A machine learning system for automated whole-brain seizure detection

Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier

Iterative Assembly of Macrocyclic Lactones using Successive Ring Expansion Reactions

Macrocyclic lactones can be prepared from lactams and hydroxyacid derivatives via an efficient 3- or 4-atom iterative ring expansion protocol. The products can also be expanded using amino acid-based linear fragments, meaning that macrocycles with precise sequences of hydroxy- and amino acids can be assembled in high yields by 'growing' them from smaller rings, using a simple procedure in which high dilution is not required. The method should significantly expedite the practical synthesis of diverse nitrogen containing macrolide frameworks

Budgeting for job creation in social welfare services: exploring EPWP opportunities

SeptemberThe topic of budgeting for social welfare services as indicated within this conference assumes that the budgeting for welfare services - in fact any public service - is not a contested terrain. The South African budget process is both a political and technical process. It is a political process because the national Cabinet, on considering a range of public financing and service delivery priorities, is responsible for budget policy decisions. These decisions are taken against the backdrop of both marginal and average public finance requests as a result of an elaborate policy and service delivery priority determination process that evolves from the Presidents' State of Nation Address to the final Budget Statement (announced during February of each year)

Sensitivity to Reward and Punishment in Borderline and Avoidant Personality Disorders

The authors compared self-reported and behavioral responses to reward and punishment in individuals diagnosed with borderline personality disorder (BPD) or avoidant personality disorder (APD) relative to a healthy comparison (HC) group. As predicted, self-reported sensitivity to reward was significantly higher in the BPD group than in the APD and HC groups. Also as predicted, self-reported sensitivity to punishment was significantly elevated in both disordered groups but significantly higher in APD than in BPD. These hypothesized patterns were also evident in responses to behavioral tasks: Participants with BPD made more errors of commission and fewer errors of omission than HC participants on a passive avoidance learning task, and participants with APD showed greater reactivity to losses than other participants on a probabilistic reversal learning task. Results help characterize differences between these two disorders