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Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

The debris flow is one of the geological hazards; its occurrence is complex, fuzzy, and random. And it is affected by many indices; a new multi-index assessment method is proposed to analyze the risk level of debris flow based on the entropy weight-normal cloud model in Banshanmen gully. The index weight is calculated by using the entropy weight method. Then, the certainty degree of each index belonging to the corresponding cloud is obtained by using the cloud model. The final risk level of debris flow is determined according to the synthetic certainty degree. The conclusions are drawn that the method is feasible and accurate rate of risk estimation for debris flow is very high, so a new method and thoughts for the risk assessment of debris flow can be provided in the future.

A bstract We analyzed the evolution data of the Daya Bay reactor neutrino experiment in terms of short-baseline active-sterile neutrino oscillations taking into account the theoretical uncertainties of the reactor antineutrino fluxes. We found that oscillations are disfavored at 2.6 σ with respect to a suppression of the 235 U reactor antineutrino flux and at 2.5 σ with respect to variations of the 235 U and 239 Pu fluxes. On the other hand, the analysis of the rates of the short-baseline reactor neutrino experiments favor active-sterile neutrino oscillations and disfavor the suppression of the 235 U flux at 3.1 σ and variations of the 235 U and 239 Pu fluxes at 2.8 σ . We also found that both the Daya Bay evolution data and the global rate data are well-fitted with composite hypotheses including variations of the 235 U or 239 Pu fluxes in addition to active-sterile neutrino oscillations. A combined analysis of the Daya Bay evolution data and the global rate data shows a slight preference for oscillations with respect to variations of the 235 U and 239 Pu fluxes. However, the best fits of the combined data are given by the composite models, with a preference for the model with an enhancement of the 239 Pu flux and relatively large oscillations.

Anna Lasorella, Raul Rabadan, Antonio Iavarone and colleagues report an integrated analysis of genomic alterations in glioblastoma. They identify and functionally validate several new driver events, including loss-of-function mutations in CTNND2 and recurrent EGFR fusions. Glioblastoma is one of the most challenging forms of cancer to treat. Here we describe a computational platform that integrates the analysis of copy number variations and somatic mutations and unravels the landscape of in-frame gene fusions in glioblastoma. We found mutations with loss of heterozygosity in LZTR1 , encoding an adaptor of CUL3-containing E3 ligase complexes. Mutations and deletions disrupt LZTR1 function, which restrains the self renewal and growth of glioma spheres that retain stem cell features. Loss-of-function mutations in CTNND2 target a neural-specific gene and are associated with the transformation of glioma cells along the very aggressive mesenchymal phenotype. We also report recurrent translocations that fuse the coding sequence of EGFR to several partners, with EGFR-SEPT14 being the most frequent functional gene fusion in human glioblastoma. EGFR-SEPT14 fusions activate STAT3 signaling and confer mitogen independence and sensitivity to EGFR inhibition. These results provide insights into the pathogenesis of glioblastoma and highlight new targets for therapeutic intervention.

The landslide hazard is one of the geological hazards in mountainous zone. Its occurrence is controlled by many factors. To assess the risk level of landslide in Shiwangmiao accurately, intuitionistic fuzzy sets-Topsis model is introduced at first; secondly, the decisive matrix about the intuitionistic fuzzy sets is established, and the index weight coefficients considering the uncertainty of assessment indices are determined by using the Entropy weight method, then the weighed decisive matrix is obtained. Finally, degree of membership at different levels about the landslide is determined based on the ranking sequence of degree of membership, the risk level corresponding to the maximum degree of membership is final assessment level. The conclusions are drawn that accurate rate of risk estimation about landslide hazards is very high based on the intuitionistic fuzzy sets model in comparison with the current specifications, and the method is feasible for the risk assessment of landslide hazards, so it provides a new method and thoughts to assess the risk level of landslide in future.

Creating oxide interfaces with precise chemical specificity at the atomic layer level is desired for the engineering of quantum phases and electronic applications, but highly challenging, owing partially to the lack of in situ tools to monitor the chemical composition and completeness of the surface layer during growth. Here we report the in situ observation of atomic layer-by-layer inner potential variations by analysing the Kikuchi lines during epitaxial growth of strontium titanate, providing a powerful real-time technique to monitor and control the chemical composition during growth. A model combining the effects of mean inner potential and step edge density (roughness) reveals the underlying mechanism of the complex and previously not well-understood reflection high-energy electron diffraction oscillations observed in the shuttered growth of oxide films. General rules are proposed to guide the synthesis of atomically and chemically sharp oxide interfaces, opening up vast opportunities for the exploration of intriguing quantum phenomena at oxide interfaces. Precisely controlled growth of oxide interfaces at the atomic layer level is critical for device applications but quite challenging. Here Sun et al. show real time monitoring and control of the surface composition of epitaxial strontium titanate perovskite films by analysing the Kikuchi lines.

Femtosecond XFEL crystallography is used to identify dynamic changes in the adenine riboswitch aptamer domain, with at least four states identified in real time, two in the apo form before binding and two with the ligand bound. Riboswitch RNA reaction-state structures The potential of nanocrystallography to offer insights into dynamics is now beginning to be realized. Yun-Xing Wang and colleagues have used femtosecond X-ray free-electron laser (XFEL) crystallography to study dynamic changes in the ligand-binding, or aptamer, domain of the Vibrio vulnificus adenine riboswitch. They identify at least four states, two in the apo form before binding and two with ligand bound, in real time. These results allow the modelling of a kinetic scheme that describes how ligand binding transmits a signal through the P1 helix. The large-scale conformational changes captured within the crystal are enabled by the time-resolved serial crystallography. Riboswitches are structural RNA elements that are generally located in the 5′ untranslated region of messenger RNA. During regulation of gene expression, ligand binding to the aptamer domain of a riboswitch triggers a signal to the downstream expression platform 1 , 2 , 3 . A complete understanding of the structural basis of this mechanism requires the ability to study structural changes over time 4 . Here we use femtosecond X-ray free electron laser (XFEL) pulses 5 , 6 to obtain structural measurements from crystals so small that diffusion of a ligand can be timed to initiate a reaction before diffraction. We demonstrate this approach by determining four structures of the adenine riboswitch aptamer domain during the course of a reaction, involving two unbound apo structures, one ligand-bound intermediate, and the final ligand-bound conformation. These structures support a reaction mechanism model with at least four states and illustrate the structural basis of signal transmission. The three-way junction and the P1 switch helix of the two apo conformers are notably different from those in the ligand-bound conformation. Our time-resolved crystallographic measurements with a 10-second delay captured the structure of an intermediate with changes in the binding pocket that accommodate the ligand. With at least a 10-minute delay, the RNA molecules were fully converted to the ligand-bound state, in which the substantial conformational changes resulted in conversion of the space group. Such notable changes in crystallo highlight the important opportunities that micro- and nanocrystals may offer in these and similar time-resolved diffraction studies. Together, these results demonstrate the potential of ‘mix-and-inject’ time-resolved serial crystallography to study biochemically important interactions between biomacromolecules and ligands, including those that involve large conformational changes.

Understanding the relative importance of top-down and bottom-up regulation of ecosystem structure is a fundamental ecological question, with implications for fisheries and water-quality management. For the Laurentian Great Lakes, where, since the early 1970s, nutrient inputs have been reduced, whereas top-predator biomass has increased, we describe trends across multiple trophic levels and explore their underlying drivers. Our analyses revealed increasing water clarity and declines in phytoplankton, native invertebrates, and prey fish since 1998 in at least three of the five lakes. Evidence for bottom-up regulation was strongest in Lake Huron, although each lake provided support in at least one pair of trophic levels. Evidence for top-down regulation was rare. Although nonindigenous dreissenid mussels probably have large impacts on nutrient cycling and phytoplankton, their effects on higher trophic levels remain uncertain. We highlight gaps for which monitoring and knowledge should improve the understanding of food-web dynamics and facilitate the implementation of ecosystem-based management.