Signing data citations enables data verification and citation persistence.

Commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to content drift, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. We show that content signatures enable independent verification of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe applications of signed citations to solve real-world data collection, identification, and citation challenges.

Digital Extended Specimens: Enabling an Extensible Network of Biodiversity Data Records as Integrated Digital Objects on the Internet.

The early twenty-first century has witnessed massive expansions in availability and accessibility of digital data in virtually all domains of the biodiversity sciences. Led by an array of asynchronous digitization activities spanning ecological, environmental, climatological, and biological collections data, these initiatives have resulted in a plethora of mostly disconnected and siloed data, leaving to researchers the tedious and time-consuming manual task of finding and connecting them in usable ways, integrating them into coherent data sets, and making them interoperable. The focus to date has been on elevating analog and physical records to digital replicas in local databases prior to elevating them to ever-growing aggregations of essentially disconnected discipline-specific information. In the present article, we propose a new interconnected network of digital objects on the Internet-the Digital Extended Specimen (DES) network-that transcends existing aggregator technology, augments the DES with third-party data through machine algorithms, and provides a platform for more efficient research and robust interdisciplinary discovery.

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. All of the mutations occurred in a KCTD1 region encoding a highly conserved bric-a-brac, tram track, and broad complex (BTB) domain that is required for transcriptional repressor activity. KCTD1 inhibits the transactivation of the transcription factor AP-2α (TFAP2A) via its BTB domain, and mutations in TFAP2A cause cutis aplasia in individuals with branchiooculofacial syndrome (BOFS), suggesting a potential overlap in the pathogenesis of SEN syndrome and BOFS. The identification of KCTD1 mutations in SEN syndrome reveals a role for this BTB-domain-containing transcriptional repressor during ectodermal development.

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. This project aimed to adapt a previously developed collaborative web-based appliance for remote, secure, collaborative review of crash investigations for use with CFR. METHODS: The user-centred design and evaluation process included: (1) key informant interviews and visits to CFR sessions to determine current practices, (2) an anonymous, qualitative, internet-based survey of 64 Pennsylvania CFR team leaders, (3) redesign and adaptation of the Telecenter appliance based on survey results and team feedback and (4) pilot-testing of the adapted Telecenter application at an actual local CFR. RESULTS: The qualitative informant interviews identified facilitators and barriers for adoption of the Telecenter. Facilitators included: team member training, improved communication and collaboration, more efficient reviews, and enhanced preventive efforts. Barriers identified included: concern for confidentiality and security, concern about accepting a novel CFR method, low interest in using technology, cost and maintenance. The survey of CFR team leaders identified themes for improving CFR team functioning including the need for evidence-based prevention resources, increased team training and efficient information gathering. The Telecenter was redesigned and adapted based on the information gathered and was successfully piloted for use with CFR. CONCLUSIONS: Telecenter met the design goal to improve information dissemination and identification of fatality prevention strategies for CFR.

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. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.