Melting of a DNA hairpin without hyperchromism

UV absorbance spectroscopy is the most common method for detecting nucleic acid structural transitions and obtaining thermodynamic parameters. UV-detected melting has been used to determine stabilities of nucleic acid hairpins, duplexes, triplexes, and higher order structures and to determine thermodynamic effects of unusual or modified bases and mismatched base-pairs. We report that in some cases UV absorbance spectroscopy is an inadequate analytical technique for these purposes. Some critical transitions are invisible to UV absorbance spectroscopy. For example, the conversion of dodecamer d(CGCAAATTCGCG) from hairpin to random coil is not accompanied by hyperchromism. Circular dichroism (CD) spectroscopy (263 nm) clearly detects two transitions for this dodecamer, each giving a pronounced change in ellipiticity. The concentration dependence of the low-temperature transition and the concentration independence of the high-temperature transition indicate that the predominant state converts from duplex to hairpin to random coil as the temperature increases. These assignments are confirmed by comparison to oligonucleotides of similar sequence that undergo a hairpin to coil transition only. In contrast to CD spectroscopy, UV absorbance spectroscopy shows only a single transition. The transition detected by UV absorbance spectroscopy corresponds to the low-temperature transition detected by CD. UV absorbance spectroscopy does not detect the second transition at any wavelength (from 218 to 310 nm) (by changes) in either absorbance or its derivative with temperature.     

On effective execution of nonuniform DOACROSS loops

It is extremely difficult to parallelize DOACROSS loops with nonuniform loop-carried dependences. In this paper, we present a static scheduling scheme with an accompanying synchronization strategy that can execute such DOACROSS loops effectively and efficiently. Our approach uses one of the parallelization techniques called Dependence Uniformization, which finds a small set of uniform dependence vectors to cover all possible nonuniform dependences in a DOACROSS loop. It differs from the previous schemes in that we demonstrate a better way to select the uniform dependence vectors. When used with the Static Strip Scheduling scheme, the proposed uniform dependence vector set allows us to enforce dependences with more locality, which reduces the requirement of explicit synchronization considerably while retaining most of the parallelism. This paper describes the uniform dependence vectors selection strategy and the static strip scheduling scheme. The performance analysis and examples are also presented     

Fractionation studies of palm oil by density gradient

The paper describes a method of fractionating vegetable, animal and fish oils, and in particular palm oil. The method involves addition of a medium comprising two common solvents to the semisolid oils. On centrifugation, the olein and stearin are separated by the medium in the middle. Thirteen media made up from binary combinations of nine solvents, viz. water, propylene glycol, glycerine, methanol, ethanol,n-propanol, isopropanol (IPA), acetone and butanone, are found to be effective in olein-stearin separation. However, only the water/IPA and water/methanol systems have been studied in detail. The aqueous IPA provides a higher yield of olein than water/ methanol but intersolubility between oil and medium is also greater. The fractionation process can be carried out at any suitable temperature. Fractionation of the special prime bleached (SPB) palm oil at 16 C yields an olein with a cloud point of 4.8 C. Some hybrid palm oils produce a large quantity of low cloud point olein which can be bleached readily. The process can be extended to include degumming and neutralization by using an alkaline medium for centrifugation. The olein fractions obtained have been found to be free of phosphatides and the free fatty acids reduced to as low as 0.02%. Metal-scavenging agents have also been added to the medium in an attempt to remove copper and iron. The development of this process into a continuous one has been demonstrated on the AlfaLaval LAPX 202 Separator. Fractionation of crude palm oil using a density gradient provides seven fractions of different characteristics. The iodine values vary from 37.5 to 57.4 and the unsaturated fatty acids range from 32.7% to 51.2%. Triglyceride analysis by carbon numbers shows great differences in the C₄₈ and C₅₂ constituents of the fractions. aThe volume ratio of oil to medium in each case was 1:1. The separation involved the oil and wax.     

LTCC‐based monolithic system‐in‐package (SiP) module for millimeter‐wave applications

A miniature LTCC system‐in‐package (SiP) module has been presented for millimeter‐wave applications. A typical heterodyne 61 GHz transmitter (Tx) has been designed and fabricated in a type of the SiP module as small as 36 × 12 × 0.9 mm³. Five active chips including a mixer, driver amplifier, power amplifier, and two frequency multipliers were mounted on the single LTCC package substrate, in which all passive circuits such as a stripline (SL) BPF, 2 × 2 array patch antenna, surface‐mount technology (SMT) pads, and intermediate frequency (IF) feeding lines have been monolithically embedded by using vertical and planar transitions. The embedded SL BPF shows the center frequency of 60.8 GHz, BW of 4.1%, and insertion loss of 3.74 dB. The gain and 3‐dB beam width of the fabricated 2 × 2 array patch antenna are 7 dBi and 36 degrees, respectively. The assembled LTCC 61 GHz Tx SiP module achieves an output power of 10.2 dBm and an up‐conversion gain of 7.3 dB. Because of the integrated BPF, an isolation level between a local oscillation (LO) and RF signal is below 26.4 dBc and the spurious level is suppressed by lower than 22.4 dBc. By using a 61 GHz receiver (Rx) consisting of off‐the‐shelf modules, wireless communication test was demonstrated by comparing measured IF spectrums at the Tx and Rx part.     

Analytic center cutting plane method for multiple kernel learning

Multiple Kernel Learning (MKL) is a popular generalization of kernel methods which allows the practitioner to optimize over convex combinations of kernels. We observe that many recent MKL solutions can be cast in the framework of oracle based optimization, and show that they vary in terms of query point generation. The popularity of such methods is because the oracle can fortuitously be implemented as a support vector machine. Motivated by the success of centering approaches in interior point methods, we propose a new approach to optimize the MKL objective based on the analytic center cutting plane method (accpm). Our experimental results show that accpm outperforms state of the art in terms of rate of convergence and robustness. Further analysis sheds some light as to why MKL may not always improve classification accuracy over naive solutions.     

Extraction of enhanced evoked potentials using wavelet filtering

In order to remove physiological artefacts and gain the improved evoked potentials, we propose a filtering method using the multi-resolution wavelet transform. The wavelet transform is repeatedly performed until all resolution levels are obtained. It decomposes the measured evoked potentials into scale coefficients corresponding to low frequency components and wavelet coefficients corresponding to high frequency components. In the wavelet domain, artefacts are dispersed mainly at the wavelet coefficients rather than the scaling coefficients. Thus, when the inverse wavelet transform is performed, this method shrinks the wavelet coefficients to reduce artefacts with shrinkage functions. By repeatedly performing the inverse wavelet transform, an evoked potential having the reduced artefacts and background noise is obtained. In this study, quantitative evaluation with simulation data and actual clinical data were conducted. As a result, characteristic peaks of evoked potential could be gained removing background EEG and artefacts using suggested shrinkage function. It was improved more than 0.2–1.6Db compared to the conventional averaging method. Also, the system for measuring and analyzing evoked potentials using DSP is implemented.     

Effects of wheelchair-based rehabilitation on the physical functions and health perception of stroke patients

The purpose of this study was to investigate the effect of wheelchair-based rehabilitation on the physical functions, health perception, and blood lipids according to the length of time since the stroke. Wheelchair-based rehabilitation progressed for 60 min per session, five times per week for 6 weeks. Physical functions, health perception (SF-36), and blood lipids were measured before and after rehabilitation. Physical fitness for physical function such as agility, grip strength, lower-body muscular endurance, flexibility, and posture control significantly improved after 6 weeks of rehabilitation. Health perception improved significantly with physical and mental health according to time since the stroke occurred. Overall, 6 weeks of wheelchair-based rehabilitation had a positive effect on the physical function and health perception regardless of stroke duration. Wheelchair-based rehabilitation had a more positive effect on the physical functions and health perception on the patients who suffered their stroke most recently. We suggested that wheelchair-based rehabilitation is necessary even through wheelchair based for chronic stroke patients, and it was useful their improved of quality of life.     

A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

Feature selection via sensitivity analysis of SVM probabilistic outputs

Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications. Editor: Risto Miikkulainen.