Cs-concentration dependence of the low-temperature saturation magnetization shows a minimum close to x=0.03. Ferromagnetic resonance studies at x-band reveal a 5% decrease in the g-value for x=0.03 relative to the end members (x=0 and 0.1). The low-field magnetostriction for x=0.03 indicates a relatively strong electron–phonon spin coupling compared to neighboring compositions.

Studies aimed at the nature of magnetic interactions in Fe and Cr substituted lanthanum deficient manganites, La 0.88 Mn 1− x Fe x O 3 ( x =0–0.15) and La 0.88 Mn 1− y Cr y O 3 ( y =0–0.175) are reported. The oxides order ferromagnetically for the entire range of x - and y -values. A decrease in the Curie temperature T c with increasing substitution is observed for both compounds, but the decrease in T c is large for Fe substitution. A large decrease was also seen in the low-temperature magnetic moment of Fe substituted samples. The data indicate possible ferromagnetic ordering of Cr and antiferromagnetic ordering of Fe moments in the oxides. Ferromagnetic resonance studies indicate (i) strong spin–lattice effects in the paramagnetic state, (ii) valence–exchange effects in the ferromagnetic state, and (iii) possible microscopic inhomogeneities in Cr-substituted samples

The observation of a substitution-induced ferromagnetic order (Tc=200 K) is reported in antiferromagnetic La0.9Sr0.1MnO3 (TN=130 K) when 0.05 of La is replaced by yttrium. Important results of ferromagnetic resonance (FMR) and high-pressure transport studies are as follows. (i) Data on FMR linewidth and resistivity provide evidence for the presence of microscopic magnetic inhomogeneities, possibly La0.9Sr0.1MnO3. (ii) For zero external pressure, a resistivity peak at 130 K due to intergrain tunneling, a secondary peak close to Tc, and magnetoresistance (MR) on the order of 30% are observed. (iii) At high pressures, the resistivity peaks shift to higher temperatures at the rate of 24–26 K/GPa and MR values decrease. (iv) An internal pressure of 3 GPa due to Y substitution is inferred from high-pressure transport studies.

The ever increasing popularity of particle swarm optimization (PSO) algorithm is recently attracting attention to the embedded computing world. Although PSO is in general considered to be computationally efficient algorithm, its direct software implementation on complex problems, targeted on low capacity embedded processors could however suffer from poor execution performance. This paper first evaluates the performance of the standard PSO algorithm on a typical embedded platform (using a 16-bit microcontroller). Then, a modular, flexible and reusable architecture for a hardware PSO engine, for accelerating the algorithm's performance, will be presented. Finally, implementation test results of the proposed architecture targeted on Field Programmable Gate Array (FPGA) technology will be presented and its performance compared against software executions on benchmark test functions.

The ever increasing popularity of the particle swarm optimization (PSO) algorithm is recently attracting attention to the embedded computing world. Although PSO is considered efficient compared to other contemporary population based optimization techniques, for many continuous multimodal and multidimensional problems, it still suffers from performance loss when it is targeted onto embedded application platforms. Examples of such target applications include small mobile robots and distributed sensor nodes in sensor network applications. In a previous work we presented a novel, modular, efficient and portable hardware architecture to accelerate the performance of the PSO for embedded applications. This paper extends the work by presenting a parallelization technique for further speedup of the PSO algorithm by dividing the swarm into a set of subswarms that are executing in parallel. The underlying communication topology and messaging protocols are described. Finally, the performance of the proposed system is evaluated on mathematical and real-world benchmark functions.

Particle Swarm Optimization (PSO) has gained growing popularity in the recent years and is finding a wide range of important applications. Like other population based, stochastic meta-heuristics, PSO has a few algorithm parameters that need to be carefully set to achieve best execution results. This paper develops an automatic parameter tuning technique for enhancing its performance. The effectiveness of the proposed method is demonstrated on mathematical benchmark functions as well as on a real world application problem.

This paper addresses the problem of emission source localization in an environment monitored by a distributed wireless sensor network. Typical application scenarios of interest include emergency response and military surveillance. A nonlinear least squares method is employed to model the problem of estimation of the emission source location and the intensity at the source. A particle swam optimization (PSO) approach to solve this problem produces solution qualities that compete well with other best known traditional approaches. Moreover, the PSO solution achieves the best runtime performance compared to the other methods investigated. However, when it is targeted on to low capacity embedded processors PSO itself suffers from poor execution performance. To address this problem a direct, flexible and efficient hardware implementation of the PSO algorithm is developed, resulting in tremendous speedup over software solutions on embedded processors.

This paper exploits the simplicity, efficiency and flexibility of the particle swarm optimization (PSO) method to propose a single and multisurface based data separation methods for classification of Breast Cancer Data. Like most artificial intelligence based techniques the first step of the proposed approaches involve the training of the PSO-based classifiers according to pre-defined data separation methods, using part of the dataset for training. The performances of the classifiers are then tested on the remaining dataset to measure the classification accuracy. The training and testing datasets are derived from the Breast Cancer database obtained from the UCI machine learning repository. Both separation methods produce good classification performance; however, the method based on multiple separating surfaces achieves the best result of 100% classification accuracy on both the training and testing datasets.

This paper describes the detection of Vesicular Stomatitis Virus using a novel capacitive immunosensor technology, whereby the respective antibodies for the antigens were used as a means for chemical detection on separate sensors. Devices were fabricated using standard etching and metal plating techniques, followed by immobilization of antibodies. Detection of antigen was performed by measuring voltage change due to changes in capacitance as antigen bound to the antibody surface. Capacitance changes were detected upon binding of specific antigen to the surface. This rugged, prototype device detected VSV down to the 2 pg/ml range.

This work describes the initial experimental setup and results involving the percentage cell lysis in SWLA-2 murine hybridomas produced by AC electric field pulses of varying amplitudes and pulse widths. Cells were cultured and separate samples examined at 24 hours. The frequency, pulse width and peak-to-peak voltage were varied. AC electric fields producing at least 1 V across the cell membrane appear to be more effective in producing cell lysis than similar fields producing lower membrane voltages. Additionally, higher frequencies, in the 10 kHz range, appear to be more effective than lower frequencies at membrane voltages above 1 V in producing cell lysis.

Over the past 10 years mathematical models have been developed using differential equations for the progression of HIV-1 to AIDS in an infected patient. Additional terms and formulations are presented over time, as the disease is better understood. Experimentation has been used to obtain many of the modeling parameters. These previous works focus primarily on modeling the progression of the disease, some with intervention such as HAART drugs. Attempts have been made to filter HIV-1 and HIV-1-infected T-cells from the blood. A model is developed that characterizes the rate of filtrations impact on disease progression. Such studies could be also used to investigate the impact of an implantable HIV-1 filter. Proliferation rates of actively and latently infected T-cells are significant and have been incorporated into the models developed in this paper.

This paper describes implementing technology used for developing a personalized treatment plan using a specific treatment planning process and methodology. This methodology involves defining categories of treatment, goals that a program can assist patients to achieve, client objectives that evidence movement towards achieving specific goals, and interventions that can be performed by the program to accomplish these objectives. Each goal is issued a start and end (review) date. At the review date, whether a patient accomplished the goal or not will be assessed by a clinician using acceptable testing methods. Staff intervene according to the objectives for which the intervention has been designated and chart against these objectives by comments and assigning a progress indicator. Using this methodology which includes these two disjoint measures, care facilities can utilize technology to employ a paperless system that will standardize the process for developing a person-centered plan, identify program and staff deficiencies, evaluate the effectiveness of changes made to the program with respect to staff and patients, and capture information necessary to produce detailed billing. Software that implements this method has successfully been employed at the Rose Hill Center for Psychiatric Treatment & Rehabilitation in less than six months. Facilities using this software can transfer patients between programs and keep a longitudinal patient record. Funding has been granted to use this software to study best practices in the treatment of schizophrenia and schizoaffective disorder.

This paper describes experimental results involving the percentage cell lysis in SWLA-2 murine hybridomas produced by square wave electric field pulses of 100, 200, and 300 V across a 1 mm gap width in a standard cuvette. Pulse lengths were of 0.2 and 0.6 ms duration; 1, 2, or 3 pulses were applied with 100 ms time interval between pulses. Cells were cultured and separate samples examined at 48 hours to determine cell mortality. Nearly 90% cell mortality was produced by applying 3 pulses at of 0.6 ms duration at 300 V.