Development of a Hydrogeological Model of the Borrowdale Volcanics at Sellafield

International audienceThis work has arisen out of recent developments within the radioactive waste research programme managed by Her Majesty's Inspectorate of Pollution, UK (HMIP)*, to develop an integrated flow and transport model for the potential deep radioactive waste repository at Sellafield. One of the largest sources of uncertainty in model predictions, is the characterisation of the hydrogeological properties of the underlying strata, in particular, of the Borrowdale Volcanic Group (BVG) within which the repository is to be located. Analysis of the available borehole data (that released by the proponent company, Nirex, by December 1995) for the BVG formation has indicated a dual regime consisting of flow within faults and flow within the matrix (or an equivalent porous medium containing micro-fractures). Significant relationships between permeability, depth and the presence and orientation of faults have been identified; they account for a variation of up to 6 orders of magnitude in mean permeability measurements. This can be explained in part by the effect of the orientation of the current maximum principal stress directions within the BVG: however, it is likely that permeability is also dependent on the existence of fracture families, which cannot be effectively identified from the data currently available. These analyses have enabled considerable insight to be gained into the dominant features of flow within the BVG. The conceptual hydrogeological model derived here will have a significant effect on the outcome and reliability of future radionuclide transport predictions in the Sellafield area

Neuropsychiatric systemic lupus erythematosus: a diagnostic challenge

A 58-year-old woman presented to neuropsychiatric services with increased frequency of confusional episodes and intermittent psychotic symptoms. She had a 19-year history of atypical epileptic seizures and cognitive decline. Detailed review of history and clinical investigations revealed that she had accumulated sufficient features to meet diagnostic criteria for systemic lupus erythematosus (SLE). She had previously had lymphopenia and a malar rash; she had positive antinuclear, anti-Ro (anti-Sjögren's-syndrome-related antigen A) and anti-SM (anti-Smith Antibody) antibodies, and elevated erythrocyte sedimentation rate. The seizures, cognitive impairment and psychosis were attributable to neuropsychiatric SLE. Treatment with immune-modulating therapy, cyclophosphamide, resulted in significant improvement in subjective and objective clinical presentation. Neuropsychiatric SLE should be considered a potential differential diagnosis for patients presenting with seizures, psychotic symptoms or cognitive decline. A detailed clinical evaluation with review of the medical history and appropriate laboratory analyses allows this diagnosis to be made, and appropriate treatment to be initiated

Concise Review: Stem Cell Therapies for Amyotrophic Lateral Sclerosis: Recent Advances and Prospects for the Future

Amyotrophic lateral sclerosis (ALS) is a lethal disease involving the loss of motor neurons. Although the mechanisms responsible for motor neuron degeneration in ALS remain elusive, the development of stem cell‐based therapies for the treatment of ALS has gained widespread support. Here, we review the types of stem cells being considered for therapeutic applications in ALS, and emphasize recent preclinical advances that provide supportive rationale for clinical translation. We also discuss early trials from around the world translating cellular therapies to ALS patients, and offer important considerations for future clinical trial design. Although clinical translation is still in its infancy, and additional insight into the mechanisms underlying therapeutic efficacy and the establishment of long‐term safety are required, these studies represent an important first step toward the development of effective cellular therapies for the treatment of ALS. S tem C ells 2014;32:1099–1109Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106861/1/stem1628.pd

Cystic fibrosis mice carrying the missense mutation G551D replicate human genotype phenotype correlations

We have generated a mouse carrying the human G551D mutation in the cystic fibrosis transmembrane conductance regulator gene (CFTR) by a one-step gene targeting procedure. These mutant mice show cystic fibrosis pathology but have a reduced risk of fatal intestinal blockage compared with 'null' mutants, in keeping with the reduced incidence of meconium ileus in G551D patients. The G551D mutant mice show greatly reduced CFTR-related chloride transport, displaying activity intermediate between that of cftr(mlUNC) replacement ('null') and cftr(mlHGU) insertional (residual activity) mutants and equivalent to approximately 4% of wild-type CFTR activity. The long-term survival of these animals should provide an excellent model with which to study cystic fibrosis, and they illustrate the value of mouse models carrying relevant mutations for examining genotype-phenotype correlations

Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses

With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining and Learning Analytics are emerging fields of research that aim to enhance the delivery of education through the application of various statistical and machine learning approaches. An extensive literature survey indicates that no significant research is available within the area of MOOC data analysis, in particular considering the behavioural patterns of users. In this paper, therefore, two sets of features, based on learner behavioural patterns, were compared in terms of their suitability for predicting the course outcome of learners participating in MOOCs. Our Exploratory Data Analysis demonstrates that there is strong correlation between click steam actions and successful learner outcomes. Various Machine Learning algorithms have been applied to enhance the accuracy of classifier models. Simulation results from our investigation have shown that Random Forest achieved viable performance for our prediction problem, obtaining the highest performance of the models tested. Conversely, Linear Discriminant Analysis achieved the lowest relative performance, though represented only a marginal reduction in performance relative to the Random Forest

Stem cell technology for neurodegenerative diseases

Over the past 20 years, stem cell technologies have become an increasingly attractive option to investigate and treat neurodegenerative diseases. In the current review, we discuss the process of extending basic stem cell research into translational therapies for patients suffering from neurodegenerative diseases. We begin with a discussion of the burden of these diseases on society, emphasizing the need for increased attention toward advancing stem cell therapies. We then explain the various types of stem cells utilized in neurodegenerative disease research, and outline important issues to consider in the transition of stem cell therapy from bench to bedside. Finally, we detail the current progress regarding the applications of stem cell therapies to specific neurodegenerative diseases, focusing on Parkinson disease, Huntington disease, Alzheimer disease, amyotrophic lateral sclerosis, and spinal muscular atrophy. With a greater understanding of the capacity of stem cell technologies, there is growing public hope that stem cell therapies will continue to progress into realistic and efficacious treatments for neurodegenerative diseases. Ann Neurol 2011;70: 353–361.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86937/1/22487_ftp.pd

Does personality affect dietary intake?

The purpose of this review is to evaluate the evidence for an association between the Big Five dimensions of personality, dietary intake, and compliance to dietary recommendations. Poor diet is a known risk factor for overweight and obesity and associated chronic lifestyle diseases and it has been proposed that personality may be linked to dietary choices. Findings from cross-sectional surveys from different countries and cultures show a positive association between Openness and consumption of fruits and vegetables and between Conscientiousness and healthy eating. Although no evidence has been found that personality dimensions are associated with adherence to dietary recommendations over time, Conscientiousness is associated with a number of prosocial and health-promoting behaviors that include avoiding alcohol-related harm, binge-drinking, and smoking, and adherence to medication regimens. With emerging evidence of an association between higher Conscientiousness and lower obesity risk, the hypothesis that higher Conscientiousness may predict adoption of healthy dietary and other lifestyle recommendations appears to be supported