Many studies have demonstrated that divergence levels generated by different mutation types vary and covary across the human genome. To improve our still-incomplete understanding of the mechanistic basis of this phenomenon, we analyze several mutation types simultaneously, anchoring their variation to specific regions of the genome. Using hidden Markov models on insertion, deletion, nucleotide substitution, and microsatellite divergence estimates inferred from human-orangutan alignments of neutrally evolving genomic sequences, we segment the human genome into regions corresponding to different divergence states-each uniquely characterized by specific combinations of divergence levels. We then parsed the mutagenic contributions of various biochemical processes associating divergence states with a broad range of genomic landscape features. We find that high divergence states inhabit guanine-and cytosine (GC)-rich, highly recombining subtelomeric regions; low divergence states cover inner parts of autosomes; chromosome X forms its own state with lowest divergence; and a state of elevated microsatellite mutability is interspersed across the genome. These general trends are mirrored in human diversity data from the 1000 Genomes Project, and departures from them highlight the evolutionary history of primate chromosomes. We also find that genes and noncoding functional marks [annotations from the Encyclopedia of DNA Elements (ENCODE)] are concentrated in high divergence states. Our results provide a powerful tool for biomedical data analysis: segmentations can be used to screen personal genome variants-including those associated with cancer and other diseases-and to improve computational predictions of noncoding functional elements. W hole-genome sequencing studies have demonstrated that divergence estimates for several mutation types (e.g., nucleotide substitutions, insertions, and deletions) vary substantially across the human genome. This phenomenon has been studied at various genomic scales and evolutionary distances (reviewed in ref. 1), and-whereas initially of interest solely to evolutionary biologists-is now entering the purview of main biomedical research. Specifically, human population (e.g., ref.2) and cancer (3, 4) genome resequencing projects have revealed that incidences of single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and copy number variants (CNVs) vary across the genome. Divergence estimates for different mutation types also covary across the genome (5, 6)-e.g., substitution rates increase in regions with high indel rates (7)-suggesting that regional variation is an important and general characteristic of mutations.Variation in divergence is often linked to genomic landscape features such as base composition, replication timing, and recombination rates (1). For instance, nucleotide substitution rates are elevated in late-replicating regions because of an accumulation of single-stranded DNA susceptible to endogenous damage (8) and are affected by chromatin structure (9) and ...

STUDY QUESTION Does increased daily energy intake lead to menstrual recovery in exercising women with oligomenorrhoea (Oligo) or amenorrhoea (Amen)? SUMMARY ANSWER A modest increase in daily energy intake (330 ± 65 kcal/day; 18 ± 4%) is sufficient to induce menstrual recovery in exercising women with Oligo/Amen. WHAT IS KNOWN ALREADY Optimal energy availability is critical for normal reproductive function, but the magnitude of increased energy intake necessary for menstrual recovery in exercising women, along with the associated metabolic changes, is not known. STUDY DESIGN, SIZE, DURATION The REFUEL study (trial # NCT00392873) is the first randomised controlled trial to assess the effectiveness of 12 months of increased energy intake on menstrual function in 76 exercising women with menstrual disturbances. Participants were randomised (block method) to increase energy intake 20–40% above baseline energy needs (Oligo/Amen + Cal, n = 40) or maintain energy intake (Oligo/Amen Control, n = 36). The study was performed from 2006 to 2014. PARTICIPANTS/MATERIALS, SETTING, METHODS Participants were Amen and Oligo exercising women (age = 21.0 ± 0.3 years, BMI = 20.8 ± 0.2 kg/m2, body fat = 24.7 ± 0.6%) recruited from two universities. Detailed assessment of menstrual function was performed using logs and measures of daily urinary ovarian steroids. Body composition and metabolic outcomes were assessed every 3 months. MAIN RESULTS AND THE ROLE OF CHANCE Using an intent-to-treat analysis, the Oligo/Amen + Cal group was more likely to experience menses during the intervention than the Oligo/Amen Control group (P = 0.002; hazard ratio [CI] = 1.91 [1.27, 2.89]). In the intent-to-treat analysis, the Oligo/Amen + Cal group demonstrated a greater increase in energy intake, body weight, percent body fat and total triiodothyronine (TT3) compared to the Oligo/Amen Control group (P < 0.05). In a subgroup analysis where n = 22 participants were excluded (ambiguous baseline menstrual cycle, insufficient time in intervention for menstrual recovery classification), 64% of the Oligo/Amen + Cal group exhibited improved menstrual function compared with 19% in the Oligo/Amen Control group (χ2, P = 0.001). LIMITATIONS, REASONS FOR CAUTION While we had a greater than expected dropout rate for the 12-month intervention, it was comparable to other shorter interventions of 3–6 months in duration. Menstrual recovery defined herein does not account for quality of recovery. WIDER IMPLICATIONS OF THE FINDINGS Expanding upon findings in shorter, non-randomised studies, a modest increase in daily energy intake (330 ± 65 kcal/day; 18 ± 4%) is sufficient to induce menstrual recovery in exercising women with Oligo/Amen. Improved metabolism, as demonstrated by a modest increase in body weight (4.9%), percent body fat (13%) and TT3 (16%), was associated with menstrual recovery. STUDY FUNDING/COMPETING INTEREST(S) This research was supported by the U.S. Department of Defense: U.S. Army Medical Research and Material Command (Grant PR054531). Additional research assistance provided by the Penn State Clinical Research Center was supported by the National Center for Advancing Translation Sciences, National Institutes of Health, through Grant UL1 TR002014. M.P.O. was supported in part by the Loretta Anne Rogers Chair in Eating Disorders at University of Toronto and University Health Network. All authors report no conflict of interest. TRIAL REGISTRATION NUMBER NCT00392873 TRIAL REGISTRATION DATE October 2006 DATE OF FIRST PATIENT’S ENROLMENT September 2006

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Background Energy deficiency can result in menstrual disturbances and compromised bone health in women, a condition known as the Female Athlete Triad. Objective The REFUEL randomized controlled trial assessed the impact of increased energy intake on bone health and menstrual function in exercising women with menstrual disturbances. Methods Exercising women with oligo/amenorrhea were randomized to an intervention group (Oligo/Amen+Cal, n=40, 21.3±0.5 yrs, 55.0±1.0kg, 20.4±0.3 kg/m2) who increased energy intake 20-40% above baseline energy needs for 12 months or a control group (Oligo/Amen Control, n=36, 20.7±0.5 yrs, 59.1±1.3kg, 21.3±0.4 kg/m2). Energy intake and expenditure, metabolic and reproductive hormones, body composition, and areal bone mineral density (aBMD) were assessed. Results The Oligo/Amen+Cal group improved energy status (increased body mass (2.6±0.4 kg), body mass index (0.9±0.2 kg/m2), fat mass (2.0±0.3 kg), body fat percentage (2.7±0.4%), and insulin-like growth factor 1 (37.4±14.6 ng/ml)) compared to the control group and experienced a greater likelihood of menses (p<0.05). Total body and spine aBMD remained unchanged (p>0.05). Both groups demonstrated decreased femoral neck aBMD at month 6 (-0.006 g/cm2, 95%CI: -0.011, -0.0002 time main effect p=0.043) and month 12 (-0.011 g/cm2, 95%CI: -0.021, -0.001, time main effect p=0.023). Both groups demonstrated a decrease in total hip aBMD at month 6 (-0.006 g/cm2, 95%CI: -0.011, -0.002, time main effect p=0.004). Conclusions Although higher dietary energy intake increased weight, body fat and menstrual frequency, bone mineral density was not improved, compared to the control group. The 12-month intervention may have been too short and the increase in energy intake (∼352 kcal/day), while sufficient to increase menstrual frequency, was insufficient to increase estrogen or improve aBMD. Future research should refine the optimal nutritional and/or pharmacological intervention(s) for the recovery of bone health in athletes and exercising women with oligo/amenorrhea. Clinical Trial Registry Number: NCT00392873 https://www.clinicaltrials.gov/ct2/show/NCT00392873

In this article, three tropical cyclones and their 120-h, 50-member ECMWF Integrated Forecasting System (IFS) ensemble track forecasts at 10 initialization times are considered. The IFS forecast tracks are clustered with a regression mixture model, and two traditional diagnostics (the Bayesian information criterion and a measure of strength of cluster assignment) are used to determine the optimal polynomial order and number of clusters to use in the model. In addition, cross-validation versions of the two diagnostics are formulated and computed to further aid in model selection. Both traditional and cross-validation diagnostics suggest that third-order polynomials and five clusters are effective options—although the evidence is less conclusive for the number of clusters than for the polynomial order, and the cross-validation diagnostics favor a smaller number of clusters than the traditional ones. Path clustering of IFS tropical cyclone track forecasts with this third-order polynomial, five-cluster regression mixture model produces interpretable partitions by direction and speed of motion for each of the storms and initialization times considered. Thus, this approach effectively synthesizes the forecast spreads within the IFS into a small number of representative trajectories. Based on how forecasts distribute across clusters, this approach also provides information on the likelihood of each such representative trajectory. If used operationally, this information has the potential to aid forecasters in parsing and quantifying the uncertainty in tropical cyclone track forecasts.