Detecting danger in roads: an immune-inspired technique to identify heavy goods vehicles incident hot spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom

Vehicle incident hot spots identification: an approach for big data

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a number of iterations that remove data redundancies and result in the detection of areas of high likelihood of vehicles incidents. It has been successfully applied to large datasets, however, as the size of the data increases to millions of instances, its performance drops significantly. Our objective therefore is to re-conceptualise the method for big data. In this paper we present the new implementation, the challenges faced when converting the method for the Apache Spark platform as well as the outcomes obtained. For our experiments we employ a large dataset containing hundreds of thousands of Heavy Good Vehicles incidents, collected via telematics. Results show a significant improvement in performance with no detriment to the accuracy of the method

An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was originally conceptualised to eliminate very similar instances in data classification tasks. We adapt the method to detect hot spots from a telematics data set containing hundreds of thousands of data points indicating incident locations involving heavy goods vehicles (HGVs) across the United Kingdom. The objective is to provide HGV drivers with information regarding areas of high likelihood of incidents in order to continuously improve road safety and vehicle economy. The problem presents several challenges and constraints. An accurate view of the hot spots produced in a timely manner is necessary. In addition, the solution is required to be adaptable and dynamic, as thousands of new incidents are included in the database daily. Furthermore, the impact on hot spots after informing drivers about their existence has to be considered. Our solution successfully addresses these constraints. It is fast, robust, and applicable to all different incidents investigated. The method is also self-adjustable, which means that if the hot spots’ configuration changes with time, the algorithm automatically evolves to present the most current topology. Our solution has been implemented by a telematics company to improve HGV safety in the United Kingdom

Predicting and analyzing HIV-1 adaptation to broadly neutralizing antibodies and the host immune system using machine learning

Thanks to its extraordinarily high mutation and replication rate, the human immunodeficiency virus type 1 (HIV-1) is able to rapidly adapt to the selection pressure imposed by the host immune system or antiretroviral drug exposure. With neither a cure nor a vaccine at hand, viral control is a major pillar in the combat of the HIV-1 pandemic. Without drug exposure, interindividual differences in viral control are partly influenced by host genetic factors like the human leukocyte antigen (HLA) system, and viral genetic factors like the predominant coreceptor usage of the virus. Thus, a close monitoring of the viral population within the patients and adjustments in the treatment regimens, as well as a continuous development of new drug components are indispensable measures to counteract the emergence of viral escape variants. To this end, a fast and accurate determination of the viral adaptation is essential for a successful treatment. This thesis is based upon four studies that aim to develop and apply statistical learning methods to (i) predict adaptation of the virus to broadly neutralizing antibodies (bNAbs), a promising new treatment option, (ii) advance antibody-mediated immunotherapy for clinical usage, and (iii) predict viral adaptation to the HLA system to further understand the switch in HIV-1 coreceptor usage. In total, this thesis comprises several statistical learning approaches to predict HIV-1 adaptation, thereby, enabling a better control of HIV-1 infections.Dank seiner außergewöhnlich hohen Mutations- und Replikationsrate ist das humane Immundefizienzvirus Typ 1 (HIV-1) in der Lage sich schnell an den vom Immunsystem des Wirtes oder durch die antiretrovirale Arzneimittelexposition ausgeübten Selektionsdruck anzupassen. Da weder ein Heilmittel noch ein Impfstoff verfügbar sind, ist die Viruskontrolle eine wichtige Säule im Kampf gegen die HIV-1-Pandemie. Ohne Arzneimittelexposition werden interindividuelle Unterschiede in der Viruskontrolle teilweise durch genetische Faktoren des Wirts wie das humane Leukozytenantigensystem (HLA) und virale genetische Faktoren wie die vorherrschende Korezeptornutzung des Virus beeinflusst. Eine genaue Überwachung der Viruspopulation innerhalb des Patienten, gegebenfalls Anpassungen der Behandlungsschemata sowie eine kontinuierliche Entwicklung neuer Wirkstoffkomponenten sind daher unerlässliche Maßnahmen, um dem Auftreten viraler Fluchtvarianten entgegenzuwirken. Für eine erfolgreiche Behandlung ist eine schnelle und genaue Bestimmung der Anpassung einer Variante essentiell. Die Thesis basiert auf vier Studien, deren Ziel es ist statistische Lernverfahren zu entwickeln und anzuwenden, um (1) die Anpassung von HIV-1 an breit neutralisierende Antikörper, eine neuartige vielversprechende Therapieoption, vorherzusagen, (2) den Einsatz von Antikörper-basierte Immuntherapien für den klinischen Einsatz voranzutreiben, und (3) die virale Anpassung von HIV-1 an das HLA-System vorherzusagen, um den Wechsel der HIV-1 Korezeptornutzung besser zu verstehen. Zusammenfassend umfasst diese Thesis mehrere statistische Lernverfahrenansätze, um HIV Anpassung vorherzusagen, wodurch eine bessere Kontrolle von HIV-1 Infektionen ermöglicht wird

Improving representations of genomic sequence motifs in convolutional networks with exponential activations.

ABSTRACT Deep convolutional neural networks (CNNs) trained on regulatory genomic sequences tend to build representations in a distributed manner, making it a challenge to extract learned features that are biologically meaningful, such as sequence motifs. Here we perform a comprehensive analysis on synthetic sequences to investigate the role that CNN activations have on model interpretability. We show that employing an exponential activation to first layer filters consistently leads to interpretable and robust representations of motifs compared to other commonly used activations. Strikingly, we demonstrate that CNNs with better test performance do not necessarily imply more interpretable representations with attribution methods. We find that CNNs with exponential activations significantly improve the efficacy of recovering biologically meaningful representations with attribution methods. We demonstrate these results generalise to real DNA sequences across several in vivo datasets. Together, this work demonstrates how a small modification to existing CNNs, i.e. setting exponential activations in the first layer, can significantly improve the robustness and interpretabilty of learned representations directly in convolutional filters and indirectly with attribution methods

Integrative Multi-Omics in Biomedical Research

Genomics technologies revolutionised biomedicine research, but the genome alone is not sufficient to capture biological complexity. Postgenomic methods, typically based on mass spectrometry, comprise the analysis of metabolites, lipids, and proteins and are an essential complement to genomics and transcriptomics. Multidimensional omics is becoming established to provide accurate and comprehensive state descriptions. This book covers the latest methodological developments for, and applications of integrative multi-omics in biomedical research

Clinical evaluation of thalamic deep brain stimulation for movement disorders in multiple sclerosis

Disorders of movement are recognized features of multiple sclerosis (MS). They often involve the upper limbs, headland trunk and can prevent a person from carrying out the simplest of daily activities such as holding a drink and feeding themselves. This may have enormous psychological consequences, often leading to frustration, embarrassment (particularly in social situations), withdrawal and increased dependence on others.Treatment of these disorders of movement, which are usually refractory to medical therapy, has been by thalamotomy, a neuroablative technique. Results have been variable and often unsatisfactory in the long term. Recently thalamic deep brain stimulation (DBS) has been proposed after its successful use in the treatment of Parkinsonian tremor. Relatively little information exists on the use of this treatment in patients with MS. Studies carried out so far have been on very small cohorts and have used non-validated outcome scales and short follow-up. There is little data on the effect of the movement disorder on a person's disability, handicap and quality of life (QOL); the perception of ability after surgery; and on the costs involved in thalamic DBSThe work presented here had 3 principal objectives: first to develop and validate a scale for measuring movement disorders in MS (MDMS); secondly to evaluate the effect of thalamic DBS on impairment, disability, handicap and aspects of quality of life (QOL) relevant to these patients; and thirdly to estimate the costs associated with thalamic DBS.The Modified Fahn's Tremor Rating Scale (MFTRS) was developed and validated for the purposes of this study. Results of the validity, reliability and responsiveness of the MFTRS, as given in the published paper, showed that it can be used with confidence in the clinical setting.Thirty seven patients with MDMS were assessed before operation. Fifteen patients underwent thalamic surgery. The target arm was assessed 1, 3, 6 and 12 months after operation using the MFTRS, which measured severity of tremor, and the Jebsen Test of Hand Function (JTHF) which measured performance of 7 subtests of upper limb function. Information concerning the influence of the movement disorder on overall disability, handicap and QOL was collected at or over 12 months and was compared with that of the pre-operative assessment using various subjective rating scales and questionnaires.Results showed that thalamic DBS significantly reduced the severity of tremor amplitude and significantly improved performance of the Jebsen subtests when the DBS was on at each post-operative assessments (1, 3, 6, and 12 months) compared with pre-operatively (all p values < 0.02). However, these symptomatic and changes in function did not translate into significant improvements in patients' performance in activities of daily living and thus there were no apparent economic benefits (ie. savings in future care-costs). Also there was no change in patients' perceptions of their handicap or in most aspects of QOL: the only significant change was that patients perceived themselves to be less anxious 12 months after the operation (p-0.03). The overall impact was therefore clinically limited.This prospective study has illustrated the benefits and limitations of thalamic DBS in patients with MDMS, and has highlighted the post-operative rehabilitation and follow-up requirements and the resulting health economic implications associated with its use. The validation of the MFTRS not only enabled the effect of thalamic DBS to be evaluated but also provided a major contribution to the assessment of MDMS

Worker and Public Health and Safety

This book on "Worker and Public Health and Safety: Current Views" brings together current scholarly work and opinions in the form of original papers and reviews related to this field of study. It provides important and recent scientific reading as well as topical medical and occupational information and research in areas of immediate relevance, such as chronic and occupational diseases, worker safety and performance, job strain, workload, injuries, accident and errors, risks and management, fitness, burnout, psychological and mental disorders including stress, therapy, job satisfaction, musculoskeletal symptoms and pain, socio-economic factors, dust pollution, pesticides, noise, pathogens, and related areas

Nutrition and Athletic Performance

Exercise necessitates increased energy production to match the elevated demand of physical activity, the magnitude of which varies significantly by activity, sport, and/or athletic position. While long term nutritional habitus is known to impact exercise performance, short term or acute nutritional strategies may also prove beneficial, or detrimental, to athletic performance. Modifications to macro- or micro-nutrient intakes likely influence athletic capacity through the altered metabolic capacity, although cardiovascular, respiratory, or neurocognitive effects are not to be discounted as possibly being influenced by altering the nutritional approach. Similarly, dietary supplementation with factors such as probiotics or antioxidants, either acutely or chronically, is also a likely avenue in which to optimize athletic performance. Supplementation, or the timing of supplementation, diurnally or with activity, may help to bridge gaps between dietary intakes and needs, perhaps as a result of either an inadequate intake and/or high level of athletic demand via high intensity, frequency, volume, or a combination thereof. Altering nutritional strategy for athletic performance is a de facto approach employed by athletes, often occurring seemingly independent of knowledge or evidence for or against a particular strategy. Rigorous studies of nutritional manipulation, supplementation, or those exploring the temporal optimization of nutrition or supplementation are desperately needed in an ever-changing sports nutrition landscape with an increasingly larger audience