An Optical Implementation of Adaptive Resonance Utilizing Phase Conjugation

A novel adaptive resonance theory (ART) device has been conceived that is fully optical in the input-output processing path. This device is based on holographic information processing in a phase-conjugating crystal. This sets up an associative pattern retrieval in a resonating loop utilizing angle-multiplexed reference beams for pattern classification. A reset mechanism is used to reject any given beam, allowing an ART search strategy. The design is similar to that of an existing nonlearning optical associative memory, but is does allow learning and makes use of information the other device discards. This new device is expected to offer higher information storage density that alternative ART implementation

An Optical Adaptive Resonance Neural Network Utilizing Phase Conjugation

An adaptive resonance (ART) device has been conceived that is fully optical in the input-output processing path. It is based on holographic information processing in a phase-conjugating crystal. This sets up an associative pattern retrieval in a resonating loop utilizing angle-multiplexed reference beams for pattern classification. A reset mechanism is used to reject any given beam, allowing an ART search strategy. The design is similar to an existing nonlearning optical associative memory, but it does allow learning and makes use of information the other device discards. This device is expected to offer higher information storage density than alternative ART implementations

A Neural Architecture for Unsupervised Learning with Shift, Scale and Rotation Invariance, Efficient Software Simulation Heuristics, and Optoelectronic Implementation

A simple modification of the adaptive resonance theory (ART) neural network allows shift, scale and rotation invariant learning. The authors point out that this can be accomplished as a neural architecture by modifying the standard ART with hardwired interconnects that perform a Fourier-Mellin transform, and show how to modify the heuristics for efficient simulation of ART architectures to accomplish the additional innovation. Finally, they discuss the implementation of this in optoelectronic hardware, using a modification of the Van der Lugt optical correlato

An Industrial Application to Neural Networks to Reusable Design

Summary form only given, as follows. The feasibility of training an adaptive resonance theory (ART-1) network to first cluster aircraft parts into families, and then to recall the most similar family when presented a new part has been demonstrated, ART-1 networks were used to adaptively group similar input vectors. The inputs to the network were generated directly from computer-aided designs of the parts and consist of binary vectors which represent bit maps of the features of the parts. This application, referred to as group technology, is of large practical value to industry, making it possible to avoid duplication of design efforts

Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient’s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Systemic outcomes of (Pyr(1))-apelin-13 infusion at mid-late pregnancy in a rat model with preeclamptic features

Preeclampsia is a syndrome with diverse clinical presentation that currently has no cure. The apelin receptor system is a pleiotropic pathway with a potential for therapeutic targeting in preeclampsia. We established the systemic outcomes of (Pyr(1))-apelin-13 administration in rats with preeclamptic features (TGA-PE, female transgenic for human angiotensinogen mated to male transgenic for human renin). (Pyr(1))-apelin-13 (2 mg/kg/day) or saline was infused in TGA-PE rats via osmotic minipumps starting at day 13 of gestation (GD). At GD20, TGA-PE rats had higher blood pressure, proteinuria, lower maternal and pup weights, lower pup number, renal injury, and a larger heart compared to a control group (pregnant Sprague-Dawley rats administered vehicle). (Pyr(1))-apelin-13 did not affect maternal or fetal weights in TGA-PE. The administration of (Pyr(1))-apelin-13 reduced blood pressure, and normalized heart rate variability and baroreflex sensitivity in TGA-PE rats compared to controls. (Pyr(1))-apelin-13 increased ejection fraction in TGA-PE rats. (Pyr(1))-apelin-13 normalized proteinuria in association with lower renal cortical collagen deposition, improved renal pathology and lower immunostaining of oxidative stress markers (4-HNE and NOX-4) in TGA-PE. This study demonstrates improved hemodynamic responses and renal injury without fetal toxicity following apelin administration suggesting a role for apelin in the regulation of maternal outcomes in preeclampsia

Predicting Radiotherapy Patient Outcomes with Real-Time Clinical Data Using Mathematical Modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient\u27s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Phase II study of induction chemotherapy with TPF followed by radioimmunotherapy with Cetuximab and intensity-modulated radiotherapy (IMRT) in combination with a carbon ion boost for locally advanced tumours of the oro-, hypopharynx and larynx - TPF-C-HIT

<p>Abstract</p> <p>Background</p> <p>Long-term locoregional control in locally advanced squamous cell carcinoma of the head and neck (SCCHN) remains challenging. While recent years have seen various approaches to improve outcome by intensification of treatment schedules through introduction of novel induction and combination chemotherapy regimen and altered fractionation regimen, patient tolerance to higher treatment intensities is limited by accompanying side-effects. Combined radioimmunotherapy with cetuximab as well as modern radiotherapy techniques such as intensity-modulated radiotherapy (IMRT) and carbon ion therapy (C12) are able to limit toxicity while maintaining treatment effects. In order to achieve maximum efficacy with yet acceptable toxicity, this sequential phase II trial combines induction chemotherapy with docetaxel, cisplatin, and 5-FU (TPF) followed by radioimmunotherapy with cetuximab as IMRT plus carbon ion boost. We expect this approach to result in increased cure rates with yet manageable accompanying toxicity.</p> <p>Methods/design</p> <p>The TPF-C-HIT trial is a prospective, mono-centric, open-label, non-randomized phase II trial evaluating efficacy and toxicity of the combined treatment with IMRT/carbon ion boost and weekly cetuximab in 50 patients with histologically proven locally advanced SCCHN following TPF induction chemotherapy. Patients receive 24 GyE carbon ions (8 fractions) and 50 Gy IMRT (2.0 Gy/fraction) in combination with weekly cetuximab throughout radiotherapy. Primary endpoint is locoregional control at 12 months, secondary endpoints are disease-free survival, progression-free survival, overall survival, acute and late radiation effects as well as any adverse events of the treatment as well as quality of life (QoL) analyses.</p> <p>Discussion</p> <p>The primary objective of TPF-C-HIT is to evaluate efficacy and toxicity of cetuximab in combination with combined IMRT/carbon ion therapy following TPF induction in locally advanced SCCHN.</p> <p>Trial Registration</p> <p>Clinical Trial Identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01245985">NCT01245985</a> (clinicaltrials.gov)</p> <p>EudraCT number: 2009 - 016489- 10</p

Swallowing, nutrition and patient-rated functional outcomes at 6 months following two non-surgical treatments for T1-T3 oropharyngeal cancer

Altered fractionation radiotherapy with concomitant boost (AFRT-CB) may be considered an alternative treatment for patients not appropriate for chemoradiation (CRT). As functional outcomes following AFRT-CB have been minimally reported, this exploratory paper describes the outcomes of patients managed with AFRT-CB or CRT at 6 months post-treatment