Beyond "Safe and Effective": The urgent need for high-impact smoking cessation medications.

Smoking cessation medications (SCMs) are an evidence-based cornerstone of comprehensive tobacco control programs globally. However, the impact of SCMs on population smoking prevalence is controversial, with inconsistencies between randomized controlled trials (RCTs) and population-based observational studies. We estimated SCM impact on permanent cessation and population smoking prevalence by extrapolating efficacy estimates from meta-analyses of RCTs, using the standard population impact formula: efficacy*reach. We calculated the potential SCM impact under a range of assumptions for permanent cessation (20%,14%), behavioral support (yes/no), reach (40%-2%), and underlying smoking prevalence. Assuming behavioral support for all, depending on reach, 8%-0.3% of smokers are expected to quit permanently. Without behavioral support, permanent cessation is estimated to be 6.4%-0.2%. Assuming an underlying population smoking prevalence of 14%, (current U.S. prevalence), the maximum impact on population smoking prevalence is 1.12%. Impact on prevalence increases with increasing underlying country-specific levels of prevalence. With current U.S. levels of reach, behavioral support and smoking prevalence, we estimate that, based on a single course of treatment, 2.3% of smokers would quit permanently, contributing to a 0.3% decrease in population level smoking prevalence. Even under ideal conditions, the potential of current first-line SCMs to increase cessation in a substantial proportion of smokers, and reduce population smoking prevalence, is limited. In order to avert the predicted billion tobacco-caused deaths in this century, "safe and effective" medications are not sufficient: SCMs with high population impact are urgently needed. Policies to ensure the availability and accessibility of highly efficacious SCMs, with behavioral support, are crucial.

Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy.

The population of adults with Alzheimer's disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, "Anosognosia dementia" and "Insightful dementia", differentiate between severe participants based on clinical characteristics and biomarkers. The "Uncompensated mild cognitive impairment (MCI)" subtype, demonstrates clinical, demographic and imaging differences from the "Affective MCI" subtype. Differences were also observed between the "Worried Well" and "Healthy" clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.

Hierarchical Data-Driven Analysis of Clinical Symptoms Among Patients With Parkinson's Disease.

Mutations in the LRRK2 and GBA genes are the most common inherited causes of Parkinson's disease (PD). Studies exploring phenotypic differences based on genetic status used hypothesis-driven data-gathering and statistical-analyses focusing on specific symptoms, which may influence the validity of the results. We aimed to explore phenotypic expression in idiopathic PD (iPD) patients, G2019S-LRRK2-PD, and GBA-PD using a data-driven approach, allowing screening of large numbers of features while controlling selection bias. Data was collected from 1525 Ashkenazi Jews diagnosed with PD from the Tel-Aviv Medical center; 161 G2019S-LRRK2-PD, 222 GBA-PD, and 1142 iPD (no G2019S-LRRK2 or any of the 7 AJ GBA mutations tested). Data included 771 measures: demographics, cognitive, physical and neurological functions, performance-based measures, and non-motor symptoms. The association of the genotypes with each of the measures was tested while accounting for age at motor symptoms onset, gender, and disease duration; p-values were reported and corrected in a hierarchical approach for an average over the selected measures false discovery rate control, resulting in 32 measures. GBA-PD presented with more severe symptoms expression while LRRK2-PD had more benign symptoms compared to iPD. GBA-PD presented greater cognitive and autonomic involvement, more frequent hyposmia and REM sleep behavior symptoms while these were less frequent among LRRK2-PD compared to iPD. Using a data-driven analytical approach strengthens earlier studies and extends them to portray a possible unique disease phenotype based on genotype among AJ PD. Such findings could help direct a more personalized therapeutic approach.

Current and Potential Approaches for Defining Disease Signatures: a Systematic Review.

Identifying disease signatures in order to facilitate accurate diagnosis/treatment has been the focus of research efforts in the last decade. However, the term "disease signature" has not been properly defined, resulting in inconsistencies between studies, as well as limited ability to fully utilize the tools/information available in the evolving field of healthcare big data. Research was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. The search (in PubMed, Cochrane, and Web of Science) was limited to English articles published up to 31/12/2016. The search string was "disease signature" OR "disease signatures" OR "disease fingerprint" OR "disease fingerprints" OR "subtype signature" OR "subtype signatures" OR "subgroup signature" OR "subgroup signatures." The full text of the articles was reviewed to determine the meaning of the phrase "disease signature" as well as the context of its use. Of 285 articles identified in the search, 129 were included in the final analysis. The term disease signature was first found in an article from 2001. In the last 10 years, the use of the term increased by approximately ninefold, which is double the general increase in the number of published articles. Only one article attempted to define the term. The two major medical fields where the term was used were oncology (31%) and neurology (20%); 71% of the identified articles used a single biomarker to define the term, 13% of the articles used a pair of biomarkers, and 16% used signatures with multiple biomarker; in 42% of the identified articles, genomic biomarkers were used for the signature, in 17% measurements of biochemical compounds in body fluids, and in 10%, changes in imaging studies were used for the signature. Our findings identified a lack of consistency in defining the term disease signature. We suggest a novel hierarchical multidimensional concept for this term that would combine both current approaches for identifying diseases (one focusing on undesired effects of the disease and the other on its causes). This model can improve disease signature definition consistency which will enable to generalize and classify diseases, resulting in more precise treatments and better outcomes. Ultimately, this model could lead to developing a statistical confidence in a disease signature that would allow physicians/patients to estimate the precision of the diagnosis, which, in turn, may have important implications on patients' prognosis and treatment.

The Importance of Nonlinear Transformations Use in Medical Data Analysis.

BACKGROUND: The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. OBJECTIVE: Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. METHODS: We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. RESULTS: Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). CONCLUSIONS: This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results.

Diminishing benefit of smoking cessation medications during the first year: a meta-analysis of randomized controlled trials.

BACKGROUND AND AIMS: Although smoking cessation medications have shown effectiveness in increasing abstinence in randomized controlled trials (RCTs), it is unclear to what extent benefits persist over time. This paper assesses whether the benefits of smoking cessation medications decline over the first year. METHODS: We selected studies from three systematic reviews published by the Cochrane Collaboration. RCTs of first-line smoking cessation medications, with 6- and 12-month follow-up, were eligible for inclusion. Meta-analysis was used to synthesize information on sustained abstinence (SA) at 6 versus 12 months and 3 versus 6 months, using the risk difference (RD) ('net benefit') between intervention and control group quit rates, the relative risk (RR) and the odds ratio (OR). RESULTS: Sixty-one studies (27 647 participants) were included. Fewer than 40% of intervention group participants were sustained abstinent at 3 months (bupropion: 37.1%; nicotine replacement therapy (NRT): 34.8%; varenicline: 39.3%); approximately a quarter were sustained abstinent at 6 months (bupropion: 25.9%; NRT: 26.6%; varenicline: 25.4%), and approximately a fifth were sustained abstinent at 12 months (bupropion: 19.9%; NRT: 19.8%%; varenicline: 18.7%). There was only a small decline in RR (3 months: 1.95 [95% confidence interval (CI) = 1.74-2.18, P < 0.0001]; 6 months: 1.87 (95% CI = 1.67-2.08 P < 0.0001); 12 months: 1.75 (95% CI = 1.56-1.95, P < 0.0001) between intervention and control groups over time, but a substantial decline in net benefit [3 months: RD = 17.3% (14.5-20.1%); 6 months: RD = 11.8% (10.0-13.7%); 12 months: RD = 8.2% (6.8-9.6%)]. The decline in net benefit was statistically significant between 3 and 6 [RD = 4.95% (95% CI = 3.49-6.41%), P < 0.0001] and 6 and 12 months [RD = 3.00% (95% CI = 2.36%-3.64%), P < 0.0001)] for medications combined and individual medications. CONCLUSIONS: The proportion of smokers who use smoking cessation medications who benefit from doing so decreases during the course of the first year, but a net benefit still remains at 12 months.

heatmaply: an R package for creating interactive cluster heatmaps for online publishing.

Summary: heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout. Availability and implementation: The heatmaply package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from: http://cran.r-project.org/package=heatmaply. Contact: tal.galili@math.tau.ac.il. Supplementary information: Supplementary data are available at Bioinformatics online.

The Proprioceptive System Masterminds Spinal Alignment: Insight into the Mechanism of Scoliosis.

Maintaining posture requires tight regulation of the position and orientation of numerous spinal components. Yet, surprisingly little is known about this regulatory mechanism, whose failure may result in spinal deformity as in adolescent idiopathic scoliosis. Here, we use genetic mouse models to demonstrate the involvement of proprioception in regulating spine alignment. Null mutants for Runx3 transcription factor, which lack TrkC neurons connecting between proprioceptive mechanoreceptors and spinal cord, developed peripubertal scoliosis not preceded by vertebral dysplasia or muscle asymmetry. Deletion of Runx3 in the peripheral nervous system or specifically in peripheral sensory neurons, or of enhancer elements driving Runx3 expression in proprioceptive neurons, induced a similar phenotype. Egr3 knockout mice, lacking muscle spindles, but not Golgi tendon organs, displayed a less severe phenotype, suggesting that both receptor types may be required for this regulatory mechanism. These findings uncover a central role for the proprioceptive system in maintaining spinal alignment.

The Proprioceptive System Regulates Morphologic Restoration of Fractured Bones.

Successful fracture repair requires restoration of bone morphology and mechanical integrity. Recent evidence shows that fractured bones of neonatal mice undergo spontaneous realignment, dubbed "natural reduction." Here, we show that natural reduction is regulated by the proprioceptive system and improves with age. Comparison among mice of different ages revealed, surprisingly, that 3-month-old mice exhibited more rapid and effective natural reduction than newborns. Fractured bones of null mutants for transcription factor Runx3, lacking functional proprioceptors, failed to realign properly. Blocking Runx3 expression in the peripheral nervous system, but not in limb mesenchyme, recapitulated the null phenotype, as did inactivation of muscles flanking the fracture site. Egr3 knockout mice, which lack muscle spindles but not Golgi tendon organs, displayed a less severe phenotype, suggesting that both receptor types, as well as muscle contraction, are required for this regulatory mechanism. These findings uncover a physiological role for proprioception in non-autonomous regulation of skeletal integrity.

An Example of an Improvable Rao-Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator.

The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].

Identifying the ligated amino acid of archaeal tRNAs based on positions outside the anticodon.

Proper recognition of tRNAs by their aminoacyl-tRNA synthetase is essential for translation accuracy. Following evidence that the enzymes can recognize the correct tRNA even when anticodon information is masked, we search for additional nucleotide positions within the tRNA molecule that potentially contain information for amino acid identification. Analyzing 3936 sequences of tRNA genes from 86 archaeal species, we show that the tRNAs' cognate amino acids can be identified by the information embedded in the tRNAs' nucleotide positions without relying on the anticodon information. We present a small set of six to 10 informative positions along the tRNA, which allow for amino acid identification accuracy of 90.6% to 97.4%, respectively. We inspected tRNAs for each of the 20 amino acid types for such informative positions and found that tRNA genes for some amino acids are distinguishable from others by as few as one or two positions. The informative nucleotide positions are in agreement with nucleotide positions that were experimentally shown to affect the loaded amino acid identity. Interestingly, the knowledge gained from the tRNA genes of one archaeal phylum does not extrapolate well to another phylum. Furthermore, each species has a unique ensemble of nucleotides in the informative tRNA positions, and the similarity between the sets of positions of two distinct species reflects their evolutionary distance. Hence, we term this set of informative positions a "tRNA cipher." It is tempting to suggest that the diverging code identified here might also serve the aminoacyl tRNA synthetase in the task of tRNA recognition.

Isometric Scaling in Developing Long Bones Is Achieved by an Optimal Epiphyseal Growth Balance.

One of the major challenges that developing organs face is scaling, that is, the adjustment of physical proportions during the massive increase in size. Although organ scaling is fundamental for development and function, little is known about the mechanisms that regulate it. Bone superstructures are projections that typically serve for tendon and ligament insertion or articulation and, therefore, their position along the bone is crucial for musculoskeletal functionality. As bones are rigid structures that elongate only from their ends, it is unclear how superstructure positions are regulated during growth to end up in the right locations. Here, we document the process of longitudinal scaling in developing mouse long bones and uncover the mechanism that regulates it. To that end, we performed a computational analysis of hundreds of three-dimensional micro-CT images, using a newly developed method for recovering the morphogenetic sequence of developing bones. Strikingly, analysis revealed that the relative position of all superstructures along the bone is highly preserved during more than a 5-fold increase in length, indicating isometric scaling. It has been suggested that during development, bone superstructures are continuously reconstructed and relocated along the shaft, a process known as drift. Surprisingly, our results showed that most superstructures did not drift at all. Instead, we identified a novel mechanism for bone scaling, whereby each bone exhibits a specific and unique balance between proximal and distal growth rates, which accurately maintains the relative position of its superstructures. Moreover, we show mathematically that this mechanism minimizes the cumulative drift of all superstructures, thereby optimizing the scaling process. Our study reveals a general mechanism for the scaling of developing bones. More broadly, these findings suggest an evolutionary mechanism that facilitates variability in bone morphology by controlling the activity of individual epiphyseal plates.

dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering.

UNLABELLED: dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing R's rich ecosystem of packages for performing hierarchical clustering of items. AVAILABILITY AND IMPLEMENTATION: The dendextend R package (including detailed introductory vignettes) is available under the GPL-2 Open Source license and is freely available to download from CRAN at: (http://cran.r-project.org/package=dendextend) CONTACT: Tal.Galili@math.tau.ac.il.

An international age- and gender-controlled model for the Spinal Cord Injury Ability Realization Measurement Index (SCI-ARMI).

Background. A quadratic formula of the Spinal Cord Injury Ability Realization Measurement Index (SCI-ARMI) has previously been published. This formula was based on a model of Spinal Cord Independence Measure (SCIM95), the 95th percentile of the SCIM III values, which correspond with the American Spinal Injury Association Motor Scores (AMS) of SCI patients. Objective. To further develop the original formula. Setting. Spinal cord injury centers from 6 countries and the Statistical Laboratory, Tel-Aviv University, Israel. Methods. SCIM95 of 661 SCI patients was modeled, using a quantile regression with or without adjustment for age and gender, to calculate SCI-ARMI values. SCI-ARMI gain during rehabilitation and its correlations were examined. Results. A new quadratic SCIM95 model was created. This resembled the previously published model, which yielded similar SCIM95 values in all the countries, after adjustment for age and gender. Without this adjustment, however, only 86% of the non-Israeli SCIM III observations were lower than those SCIM95 values (P < .0001). Adding the variables age and gender to the new model affected the SCIM95 value significantly (P < .04). Adding country information did not add a significant effect (P > .1). SCI-ARMI gain was positive (38.8 ± 22 points, P < .0001) and correlated weakly with admission age and AMS. Conclusions. The original quadratic SCI-ARMI formula is valid for an international population after adjustment for age and gender. The new formula considers more factors that affect functional ability following SCI.

Impaired social processing in autism and its reflections in memory: a deeper view of encoding and retrieval processes.

Previous studies of memory in autism spectrum conditions (ASC) have consistently shown that persons with ASC have reduced memories for social information, relative to a spared memory for non-social facts. The current study aims to reproduce these findings, while examining the possible causes leading to this difference. Participants' memory for trait-words was tested after they had viewed the words in three study contexts: visuo-motor, letter-detection, and social judgment. While participants with ASC showed a levels-of-processing effect, such that their memory for words viewed in the social judgment context was greater than their memory for words viewed in the letter-detection context, their memory for socially-processed words was reduced relative to comparison participants. This interaction effect could not be explained by a speed/accuracy trade-off, nor could it be explained solely by differences in encoding. These results suggest that social memory deficits in ASC arise from difficulties both in orienting towards and encoding social content, as well as retaining and retrieving it. Implications for theory and clinical practice are discussed.

Quantifying the buildup in extent and complexity of free exploration in mice.

To obtain a perspective on an animal's own functional world, we study its behavior in situations that allow the animal to regulate the growth rate of its behavior and provide us with the opportunity to quantify its moment-by-moment developmental dynamics. Thus, we are able to show that mouse exploratory behavior consists of sequences of repeated motion: iterative processes that increase in extent and complexity, whose presumed function is a systematic active management of input acquired during the exploration of a novel environment. We use this study to demonstrate our approach to quantifying behavior: targeting aspects of behavior that are shown to be actively managed by the animal, and using measures that are discriminative across strains and treatments and replicable across laboratories.