Monolithic Photoelectrochemical Device for Direct Water Splitting with 19% Efficiency

Recent rapid progress in efficiencies for solar water splitting by photoelectrochemical devices has enhanced its prospects to enable storable renewable energy. Efficient solar fuel generators all use tandem photoelectrode structures, and advanced integrated devices incorporate corrosion protection layers as well as heterogeneous catalysts. Realization of near thermodynamic limiting performance requires tailoring the energy band structure of the photoelectrode and also the optical and electronic properties of the surface layers exposed to the electrolyte. Here, we report a monolithic device architecture that exhibits reduced surface reflectivity in conjunction with metallic Rh nanoparticle catalyst layers that minimize parasitic light absorption. Additionally, the anatase TiO2 protection layer on the photocathode creates a favorable internal band alignment for hydrogen evolution. An initial solar-to-hydrogen efficiency of 19.3 % is obtained in acidic electrolyte and an efficiency of 18.5 % is achieved at neutral pH condition (under simulated sunlight)

Human amniotic fluid glycoproteins expressing sialyl Lewis carbohydrate antigens stimulate progesterone production in human trophoblasts in vitro

Background: Progesterone is thought to mediate immune modulator effects by regulating uterine responsiveness. The aim of the study was to clarify the effect of transferrin and glycodelin A (former name PP14) as sialyl Lewis X-expressing glycoproteins on the release of progesterone by trophoblast cells in vitro. Methods: Cytotrophoblast cells were prepared from human term placentas by standard dispersion of villous tissue followed by a Percoll gradient centrifugation step. Trophoblasts were incubated with varying concentrations (50-300 mug/ml) of human amniotic fluid- and serum-transferrin as well as with glycodelin A. Culture supernatants were assayed for progesterone, human chorionic gonadotropin (hCG) and cortisol by enzyme immunometric methods. Results: The release of progesterone is increased in amniotic fluid transferrin- and glycodelin A-treated trophoblast cell cultures compared to untreated trophoblast cells. There is no relation between transferrin and the hCG or cortisol production of trophoblast cells. Conclusion: The results suggest that sialyl Lewis carbohydrate antigen-expressing amniotic fluid glycoproteins modulate the endocrine function of trophoblasts in culture by upregulating progesterone production. Copyright (C) 2004 S. Karger AG, Basel

Hsp21potentiates antifungal drug tolerance in Candida albicans

Peer reviewedPublisher PD

Secular evolution versus hierarchical merging: galaxy evolution along the Hubble sequence, in the field and rich environments

In the current galaxy formation scenarios, two physical phenomena are invoked to build disk galaxies: hierarchical mergers and more quiescent external gas accretion, coming from intergalactic filaments. Although both are thought to play a role, their relative importance is not known precisely. Here we consider the constraints on these scenarios brought by the observation-deduced star formation history on the one hand, and observed dynamics of galaxies on the other hand: the high frequency of bars and spirals, the high frequency of perturbations such as lopsidedness, warps, or polar rings. All these observations are not easily reproduced in simulations without important gas accretion. N-body simulations taking into account the mass exchange between stars and gas through star formation and feedback, can reproduce the data, only if galaxies double their mass in about 10 Gyr through gas accretion. Warped and polar ring systems are good tracers of this accretion, which occurs from cold gas which has not been virialised in the system's potential. The relative importance of these phenomena are compared between the field and rich clusters. The respective role of mergers and gas accretion vary considerably with environment.Comment: 18 pages, 8 figures, review paper to "Penetrating Bars through Masks of Cosmic Dust: the Hubble Tuning Fork Strikes a New Note", Pilanesberg, ed. D. Block et al., Kluwe

Quality of life in patients with personality disorders seen at an ordinary psychiatric outpatient clinic

BACKGROUND: Epidemiological studies have found reduced health-related quality of life (QoL) in patients with personality disorders (PDs), but few clinical studies have examined QoL in PDs, and none of them are from an ordinary psychiatric outpatient clinic (POC). We wanted to examine QoL in patients with PDs seen at a POC, to explore the associations of QoL with established psychiatric measures, and to evaluate QoL as an outcome measure in PD patients. METHODS: 72 patients with PDs at a POC filled in the MOS Short Form 36 (SF-36), and two established psychiatric self-rating measures. A national norm sample was compared on the SF-36. An independent psychiatrist diagnosed PDs and Axis-I disorders by structured interviews and rated the Global Assessment of Functioning (GAF). All measurements were repeated in the 39 PD patients that attended the 2 years follow-up examination. RESULTS: PD patients showed high co-morbidity with other PDs and Axis I mental disorders, and they scored significantly lower on all the SF-36 dimensions than age- and gender-adjusted norms. Adjustment for co-morbid Axis I disorders had some influence, however. The SF-36 mental health, vitality, and social functioning were significantly associated with the GAF and the self-rated psychiatric measures. Significant changes at follow-up were found in the psychiatric measures, but only on the mental health and role-physical of the SF-36. CONCLUSION: Patients with PDs seen for treatment at a POC have globally poor QoL. Both physical and mental dimensions of the SF-36 are correlated with established psychiatric measures in such patients, but significant changes in these measures are only partly associated with changes in the SF-36 dimensions

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p