We are introducing Pep McConst-a software that employs a Monte-Carlo algorithm to construct 3D structures of polypeptide chains which could subsequently be studied as stand-alone macromolecules or complement the structure of known proteins. Using an approach to avoid steric clashes, Pep McConst allows to create multiple structures for a predefined primary sequence of amino acids. These structures could then effectively be used for further structural analysis and investigations. The article introduces the algorithm and describes its user-friendly approach that was made possible through the VIKING online platform. Finally, the manuscript provides several highlight examples where Pep McConst was used to predict the structure of the C-terminal of a known protein, generate a missing bit of already crystallized protein structures and simply generate short polypeptide chains.
  Case-control studies from the early 2000s demonstrated that human papillomavirus-related oropharyngeal cancer (HPV-OPC) is a distinct entity associated with number of oral sex partners. Using contemporary data, we investigated novel risk factors (sexual debut behaviors, exposure intensity, and relationship dynamics) and serological markers on odds of HPV-OPC.
 
  HPV-OPC patients and frequency-matched controls were enrolled in a multicenter study from 2013 to 2018.  https://www.selleckchem.com/products/s-gsk1349572.html  Participants completed a behavioral survey. Characteristics were compared using a chi-square test for categorical variables and a t test for continuous variables. Adjusted odds ratios (aOR) were calculated using logistic regression.
 
  A total of 163 HPV-OPC patients and 345 controls were included. Lifetime number of oral sex partners was associated with significantly increased odds of HPV-OPC (>10 partners odds ratio [OR], 4.3 [95% CI, 2.8-6.7]). After adjustment for number of oral sex partners and smoking, younger age at first oral sex (<18 ls develop HPV-OPC.
  Events of accelerated species diversification represent one of Earth's most celebrated evolutionary outcomes. Northern Andean high-elevation ecosystems, or páramos, host some plant lineages that have experienced the fastest diversification rates, likely triggered by ecological opportunities created by mountain uplifts, local climate shifts, and key trait innovations. However, the mechanisms behind rapid speciation into the new adaptive zone provided by these opportunities have long remained unclear.
 
  We address this issue by studying the Venezuelan clade of Espeletia, a species-rich group of páramo-endemics showing a dazzling ecological and morphological diversity. We performed several comparative analyses to study both lineage and trait diversification, using an updated molecular phylogeny of this plant group.
 
  We showed that sets of either vegetative or reproductive traits have conjointly diversified in Espeletia along different vegetation belts, leading to adaptive syndromes. Diversification in vegetative traits occurred earlier than in reproductive ones. The rate of species and morphological diversification showed a tendency to slow down over time, probably due to diversity dependence. We also found that closely related species exhibit significantly more overlap in their geographic distributions than distantly related taxa, suggesting that most events of ecological divergence occurred at close geographic proximity within páramos.
 
  These results provide compelling support for a scenario of small-scale ecological divergence along multiple ecological niche dimensions, possibly driven by competitive interactions between species, and acting sequentially over time in a leapfrog pattern.
 These results provide compelling support for a scenario of small-scale ecological divergence along multiple ecological niche dimensions, possibly driven by competitive interactions between species, and acting sequentially over time in a leapfrog pattern.Retinopathy of prematurity is a vision-threatening disease associated with retinal hypoxia-ischemia, leading to the death of retinal neurons and chronic neuronal degeneration. During this study, we used the oxygen-induced retinopathy mice model to mimic retinal hypoxia-ischemia phenotypes to investigate further the neuroprotective effect of melatonin on neonatal retinal neurons. Melatonin helped maintain relatively normal inner retinal architecture and thickness and preserve inner retinal neuron populations in avascular areas by rescuing retinal ganglion and bipolar cells, and horizontal and amacrine neurons, from apoptosis. Meanwhile, melatonin recovered visual dysfunction, as reflected by the improved amplitudes and implicit times of a-wave, b-wave, and oscillatory potentials. Additionally, elevated cleaved caspase-3 and Bax protein levels and reduced Bcl-2 protein levels in response to hypoxia-ischemia were diminished after melatonin treatment. Moreover, melatonin increased BDNF and downstream phospho-TrkB/Akt/ERK/CREB levels. ANA-12, a TrkB receptor antagonist, antagonized these melatonin actions and reduced melatonin-induced neuroprotection. Furthermore, melatonin rescued the reduction in melatonin receptor expression. This study suggests that melatonin exerted anti-apoptotic and neuroprotective effects in inner retinal neurons after hypoxia-ischemia, at least partly due to modulation of the BDNF-TrkB pathway.As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This leads to a gap between research results and real-world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high-dimensional integration, and propose a novel computational solution based on Monte Carlo and smoothing methods. Our method utilizes the modern techniques of general purpose computing on graphics processing units for large-scale parallel computing. Compared to a published method in three-dimensional problems, our approach is more accurate and 133 times faster.