Utilization and transformation of aquatic humic substances by autochthonous microorganisms

Aquatic humic substances (HS) from a bog lake water, a riverwater, and a groundwater were isolated after enrichment on XAD 8 columns and added to a Czapek-Dox nutrient broth which was used either in full strength or without glucose and/or NaNO3. The individual flasks were inoculated with natural microbial populations of corresponding water samples or with a Pseudomonas fluorescens strain isolated from groundwater. The presence of HS resulted in an increase of bacterial numbers in nearly all cultures incubated for 3 weeks at 25 degrees C on a shaker. HS reisolated from cultures without glucose or NaNO3 showed no or only minor quantitative differences as compared to those from sterile controls. In full strength nutrient broth up to 27% of HS were utilized. Data obtained by spectroscopic methods (UV/vis/FTIR) and elemental analysis indicated a decrease in particle size and a loss in aromaticity and aliphatic carbon in HS reisolated from the microbial cultures. Simultaneously an increase in the N content of HS was observed, which probably originated from some constituents of microbial biomass such as proteins and amino sugars. The NMR data also documented that significant transformations of HS occurred in the individual microbial cultures. After incubation, increased amounts of aromatic acids were detected in some liquid media and residual HS by GC/MS or capillary electrophoresis. 1H NMR spectroscopy was less effective in indicating structural differences in the HS than 13C NMR but revealed considerable detail of the microbial degradation of riverine HS, when limited sample was available. The newly developed NMR increment analysis provided substantial detail of aromatic structures in a microbially altered HS. The microbial degradation of HS strongly depended on the composition of the HS, the species selection of the microorganisms, and to a lesser extent on the culture conditions. For any series of identical inoculum and HS, full broth media initiated the most extensive alteration of HS.     

Optical Kerr switching technique for the production of a picosecond,multiwavelength CO2 laser pulse

A wavelength-independent method for optical gating, based on the optical Kerr effect, has been demonstrated. Using this method, we produced 100-ps, 10-kW, two-wavelength pulses (10.3 and 10.6 microm) with a signal-to-background ratio contrast of 10(5) by slicing a long CO2 pulse. The capability of gating consecutive pulses separated on a picosecond time scale with this method is also shown.     

Reduced autobiographical memory specificity and rumination in predicting the course of depression.

Reduced autobiographical memory (AM) specificity is a known vulnerability factor for depression. AM specificity was investigated as a predictor of depression with the Autobiographical Memory Test (J. M. G. Williams & K. Broadbent, 1986). When baseline depression scores were partialed, reduced AM specificity to negative cue words predicted higher levels of depression at 7-month follow-up. Once rumination was taken into account by means of the Rumination on Sadness Scale (M. Conway, P. A. R. Csank, S. L. Holm, & C. K. Blake, 2000), AM specificity no longer predicted depression, suggesting that the predictive value of AM specificity observed in previous studies might be--at least partly--explained as an effect of rumination. Further mediation analyses indeed revealed support for rumination as a mediator of the relation between reduced AM specificity and poor outcome of depression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

薄膜晶体硅太阳能电池的潜力

如果效率和成本目标能够实现，薄膜晶体硅太阳能电池有潜力替代目前在光伏市场上占主导地位的多晶硅太阳能电池。     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Evaluation of a Novel Thiol–Norbornene-Functionalized Gelatin Hydrogel for Bioprinting of Mesenchymal Stem Cells

Introduction: Three-dimensional bioprinting can be considered as an advancement of the classical tissue engineering concept. For bioprinting, cells have to be dispersed in hydrogels. Recently, a novel semi-synthetic thiolene hydrogel system based on norbornene-functionalized gelatin (GelNB) and thiolated gelatin (GelS) was described that resulted in the photoclick hydrogel GelNB/GelS. In this study, we evaluated the printability and biocompatibility of this hydrogel system towards adipose-tissue-derived mesenchymal stem cells (ASCs). Methods: GelNB/GelS was synthesized with three different crosslinking densities (low, medium and high), resulting in different mechanical properties with moduli of elasticity between 206 Pa and 1383 Pa. These hydrogels were tested for their biocompatibility towards ASCs in terms of their viability, proliferation and differentiation. The extrusion-based bioprinting of ASCs in GelNB/GelS-high was performed to manufacture three-dimensional cubic constructs. Results: All three hydrogels supported the viability, proliferation and chondrogenic differentiation of ASCs to a similar extent. The adipogenic differentiation of ASCs was better supported by the softer hydrogel (GelNB/GelS-low), whereas the osteogenic differentiation was more pronounced in the harder hydrogel (GelNB/GelS-high), indicating that the differentiation fate of ASCs can be influenced via the adaption of the mechanical properties of the GelNB/GelS system. After the ex vivo chondrogenic differentiation and subcutaneous implantation of the bioprinted construct into immunocompromised mice, the production of negatively charged sulfated proteoglycans could be observed with only minimal inflammatory signs in the implanted material. Conclusions: Our results indicate that the GelNB/GelS hydrogels are very well suited for the bioprinting of ASCs and may represent attractive hydrogels for subsequent in vivo tissue engineering applications.