Terry Hermiston, Ph.D.
Vice President, US Biologics Research Site Head, US Innovation Center Bayer Healthcare, USA
ColoAd1 – An oncolytic adenovirus derived by directed evolution
Attempts at developing oncolytic viruses have been primarily based on rational design. However, this approach has been met with limited success. An alternative approach employs directed evolution as a means of producing highly selective and potent anticancer viruses. In this method, viruses are grown under conditions that enrich and maximize viral diversity and then passaged under conditions meant to mimic those encountered in the human cancer microenvironment. Using the “Directed Evolution” methodology, we have generated ColoAd1, a novel chimeric oncolytic adenovirus. In vitro, this virus demonstrated a >2 log increase in both potency and selectivity when compared to ONYX-015 on colon cancer cells. These results were further supported by in vivo and ex vivo studies. Importantly, these results have validated this methodology as a new general approach for deriving clinically-relevant, highly potent anti-cancer virotherapies. This virus is currently in clinical trials as a novel treatment for cancer.
David Ibanez, Laura Dubreuil and Alejandro Rier
Neurofeedback (NF) is a type of biofeedback that uses real time display of electroencephalography to illustrate brain activity. EEG features are extracted and displayed allowing the user to, with practice, modulate their temporal evolution. Neurofeedback training has many therapeutic applications such as attention deficit hyperactivity disorder (ADHD), migraine, depression or conduct disorders. This document presents NeuroSurfer, a novel general-purpose tool for neurofeedback training with a use case of attention deficit hyperactivity disorder (ADHD) treatment.
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.