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For centuries, herbal medicines have been used to treat humans and animals. Veterinary medicines are well-regulated; inspectors monitor milk and associated dairy products to screen for unacceptable contaminants. However, few scholars have discussed how the by-products of veterinary herbal medicine can be found in food products. In this paper, we applied nontargeted profiling of the UHPLC–ESI–TOF platform to screen possible by-products originating from veterinary herbal medicine in commercial milk samples. These tentatively identified alkaloids showed high consistency with the plant extract of Holarrhena antidysenterica (L.) Wall. ex A. DC., which indicated the possibility that dairy farmers had used plants from genus Holarrhena as veterinary herbal medicine. This is the first report showing the presence of by-products originating from veterinary herbal medicine in commercial milk. This finding could help authorities to evaluate the use of veterinary herbal medicine in farm animals.

Fibromyalgia syndrome (FM) is a multifactorial disorder whose pathogenesis and diagnosis are poorly understood. This study investigated differential serum proteome profiles in patients with FM and healthy pain-free controls and explored the association between serum proteome and clinical profiles in patients with FM. Twenty patients with FM (according to the American College of Rheumatology criteria, 2010) and 20 healthy pain-free controls were recruited for optimized quantitative serum proteomics analysis. The levels of pain, pressure pain threshold, sleep, anxiety, depression, and functional status were evaluated for patients with FM. We identified 22 proteins differentially expressed in FM when compared with healthy pain-free controls and propose a panel of methyltransferase-like 18 (METTL18), immunoglobulin lambda variable 3–25 (IGLV3–25), interleukin-1 receptor accessory protein (IL1RAP), and IGHV1OR21-1 for differentiating FM from controls by using a decision tree model (accuracy: 0.97). In addition, we noted several proteins involved in coagulation and inflammation pathways with distinct expression patterns in patients with FM. Novel proteins were also observed to be correlated with the levels of pain, depression, and dysautonomia in patients with FM. We suggest that upregulated inflammation can play a major role in the pathomechanism of FM. The differentially expressed proteins identified may serve as useful biomarkers for diagnosis and evaluation of FM in the future.

Due to the development of modern techniques, in the recent years, electronic vehicles and autopilot systems have beensignificant emerged in automobile and IT industrial. This leads the electronics automotive systems and auto-control systems consistedof a lot of high performance Electronic Control Units(ECUs) connected by controller area network (CAN). For realizing morecomplicated design in ECUs, this work integrates real-time OS and network management function. The results improve the CANbusnodes' designing level to as a gateway to interconnect CANbus nodes. As the number of CANbus nodes increase, the verification processis more and more complicated and takes much time. For speeding up the verification process, this work uses CANoe package toprogram the testing script for automotive verification environment. Then the engineer can connect the testing device by CAN to theenvironment for automatic verification. The engineer can define the network messages of the CANbus nodes and tune the design asthe validating progress. The testing results present as XML format and can be transferred to HTML pages for readability. Hence, thiswork realizes an automatic verification environment for CANbus in-vehicle networks.

Fibromyalgia syndrome (FM) is a multifactorial disorder whose pathogenesis and diagnosis are poorly understood. This study investigated differential serum proteome profiles in patients with FM and healthy pain-free controls and explored the association between serum proteome and clinical profiles in patients with FM. Twenty patients with FM (according to the American College of Rheumatology criteria, 2010) and 20 healthy pain-free controls were recruited for optimized quantitative serum proteomics analysis. The levels of pain, pressure pain threshold, sleep, anxiety, depression, and functional status were evaluated for patients with FM. We identified 22 proteins differentially expressed in FM when compared with healthy pain-free controls and propose a panel of methyltransferase-like 18 (METTL18), immunoglobulin lambda variable 3-25 (IGLV3-25), interleukin-1 receptor accessory protein (IL1RAP), and IGHV1OR21-1 for differentiating FM from controls by using a decision tree model (accuracy: 0.97). In addition, we noted several proteins involved in coagulation and inflammation pathways with distinct expression patterns in patients with FM. Novel proteins were also observed to be correlated with the levels of pain, depression, and dysautonomia in patients with FM. We suggest that upregulated inflammation can play a major role in the pathomechanism of FM. The differentially expressed proteins identified may serve as useful biomarkers for diagnosis and evaluation of FM in the future.

Background: Exposure to particulate matter (PM) pollution has direct impacts on the respiratory organs, yet the molecular alterations underlying PM-induced pulmonary injury remain unclear. In this study, we investigated the effect of PM on lung tissues of SD rats with whole-body exposure to traffic-related PM1 (< 1 mm in aerodynamic diameter) pollutants and compared it with rats exposed to high-efficiency particulate air-filtered gaseous pollutants and clean air control for 3 and 6 months. Lung function and histological examinations as well as quantitative proteomics analysis and functional validation were performed. Results: The rats in 6-month PM1-exposed group showed significant decline in lung function by decreased forced expiratory flow and forced expiratory volume, but the histological analysis revealed an earlier lung damage evidenced by increased congestion and macrophage infiltration in 3-month PM1-exposed rat lungs. The lung tissue proteomics analysis identified 2,673 proteins which highlighted dysregulations on proteins involved in oxidative stress, cellular metabolisms, calcium signaling, inflammatory responses, and actin dynamics. The presence of fine particles specifically enhanced the oxidative stress and inflammatory reactions under sub-chronic exposure of traffic-related PM1 and suppressed the glucose metabolism and actin cytoskeleton signaling which might lead to repair failure and thus lung function decline after chronic exposure of traffic-related PM1. A detailed pathogenic mechanism was proposed to depict the temporal and dynamic molecular regulations associated with PM1-induced lung injury.Conclusion: Our study explored the earlier lung injury prior to lung function decline and proposed potential molecular features for traffic-related PM1-induced lung injury.

Background A. baumannii has become an emerging pathogen of healthcare-associated infection with significant mortality. The present study aimed to identify specific biomarkers to predict patient survival of A. baumannii bacteremia by metabolomics. Methods From July 2011 to November 2014, a total of 60 patients with A. baumannii bacteremia and available blood samples within 4 days of the onset (Day 0) of bacteremia were included for analysis. They were categorized into two groups depending on their survival at Day 14. Metabolomic profiles of the blood specimens collected at Day 0–4 of survival and death groups were compared to identify specific biomarkers to predict patient survival at Day 14. The patients were divided in the training (n=40) and validation (n=20) sets, and the logistic regression-based receiver-operation characteristic (ROC) was used to find the potential markers. Results The Day 14 mortality of the included patients was 20.0% (12/60). The partial least square-discriminate analysis (PLS-DA) scores plot separated the survival and death groups (Figure 1). Thirteen metabolites, L-Isoleucine, Ofloxacin, P-Hydroxybenzaldehyde, Hippurate, Indolelactic acid, Kynurenate, N-Acetyl-L-alanine, Sebacic acid, N-Acetylaspartylglutamic acid, Hematoporphyrin IX, and Urocanic acid reached the statistical significance (p < 0.05) and the accuracies of training and validation sets were greater than 0.8 and 0.6, respectively (Figure 2 and Table 1). Moreover, the Wilcoxon rank sum test results of those metabolites reached the statistical significance (Table 1). Future 1: (A) PLS-DA scores plot for death and survival groups. (B) The loading plot of PLS-DA and the distribution of the thirty important metabolites with VIP values of >1.5. Figure 2. Box plots showing significant changes of the 13 potential markers in the level of metabolites among the death and survival groups. Table 2: Identified metabolites, p value, VIP, and the result of logistic regression. Conclusion Metabolomics had the potential to identify metabolites to predict survival in patients with A. baumannii bacteremia. Disclosures All Authors: No reported disclosures

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC–MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies. Keywords: Gut microbiota, Metabolomics, Sample collection, Mass spectrometry, Data processing

Background Misdiagnosis of autoimmune pancreatitis (AIP) as pancreatic cancer (PDAC) or vice versa can cause dismal patents’ outcomes. Changes in IgG glycosylation are associated with cancers and autoimmune diseases. This study investigated the IgG glycosylation profiles as diagnostic and prognostic biomarkers in PDAC and AIP. Methods Serum IgG-glycosylation profiles from 86 AIP patients, 115 PDAC patients, and 57 controls were analyzed using liquid chromatography–electrospray ionization mass spectrometry. Classification and regression tree (CART) analysis was applied to build a decision tree for discriminating PDAC from AIP. The result was validated in an independent cohort. Results Compared with AIP patients and controls, PDAC patients had significantly higher agalactosylation, lower fucosylation, and sialylation of IgG1, a higher agalactosylation ratio of IgG1 and a higher agalactosylation ratio of IgG2. AIP patients had significantly higher fucosylation of IgG1 and a higher sialylation ratio of IgG subclasses 1, 2 and 4. Using the CART analysis of agalactosylation and sialylation ratios in the IgG to discriminate AIP from PDAC, the diagnostic accuracy of the glycan markers was 93.8% with 94.6% sensitivity and 92.9% specificity. There were no statistically significant difference of IgG-glycosylation profiles between diffuse type and focal type AIP. Conclusions AIP and PDAC patients have distinct IgG-glycosylation profilings. IgG-glycosylation could different PDAC from AIP with high accuracy. Electronic supplementary material The online version of this article (10.1186/s12014-018-9221-1) contains supplementary material, which is available to authorized users.

Food safety control can be realized through contaminant monitoring and regulation in food matrices and food-associated products. Untargeted profiling methods can provide comprehensive results through screening and identification of unexpected compound. In this study, we have successfully employed the untargeted profiling method to screen unexpected compounds, and identified an unexpected contaminant, Irgacure-907, in two of seven commercial cow milk samples. The Irgacure-907 was further confirmed and quantified as 7.9 ± 0.4 ng mL−1 and 21.2 ± 1.9 ng mL−1 through standard addition in two commercial cow milk samples. This is the first study to use untargeted profiling to screen unknown contaminants present in commercial milk. Moreover, the presence of Irgacure-907 in commercial milk has not been reported previously in Taiwan. This untargeted profiling approach can be used to reveal other unknown food contaminants in various food matrices in the future and is beneficial to public health worldwide.

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC2MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC2MS consists of blob/peak detection, baseline correction, and blob alignment. GC2MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC2MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC2MS also directly corrects the raw data baseline. The analytical performance of GC2MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC2MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC2MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC2MS is freely accessible at http://gc2ms.web.cmdm.tw .

With the help of micro-electromechanical systems (MEMS) and complementary metal-oxide-semiconductor (CMOS) technology, a portable micro gas chromatography ( $\upmu {\text{GC}}$ ) system for lung cancer associated volatile organic compounds (VOCs) detection is realized for the first time. The system is composed of an MEMS preconcentrator, an MEMS separation column, and a CMOS system-on-chip (SoC). The preconcentrator provides a concentration ratio of 2170. The separation column can separate more than seven types of lung cancer associated VOCs. The SoC is fabricated by a TSMC $0.35\;\upmu {\text{m}}$ 2P4M process including the CMOS VOCs detector, sensor calibration circuit, low-noise chopper instrumentation amplifier (IA), 10 bit analog to digital converter, and the microcontrol unit (MCU). Experimental results show that the system is able to detect seven types of lung cancer associated VOCs (acetone, 2-butanone, benzene, heptane, toluene, m-xylene, 1,3,5-trimethylbenzene). The concentration linearity is ${{\text{R}}^{2}} = 0.985$ and the detection sensitivity is up to 15 ppb with 1,3,5-trimethylbenzene.

Nowadays, redundant topologies with multiple forwarding paths between original-destination (OD) pairs have been proposed for datacentre networks, such as fat-tree topology. To achieve resultant fairness (fair utilisation) in this topology, previous algorithms were based on traditional shortest path algorithms or equal-cost multiple paths algorithms (ECMP). However, these algorithms are facing with take up a lot of resources of the SDN controller and may cause a large amount of loss of resultant fairness for the network that has different configurations of link capacity. In this paper, we present out port selection (OPS) mechanisms, including modular out port selection (MOPS) and random out port selection (ROPS), novel methods to improve resultant fairness of fat-tree topology SDN-based datacentre network. Compare with static load-balance methods, our MOPS and ROPS can reduce 65% and 33% loss of fairness, respectively. Further, combining with elephant flows detecting and handling mechanisms, MOPSE and ROPSE can dynamically assign better forwarding path for elephant flows.

Baseline distortion in 1D (1)H NMR data complicates the quantification of individual components of biofluids in metabolomic experiments. Current 1D (1)H NMR baseline correction methods usually require manual parameter and filter tuning by experienced users to obtain desirable results from complex metabolomic spectra, thus becoming prone to correction variation and biased quantification. We present a novel alternative method, BaselineCorrector, for automatically estimating the baselines of 1D (1)H NMR metabolomic data. By collecting the standard deviations of spectral intensities, using a moving window to slide through a spectrum, BaselineCorrector can model the distribution of noise standard deviation as a derived chi-squared distribution in each window and then determine optimal parameters for least-error classification of signal and noise. Due to the universal property of noise distributions, BaselineCorrector can robustly recognize the baseline segments in various spectra. In addition to the commonly used 1D NOESY and CPMG pulse sequences, BaselineCorrector also provides an algorithm for correcting diffusion-edited NMR spectra. Using its classification model, BaselineCorrector is able to preserve low signal peaks and correctly handle wide, overlapping peaks in complex metabolomic spectra.

Due to the hardware cost and site constraints, the underwater transmission cannot be easily achieved in the terrestrial environment. In addition, network simulators also cannot precisely evaluate the network performance. As a result, a ZigBee-based Emulation Platform (ZEP) for underwater acoustic sensor networks is developed to estimate the network performance. Based on the evaluation results, some underwater collision problems can be correctly implemented in ZEP.

The complex composition of welding fumes, multiplicity of molecular targets, diverse cellular effects, and lifestyles associated with laborers vastly complicate the assessment of welding fume exposure. The urinary metabolomic profiles of 35 male welders and 16 male office workers at a Taiwanese shipyard were characterized via (1)H NMR spectroscopy and pattern recognition methods. Blood samples for the same 51 individuals were also collected, and the expression levels of the cytokines and other inflammatory markers were examined. This study dichotomized the welding exposure variable into high (welders) versus low (office workers) exposures to examine the differences of continuous outcome markers-metabolites and inflammatory markers-between the two groups. Fume particle assessments showed that welders were exposed to different concentrations of chromium, nickel, and manganese particles. Multivariate statistical analysis of urinary metabolomic patterns showed higher levels of glycine, taurine, betaine/TMAO, serine, S-sulfocysteine, hippurate, gluconate, creatinine, and acetone and lower levels of creatine among welders, while only TNF-α was significantly associated with welding fume exposure among all cytokines and other inflammatory markers measured. Of the identified metabolites, the higher levels of glycine, taurine, and betaine among welders were suspected to play some roles in modulating inflammatory and oxidative tissue injury processes. In this metabolomics experiment, we also discovered that the association of the identified metabolites with welding exposure was confounded by smoking, but not with drinking, which is a finding consistent with known modified response of inflammatory markers among smokers. Our results correspond with prior studies that utilized nonmetabolomic analytical techniques and suggest that the metabolomic profiling is an efficient method to characterize the overall effect of welding fume exposure and other confounders.

The Internet is gradually and constantly becoming a multimedia network that needs mechanisms to provide effective quality of service (QoS) requirements to users. The service curve (SC) is an efficient description of QoS and the service curve based earliest deadline first policy (SCED) is a scheduling algorithm to guarantee SCs specified by users. In SCED, deadline calculation is the core. However, not every SC has a treatable deadline calculation; currently the only known treatable SC is the concave piecewise linear SC (CPLSC). In this paper, we propose an algorithm to translate all kinds of SCs into CPLSCs. In this way, the whole Internet can have improved performance. Moreover, a modification of the deadline calculation of the original SCED is developed to obtain neat and precise results. The results combining with our proposed algorithm can make the deadline calculation smooth and the multimedia Internet possible.

Existing non-invasive lung cancer diagnostic equipment has difficulty in detecting early stage lung cancer as abnormal tissue is smaller than 0.5cm in size. According to studies showing that volatile organic compounds (VOCs) from human breath gas can provide biomarkers for human disease, especially for lung cancer [1], a non-invasive method was developed to measure the exhaled air from cancer patients using chromatography-mass spectrometry (GC-MS). However, traditional GC-MS equipment is very expensive and requires specialists to operate. In this research we demonstrate a portable micro gas chromatography (μGC) system to overcome the limitations of conventional methods and realize small chips for big data.

The one-to-all broadcast is the most primary collective communication pattern in a multicomputer network. This paper studies this problem in a circuit-switched torus with α-port capability, where a node can simultaneously send and receive α messages at one time. This is a generalization of the one-port and all-port models. We show how to efficiently perform broadcast in tori of any dimension, any size, square or nonsquare, using near optimal numbers of steps. The main techniques used are: (i) a “span-by-dimension” approach, which makes our solution scalable to torus dimensions, and (ii) a “squeeze-then-expand” approach, which makes possible solving the difficult cases where tori are non-square. Existing results, as compared to ours, can only solve very restricted sizes or dimensions of tori, or use more numbers of steps.

The one-to-all broadcast is the most primary collective communication pattern in a multicomputer network. We consider this problem in a wormhole-routed torus which uses the all-port and dimension-ordered routing model. We derive our routing algorithms based on the concept of "span of vector spaces" in linear algebra. For instance, in a 3D torus, the nodes receiving the broadcast message will be "spanned" from the source node to a line of nodes, to a plane of nodes, and then to a cube of nodes. Our results require at most 2(k-1) steps more than the optimal number of steps for any square k-D torus. Existing results, as compared to ours, can only be applied to tori of very restricted dimensions or sizes and either rely on an undesirable non-dimension-ordered routing or require more numbers of steps.

In this paper, a network-partitioning approach for one-to-all broadcasting on wormhole-routed networks is proposed. To broadcast a message, the scheme works in three phases. First, a number of data-distributing networks (DDNs), which can work independently, are constructed. Then the message is evenly divided into submessages, each being sent to a representative node in one DDN. Second, the submessages are broadcast on the DDNs concurrently. Finally, a number of data-collecting networks (DCNs), which can work independently too, are constructed. Then, concurrently on each DCN, the submessages are collected and combined into the original message. Our approach, especially designed for wormhole-routed networks, is conceptually similar but fundamentally very different from the traditional approach of using multiple edge-disjoint spanning trees in parallel for broadcasting in store-and-forward networks. One interesting issue is on the definition of independent DDNs and DCNs, in the sense of wormhole routing. We show how to apply this approach to tori, meshes, and hypercubes. Thorough analyses and comparisons based on different system parameters and configurations are conducted. The results do confirm the advantage of our scheme, under various system parameters and conditions, over other existing broadcasting algorithms.

Aristolochic acid nephropathy is caused by aristolochic acid (AA) and AA-containing herbs. In traditional Chinese medicine, a principle called “Jun-Chen-Zou-Shi” may be utilized to construct a remedial herbal formula that attempts to mitigate the toxicity of the main ingredient. This study usedBu-Fei-A-Jiao-Tang(BFAJT) to test if the compound remedy based on a principle of “Jun-Chen-Zou-Shi” can decrease the toxicity of AA-containing herbs. We compared the three toxicities of AA standard,Madouling(anAristolochiaherb), and a herbal formula BFAJT. AA standard was given for BALB/c mice at a dose of 5 mg/kg bw/day or 7.5 mg/kg bw/day for 10 days.Madoulingand BFAJT were given at an equivalence of AA 0.5 mg/kg bw/day for 21 days. Nephrotoxicity was evaluated by metabolomics and histopathology. The urinary metabolomics profiles were characterized by1H NMR spectroscopy. The spectral data was analyzed with partial least squares discriminant analysis, and the significant differential metabolites between groups were identified. The result showed different degrees of acute renal tubular injuries, and metabolomics analysis found that the kidney injuries were focused in proximal renal tubules. Both metabolomics and pathological studies revealed that AA standard,Madouling, and BFAJT were all nephrotoxicants. The compositions of the compound remedy did not diminish the nephrotoxicity caused by AA.

The future multimedia Internet needs mechanisms to provide QoS (Quality of Service) for users. Service curve (SC) is an effective description of QoS and SCED (service curve based earliest deadline first policy) is an efficient scheduling algorithm to guarantee SCs specified by users. Deadline calculation is the core of SCED. The unique treatable SC as we know is the concave piecewise linear SC (CPLSC). This paper re-derives out a more compact and clearer recursive relation of deadline calculation than that in the original SCED and also modifies some defects of the SCED.

Multimedia applications will be common in the future Internet and they induce diverse requirements of quality of service (QoS). Hence, the QoS issues are important and worth studying. Scheduling algorithm plays an important role in the provision of QoS. In general, good performed scheduling algorithms are with high complexity, for example Weighted Fair Queueing. The complexity comes from calculating virtual time and selecting next queue. There are some scheduling algorithms, e.g. VirtualClock, Self-Clock Fair Queueing, and Leap Forward Virtual Clock, are developed to reduce the complexity of calculating virtual time. Except there are some problems in these algorithms, the complexity of selecting next queue still exists. In this paper, a hybrid scheduling algorithm is developed to solve these problems and still has O(1) complexity of calculating virtual time and selecting next queue. It is Jumping Virtual Clock Round Robin (JVCRR), which combines dynamic JVC and static RR.

Mobile ad hoc networks (MANET) have many applications in the civilian environment, law enforcement, military and disaster recovery arenas. To facilitate these applications, with heavy traffic among nodes, a stable and efficient routing protocol is crucial. In this paper, a signal strength-based, on-demand routing protocol has been proposed, which first uses the earliest established path to forward packets, then changes to the strongest signal strength path for long transmissions. Through exhaustive simulation on ns-2, our routing showed superior performance for various system parameters in a Ricean fading environment. When combined with a multi-rate MAC protocol, the performance was even more distinguished.

In this paper a network-partitioning scheme for single-node broadcasting on wormhole-routed networks is proposed. To broadcast a message, the scheme works in three phases. First, a number of data-distributing networks (DDNs), which can work independently, are constructed. Then the message is evenly divided into sub-messages each being sent to a representative node in one DDN. Second, the sub-messages are broadcast on the DDNs concurrently. Finally, a number of data-collecting networks (DCNs) which can work independently too, are constructed. Then concurrently on each DCN the sub-messages are re-collected and combined into the original message. One interesting issue is on the definition of independent, in the sense of wormhole routing, DDNs and DCNs. We show how to apply this scheme to tori, meshes, and hypercubes. Thorough analyses and experiments based on different system parameters and configurations are conducted. The results do confirm the advantage of our scheme, under various system parameters and conditions, over other existing broadcasting algorithms.

The one-to-all broadcast is the most primary operation in a multicomputer network. In this paper we study this problem in a wormhole-routed 3D torus which provides the dimension-ordered routing and the all-port communication capability. We propose an efficient scheme to utilize the distance-insensitive characteristic of wormhole routing. The scheme requires at most 4 steps more than the optimal number of steps for any square 3D tools. The best known results, as compared to ours, can only be applied to cubic tori of very restricted network sizes and either rely on an undesirable non-dimension-ordered routing or require more numbers of steps.

This paper studies the one-to-all broadcast problem in a circuit-switched torus with ?-port capability, where a node can simultaneously send and receive ? messages at one time. We show how to efficiently perform broadcast in 2-D and 3-D tori of any size, square or non-square, using near optimal numbers of steps. The main techniques used are: (i) a "span-by-dimension" approach, which makes our solution scalable to torus dimensions, and (ii) a "squeeze-then-expand" approach, which makes possible solving the difficult cases where tori are non-square. Existing results, as compared to ours, can only solve very restricted sizes or dimensions of tori, or use more numbers of steps.

This paper studies the one-to-all broadcast in a circuit-switched 2D torus of any size with α-port capability. This is a generalization of the one-port and all-port models. Existing results, as compared to ours, can only solve very restricted sizes of tori, and use more numbers of steps.

Background Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality. Methods We completed a case-control study using 1 H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population. Results Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals. Conclusions Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.

Owing to the characteristics of rapid deployment and flexible configuration, Mobile Ad Hoc Networks (MANETs) have many applications in civilian environment, law enforcement, military arena, etc. Among these applications, data packets have to disseminate from nodes to nodes hop by hop. To facilitate these applications with much traffic among nodes, the crucial operation of MANET is routing. This paper investigates the chance to enhance the performance of routing protocol by modifying the Media Access Control (MAC) protocol. The basic idea is to increase the transmission chance for those nodes that want to relay data. Through exhaust simulation on ns-2, our novel MAC protocol enhances the on-demand routing protocols more than that of the IEEE 802.11 Distributed Coordination Function (DCF) in various system parameters.

Bluetooth is a newly developed technology designed as low-power, short-range, low-cost radios fitted for wireless connectivity among mobile devices. This paper addresses multicast routing for Bluetooth scatternets. Multicast is the technique of delivering fountainhead data to multiple destinations, which is widely used on wireless applications such as military command transmittal, video conference, mobile commerce, mobile learning, etc. As far as I know, there is no literature discussing multicast routing for Bluetooth scatternets. This paper proposes a proactive, source-routing multicast algorithm based on a pre-constructed scheduled shared core tree on scatternets, called scheduled shared-tree multicast (SSTM) algorithm. Before applying the SSTM algorithm, one should construct a shared core-based tree with simple scatternet-wide time-slot scheduling so that packet transmission is not affected by masters' polling delay and bridges' switching delay to reduce the multicast latency.