A study of the problems associated with Dalangdian reservoir, China

There are over 2,300 lakes over 1 km2 in China (total area 80 000 km2). In addition there are approximately 87 000 reservoirs with a storage capacity of 413 billion m3. These form the main supply of drinking water as well as water for industrial and agricultural production and aquaculture. Because of a lack of understanding of the frailty of lake ecosystems and poor environmental awareness, human activities have greatly affected freshwater systems. This article focuses on the problems of one water supply reservoir, Dalangdian Reservoir, and considers options for improving its management. Dalangdian Reservoir is described and occurrence of algal genera given. The authors conclude with remarks on the future of the Dalangdian Reservoir

Protein function prediction by integrating sequence, structure and binding affinity information

Indiana University-Purdue University Indianapolis (IUPUI)Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig

Fast Iterative Reconstruction for Multi-spectral CT by a Schmidt Orthogonal Modification Algorithm (SOMA)

Multi-spectral CT (MSCT) is increasingly used in industrial non-destructive testing and medical diagnosis because of its outstanding performance like material distinguishability. The process of obtaining MSCT data can be modeled as nonlinear equations and the basis material decomposition comes down to the inverse problem of the nonlinear equations. For different spectra data, geometric inconsistent parameters cause geometrical inconsistent rays, which will lead to mismatched nonlinear equations. How to solve the mismatched nonlinear equations accurately and quickly is a hot issue. This paper proposes a general iterative method to invert the mismatched nonlinear equations and develops Schmidt orthogonalization to accelerate convergence. The validity of the proposed method is verified by MSCT basis material decomposition experiments. The results show that the proposed method can decompose the basis material images accurately and improve the convergence speed greatly

Indomethacin inhibits PGE2, regulates inflammatory response, participates in adipogenesis regulation, and improves success rate of fat transplantation in C57/B6 mice

Purpose: To investigate the effect of indomethacin on prostaglandin E2, regulation of inflammation and adipogenesis, and success of fat transplantation in mice. Methods: The mice were randomly divided into 4 groups: group A (free fat group), group B (free fat + stromal vascular fragments group (SVF)), group C (free fat + 200 μM indomethacin group), and group D (free fat + 200 μM indomethacin + SVF group), with 21 mice in each group. Expression levels of adipogenic genes CEBP-α, FABP4 and LPL in each group were determined. Changes in PGE2 level in transplanted adipose tissue, and changes in the expression of NF-κB in apoptotic stem cells induced by different pro-inflammatory treatments were assayed. Results: Compared with group B, the expression levels of adipogenic genes CEBP-α, FABP4 and LPL significantly decreased in groups A, C and D, with group A as the lowest (p < 0.05). Compared with the indomethacin treatment group, the level of inhibition of PGE2 in mice adipose tissue in the indomethacin-free group increased significantly (p < 0.01). The expression of NF-κB in the adipose stem cells from the indomethacin-treated group was significantly lower than that in the indomethacin-treated group after pretreatment with IL-17 or INF-γ + TNF-α. Conclusion: Indomethacin regulates adipogenesis by inhibiting the production of COX2 metabolite, PGE2. It also regulates the local microenvironment, inhibits the inflammatory process, and protects various stem cells. Therefore, it may improve the success rate of fat transplantation

Shared genetic factors underlie migraine and depression

Free to read\ud \ud Migraine frequently co-occurs with depression. Using a large sample of Australian twin pairs, we aimed to characterize the extent to which shared genetic factors underlie these two disorders. Migraine was classified using three diagnostic measures, including self-reported migraine, the ID migraine screening tool, or migraine without aura (MO) and migraine with aura (MA) based on International Headache Society (IHS) diagnostic criteria. Major depressive disorder (MDD) and minor depressive disorder (MiDD) were classified using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. Univariate and bivariate twin models, with and without sex-limitation, were constructed to estimate the univariate and bivariate variance components and genetic correlation for migraine and depression. The univariate heritability of broad migraine (self-reported, ID migraine, or IHS MO/MA) and broad depression (MiDD or MDD) was estimated at 56% (95% confidence interval [CI]: 53-60%) and 42% (95% CI: 37-46%), respectively. A significant additive genetic correlation (r G = 0.36, 95% CI: 0.29-0.43) and bivariate heritability (h 2 = 5.5%, 95% CI: 3.6-7.8%) was observed between broad migraine and depression using the bivariate Cholesky model. Notably, both the bivariate h 2 (13.3%, 95% CI: 7.0-24.5%) and r G (0.51, 95% CI: 0.37-0.69) estimates significantly increased when analyzing the more narrow clinically accepted diagnoses of IHS MO/MA and MDD. Our results indicate that for both broad and narrow definitions, the observed comorbidity between migraine and depression can be explained almost entirely by shared underlying genetically determined disease mechanisms

Familial aggregation of migraine and depression: Insights from a large Australian twin sample

Free to read\ud \ud Objectives: This research examined the familial aggregation of migraine, depression, and their co-occurrence.\ud \ud Methods: Diagnoses of migraine and depression were determined in a sample of 5,319 Australian twins. Migraine was diagnosed by either self-report, the ID migraine™ Screener, or International Headache Society (IHS) criteria. Depression was defined by fulfilling either major depressive disorder (MDD) or minor depressive disorder (MiDD) based on the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. The relative risks (RR) for migraine and depression were estimated in co-twins of twin probands reporting migraine or depression to evaluate their familial aggregation and co-occurrence.\ud \ud Results: An increased RR of both migraine and depression in co-twins of probands with the same trait was observed, with significantly higher estimates within monozygotic (MZ) twin pairs compared to dizygotic (DZ) twin pairs. For cross-trait analysis, the RR for migraine in co-twins of probands reporting depression was 1.36 (95% CI: 1.24–1.48) in MZ pairs and 1.04 (95% CI: 0.95–1.14) in DZ pairs; and the RR for depression in co-twins of probands reporting migraine was 1.26 (95% CI: 1.14–1.38) in MZ pairs and 1.02 (95% CI: 0.94–1.11) in DZ pairs. The RR for strict IHS migraine in co-twins of probands reporting MDD was 2.23 (95% CI: 1.81–2.75) in MZ pairs and 1.55 (95% CI: 1.34–1.79) in DZ pairs; and the RR for MDD in co-twins of probands reporting IHS migraine was 1.35 (95% CI: 1.13–1.62) in MZ pairs and 1.06 (95% CI: 0.93–1.22) in DZ pairs.\ud \ud Conclusions: We observed significant evidence for a genetic contribution to familial aggregation of migraine and depression. Our findings suggest a bi-directional association between migraine and depression, with an increased risk for depression in relatives of probands reporting migraine, and vice versa. However, the observed risk for migraine in relatives of probands reporting depression was considerably higher than the reverse. These results add further support to previous studies suggesting that patients with comorbid migraine and depression are genetically more similar to patients with only depression than patients with only migraine