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There is now an unprecedented opportunity to improve the care of the over 5 million people who are living with Alzheimer's disease and related dementias and many more with cognitive impairment due to brain injury, systemic diseases, and other causes. The introduction of a new Medicare care planning benefit-long sought openly by advocacy organizations and clinicians and badly needed by patients and families-could greatly improve health care quality, but only if widely and fully implemented. We describe the components of this new benefit and its promise of better clinical care, as well as its potential to create a new platform for clinical and health outcomes research. We highlight external factors-and some that are internal to the benefit structure itself-that challenge the full realization of its value, and we call for broad public and professional engagement to ensure that it will not fail.

With increasing numbers of people with Alzheimer's and other dementias across the globe, many countries have developed national plans to deal with the resulting challenges. In the United States, the National Alzheimer's Project Act, signed into law in 2011, required the creation of such a plan with annual updates thereafter. Pursuant to this, the US Department of Health and Human Services (HHS) released the National Plan to Address Alzheimer's Disease in 2012, including an ambitious research goal of preventing and effectively treating Alzheimer's disease by 2025. To guide investments, activities, and the measurement of progress toward achieving this 2025 goal, in its first annual plan update (2013) HHS also incorporated into the plan a set of short, medium and long-term milestones. HHS further committed to updating these milestones on an ongoing basis to account for progress and setbacks, and emerging opportunities and obstacles. To assist HHS as it updates these milestones, the Alzheimer's Association convened a National Plan Milestone Workgroup consisting of scientific experts representing all areas of Alzheimer's and dementia research. The workgroup evaluated each milestone and made recommendations to ensure that they collectively constitute an adequate work plan for reaching the goal of preventing and effectively treating Alzheimer's by 2025. This report presents these Workgroup recommendations.

With increasing numbers of people with Alzheimer's and other dementias across the globe, many countries have developed national plans to deal with the resulting challenges. In the United States, the National Alzheimer's Project Act, signed into law in 2011, required the creation of such a plan with annual updates thereafter. Pursuant to this, the US Department of Health and Human Services (HHS) released the National Plan to Address Alzheimer's Disease in 2012, including an ambitious research goal of preventing and effectively treating Alzheimer's disease by 2025. To guide investments, activities, and the measurement of progress toward achieving this 2025 goal, in its first annual plan update (2013) HHS also incorporated into the plan a set of short, medium and long-term milestones. HHS further committed to updating these milestones on an ongoing basis to account for progress and setbacks, and emerging opportunities and obstacles. To assist HHS as it updates these milestones, the Alzheimer's Association convened a National Plan Milestone Workgroup consisting of scientific experts representing all areas of Alzheimer's and dementia research. The workgroup evaluated each milestone and made recommendations to ensure that they collectively constitute an adequate work plan for reaching the goal of preventing and effectively treating Alzheimer's by 2025. This report presents these Workgroup recommendations.

Revised diagnostic criteria for Alzheimer disease (AD) acknowledge a key role of imaging biomarkers for early diagnosis. Diagnostic accuracy depends on which marker (i.e., amyloid imaging, ¹⁸F-fluorodeoxyglucose [FDG]-PET, SPECT, MRI) as well as how it is measured ("metric": visual, manual, semiautomated, or automated segmentation/computation). We evaluated diagnostic accuracy of marker vs metric in separating AD from healthy and prognostic accuracy to predict progression in mild cognitive impairment. The outcome measure was positive (negative) likelihood ratio, LR+ (LR-), defined as the ratio between the probability of positive (negative) test outcome in patients and the probability of positive (negative) test outcome in healthy controls. Diagnostic LR+ of markers was between 4.4 and 9.4 and LR- between 0.25 and 0.08, whereas prognostic LR+ and LR- were between 1.7 and 7.5, and 0.50 and 0.11, respectively. Within metrics, LRs varied up to 100-fold: LR+ from approximately 1 to 100; LR- from approximately 1.00 to 0.01. Markers accounted for 11% and 18% of diagnostic and prognostic variance of LR+ and 16% and 24% of LR-. Across all markers, metrics accounted for an equal or larger amount of variance than markers: 13% and 62% of diagnostic and prognostic variance of LR+, and 29% and 18% of LR-. Within markers, the largest proportion of diagnostic LR+ and LR- variability was within ¹⁸F-FDG-PET and MRI metrics, respectively. Diagnostic and prognostic accuracy of imaging AD biomarkers is at least as dependent on how the biomarker is measured as on the biomarker itself. Standard operating procedures are key to biomarker use in the clinical routine and drug trials.

We present a unified mathematical framework for analyzing the tradeoffs between parallelism and storage allocation within a parallelizing compiler. Using this framework, we show how to find a good storage mapping for a given schedule, a good schedule for a given storage mapping, and a good storage mapping that is valid for all legal (one-dimensional affine) schedules. We consider storage mappings that collapse one dimension of a multidimensional array, and programs that are in a single assignment form and accept a one-dimensional affine schedule. Our method combines affine scheduling techniques with occupancy vector analysis and incorporates general affine dependences across statements and loop nests. We formulate the constraints imposed by the data dependences and storage mappings as a set of linear inequalities, and apply numerical programming techniques to solve for the shortest occupancy vector. We consider our method to be a first step towards automating a procedure that finds the optimal tradeoff between parallelism and storage space. Categories and Subject Descriptors: D.3.4 [Programming Languages]: Processors--compilation; optimization; memory management General Terms: Languages Additional Key Words and Phrases: Automatic parallelization, affine scheduling, storage optimization, occupancy vectors, polyhedral model, affine recurrence equations ACM Reference Format: DOI = 10.1145/1286821.1286825 http://doi.acm.org/10.1145/1286821.1286825

Abstract Background Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Results Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Qα value of 77.6% and an SOVα value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. Conclusion The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.

Abstract Background Published descriptions of biology protocols are often ambiguous and incomplete, making them difficult to replicate in other laboratories. However, there is increasing benefit to formalizing the descriptions of protocols, as laboratory automation systems (such as microfluidic chips) are becoming increasingly capable of executing them. Our goal in this paper is to improve both the reproducibility and automation of biology experiments by using a programming language to express the precise series of steps taken. Results We have developed BioCoder, a C++ library that enables biologists to express the exact steps needed to execute a protocol. In addition to being suitable for automation, BioCoder converts the code into a readable, English-language description for use by biologists. We have implemented over 65 protocols in BioCoder; the most complex of these was successfully executed by a biologist in the laboratory using BioCoder as the only reference. We argue that BioCoder exposes and resolves ambiguities in existing protocols, and could provide the software foundations for future automation platforms. BioCoder is freely available for download at http://research.microsoft.com/en-us/um/india/projects/biocoder/. Conclusions BioCoder represents the first practical programming system for standardizing and automating biology protocols. Our vision is to change the way that experimental methods are communicated: rather than publishing a written account of the protocols used, researchers will simply publish the code. Our experience suggests that this practice is tractable and offers many benefits. We invite other researchers to leverage BioCoder to improve the precision and completeness of their protocols, and also to adapt and extend BioCoder to new domains.