A Commitment to Open Source in Neuroscience

Modern neuroscience increasingly relies on custom-developed software, but much of this is not being made available to the wider community. A group of researchers are pledging to make code they produce for data analysis and modeling open source, and are actively encouraging their colleagues to follow suit

libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.

NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment

SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models.

There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a variety of simulation engines and hardware platforms. Layered XML model specification formats solve many of these problems, however they are difficult to write and visualise without tools. Here we describe a new graphical software tool, SpineCreator, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming. We demonstrate the tool through the reproduction and visualisation of published models and show simulation results using code generation interfaced directly into SpineCreator. As a unique application for the graphical creation of neural networks, SpineCreator represents an important step forward for neuronal modelling

Extended abdominoperineal resection in women: the barbadian experience

BACKGROUND AND OBJECTIVES: We report our results of a selective approach to primary direct appositional vaginal repair versus transverse rectus abdominis flap repair (TRAM) in patients with extensive rectal/anal cancer or in cases with primary cancer of cervix, vagina or vulva involving the anal canal and anal sphincters. METHODS: Eighteen female patients (mean age: 62.9 years; range: 44–81 years) with a median follow-up of 14 months (range: 2–36 months) undergoing extended abdominoperineal reconstruction with total mesorectal excision between May 2002 and September 2005, were studied. RESULTS: Twelve patients underwent an extended abdominoperineal resection with hysterectomy and vaginectomy, with 6 patients undergoing primary TRAM flap reconstruction following pelvic exenteration. Exenterative procedures were performed in 2 cases of primary vaginal cancer, following Wertheim hysterectomy for carcinoma of the cervix with recurrence after radiation and in 2 further cases of anal cancer with extensive pelvic recurrence after primary chemoradiation. Fifteen cases are alive on follow-up with no evidence of disease; 2 patients who had recurrent carcinoma of the cervix and who underwent TRAM flap reconstruction, have recurrent disease after 5 and 6 months of follow-up, respectively. DISCUSSION: Our experience shows that careful primary closure of an extended abdominoperineal resection wound is effective and safe. Our one case of wound breakdown after primary repair underwent external beam and intracavitary irradiation primarily with wound breakdown of a primary repair followed by a delayed pedicled graciloplasty. TRAM flap reconstruction has been reserved in our unit for patients undergoing total pelvic extenteration. In general, we would recommend the use of TRAM flap reconstruction in younger sexually active patients where there has been external irradiation combined with brachytherapy

Toward standard practices for sharing computer code and programs in neuroscience

Computational techniques are central in many areas of neuroscience and are relatively easy to share. This paper describes why computer programs underlying scientific publications should be shared and lists simple steps for sharing. Together with ongoing efforts in data sharing, this should aid reproducibility of research.This article is based on discussions from a workshop to encourage sharing in neuroscience, held in Cambridge, UK, December 2014. It was financially supported and organized by the International Neuroinformatics Coordinating Facility (http://www.incf.org), with additional support from the Software Sustainability institute (http://www.software.ac.uk). M.H. was supported by funds from the German federal state of Saxony-Anhalt and the European Regional Development Fund (ERDF), Project: Center for Behavioral Brain Sciences

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience

Exhaustive exercise training enhances aerobic capacity in American alligator (Alligator mississippiensis)

The oxygen transport system in mammals is extensively remodelled in response to repeated bouts of activity, but many reptiles appear to be ‘metabolically inflexible’ in response to exercise training. A recent report showed that estuarine crocodiles (Crocodylus porosus) increase their maximum metabolic rate in response to exhaustive treadmill training, and in the present study, we confirm this response in another crocodilian, American alligator (Alligator mississippiensis). We further specify the nature of the crocodilian training response by analysing effects of training on aerobic [citrate synthase (CS)] and anaerobic [lactate dehydrogenase (LDH)] enzyme activities in selected skeletal muscles, ventricular and skeletal muscle masses and haematocrit. Compared to sedentary control animals, alligators regularly trained for 15 months on a treadmill (run group) or in a flume (swim group) exhibited peak oxygen consumption rates higher by 27 and 16%, respectively. Run and swim exercise training significantly increased ventricular mass (~11%) and haematocrit (~11%), but not the mass of skeletal muscles. However, exercise training did not alter CS or LDH activities of skeletal muscles. Similar to mammals, alligators respond to exercise training by increasing convective oxygen transport mechanisms, specifically heart size (potentially greater stroke volume) and haematocrit (increased oxygen carrying-capacity of the blood). Unlike mammals, but similar to squamate reptiles, alligators do not also increase citrate synthase activity of the skeletal muscles in response to exercise

Comprehensive early intervention for patients with first-episode psychosis in Japan (J-CAP): study protocol for a randomised controlled trial

<p>Abstract</p> <p>Introduction</p> <p>Comprehensive approaches for patients with psychotic symptoms play essential roles in the symptomatic and functional outcomes of patients, especially during disease onset. In Japan, the shortage of mental health services, particularly for outpatients, and community-based supports has been a major problem. The purpose of this trial is to investigate the effectiveness and affordability of 18-month comprehensive early intervention services for patients with first-episode psychosis compared with typical treatment.</p> <p>Methods</p> <p>This interventional, parallel, single-blinded (open but blinded raters trial) was effectively designed. The participants are patients with a diagnosis of F2 or F3 (International Classification of Disease, 10 th revision), with psychotic symptoms. The inclusion criteria were an age of 15-35 years, onset of psychotic symptoms within 5 years, first-episode psychosis, and residence in the catchment area of each site. Allocation will be conducted equally between case management and standard care groups. After enrollment, standard care will be provided for both groups, and community-based care to promote recovery for 18 months will be provided for the comprehensive approach group. The primary outcome will be the function domain of the global assessment of functioning scores at 18 months after enrollment. Data assessment will be performed at enrollment and 18, 36, and 60 months after enrollment. The target sample size will be 150, and registration will occur from March 1, 2011, to September 30, 2012.</p> <p>Discussion</p> <p>This trial will provide promising results about the effectiveness and cost-effectiveness of early intervention services in Japan to improve the quality and quantity of community mental health services.</p> <p>Trial registration</p> <p>This trial was registered in The University Hospital Medical Information Network Clinical Trials Registry (No. UMIN000005092).</p

The role of ligand efficiency metrics in drug discovery

The judicious application of ligand or binding efficiencies, which quantify the molecular properties required to gain binding affinity for a drug target, is gaining traction in the selection and optimisation of fragments, hits, and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimised ligand efficiency values for their target. Optimising ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the developability of drug candidates