"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

SchussenAktivplus: reduction of micropollutants and of potentially pathogenic bacteria for further water quality improvement of the river Schussen, a tributary of Lake Constance, Germany

The project focuses on the efficiency of combined technologies to reduce the release of micropollutants and bacteria into surface waters via sewage treatment plants of different size and via stormwater overflow basins of different types. As a model river in a highly populated catchment area, the river Schussen and, as a control, the river Argen, two tributaries of Lake Constance, Southern Germany, are under investigation in this project. The efficiency of the different cleaning technologies is monitored by a wide range of exposure and effect analyses including chemical and microbiological techniques as well as effect studies ranging from molecules to communities

Review of risk from potential emerging contaminants in UK groundwater

This paper provides a review of the types of emerging organic groundwater contaminants (EGCs) which are beginning to be found in the UK. EGCs are compounds being found in groundwater that were previously not detectable or known to be significant and can come from agricultural, urban and rural point sources. EGCs include nanomaterials, pesticides, pharmaceuticals, industrial compounds, personal care products, fragrances, water treatment by-products, flame retardants and surfactants, as well as caffeine and nicotine. Many are relatively small polar molecules which may not be effectively removed by drinking water treatment. Data from the UK Environment Agency’s groundwater screening programme for organic pollutants found within the 30 most frequently detected compounds a number of EGCs such as pesticide metabolites, caffeine and DEET. Specific determinands frequently detected include pesticides metabolites, pharmaceuticals including carbamazepine and triclosan, nicotine, food additives and alkyl phosphates. This paper discusses the routes by which these compounds enter groundwater, their toxicity and potential risks to drinking water and the environment. It identifies challenges that need to be met to minimise risk to drinking water and ecosystems

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Signal processing of EEG data and AI assisted classification of emotional responses based on visual stimuli

This report outlines the research conducted to explore on the topic of classification of human neurological data using machine learning models. The primary objective was to investigate alternative approaches for efficiently interpreting EEG data and test the possibilities for predicting human emotions. During the study, data was collected by recording the brain activity of volunteering respondents using electroencephalography. These participants were exposed to visual stimuli in the purpose of provoking specific neural activity as a result of emotional responses in the brain. The collected data underwent traditional signal preprocessing techniques typically employed in EEG data analysis. Subsequently, the filtered data was subjected to wavelet transformation, both with and without synchrosqueezing. Principal components analysis was used to perform dimensionality reduction on the resulting features extracted from the data. The final model achieved a prediction accuracy of 32% when classifying between eight different classes of emotional responses based on training data from three respondents

Fish behavior and its use in the capture and culture of fishes

Fishery management, Behaviour, Food fish, Fish culture, Conferences

Deep learning in breast cancer screening

Breast cancer is the most common cancer form among women worldwide and the incidence is rising. When mammography was introduced in the 1980s, mortality rates decreased by 30% to 40%. Today all women in Sweden between 40 to 74 years are invited to screening every 18 to 24 months. All women attending screening are examined with mammography, using two views, the mediolateral oblique (MLO) view and the craniocaudal (CC) view, producing four images in total. The screening process is the same for all women and based purely on age, and not on other risk factors for developing breast cancer. Although the introduction of population-based breast cancer screening is a great success, there are still problems with interval cancer (IC) and large screen detected cancers (SDC), which are connected to an increased morbidity and mortality. To have a good prognosis, it is important to detect a breast cancer early while it has not spread to the lymph nodes, which usually means that the primary tumor is small. To improve this, we need to individualize the screening program, and be flexible on screening intervals and modalities depending on the individual breast cancer risk and mammographic sensitivity. In Sweden, at present, the only modality in the screening process is mammography, which is excellent for a majority of women but not for all. The major lack of breast radiologists is another problem that is pressing and important to address. As their expertise is in such demand, it is important to use their time as efficiently as possible. This means that they should primarily spend time on difficult cases and less time on easily assessed mammograms and healthy women. One challenge is to determine which women are at high risk of being diagnosed with aggressive breast cancer, to delineate the low-risk group, and to take care of these different groups of women appropriately. In studies II to IV we have analysed how we can address these challenges by using deep learning techniques. In study I, we described the cohort from which the study populations for study II to IV were derived (as well as study populations in other publications from our research group). This cohort was called the Cohort of Screen Aged Women (CSAW) and contains all 499,807 women invited to breast cancer screening within the Stockholm County between 2008 to 2015. We also described the future potentials of the dataset, as well as the case control subset of annotated breast tumors and healthy mammograms. This study was presented orally at the annual meeting of the Radiological Society of North America in 2019. In study II, we analysed how a deep learning risk score (DLrisk score) performs compared with breast density measurements for predicting future breast cancer risk. We found that the odds ratios (OR) and areas under the receiver operating characteristic curve (AUC) were higher for age-adjusted DLrisk score than for dense area and percentage density. The numbers for DLrisk score were: OR 1.56, AUC, 0.65; dense area: OR 1.31, AUC 0.60, percent density: OR 1.18, AUC, 0.57; with P < .001 for differences between all AUCs). Also, the false-negative rates, in terms of missed future cancer, was lower for the DLrisk score: 31%, 36%, and 39% respectively. This difference was most distinct for more aggressive cancers. In study III, we analyzed the potential cancer yield when using a commercial deep learning software for triaging screening examinations into two work streams – a ‘no radiologist’ work stream and an ‘enhanced assessment’ work stream, depending on the output score of the AI tumor detection algorithm. We found that the deep learning algorithm was able to independently declare 60% of all mammograms with the lowest scores as “healthy” without missing any cancer. In the enhanced assessment work stream when including the top 5% of women with the highest AI scores, the potential additional cancer detection rate was 53 (27%) of 200 subsequent IC, and 121 (35%) of 347 next-round screen-detected cancers. In study IV, we analyzed different principles for choosing the threshold for the continuous abnormality score when introducing a deep learning algorithm for assessment of mammograms in a clinical prospective breast cancer screening study. The deep learning algorithm was supposed to act as a third independent reader making binary decisions in a double-reading environment (ScreenTrust CAD). We found that the choice of abnormality threshold will have important consequences. If the aim is to have the algorithm work at the same sensitivity as a single radiologist, a marked increase in abnormal assessments must be accepted (abnormal interpretation rate 12.6%). If the aim is to have the combined readers work at the same sensitivity as before, a lower sensitivity of AI compared to radiologists is the consequence (abnormal interpretation rate 7.0%). This study was presented as a poster at the annual meeting of the Radiological Society of North America in 2021. In conclusion, we have addressed some challenges and possibilities by using deep learning techniques to make breast cancer screening programs more individual and efficient. Given the limitations of retrospective studies, there is a now a need for prospective clinical studies of deep learning in mammography screening

Functional and cosmetic outcome of two-stage hypospadias repair : an objective scoring evaluation and uroflowmetry

Introduction Hypospadias is characterized by an abnormally located urethral opening that could occur anywhere proximal to its normal location on the ventral surface of glans penis to the perineum and usually accompanied with chordee. Distal hypospadias is including glans, coronal and distal penile hypospadias. Proximal hypospadias is including proximal penile and penoscrotal hypospadias. It is the most common congenital anomaly affecting the penis (Wilcox & Ransley, 2000) with an incidence of 0.7% of male live births (Michael et al., 2001 ). There have been many operations described for hypospadias involving many surgical subspecialties. This reflects the difficulty in getting optimum results from the surgery and implies that there is no gold standard technique for hypospadias repair (Arshad, 2005, Oztruk et al., 2005). There is also no standardized objective method to assess the outcome of hypospadias repair until Holland et al. (200 1) can1e with hypospadias objective scoring evaluation (HOSE). Objective The main objective of this study is to evaluate the functional and cosmetic outcome of patients who underwent two-stage hypospadias repair in Hospital Universiti Sains Malaysia and Hospital Raja Perempuan Zainab between January 1997 and December 2004, using HOSE (hypospadias objective scoring evaluation) and uroflowmetry and also to determine the factors that could influenced the outcome. Methodology This is an historical cohort study among hypospadias patients who have undergone two-stage hypospadias repair in Hospital Universiti Sains Malaysia and Hospital Raja Perempuan Zainab II between January 1997 and December 2004. Over the eight years 90 hypospadias patients underwent two-stage repair. Only 55 patients out of 90 patients (61.1 %) with complete record and agree to participate were included in the study. They were examined to evaluate the functional and cosmetic outcome using HOSE: hypospadias objective scoring evaluation and uroflowmetry (if they were able to void volitionally and had no fistula). Five factors that may have influenced the outcome of hypospadias were studied, including type of hyposapadias, age at the completion of repair, duration between the first and the second-stage repair, techniques of hypospadias repair and surgeon. Results 53 of the 55 patients were Malay, one Chinese and one Siamese. The age of patients at the time of the study ranged from 8 to 23 year-old (mean age 14.89 year). 35 patients (63.6%) had proximal type hypospadias (23 penoscrotal and 12 proximal penile) and 20 patients (36.4%) had distal hypospadias (12 distal penile, 7 subcoronal and one glannular) Four patients underwent circumcision in one to two years before hypospadias repair and two patients underwent previous unsuccessful hypospadias repair. The types of operations performed were Bracka' s two-stage procedure (3 7) and Byar's two-stage procedure (18). The complications encountered were urethralcutaneous fistula 17 patients (30.9%), followed by meatal stenosis 2 patients (3.6%), urethtal stricture one patient (1.8%) and wide meatal opening at subcoronal one patient (1.8%). Of the 17 patients with fistula, 9 underwent fistula repair and three had recurrence. Using the assessment criteria in HOSE, 34.5% had acceptable score and 65.5% had unacceptable score. The meatal openings were located at the tip of glans penis in 17 patients (30.9%), the meatal opening were vertical slit in 12 patients (21.8%), single urinary stream were obtained in 50 patients (90.9%), straight penis on erection were documented in 20 patients (36.4%) and there were no fistula in 44 patients (80%). Only 43 patients who were able to underwent uroflowmetry examination, in which 36 patients (83.7.0%) were considered normal, four patients (9.3%) as equivocal and three patients (7.0%) were obstructed. Only surgeon factor was found to have statistically significant influence on the outcome. Conclusion In conclusion, there seem to be a higher occurrence e of penoscrotal hypospadias in the Eastern side of Peninsula of Malaysia. HOSE and uroflowmerty are important objective tools to evaluate the functional and cosmetic outcome. The only factor that had a statistically significant influence on the outcome was the surgeon factor; other factors were found to be insignificant statistically

Published work on freshwater science from the FBA, IFE and CEH, 1929-2006

A new listing of published scientific contributions from the Freshwater Biological Association (FBA) and its later Research Council associates – the Institute of Freshwater Ecology (1989–2000) and the Centre for Ecology and Hydrology (2000+) is provided. The period 1929–2006 is covered. The compilation extends an earlier list assembled by in 1979