Vitamin K1 intake is associated with lower risk for all-cause and cardiovascular disease mortality in community-dwelling older Australian women.

BACKGROUND AND AIMS: Assessing the relationship between vitamin K1 intakes, using region-specific food databases, with both all-cause, and cardiovascular disease (CVD) mortality warrants further investigation to inform future preventative strategies. Consequently, we examined the aforementioned associations in the Perth Longitudinal Study of Ageing Women (PLSAW). METHODS AND RESULTS: 1436 community-dwelling older Australian women (mean ± SD age 75.2 ± 2.7 years) completed a validated food frequency questionnaire at baseline (1998). Vitamin K1 intake was calculated based on an Australian vitamin K food database, supplemented with published data. All-cause and CVD mortality data was obtained from linked health records. Associations were examined using restricted cubic splines within Cox-proportional hazard models, adjusted for a range of cardiovascular and lifestyle related risk factors. Over 15 years of follow-up, 601 (41.9%) women died, with 236 deaths (16.4%) due to CVD. Compared to women with the lowest vitamin K1 intakes (Quartile 1, median 49.1 µg/day), those with the highest intakes (Quartile 4, median 119.3 µg/day) had lower relative hazards for all-cause mortality (HR 0.66 95%CI 0.51-0.86) and CVD mortality (HR 0.61 95%CI 0.41-0.92). A plateau in the inverse association was observed from vitamin K1 intakes of approximately ≥80 µg/day. CONCLUSION: Higher vitamin K1 intakes were associated with lower risk for both all-cause and CVD mortality in community-dwelling older women, independent of CVD related risk factors. A higher intake of vitamin K1 rich foods, such as leafy green vegetables, may support cardiovascular health.

Medical Therapy to Prevent or Slow Progression of Aortic Stenosis: Current Evidence and Future Directions.

Degenerative aortic stenosis is a growing clinical problem owing to the high incidence in an aging population and its significant morbidity and mortality. Currently, aortic valve replacement remains the only treatment. Despite promising observational data, pharmacological management to slow or halt progression of aortic stenosis has remained elusive. Nevertheless, with a greater understanding of the mechanisms which underpin aortic stenosis, research has begun to explore novel treatment strategies. This review will explore the historical agents used to manage aortic stenosis and the emerging agents that are currently under investigation.

Time-crystalline eigenstate order on a quantum processor.

Quantum many-body systems display rich phase structure in their low-temperature equilibrium states1. However, much of nature is not in thermal equilibrium. Remarkably, it was recently predicted that out-of-equilibrium systems can exhibit novel dynamical phases2-8 that may otherwise be forbidden by equilibrium thermodynamics, a paradigmatic example being the discrete time crystal (DTC)7,9-15. Concretely, dynamical phases can be defined in periodically driven many-body-localized (MBL) systems via the concept of eigenstate order7,16,17. In eigenstate-ordered MBL phases, the entire many-body spectrum exhibits quantum correlations and long-range order, with characteristic signatures in late-time dynamics from all initial states. It is, however, challenging to experimentally distinguish such stable phases from transient phenomena, or from regimes in which the dynamics of a few select states can mask typical behaviour. Here we implement tunable controlled-phase (CPHASE) gates on an array of superconducting qubits to experimentally observe an MBL-DTC and demonstrate its characteristic spatiotemporal response for generic initial states7,9,10. Our work employs a time-reversal protocol to quantify the impact of external decoherence, and leverages quantum typicality to circumvent the exponential cost of densely sampling the eigenspectrum. Furthermore, we locate the phase transition out of the DTC with an experimental finite-size analysis. These results establish a scalable approach to studying non-equilibrium phases of matter on quantum processors.

Information scrambling in quantum circuits.

Interactions in quantum systems can spread initially localized quantum information into the exponentially many degrees of freedom of the entire system. Understanding this process, known as quantum scrambling, is key to resolving several open questions in physics. Here, by measuring the time-dependent evolution and fluctuation of out-of-time-order correlators, we experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We engineer quantum circuits that distinguish operator spreading and operator entanglement and experimentally observe their respective signatures. We show that whereas operator spreading is captured by an efficient classical model, operator entanglement in idealized circuits requires exponentially scaled computational resources to simulate. These results open the path to studying complex and practically relevant physical observables with near-term quantum processors.

Establishing and Leveraging the Expertise of Advisory Boards.

The advisory board's makeup tends to consist of professionals with domains of expertise relevant to the organization they serve. Organizations invite advisory board members to guide the organization's efforts over an extended period. Still, they do not have fiduciary, operational, or decision-making responsibilities for the organization in the way that a board of directors does. This article provides a framework for a behavior-analytic organization to (a) identify whether an advisory board would be feasible and beneficial, (b) establish an advisory board, and (c) engage and manage an advisory board to best leverage the unique skills and interests of the members. The article includes 2 examples of advisory boards to illustrate the suggested framework and recommendations.

Quantum supremacy using a programmable superconducting processor.

The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2-7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times-our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8-14 for this specific computational task, heralding a much-anticipated computing paradigm.

Using activity schedules to promote varied application use in children with autism.

We evaluated the effects of an activity schedule embedded within an iPad on varied play across applications. After establishing a pattern of repetitive gameplay, we taught three children with autism to follow the activity schedule using physical guidance. All participants increased their varied play to four applications per session and demonstrated independent and accurate activity schedule usage. The activity schedule was removed, and responding decreased to baseline levels, demonstrating the activity schedule's control over varied responding. The activity schedule was reintroduced and participant responding maintained when engaging with novel applications.

The Apprentice: an Innovative Approach to Meet the Behavior Analysis Certification Board's Supervision Standards.

The Behavior Analysis Certification Board continues to increase the standards for supervision of trainees, which is needed in order for the field to continually improve. However, this presents a challenge for organizations to meet the needs of both their clients and their supervisees based on these increasing standards. Throughout the ages, experts in all trades have passed along their knowledge and skill through apprenticeship opportunities. An apprenticeship supervision model is described that allows Board Certified Behavior Analysts to supervise future behavior analysts by mentoring, educating, and training supervisees on the science of human behavior in a format that is mutually beneficial. This innovative supervision model is discussed as it applies to an applied behavior analysis human service organization with the goal of creating a system that results in high-quality supervision in a cost-effective manner while providing maximal learning for the supervisee. The organization's previous supervision difficulties are described prior to implementing the apprenticeship supervision model, and the benefits of developing and using the apprenticeship supervision model are outlined.

COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data.

Neuroimaging data collection is inherently expensive. Maximizing the return on investment in neuroimaging studies requires that neuroimaging data be re-used whenever possible. In an effort to further scientific knowledge, the COINS Data Exchange (DX) (http://coins.mrn.org/dx) aims to make data sharing seamless and commonplace. DX takes a three-pronged approach towards improving the overall state of data sharing within the neuroscience community. The first prong is compiling data into one location that has been collected from all over the world in many different formats. The second prong is curating the data so that it can be stored in one consistent format and so that data QA/QC measures can be assured. The third prong is disseminating the data so that it is easy to consume and straightforward to interpret. This paper explains the concepts behind each prong and describes some challenges and successes that the Data Exchange has experienced.

Harnessing modern web application technology to create intuitive and efficient data visualization and sharing tools.

Neuroscientists increasingly need to work with big data in order to derive meaningful results in their field. Collecting, organizing and analyzing this data can be a major hurdle on the road to scientific discovery. This hurdle can be lowered using the same technologies that are currently revolutionizing the way that cultural and social media sites represent and share information with their users. Web application technologies and standards such as RESTful webservices, HTML5 and high-performance in-browser JavaScript engines are being utilized to vastly improve the way that the world accesses and shares information. The neuroscience community can also benefit tremendously from these technologies. We present here a web application that allows users to explore and request the complex datasets that need to be shared among the neuroimaging community. The COINS (Collaborative Informatics and Neuroimaging Suite) Data Exchange uses web application technologies to facilitate data sharing in three phases: Exploration, Request/Communication, and Download. This paper will focus on the first phase, and how intuitive exploration of large and complex datasets is achieved using a framework that centers around asynchronous client-server communication (AJAX) and also exposes a powerful API that can be utilized by other applications to explore available data. First opened to the neuroscience community in August 2012, the Data Exchange has already provided researchers with over 2500 GB of data.

Automated collection of imaging and phenotypic data to centralized and distributed data repositories.

Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite). COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010; Scott et al., 2011). It was initially developed for the investigators at the Mind Research Network (MRN), but is now available to neuroimaging institutions worldwide. Self Assessment (SA) is an application embedded in the Assessment Manager (ASMT) tool in COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. Instruments (surveys) are created through ASMT and include many unique question types and associated SA features that can be implemented to help the flow of assessment administration. SA provides an instrument queuing system with an easy-to-use drag and drop interface for research staff to set up participants' queues. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at MRN. This data is only accessible by users that have explicit permission to access the data through their COINS user accounts and access to MRN network. This allows for high volume data collection and with minimal user access to PHI (protected health information). An added benefit to using COINS is the ability to collect, store and share imaging data and assessment data with no interaction with outside tools or programs. All study data collected (imaging and assessment) is stored and exported with a participant's unique subject identifier so there is no need to keep extra spreadsheets or databases to link and keep track of the data. Data is easily exported from COINS via the Query Builder and study portal tools, which allow fine grained selection of data to be exported into comma separated value file format for easy import into statistical programs. There is a great need for data collection tools that limit human intervention and error while at the same time providing users with intuitive design. COINS aims to be a leader in database solutions for research studies collecting data from several different modalities.

An open science resource for establishing reliability and reproducibility in functional connectomics.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

Virtual and concrete manipulatives: a comparison of approaches for solving mathematics problems for students with autism spectrum disorder.

Students with autism spectrum disorder (ASD) are included in general education classes and expected to participate in general education content, such as mathematics. Yet, little research explores academically-based mathematics instruction for this population. This single subject alternating treatment design study explored the effectiveness of concrete (physical objects that can be manipulated) and virtual (3-D objects from the Internet that can be manipulated) manipulatives to teach single- and double-digit subtraction skills. Participants in this study included three elementary-aged students (ages ranging from 6 to 10) diagnosed with ASD. Students were selected from a clinic-based setting, where all participants received medically necessary intensive services provided via one-to-one, trained therapists. Both forms of manipulatives successfully assisted students in accurately and independently solving subtraction problem. However, all three students demonstrated greater accuracy and faster independence with the virtual manipulatives as compared to the concrete manipulatives. Beyond correctly solving the subtraction problems, students were also able to generalize their learning of subtraction through concrete and virtual manipulatives to more real-world applications.

Prolonged avoidance of repeat surgery with endoscopic balloon dilatation of anastomotic strictures in Crohn's disease.

BACKGROUND AND AIMS: There is a high rate of stricturing post-operative recurrence in Crohn's disease (CD) particularly at sites of surgical anastomosis, and over 50% of these patients will require a repeat resection. Endoscopic dilatation of anastomotic strictures is an alternative to surgical resection in selected patients. We aimed to evaluate the safety and long term efficacy of endoscopic balloon dilatation of symptomatic anastomotic strictures in CD. METHODS: Retrospective analysis of a prospectively maintained inflammatory bowel disease database of patients attending a single academic centre (n=1244 patients with CD) who underwent dilatation. RESULTS: Fifty-five dilatations were performed in 31 patients (mean age 43 ± SD 12, 47% female). Median follow-up period was 46 months (IQR 14-62). Ninety percent of patients had successful initial dilatation and no complications occurred. Six (21%) avoided further dilatations or surgery in the follow-up period. Stricture recurrence was detected in 22 patients; 15 (54%) patients had repeat dilatations and seven (25%) went straight to surgery. Eight (28%) patients were managed with repeat dilatations of the stricture (median dilatations=2 range 2-6) and seven (25%) required surgery despite repeat dilatations. Median time from first dilatation to repeat surgery was 14.5 months (IQR 3-28) and to repeat dilatation was 13.8 months (IQR 4-28). There was no difference in immunomodulator use, biologic use and smoking status between the groups requiring surgery versus dilatation only. CONCLUSION: Endoscopic balloon dilatation of anastomotic strictures is safe and effective in providing symptomatic relief in CD patients. Forty-five percent of patients had a sustained response to single/serial balloon dilatation with avoidance of further surgical resection for a median interval of 46 months. Post-operative medical therapy and smoking status did not predict requirement for recurrent dilatation or surgery.

The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry.

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6-85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources.

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining.

Detailed analysis of a contiguous 22-Mb region of the maize genome.

Most of our understanding of plant genome structure and evolution has come from the careful annotation of small (e.g., 100 kb) sequenced genomic regions or from automated annotation of complete genome sequences. Here, we sequenced and carefully annotated a contiguous 22 Mb region of maize chromosome 4 using an improved pseudomolecule for annotation. The sequence segment was comprehensively ordered, oriented, and confirmed using the maize optical map. Nearly 84% of the sequence is composed of transposable elements (TEs) that are mostly nested within each other, of which most families are low-copy. We identified 544 gene models using multiple levels of evidence, as well as five miRNA genes. Gene fragments, many captured by TEs, are prevalent within this region. Elimination of gene redundancy from a tetraploid maize ancestor that originated a few million years ago is responsible in this region for most disruptions of synteny with sorghum and rice. Consistent with other sub-genomic analyses in maize, small RNA mapping showed that many small RNAs match TEs and that most TEs match small RNAs. These results, performed on approximately 1% of the maize genome, demonstrate the feasibility of refining the B73 RefGen_v1 genome assembly by incorporating optical map, high-resolution genetic map, and comparative genomic data sets. Such improvements, along with those of gene and repeat annotation, will serve to promote future functional genomic and phylogenomic research in maize and other grasses.