Towards real-time communication between neurophysiological data sources and simulator-based brain biomimetic models.

Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms.

Mutations in KCTD1 cause scalp-ear-nipple syndrome.

Scalp-ear-nipple (SEN) syndrome is a rare, autosomal-dominant disorder characterized by cutis aplasia of the scalp; minor anomalies of the external ears, digits, and nails; and malformations of the breast. We used linkage analysis and exome sequencing of a multiplex family affected by SEN syndrome to identify potassium-channel tetramerization-domain-containing 1 (KCTD1) mutations that cause SEN syndrome. Evaluation of a total of ten families affected by SEN syndrome revealed KCTD1 missense mutations in each family tested.

Telecenter for secure, remote, collaborative child fatality review.

Objectives: Child fatality review (CFR) is the systematic, interdisciplinary, multi-agency examination of paediatric deaths. While CFR findings may influence policies and reduce preventable fatalities, limited resources challenge accurate CFR data collection and prevention recommendations. Therefore, using technology to improve efficiency of reviews and access to remote participants could enhance the CFR experience.

Apical ballooning syndrome secondary to nasal decongestant abuse.

We describe a typical case of apical ballooning syndrome in an octogenarian female patient with left ventricular wall motion abnormality on electrocardiography, whose ventricular function returned to normal. The patient has allergic rhinitis and had used nasal decongestant excessively a few hours prior to the episode of pain.

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control.