Selection of fusion levels in adolescent idiopathic scoliosis (AIS) using the fulcrum bending radiograph prediction: verification based on pedicle screw strategy

E-Poster - Adolescent Idiopathic Scoliosis: no. 297Utilizing the fulcrum bending radiographic technique to assess curve flexibility to aid in the selection of fusion levels, a prospective radiographic study was performed to assess the safety and effectiveness of pedicle screw fixation with alternate level screw strategy (ALSS) for thoracic AIS. This study suggests that ALSS obtains greater deformity correction than hook and hybrid systems, and improves balance without compromising fusion levels.postprin

'Clinical Triad' findings in Klippel-feil patients

E-Poster - Congenital Deformity: no. 530It has been propagated that Klippel-Feil Syndrome (KFS) is associated with the clinical triad findings (CTF) of short neck, low posterior hairline, and limited range of motion. This study noted that CTFs are not consistently noted in KFS patients. KFS patients with extensive congenitally fused cervical segments were more likely to exhibit one of the components of CTF.postprin

The safety and efficacy of a remotely distractible, magnetic controlled growing rod (MCGR) for the treatment of scoliosis in children: a prospective case series with minimum two year follow-up

Concurrent Session 2B - Early Onset Scoliosis: paper no. 26SUMMARY: The growing rod has been the gold standard for the treatment of scoliosis in young children. However, such management requires multiple open surgeries under general anesthesia for rod distraction and is associated with numerous postoperative complications. To avoid such pitfalls, we utilized a magnetically-controlled growing rod (MCGR) implant. Our study found that the MCGR was safe and effective, allowing for distractions on a non-invasive out-patient basis at monthly intervals, eliminating the need for surgeries and their associated complications. Introduction: Traditionally, growing rods are the standard of treatment for young children with severe spinal deformities and significant residual growth potential. However, this requires repeated open distractions under general anesthesia and is associated with numerous post-operative complications. This report addresses the safety and efficacy of the MCGR implant for non-invasive out-patient distractions for scoliosis correction in young children. METHODS: This was a prospective, patient series of the MCGR procedure. From November 2009 to March 2011, five patients (n=3 female; n=2 male) were treated with the MCGR. In this study, we report the first three patients (2 females and 1 male) with minimum 2 years follow-up. All cases were non-invasively distracted using an external magnet on a monthly basis. Pre and post distraction radiographs were carried out to assess the Cobb’s angle, predicted versus achieved rod distraction length and spinal length. Clinical outcome assessment was performed with the pain score (Visual Analogue Scale) and the SRS-30 questionnaire. All procedural or rod related complications were recorded. RESULTS: The main correction of the Cobb’s angle was obtained in the initial surgery and was maintained. The mean monthly increase in T1-T12, T1-S1 and instrumented segment length was 1.6mm, 2.5mm and 1.2mm, respectively. Predicted versus actual length gain per distraction were similar. One case had a superficial wound infection and there was one event of loss of distraction. On last follow-up, no pain was noted and SRS-30 scores remained unchanged to baseline. CONCLUSION: The MCGR is a safe and effective procedure for the surgical treatment of scoliosis in children. The MCGR provides external distractions on an out-patient basis without the need for sedation or anesthesia, and that remote distraction allows more frequent lengthening of the rod that may more closely mimic physiologic growth.postprin

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

The characterisation of actinomycetes isolated from diverse South African sources, with emphasis on the genus Kribbella

Includes abstract.Includes bibliographical references (p. 169-171).Actinomycetes were isolated from the leaves of indigenous plants, aquatic sediment and soil samples, using alternative isolation methods to select for actinomycetes belonging to the rarer genera. Thirty actinomycete strains belonging to the genera Gordonia, Kineococcus, Kribbella, Micromonospora, Nocardia and Streptomyces were selected for full characterisation. A polyphasic approach combining physiology, chemotaxonomy and phylogenetic analysis was used to characterise these isolates. A number of potentially novel strains belonging to the rarer genera were identified, including two Kineococcus and three Micromonospora strains. Two novel Kribbella species were isolated from soil samples and the species descriptions of Kribbella karoonensis Q41T and Kribbella swartbergensis HMC25T were published in 2006

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

The Second Conference on Lunar Bases and Space Activities of the 21st Century, volume 1

These papers comprise a peer-review selection of presentations by authors from NASA, LPI industry, and academia at the Second Conference (April 1988) on Lunar Bases and Space Activities of the 21st Century, sponsored by the NASA Office of Exploration and the Lunar Planetary Institute. These papers go into more technical depth than did those published from the first NASA-sponsored symposium on the topic, held in 1984. Session topics covered by this volume include (1) design and operation of transportation systems to, in orbit around, and on the Moon, (2) lunar base site selection, (3) design, architecture, construction, and operation of lunar bases and human habitats, and (4) lunar-based scientific research and experimentation in astronomy, exobiology, and lunar geology

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Studies of verticillium wilt and characterization of candidate verticillium wilt resistance genes in the mint species Mentha longifolia (L) Huds

To investigate the genetic basis of verticillium wilt resistance in mint (Mentha L., Lamiaceae), wild-collected germplasm obtained from the United States Department of Agriculture was employed to develop breeding populations for wilt resistance screening and molecular genetic study, including cloning of candidate verticillium wilt resistance genes. A collection of fourteen Mentha longifolia accessions from Europe, Asia and South Africa was analyzed for morphological traits, oil composition, and verticillium wilt resistance. In addition, a preliminary molecular diversity assessment was conducted utilizing randomly amplified polymorphic DNA (RAPD) markers. The accessions were found to be diverse regarding all observed traits and the South African accessions in particular were shown to possess unique features. Most importantly, highly wilt-resistant and highly wiltsusceptible accessions were observed. A collection of twenty-seven resistance gene analogs (RGAs) was isolated from M. longifolia accessions using a PCR-based approach with primers targeting the conserved nucleotide binding site (NBS) domain found in most plant disease resistance genes. The mint RGAs shared predicted amino acid sequence similarity with disease resistance genes and RGAs from various other plant species, and were grouped into seven distinct families based on DNA and predicted amino acid sequence similarity. In addition to the NBS-related RGAs, a fragment of a verticillium wilt resistance gene candidate was isolated from a verticillium-resistant M. longifolia accession using a combination of PCR-based approaches that exploited known sequences of tomato Ve (Verticillium resistance) genes. Finally, the complete coding region of the mint verticillium wilt resistance candidate gene, mVe1, was cloned and sequenced. Alleles of mVe1 were compared among four M. longifolia accessions used as crossing parents. These seven alleles were highly similar to each other (96.2-99.6% nucleotide identity) and had ∼50% predicted amino acid sequence identity to the tomato Ve genes. F1 and F2 populations were genotyped with respect to mVe1 alleles, and individuals from these populations were screened for wilt resistance. No correlation was found between any mVe allele and resistance or susceptibility to verticillium wilt in plants in the studied populations. However, this result does not discount the possibility that an mVe1 gene product plays a role in mint verticillium wilt resistance

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed