Taxonomy of rare congenital metabolic bone disorders

International audience

ENEOLITHIC, YAMNAYA, CATACOMB AND BABYNO CULTURE CEMETERIES, PIDLISIVKA, BARROW 1, YAMPIL REGION, VINNITSA OBLAST: ARCHAEOMETRY, CHRONOMETRY AND TAXONOMY

The paper presents excavation results and analytical studies concerning the taxonomic classification of a funerary site identified with the communities of the early ‘barrow cultures’ settling the north-western Black Sea Coast in the 4th/3rd-2nd millennium BC. The study focuses on the ceremonial centres of the Eneolithic, Yamnaya, Catacomb and Babyno cultures.

ENEOLITHIC, BABYNO AND NOUA CULTURE CEMETERIES, KLEMBIVKA, SITE 1, YAMPIL REGION, VINNITSA OBLAST: ARCHAEOMETRY, TAXONOMY AND TOPOGENETICS

The paper presents excavation results and analytical studies concerning the taxonomic classification of a funerary site identified with the communities of the ‘barrow cultures’ settling the north-western Black Sea Coast in the first half of the 3rd and the middle of the 2nd millennia BC . The study focuses on the ceremonial centres of the Eneolithic communities of the Babyno and Noua cultures .

TAXONOMY, MORPHOLOGY, AND RNA-SEQ TRANSCRIPTOMICS OF THE CUBOZOAN ALATINA ALATA, AN EMERGING MODEL CNIDARIAN

Cnidarians are often considered simple animals, but the more than 13,000 estimated species (e.g., corals, hydroids and jellyfish) of the early diverging phylum exhibit a broad diversity of forms, functions and behaviors, some of which are demonstrably complex. In particular, cubozoans (box jellyfish) are cnidarians that have evolved a number of distinguishing features. Some cubozoan species possess complex mating behaviors or particularly potent stings, and all possess well-developed light sensation involving image-forming eyes. Like all cnidarians, cubozoans have specialized subcellular structures called nematocysts that are used in prey capture and defense. The objective of this study is to contribute to the development of the box jellyfish Alatina alata as a model cnidarian. This cubozoan species offers numerous advantages for investigating morphological and molecular traits underlying complex processes and coordinated behavior in free-living medusozoans (i.e., jellyfish), and more broadly throughout Metazoa. First, I provide an overview of Cnidaria with an emphasis on the current understanding of genes and proteins implicated in complex biological processes in a few select cnidarians. Second, to further develop resources for A. alata, I provide a formal redescription of this cubozoan and establish a neotype specimen voucher, which serve to stabilize the taxonomy of the species. Third, I generate the first functionally annotated transcriptome of adult and larval A. alata tissue and apply preliminary differential expression analyses to identify candidate genes implicated broadly in biological processes related to prey capture and defense, vision and the phototransduction pathway and sexual reproduction and gametogenesis. Fourth, to better understand venom diversity and mechanisms controlling venom synthesis in A. alata, I use bioinformatics to investigate gene candidates with dual roles in venom and digestion, and review the biology of prey capture and digestion in cubozoans. The morphological and molecular resources presented herein contribute to understanding the evolution of cubozoan characteristics and serve to facilitate further research on this emerging cubozoan model.

Empathy in the field: towards a taxonomy of empathic communication in information gathering interviews with suspected sex offenders

Research suggests that those suspected of sexual offending might be more willing to reveal information about their crimes if interviewers display empathic behaviour. However, the literature concerning investigative empathy is in its infancy, and so as yet is not well understood. This study explores empathy in a sample of real-life interviews conducted by police officers in England with suspected sex offenders. Using qualitative methodology, the presence and type of empathic verbal behaviours displayed was examined. Resulting categories were quantitatively analysed to investigate their occurrence overall, and across interviewer gender. We identified four distinct types of empathy, some of which were used significantly more often than others. Female interviewers displayed more empathic behaviour per se by a considerable margin.

BioKin: an ambulatory platform for gait kinematic and feature assessment

A platform to move gait analysis, which is normally restricted to a clinical environment in a well-equipped gait laboratory, into an ambulatory system, potentially in non-clinical settings is introduced. This novel system can provide functional measurements to guide therapeutic interventions for people requiring rehabilitation with limited access to such gait laboratories. BioKin system consists of three layers: a low-cost wearable wireless motion capture sensor, data collection and storage engine, and the motion analysis and visualisation platform. Moreover, a novel limb orientation estimation algorithm is implemented in the motion analysis platform. The performance of the orientation estimation algorithm is validated against the orientation results from a commercial optical motion analysis system and an instrumented treadmill. The study results demonstrate a root-mean-square error less than 4° and a correlation coefficient more than 0.95 when compared with the industry standard system. These results indicate that the proposed motion analysis platform is a potential addition to existing gait laboratories in order to facilitate gait analysis in remote locations.

Acoustic and device feature fusion for load recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multi-layer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only.

A taxonomy of prestige-seeking university students: strategic insights for higher education

This study explores the importance of psychographic characteristics as potential segmentation bases in the higher education sector. In particular, we develop a taxonomy of university students based on their achievement orientation and prestige sensitivity. The study analyses the survey data obtained from 948 respondents using cluster analyses and multiple analysis of variance (MANOVA), indicating interesting findings. Three distinct clusters emerge, namely Strivers, Modest Achievers and Prestige-seeking Innovators. Findings reveal that Prestige-seeking Innovators have a more positive attitude towards the university, whereas Strivers have the strongest sense of regret over their decision to enrol at their current university and would seize the opportunity to enrol in a more prestigious university. The taxonomy is highly relevant to marketers of higher education institutions as it gives insights into potential bases for segmentation, positioning and communication strategies targeting the specific characteristics of each segment.

Fuzzy-based feature and instance recovery

The severe class distribution shews the presence of underrepresented data, which has great effects on the performance of learning algorithm, is still a challenge of data mining and machine learning. Lots of researches currently focus on experimental comparison of the existing re-sampling approaches. We believe it requires new ways of constructing better algorithms to further balance and analyse the data set. This paper presents a Fuzzy-based Information Decomposition oversampling (FIDoS) algorithm used for handling the imbalanced data. Generally speaking, this is a new way of addressing imbalanced learning problems from missing data perspective. First, we assume that there are missing instances in the minority class that result in the imbalanced dataset. Then the proposed algorithm which takes advantages of fuzzy membership function is used to transfer information to the missing minority class instances. Finally, the experimental results demonstrate that the proposed algorithm is more practical and applicable compared to sampling techniques.

Enhanced Twitter sentiment analysis by using feature selection and combination

Tweet sentiment analysis is an important research topic. An accurate and timely analysis report could give good indications on the general public's opinions. After reviewing the current research, we identify the need of effective and efficient methods to conduct tweet sentiment analysis. This paper aims to achieve a high level of performance for classifying tweets with sentiment information. We propose a feasible solution which improves the level of accuracy with good time efficiency. Specifically, we develop a novel feature combination scheme which utilizes the sentiment lexicons and the extracted tweet unigrams of high information gain. We evaluate the performance of six popular machine learning classifiers among which the Naive Bayes Multinomial (NBM) classifier achieves the accuracy rate of 84.60% and takes a few minutes to complete classifying thousands of tweets.

Novel feature of nano-titanium dioxide on textiles: antifelting and antibacterial wool

In this study, the antifelting and antibacterial features of wool samples treated with nanoparticles of titanium dioxide (TiO2) were evaluated. To examine the antifelting properties of the treated samples, the fabric shrinkage after washing was determined. The antimicrobial activity was assessed through the calculation of bacterial reduction against Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) bacteria. TiO 2 was stabilized on the wool fabric surface by means of carboxylic acids, including citric acid (CA) and butane tetracarboxylic acid (BTCA). Both oxidized samples with potassium permanganate and nonoxidized wool fabrics were used in this study. The relations between both the TiO2 and carboxylic acid concentrations in the impregnated bath and the antifelting and antibacterial properties are discussed. With increasing concentration in the impregnated bath, the amount of TiO2 nanoparticles on the surface of the wool increased; subsequently, lower shrinkage and higher antibacterial properties were obtained. The existence of TiO2 nanoparticles on the surface of the treated samples was proven with scanning electron microscopy images and energy-dispersive spectrometry.

Vector autoregressions and macroeconomic modeling: an error taxonomy

In this article we investigate the theoretical behaviour of finite lag VAR(n) models fitted to time series that in truth come from an infinite order VAR(∞) data generating mechanism. We show that the overall error can be broken down into two basic components, an estimation error that stems from the difference between the parameter estimates and their population ensemble VAR(n) counterparts, and an approximation error that stems from the difference between the VAR(n) and the true VAR(∞). The two sources of error are shown to be present in other performance indicators previously employed in the literature to characterize, so called, truncation effects. Our theoretical analysis indicates that the magnitude of the estimation error exceeds that of the approximation error, but experimental results based upon a prototypical real business cycle model and a practical example indicate that the approximation error approaches its asymptotic position far more slowly than does the estimation error, their relative orders of magnitude notwithstanding. The experimental results suggest that with sample sizes and lag lengths like those commonly employed in practice VAR(n) models are likely to exhibit serious errors of both types when attempting to replicate the dynamics of the true underlying process and that inferences based on VAR(n) models can be very untrustworthy.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.