The effect of implanting an antigen release device on lactoferrin concentration in serum and milk

The objective of this study was to evaluate the effect of implanting an Antigen Release Devices (ARD) into dairy cows during the lactation cycle to induce an immune response. Subsequently, the concentrations of lactoferrin in serum and milk were measured. Forty healthy adult Chinese Holstein cows were divided into two equal groups: a test group and a control group. Animals in the test group received ARD implants, whereas the control group animals were not treated. An even spread across the two groups was maintained with animal selection based on parity, the lactation days and milk yields. The concentrations of lactoferrin in the serum and milk of all forty animals were measured using an Enzyme-Linked Immunosorbent Assay (ELISA). The results show that the implantation of an ARD did not significantly increase the concentration of lactoferrin in the serum and milk throughout the whole experiment period except on two occasions. The levels of lactoferrin in the milk and serum significantly increased on day 7 and on day 11 after implantation (p<0.05). There was a strong correlation between milk lactoferrin and serum lactoferrin (r=0.564, P<0.01). Three separate ARDs were used releasing its antigen load on day 0, 14 and 28 to induce a primary, secondary and tertiary response respectively. As the significant increases in the lactoferrin levels were only observed after the first ARD release, the effects of lactoferrin appears to be associated with the early phase of the immune response, consistent with its role in the host's innate defense system.

Automated breast cancer classification using near-infrared optical tomographic images

An automated procedure for detecting breast cancer using near-infrared (NIR) tomographic images is presented. This classification procedure automatically extracts attributes from three imaging parameters obtained by an NIR imaging system. These parameters include tissue absorption and reduced scattering coefficients, as well as a tissue refractive index obtained by a phase-contrast-based reconstruction approach. A support vector machine (SVM) classifier is utilized to distinguish the malignant from the benign lesions using the automatically extracted attributes. The classification results of in vivo tomographic images from 35 breast masses using absorption, scattering, and refractive index attributes demonstrate high sensitivity, specificity, and overall accuracy of 81.8%, 91.7%, and 88.6% respectively, while the classification sensitivity, specificity, and overall accuracy are 63.6%, 83.3%, and 77.1%, respectively, when only the absorption and scattering attributes are used. Furthermore, the automated classification procedure provides significantly improved specificity and overall accuracy for breast cancer detection compared to those by an experienced technician through visual examination.

Real-time computerized annotation of pictures

Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the Automatic Linguistic Indexing of Pictures - Real Time (ALIPR) system of fully automatic and high speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as k-means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for non-vector data is developed using the novel concept of Hypothetical Local Mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demo site where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real-time and with good accuracy.

Factors affecting the lactoferrin concentration in bovine milk

Lactoferrin (LF) concentrations in the milk with different levels of the somatic cell count score were examined using an ELISA to determine whether milk LF concentration is influenced by parity of the cow, stage of lactation, and the somatic cell count. The study animals were 198 Chinese Holstein cows randomly chosen from more than 1,600 cows in 4 dairy farms in the Beijing area. The cows had shown no sign of mastitis for 2 mo. Daily milk production was recorded, and milk samples were taken from individual cow samples. The LF concentration varied between 31.78 and 485.63 microg/mL in milk from normal animals. Lactoferrin was significantly associated with stage of lactation (r = 0.557) and daily milk production (r = -0.472). Nevertheless, there was no significant relationship with parity. Moreover, milk LF concentration tended to be correlated with the somatic cell count score (r = 0.375). This finding suggests that milk LF may be helpful as an indicator for intramammary infection in dairy cows.

A new method to measure the semantic similarity of GO terms

MOTIVATION

Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources.

RESULTS

To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery.

AVAILABILITY

The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME.

SUPPLEMENTARY INFORMATION

http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.

EMG signal classification for myoelectric teleoperating a dexterous robot hand

This paper details a strategy of discriminating finger motions using surface electromyography (EMG) signals, which could be applied to teleoperating a dexterous robot hand or controlling the advanced multi-fingered myoelectric prosthesis for hand amputees. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG signal classification system was established based on the surface EMG signals from the subject's forearm. Four pairs of electrodes were attached on the subjects to acquire the signals during six types of finger motions, i.e. thumb extension, thumb flexion, index finger extension, index finger flexion, middle finger extension, and middle finger flexion. In order to distinguish these finger motions. A combination of autoregressive (AR) model and an Artificial Neural Network (ANN) was used in the system. The discrimination procedure consists of two steps. Firstly, the AR model is used to preprocess the surface EMG signals to reduce the scale of the data. These data will be imported into the myoelectric pattern classifier. Secondly the coefficients of AR model are imported into the ANN to identify the finger motions. The experimental results show that the discrimination system works with satisfaction.

Effects of seed migration on post-implant dosimetry of prostate brachytherapy

Brachytherapy using permanent seed implants has been an effective treatment for prostate cancer. However, seeds will migrate after implant, thus making the evaluation of post-implant dosimetry difficult. In this study, we developed a computer program to simulate seed migration and analyzed dosimetric changes due to seed migration at various migration amounts. The study was based on 14 patients treated with Pd-103 at the James Cancer Hospital. Modeling of seed migration, including direction, distance as well as day of migration, was based on clinical observations. Changes of commonly used dosimetric parameters as a function of migration amount (2, 4, 6 mm respectively), prostate size (from 20 to 90 cc), and prostate region (central vs peripheral) were studied. Change of biological outcome (tumor control probability) due to migration was also estimated. Migration reduced prostate D90 to 99+/-2% of original value in 2 mm migration, and the reduction increased to 94+/-6% in 6 mm migration. The reduction of prostate dose led to a 14% (40%) drop in the tumor control probability for 2 mm (6 mm) migration, assuming radiosensitive tumors. However, migration has less effect on a prostate implanted with a larger number of seeds. Prostate V100 was less sensitive to migration than D90 since its mean value was still 99% of original value even in 6 mm migration. Migration also showed a different effect in the peripheral region vs the central region of the prostate, where the peripheral mean dose tended to drop more significantly. Therefore, extra activity implanted in the peripheral region during pre-plan can be considered. The detrimental effects of migration were more severe in terms of increasing the dose to normal structures, as rectum V50 may be 70% higher and urethra V100 may be 50% higher in the case of 6 mm migration. Quantitative knowledge of these effects is helpful in treatment planning and post-implant evaluation.

MILES: multiple-instance learning via embedded instance selection

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (Multiple-Instance Learning via Embedded instance Selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty.

Observation of two new N* peaks in J/psi-->ppi-n and ppi+n decays

The decay J/psi-->NNpi provides an effective isospin 1/2 filter for the piN system due to isospin conservation. Using 58x10(6) J/psi decays collected with the Beijing Electromagnetic Spectrometer at the Beijing Electron Positron Collider, more than 100 thousand J/psi-->ppi-n+c.c. events are obtained. Besides the two well-known N* peaks at around 1500 MeV/c2 and 1670 MeV/c2, there are two new, clear N* peaks in the ppi invariant mass spectrum around 1360 MeV/c2 and 2030 MeV/c2 with statistical significance of 11sigma and 13sigma, respectively. We identify these as the first direct observation of the N*(1440) peak and a long-sought missing N* peak above 2 GeV/c2 in the piN invariant mass spectrum.

Dose escalation to combat hypoxia in prostate cancer: a radiobiological study on clinical data

Earlier studies have demonstrated that hypoxic regions exist in human prostate cancer and the degree of hypoxia correlates with the treatment outcome of radiotherapy. Using the concept of the clinical oxygen enhancement ratio (COER), the linear-quadratic (LQ) model was extended to account for the effect of tumour hypoxia. The clinical data collected at the Fox Chase Cancer Center for prostate cancer were analysed based on the LQ model as well as the tumour control probability (TCP) model. The LQ and TCP parameters (alpha = 0.15 Gy (-1), alpha/beta = 3.1 Gy and the number of clonogens K = 10(6) approximately 10(7) cells) determined in earlier studies were used to derive the COER for prostate cancer: COER = 1.4 with a standard confidence interval (CI) of (1.2, 1.8). The result is consistent with the in vitro OER measurements of human tumour cell lines under chronic hypoxia conditions. This implies that a higher dose is needed to overcome tumour hypoxia. For prostate tumours, the prescription dose required to overcome tumour hypoxia is 165 Gy (CI: 153 approximately 186 Gy) for permanent 125I implants and 88 Gy (CI: 74 approximately 118 Gy) in 2 Gy fractions for external-beam radiotherapy. The impact of LQ parameters on the calculations of COER and dose escalation was discussed. This study provides a preliminary estimate of the dose escalation needed to overcome tumour hypoxia based on clinical data. More clinical data with better statistics and longer follow-up time are required to further tune the radiobiological modelling of hypoxia for prostate cancer.

A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models

Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.

Dose Escalation of Three-dimensional Conformal Radiotherapy for Locally Recurrent Nasopharyngeal Carcinoma: A Prospective Randomised Study

AIMS

To investigate prospectively the feasibility and efficacy of dose escalation using three-dimensional conformal radiotherapy (3D-CRT) boost technique for locally recurrent nasopharyngeal carcinoma (NPC) in a randomised study.

MATERIALS AND METHODS

Thirty-six patients with locally recurrent NPC (>6 months interval from previous radical radiotherapy, no cervical lymph-node involvement and no distant metastasis) were enrolled. Treatment included conventional external-beam radiotherapy to 54 Gy, followed by a 3D-CRT boost to the gross tumour region. Patients were randomised to three boost dose levels: 16 Gy, 20 Gy and 24 Gy for groups I, II and III, respectively, with 12 patients in each group. All boost doses were delivered in 4-Gy fractions and 3 fractions per week. Median follow-up was 27 months (range 14-44 months).

RESULTS

Three-year, local-recurrence-free survival rate was significantly higher (72%) for the high-dose group III than for groups I and II (37% and 28%, respectively, P = 0.047). No significant difference was found in the 3-year overall survival rate among the three groups (72%, 59% and 82% for groups I, II and III, respectively). Three-year distant metastases rates were 17%, 0% and 18%, respectively. Skull-base invasion (P = 0.017) and pathology (P = 0.0006) correlated with overall survival. Treatment was well tolerated and no significant difference was observed among the three groups in acute and late toxicities (grade III toxicity is minimal: 17%, 17%, 0% of oral mucositis and 25%, 17%, 17% of nasopharyngeal mucositis in groups I, II, III, respectively, and 8% leukocytopenia only in group II; no grade IV toxicity occurred in any of the groups except for a fatal bleeding in group III).

CONCLUSIONS

Re-irradiation with high-dose 3D-CRT boost technique results in high local control rate and acceptable toxicity in patients with recurrent NPC. Dose escalation to the boost volume to 78 Gy (54 Gy + 24 Gy boost) results in improved recurrence-free survival compared with lower doses.

Electrochemical hydrogen storage in single-walled carbon nanotube paper

Single-walled carbon nanotube (SWNT) papers were successfully prepared by dispersing SWNTs in Triton X-100 solution, then filtered by PVDF membrane (0.22 microm pore size). The electrochemical behavior and the reversible hydrogen storage capacity of single-walled carbon nanotube (SWNT) papers have been investigated in alkaline electrolytic solutions (6 N KOH) by cyclic voltammetry, linear micropolarization, and constant current charge/discharge measurements. The effect of thickness and the addition of carbon black on hydrogen adsorption/desorption were also investigated. It was found that the electrochemical charge-discharge mechanism occurring in SWNT paper electrodes is somewhere between that of carbon nanotubes (physical process) and that of metal hydride electrodes (chemical process), and consists of a charge-transfer reaction (Reduction/Oxidation) and a diffusion step (Diffusion).

CLUE: cluster-based retrieval of images by unsupervised learning

In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.

Enhancement of ionic conductivity of PEO based polymer electrolyte by the addition of nanosize ceramic powders

The ionic conductivity of polyethylene oxide (PEO) based solid polymer electrolytes (SPEs) has been improved by the addition of nanosize ceramic powders (TiO2 and AL2O3). The PEO based solid polymer electrolytes were prepared by the solution-casting method. Electrochemical measurement shows that the 10 wt% TiO2 PEO-LiClO4 polymer electrolyte has the best ionic conductivity (about 10(-4) S cm(-1) at 40-60 degrees C). The lithium transference number of the 10 wt% TiO2 PEO-LiClO4 polymer electrolyte was measured to be 0.47, which is much higher than that of bare PEO polymer electrolyte. Ac impedance testing shows that the interface resistance of ceramic-added PEO polymer electrolyte is stable. Linear sweep voltammetry measurement shows that the PEO polymer electrolytes are electrochemically stable in the voltage range of 2.0-5.0 V versus a Li/Li+ reference electrode.

The requirements and mechanism for capsid assembly and budding of bovine foamy virus

Little is known about assembly of non-primate foamy virus (FV) such as bovine foamy virus (BFV). To help determine the requirements for assembly of BFV, we constructed BFV-Gag expression plasmids containing all or part of the gag gene, with or without modification by addition of myristate (Myr). Each construct was transfected alone, and with pFenv, into Sf-9 insect cells. The results showed that only the entire Gag could transit through nucleus, which is required for BFV viral assembly in the cytoplasm. Unlike other retroviruses (but like primate foamy viruses), BFV requires the coexpression of the Env protein for viral particle budding. In the case of BFV, this occurs at the plasma membrane rather than the endoplasmic reticulum (ER), due to lack of a functional ER retrieval signal (ERRS). The results also showed that addition of a Myr-membrane targeting signal to the C-terminus of Gag could restore the budding from plasma membrane, implying that Myr-membrane targeting signal could substitute for Env protein in budding.

Observation of a threshold enhancement in the plambda invariant-mass spectrum

An enhancement near the m(p)+M(Lambda) mass threshold is observed in the combined pLambda and pLambda invariant-mass spectrum from J/psi-->pK(-)Lambda;+c.c. decays. It can be fit with an S-wave Breit-Wigner resonance with a mass m=2075+/-12(stat)+/-5(syst) MeV and a width of Gamma=90+/-35(stat)+/-9(syst) MeV; it can also be fit with a P-wave Breit-Wigner resonance. Evidence for a similar enhancement is also observed in psi(')-->pK(-)Lambda;+c.c. decays. The analysis is based on samples of 5.8x10(7)J/psi and 1.4x10(7)psi(') decays accumulated in the BES II detector at the Beijing Electron-Positron Collider.

Dewetting of conducting polymer inkjet droplets on patterned surfaces

The manufacture of high-performance electronic devices with micrometre or even submicrometre dimensions by solution processing and direct printing, requires the ability to control accurately the flow and spread of functional liquid inks on surfaces. This can be achieved with the help of surface-energy patterns causing inks to be repelled and dewetted from pre-defined regions of the substrate. To exploit this principle for the fabrication of submicrometre device structures, a detailed understanding of the factors causing ink droplets to dewet on patterned surfaces is required. Here, we use hydrophobic surface-energy barriers of different geometries to study the influence of solution viscosity, ink volume, and contact angle on the process of dewetting of inkjet-printed droplets of a water-based conducting polymer. We demonstrate polymer field-effect transistor devices with channel length of 500 nm fabricated by surface-energy-assisted inkjet printing.

Studying digital imagery of ancient paintings by mixtures of stochastic models

This paper addresses learning-based characterization of fine art painting styles. The research has the potential to provide a powerful tool to art historians for studying connections among artists or periods in the history of art. Depending on specific applications, paintings can be categorized in different ways. In this paper, we focus on comparing the painting styles of artists. To profile the style of an artist, a mixture of stochastic models is estimated using training images. The two-dimensional (2-D) multiresolution hidden Markov model (MHMM) is used in the experiment. These models form an artist's distinct digital signature. For certain types of paintings, only strokes provide reliable information to distinguish artists. Chinese ink paintings are a prime example of the above phenomenon; they do not have colors or even tones. The 2-D MHMM analyzes relatively large regions in an image, which in turn makes it more likely to capture properties of the painting strokes. The mixtures of 2-D MHMMs established for artists can be further used to classify paintings and compare paintings or artists. We implemented and tested the system using high-resolution digital photographs of some of China's most renowned artists. Experiments have demonstrated good potential of our approach in automatic analysis of paintings. Our work can be applied to other domains.