The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

An all-electric single-molecule hybridisation detector for short DNA fragments

In combining DNA nanotechnology and high-bandwidth single-molecule detection in nanopipettes, we demonstrate an all-electric, label-free hybridisation sensor for short DNA sequences (< 100 nt). Such short fragments are known to occur as circulating cell-free DNA in various bodily fluids, such as blood plasma and saliva, and have been identified as disease markers for cancer and infectious diseases. To this end, we use as a model system a 88-mer target from the RV1910c gene in Mycobacterium tuberculosis that is associated with antibiotic (isoniazid) resistance in TB. Upon binding to short probes attached to long carrier DNA, we show that resistive pulse sensing in nanopipettes is capable of identifying rather subtle structural differences, such as the hybridisation state of the probes, in a statistically robust manner. With significant potential towards multiplexing and high-throughput analysis, our study points towards a new, single-molecule DNA assay technology that is fast, easy to use and compatible with point of care environments

Electric single-molecule hybridization detector for short DNA fragments

By combining DNA nanotechnology and high-bandwidth single-molecule detection in nanopipets, we demonstrate an electric, label-free hybridization sensor for short DNA sequences (<100 nucleotides). Such short fragments are known to occur as circulating cell-free DNA in various bodily fluids, such as blood plasma and saliva, and have been identified as disease markers for cancer and infectious diseases. To this end, we use as a model system an 88-mer target from the RV1910c gene in Mycobacterium tuberculosis, which is associated with antibiotic (isoniazid) resistance in TB. Upon binding to short probes attached to long carrier DNA, we show that resistive-pulse sensing in nanopipets is capable of identifying rather subtle structural differences, such as the hybridization state of the probes, in a statistically robust manner. With significant potential toward multiplexing and high-throughput analysis, our study points toward a new, single-molecule DNA-assay technology that is fast, easy to use, and compatible with point-of-care environments

Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification

This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features (”fingerprints”) that are inherently unique to specific devices and that may be used to provide hardware specific identification (manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A ”Denoised VT” technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification. The statistical fingerprint features are extracted from coefficients of a DT-CWT decomposition. Relative to previous Time Domain (TD) results, the enhanced WD statistical features provide improved device classification performance. Additional performance sensitivity results are presented to demonstrate WD fingerprinting robustness for variation in burst location error, MDA/ML training and classification SNRs, and MDA/ML training and classification signal types. For all cases considered, the WD technique proved to be more robust and exhibited less sensitivity when compared with the TD Technique

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

The doctoral research abstracts. Vol:7 2015 / Institute of Graduate Studies, UiTM

Foreword: The Seventh Issue of The Doctoral Research Abstracts captures the novelty of 65 doctorates receiving their scrolls in UiTM’s 82nd Convocation in the field of Science and Technology, Business and Administration, and Social Science and Humanities. To the recipients I would like to say that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into high impact innovative research for the community and country from knowledge gained during this academic journey. As alumni of UiTM, we will always hold you dear to our hearts. A new ‘handshake’ is about to take place between you and UiTM as joint collaborators in future research undertakings. I envisioned a strong research pact between you as our alumni and UiTM in breaking the frontier of knowledge through research. I wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ / Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

Investigation of Electromagnetic Signatures of a FPGA Using an APREL EM-ISIGHT System

Large military platforms have encountered major performance and reliability issues due to an increased number of incidents with counterfeit electronic parts. This has drawn the attention of Department of Defense (DOD) leadership making detection and avoidance of counterfeit electronic parts a top issue for national defense. More defined regulations and processes for identifying, reporting, and disposing of counterfeit electronic parts are being revised to raise awareness for this aggregating issue, as well as enhance the detection of these parts. Multiple technologies are currently employed throughout the supply chain to detect counterfeit electronic parts. These methods are often costly, time-consuming, and destructive. This research investigates a non-destructive test method that collects unintentionally radiated electromagnetic emissions from functional devices using a commercially available system, the APREL EM-ISight. A design of experiments (DOE) is created and exploited to determine the optimal test settings for measuring devices. The sensitivity of the system is analyzed by scanning a commercial-off-the-shelf (COTS) field-programmable gate array (FPGA) at the optimal test settings established from the DOE and varying the programmed signal. This research established the viability of using APRELs EM-ISight to detect a devices inherent electromagnetic signature. Another take away from this research is the tradeoff between resolution and scantime