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Language, which allows complex ideas to be communicated through symbolic sequences, is a characteristic feature of our species and manifested in a multitude of forms. Using large written corpora for many different languages and scripts, we show that the occurrence probability distributions of signs at the left and right ends of words have a distinct heterogeneous nature. Characterizing this asymmetry using quantitative inequality measures, viz. information entropy and the Gini index, we show that the beginning of a word is less restrictive in sign usage than the end. This property is not simply attributable to the use of common affixes as it is seen even when only word roots are considered. We use the existence of this asymmetry to infer the direction of writing in undeciphered inscriptions that agrees with the archaeological evidence. Unlike traditional investigations of phonotactic constraints which focus on language-specific patterns, our study reveals a property valid across languages and writing systems. As both language and writing are unique aspects of our species, this universal signature may reflect an innate feature of the human cognitive phenomenon.

Neutrophil gelatinase-associated lipocalin (NGAL) has emerged as an early marker protein for kidney dysfunction in various clinical settings. In this prospective study we evaluated serial changes of serum and urinary NGAL within the first 7 days after kidney transplantation in 170 consecutive recipients. The main focus of this study was to assess the performance of serum and urinary NGAL in the prediction of delayed graft function (DGF) and two-year graft and patient survival. Serum and urine samples of 170 patients undergoing primary kidney transplantation from October 2010 to December 2012 were prospectively collected from day 0 to 7. NGAL was analyzed by ELISA. Multivariate regression models, receiver-operating characteristics (ROC), and areas under ROC curves (AUC) were used to identify predictors of DGF. DGF occurred in 52 patients (30.6%). Serum (AUC = 0.869) and urinary NGAL (AUC = 0.872) on postoperative day (POD) 2 could accurately predict DGF compared to serum creatinine (AUC = 0.619). Multivariate analyses revealed donor age, serum and urinary NGAL significantly associated with DGF (p<0.001). Recipient age was the only significant factor in a cox regression model influencing two-year graft and patient survival. In conclusion, serum and urinary NGAL are early predictors of DGF after kidney transplantation.

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling.