Unimolecular reaction chemistry of a charge-tagged beta-hydroxyperoxyl radical

β-Hydroxyperoxyl radicals are formed during atmospheric oxidation of unsaturated volatile organic compounds such as isoprene. They are intermediates in the combustion of alcohols. In these environments the unimolecular isomerization and decomposition of β-hydroxyperoxyl radicals may be of importance, either through chemical or thermal activation. We have used ion-trap mass spectrometry to generate the distonic charge-tagged β-hydroxyalkyl radical anion, ˙CH2C(OH)(CH3)CH2C(O)O−, and investigated its subsequent reaction with O2 in the gas phase under conditions that are devoid of complicating radical–radical reactions. Quantum chemical calculations and master equation/RRKM theory modeling are used to rationalize the results and discern a reaction mechanism. Reaction is found to proceed via initial hydrogen abstraction from the γ-methylene group and from the β-hydroxyl group, with both reaction channels eventually forming isobaric product ions due to loss of either ˙OH + HCHO or ˙OH + CO2. Isotope labeling studies confirm that a 1,5-hydrogen shift from the β-hydroxyl functionality results in a hydroperoxyalkoxyl radical intermediate that can undergo further unimolecular dissociations. Furthermore, this study confirms that the facile decomposition of β-hydroxyperoxyl radicals can yield ˙OH in the gas phase.

Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

Pancreatic islet basement membrane loss and remodeling after mouse islet isolation and transplantation: Impact for allograft rejection

The isolation of islets by collagenase digestion can cause damage and impact the efficiency of islet engraftment and function. In this study, we assessed the basement membranes (BMs) of mouse pancreatic islets as a molecular biomarker for islet integrity, damage after isolation, and islet repair in vitro as well as in the absence or presence of an immune response after transplantation. Immunofluorescence staining of BM matrix proteins and the endothelial cell marker platelet endothelial cell adhesion molecule-1 (PECAM-1) was performed on pancreatic islets in situ, isolated islets, islets cultured for 4 days, and islet grafts at 3-10 days posttransplantation. Flow cytometry was used to investigate the expression of BM matrix proteins in isolated islet β-cells. The islet BM, consisting of collagen type IV and components of Engelbreth-Holm-Swarm (EHS) tumor laminin 111, laminin α2, nidogen-2, and perlecan in pancreatic islets in situ, was completely lost during islet isolation. It was not reestablished during culture for 4 days. Peri- and intraislet BM restoration was identified after islet isotransplantation and coincided with the migration pattern of PECAM-1(+) vascular endothelial cells (VECs). After islet allotransplantation, the restoration of VEC-derived peri-islet BMs was initiated but did not lead to the formation of the intraislet vasculature. Instead, an abnormally enlarged peri-islet vasculature developed, coinciding with islet allograft rejection. The islet BM is a sensitive biomarker of islet damage resulting from enzymatic isolation and of islet repair after transplantation. After transplantation, remodeling of both peri- and intraislet BMs restores β-cell-matrix attachment, a recognized requirement for β-cell survival, for isografts but not for allografts. Preventing isolation-induced islet BM damage would be expected to preserve the intrinsic barrier function of islet BMs, thereby influencing both the effector mechanisms required for allograft rejection and the antirejection strategies needed for allograft survival.

Threat of resource loss: The role of self-regulation in adaptive task performance

Changes at work are often accompanied with the threat of, or actual, resource loss. Through an experiment, we investigated the detrimental effect of the threat of resource loss on adaptive task performance. Self-regulation (i.e., task focus and emotion control) was hypothesized to buffer the negative relationship between the threat of resource loss and adaptive task performance. Adaptation was conceptualized as relearning after a change in task execution rules. Threat of resource loss was manipulated for 100 participants undertaking an air traffic control task. Using discontinuous growth curve modeling, 2 kinds of adaptation—transition adaptation and reacquisition adaptation—were differentiated. The results showed that individuals who experienced the threat of resource loss had a stronger drop in performance (less transition adaptation) and a subsequent slower recovery (less reacquisition adaptation) compared with the control group who experienced no threat. Emotion control (but not task focus) moderated the relationship between the threat of resource loss and transition adaptation. In this respect, individuals who felt threatened but regulated their emotions performed better immediately after the task change (but not later on) compared with those individuals who felt threatened and did not regulate their emotions as well. However, later on, relearning (reacquisition adaptation) under the threat of resource loss was facilitated when individuals concentrated on the task at hand.

Structure matters: Adoption of structured classification Approach in the context of cognitive presence classification

Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" – the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.

Genetic, functional, and histopathological evaluation of two C-terminal BRCA1 missense variants

Background: The vast majority of BRCA1 missense sequence variants remain uncharacterised for their possible effect on protein expression and function, and therefore are unclassified in terms of their pathogenicity. BRCA1 plays diverse cellular roles and it is unlikely that any single functional assay will accurately reflect the total cellular implications of missense mutations in this gene. Objective: To elucidate the effect of two BRCA1 variants, 5236G>C (G1706A) and 5242C>A (A1708E) on BRCA1 function, and to survey the relative usefulness of several assays to direct the characterisation of other unclassified variants in BRCA genes. Methods and Results: Data from a range of bioinformatic, genetic, and histopathological analyses, and in vitro functional assays indicated that the 1708E variant was associated with the disruption of different cellular functions of BRCA1. In transient transfection experiments in T47D and 293T cells, the 1708E product was mislocalised to the cytoplasm and induced centrosome amplification in 293T cells. The 1708E variant also failed to transactivate transcription of reporter constructs in mammalian transcriptional transactivation assays. In contrast, the 1706A variant displayed a phenotype comparable to wildtype BRCA1 in these assays. Consistent with functional data, tumours from 1708E carriers showed typical BRCA1 pathology, while tumour material from 1706A carriers displayed few histopathological features associated with BRCA1 related tumours. Conclusions: A comprehensive range of genetic, bioinformatic, and functional analyses have been combined for the characterisation of BRCA1 unclassified sequence variants. Consistent with the functional analyses, the combined odds of causality calculated for the 1706A variant after multifactorial likelihood analysis (1:142) indicates a definitive classification of this variant as "benign". In contrast, functional assays of the 1708E variant indicate that it is pathogenic, possibly through subcellular mislocalisation. However, the combined odds of 262:1 in favour of causality of this variant does not meet the minimal ratio of 1000:1 for classification as pathogenic, and A1708E remains formally designated as unclassified. Our findings highlight the importance of comprehensive genetic information, together with detailed functional analysis for the definitive categorisation of unclassified sequence variants. This combination of analyses may have direct application to the characterisation of other unclassified variants in BRCA1 and BRCA2.

Characteristics of on-road driving by persons with central vision loss: Learning to drive with a bioptic telescope

There is limited research on the driving performance and safety of bioptic drivers and even less regarding the driving skills that are most challenging for those learning to drive with bioptic telescopes. This research consisted of case studies of five trainee bioptic drivers whose driving skills were compared with those of a group of licensed bioptic drivers (n = 23) while they drove along city, suburban, and controlled-access highways in an instrumented dual-brake vehicle. A certified driver rehabilitation specialist was positioned in the front passenger seat to monitor safety and two backseat evaluators independently rated driving using a standardized scoring system. Other aspects of performance were assessed through vehicle instrumentation and video recordings. Results demonstrate that while sign recognition, lane keeping, steering steadiness, gap judgments and speed choices were significantly worse in trainees, some driving behaviors and skills, including pedestrian detection and traffic light recognition were not significantly different to those of the licensed drivers. These data provide useful insights into the skill challenges encountered by a small sample of trainee bioptic drivers which, while not generalizable because of the small sample size, provide valuable insights beyond that of previous studies and can be used as a basis to guide training strategies.

Assistive classification for improving the efficiency of avian species richness surveys

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Ultrafast Raman loss spectroscopy

This paper deals with a new form of nonlinear Raman spectroscopy called `ultrafast Raman loss spectroscopy (URLS)'. URLS is analogous to stimulated Raman spectroscopy (SRS) but is much more sensitive than SRS. The signals are background (noise) free unlike in coherent anti-Stokes Raman spectroscopy (CARS) and it provides natural fluorescence rejection, which is a major problem in Raman spectroscopy. In addition, being a self-phase matching process, the URLS experiment is much easier than CARS, which requires specific phase matching of the laser pulses. URLS is expected to be alternative if not competitive to CARS microscopy, which has become a popular technique in applications to materials, biology and medicine.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

A Classification of Homogeneous Operators in the Cowen-Douglas Class

A complete list of homogeneous operators in the Cowen-Douglas class B-n(D) is given. This classification is obtained from an explicit realization of all the homogeneous Hermitian holomorphic vector bundles on the unit disc under the action of the universal covering group of the bi-holomorphic automorphism group of the unit disc.

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).