A combination of methods, including laser-induced fluorescence excitation, fluorescence-dip infrared ͑FDIR͒ spectroscopy, and UV-UV hole-burning spectroscopy, have been used to study the infrared and ultraviolet spectra of single conformations of two methyl-capped dipeptides: N-acetyl tryptophan amide ͑NATA͒ and N-acetyl tryptophan methyl amide ͑NATMA͒. Density functional theory calculations predict that all low-energy conformers of NATA and NATMA belong to one of two conformational families: C5, with its extended dipeptide backbone, or C7 eq , in which the dipeptide backbone forms a seven-membered ring joined by a H bond between the-amide NH and the-amide carbonyl groups. In NATA ͑NATMA͒, the LIF spectrum has contributions from two ͑three͒ conformers. FDIR spectroscopy has been used to record infrared spectra of the individual conformers over the 2800-3600 cm Ϫ1 region, free from interference from one another. The NH stretch region provides unequivocal evidence that one of the conformers of NATA is C5, while the other is C7 eq. Similarly, in NATMA, there are two C5 conformers, and one C7 eq structure. Several pieces of evidence are used to assign spectra to particular C5 and C7 eq conformers. NATA͑A͒ and NATMA͑B͒ are both assigned as C5͑AP͒ structures, NATA͑B͒ and NATMA͑C͒ are assigned as C7 eq ͑⌽P͒, and NATMA͑A͒ is assigned as C5͑A⌽͒. In both molecules, the C5 structures have sharp vibronic spectra, while the C7 eq conformers are characterized by a dense, highly congested spectrum involving long progressions that extend several hundred wave numbers to the red of the C5 S 1-S 0 origins. N-acetyl tryptophan ethyl ester ͑NATE͒, which can only form C5 conformers, shows only sharp transitions in its LIF spectrum due to four C5 conformers, with no evidence for the broad absorption due to C7 eq. This provides direct experimental evidence for the importance of the peptide backbone conformation in controlling the spectroscopic and photophysical properties of tryptophan.

A parallel searching algorithm based on eigenvector-following is used to generate databases of minima and transition states for an all-atom model of the peptide Ac(ala) 3 NHMe and for a simplified bead model of a protein. We analyze the energy landscapes of both systems using disconnectivity graphs based upon both potential energy and free energy. This approach highlights the role of vibrational entropy in determining the relative free energy of local minima. Thermodynamic properties for Ac(ala) 3 NHMe calculated using the superposition approach are in reasonable agreement with parallel-tempering Monte Carlo simulations.

The discrete path sampling technique is used to calculate folding pathways of the 16-amino acid beta hairpin-forming sequence from residues 41-56 of the B1 domain of protein G. The folding time is obtained using master equation dynamics and kinetic Monte Carlo simulations, and the time evolution of different order parameters and occupation probabilities of groups of minima are calculated and used to characterize intermediates on the folding pathway.

SummaryBackgroundSurgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world.MethodsThis international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231.FindingsBetween Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p<0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p<0·001).InterpretationCountries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication.FundingDFID-MRC-Wellcome Trust Joint Global Health Trial Development Grant,...

Summary1. Pacific leatherback turtle Dermochelys coriacea populations have been declining precipitously. It has been suggested that fishery-associated mortality is the leading factor causing the decline; however, the sensitivity of leatherbacks to climate variability relative to their population ecology is unknown. 2. We investigated the effects of interannual climate variability, as governed by the El Niño Southern Oscillation (ENSO), on leatherback nesting ecology. We used equatorial Pacific sea surface temperature (SST) anomaly data over various time scales derived from both moored buoys and remote satellites as signals of ENSO. We then incorporated these data into a remigration probability model for the largest nesting population of eastern Pacific leatherbacks at Parque Nacional Marino Las Baulas (PNMB), Costa Rica. 3. Our results showed that nesting females of PNMB exhibited a strong sensitivity to ENSO, as reflected in their nesting remigration probabilities. Cool La Niña events corresponded with a higher remigration probability and warm El Niño events corresponded with a lower remigration probability. 4. We suggest that productivity transitions at leatherback foraging areas in the eastern equatorial and south-eastern Pacific in response to El Niño/La Niña events result in variable remigration intervals and thus variable annual egg production. This phenomenon may render the eastern Pacific leatherback population more vulnerable to anthropogenic mortality than other populations. 5. Synthesis and applications . Physical indices of environmental variation can be used to estimate the probability of leatherbacks remigrating to nest at PNMB. This type of modelling approach can be extremely useful for understanding the effects of climatic variation on the population dynamics of sea turtles. Our remigration probability model can be applied to any monitored sea turtle nesting population where nesting site fidelity and beach monitoring coverage remains high. This modelling approach can help nesting beach monitoring programmes forecast remigrant numbers based on prior climate data, and can further quantify anthropogenic mortality by validating survival estimates.