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Congenital anomalies, congenital defects, or birth defects are significant causes of death in infants. The most common congenital defects are congenital heart defects (CHDs) and neural tube defects (NTDs). Defects induced by genetic mutations, environmental exposure to toxins, or a combination of these effects can result in congenital malformations, leading to infant death or long‐term disabilities. These defects produce significant mortality and morbidity in the affected individuals, and families are affected emotional and financially. Also, society is impacted on many levels. Congenital anomalies may be reduced by dietary supplements of folic acid and other vitamins. Here, we review the evidence for specific roles of toxins (alcohol, cigarette smoke) in causing common severe congenital anomalies like CHDs, NTDs, and ocular defects. We also review the evidence for beneficial effects for dietary supplementation, and highlight gaps in our knowledge, where research may contribute to additional benefits of intervention that can reduce birth defects. Extensive discussion of common severe congenital anomalies (CHDs, NTDs, and ocular defects) illustrates the effects of diet on the frequency and severity of these defects. Birth Defects Research (Part C) 108:274–286, 2016. © 2016 Wiley Periodicals, Inc.  相似文献   
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Glial Fibrillary Acidic Protein (GFAP) is an intermediate-filament (IF) protein that maintains the astrocytes of the Central Nervous System in Human. This is differentially expressed during serological studies in inflamed condition such as Rheumatoid Arthritis (RA). Therefore, it is of interest to glean molecular insight using a model of GFAP (49.88 kDa) due to its crystallographic nonavailability. The present study has been taken into consideration to construct computational protein model using Modeller 9.11. The structural relevance of the protein was verified using Gromacs 4.5 followed by validation through PROCHECK, Verify 3D, WHAT-IF, ERRAT and PROVE for reliability. The constructed three dimensional (3D) model of GFAP protein had been scrutinized to reveal the associated functions by identifying ligand binding sites and active sites. Molecular level interaction study revealed five possible surface cavities as active sites. The model finds application in further computational analysis towards drug discovery in order to minimize the effect of inflammation.  相似文献   
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Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.  相似文献   
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Background

Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease.

Results

In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap.

Conclusions

The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.
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Hybridization between the nearly extinct speciesDiplotaxis siettiana andBrassica juncea is prevented because of strong prefertilization barriers. Use of mentor pollen ofD. siettiana irradiated with 1000 Gy gamma radiation before the incompatible pollination led to fertilization. 5 d after pollination 17% ovules showed entry of pollen tubes, 10 d after pollination 27% ovules showed small globular embryos which grew no further. No embryos were found in control pollinations. Thus , use of irradiated mentor pollen brings about fertilization in this difficult cross and hybrids can be obtained if embryos are rescued.  相似文献   
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Buffalo spermatozoa were subjected to cold shock and freezing treatments to assess loss of lipids and phospholipids. Cold shock and freezing resulted in significant loss of 15.8% and 34.6% of total lipids and 6.4% and 19.1% of phospholipids, respectively.  相似文献   
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