Proteomic Responses in the Gill of Zebrafish Following Exposure to Ibuprofen and Naproxen

Non-steroidal anti-inflammatory drugs (NSAIDs) are among the most abundant environmental pharmaceutical contaminants. In this study, a proteomic analysis was conducted to identify proteins differentially expressed in gill tissue of zebrafish (Danio rerio) after a 14-day exposure to the NSAIDs ibuprofen or naproxen. A total of 104 proteins with altered expression as indicated by 2-dimensional electrophoresis were analyzed by liquid chromatography with ion trap mass spectrometry (MS/MS). A total of 14 proteins fulfilled our requirements for identification which included consistency among replicate gels as well as successful MS/MS ion searches with the MASCOT database. The most prominent feature of the differential protein expression observed after NSAID exposure was an up-regulation of proteins belonging to the globin family which are involved in the transport of oxygen from gills and availability of heme molecules required for synthesis of cyclooxygenase. Differential expression was observed at exposure concentrations as low as 1-10 µg/L indicating that altered gene expression may occur in fish subjected to environmentally realistic levels of NSAID exposure

Structural insights into the repair mechanism of AGT for methyl-induced DNA damage

Methylation induced DNA base-pairing damage is one of the major causes of cancer. O6-alkylguanine-DNA alkyltransferase (AGT) is considered a demethylation agent of the methylated DNA. Structural investigations with thermodynamic properties of the AGT-DNA complex are still lacking. In this report, we modeled two catalytic states of AGT-DNA interactions and an AGT-DNA covalent complex and explored structural features using molecular dynamics (MD) simulations. We utilized the umbrella sampling method to investigate the changes in the free energy of the interactions in two different AGT-DNA catalytic states, one with methylated GUA in DNA and the other with methylated CYS145 in AGT. These non-covalent complexes represent the pre- A nd post-repair complexes. Therefore, our study encompasses the process of recognition, complex formation, and separation of the AGT and the damaged (methylated) DNA base. We believe that the use of parameters for the amino acid and nucleotide modifications and for the protein-DNA covalent bond will allow investigations of the DNA repair mechanism as well as the exploration of cancer therapeutics targeting the AGT-DNA complexes at various functional states as well as explorations via stabilization of the complex

Assessing Restoration Potential of Fragmented and Degraded Fagaceae Forests in Meghalaya, North-East India

The montane subtropical broad-leaved humid forests of Meghalaya (Northeast India) are highly diverse and situated at the transition zone between the Eastern Himalayas and Indo-Burma biodiversity hotspots. In this study, we have used inventory data from seedlings to canopy level to assess the impact of both biotic and abiotic disturbances on structure, composition, and regeneration potential of the Fagaceae trees of these forests. Fagaceae trees are considered as the keystone species in these forests due to their regional dominance and their importance as a fuel wood source, and also because they form an important component of climax community in these forests. Unfortunately, these forests are highly degraded and fragmented due to anthropogenic disturbances. We have assessed, for the first time, the restoration potential (i.e., capacity to naturally regenerate and sustain desired forest structure) of Fagaceae species in the genera Lithocarpus Blume, Castanopsis (D. Don) Spach, and Quercus Linn. We also evaluated how biotic and abiotic factors, as well as anthropogenic disturbances, influence the restoration potential of these species in six fragmented forest patches located along an elevational gradient on south-facing slopes in the Khasi Hills, Meghalaya. Fagaceae was the most dominant family at all sites except one site (Laitkynsew), where it was co-dominant with Lauraceae. Fagaceae forests have shown high diversity and community assemblages. Fagaceae species had high levels of natural regeneration (i.e., seedlings and saplings) but low recruitment to large trees (diameter at breast height or DBH ≥ 10 cm) at all sites. The ability to sprout was higher in Fagaceae tree species than non-Fagaceae tree species. We have shown that human disturbance and structural diversity were positively related to regeneration of Fagaceae tree species due to high sprouting. However, with increasing human disturbance, recruitment of saplings and pole-sized trees to mature trees hampered the resulting proportion of mature Fagaceae tree species. This study provides a means for assessing regeneration and a basis for forest management strategies in degraded and fragmented forests of Meghalaya

A relational database to identify differentially expressed genes in the endometrium and endometriosis lesions

Endometriosis is a common inflammatory estrogen-dependent gynecological disorder, associated with pelvic pain and reduced fertility in women. Several aspects of this disorder and its cellular and molecular etiology remain unresolved. We have analyzed the global gene expression patterns in the endometrium, peritoneum and in endometriosis lesions of endometriosis patients and in the endometrium and peritoneum of healthy women. In this report, we present the EndometDB, an interactive web-based user interface for browsing the gene expression database of collected samples without the need for computational skills. The EndometDB incorporates the expression data from 115 patients and 53 controls, with over 24000 genes and clinical features, such as their age, disease stages, hormonal medication, menstrual cycle phase, and the different endometriosis lesion types. Using the web-tool, the end-user can easily generate various plot outputs and projections, including boxplots, and heatmaps and the generated outputs can be downloaded in pdf-format.Peer reviewe

Variation in grain zinc and iron concentrations, grain yield and associated traits of biofortified bread wheat genotypes in Nepal

Wheat (Triticum aestivum L.) is one of the major staples in Nepal providing the bulk of food calories and at least 30% of Fe and Zn intake and 20% of dietary energy and protein consumption; thus, it is essential to improve its nutritional quality. To select high-yielding genotypes with elevated grain zinc and iron concentration, the sixth, seventh, eighth, and ninth HarvestPlus Yield Trials (HPYTs) were conducted across diverse locations in Nepal for four consecutive years: 2015–16, 2016–17, 2017–18, and 2018–19, using 47 biofortified and 3 non-biofortified CIMMYT-bred, bread wheat genotypes: Baj#1, Kachu#1, and WK1204 (local check). Genotypic and spatial variations were found in agro-morphological traits; grain yield and its components; and the grain zinc and iron concentration of tested genotypes. Grain zinc concentration was highest in Khumaltar and lowest in Kabre. Likewise, grain iron concentration was highest in Doti and lowest in Surkhet. Most of the biofortified genotypes were superior for grain yield and for grain zinc and iron concentration to the non-biofortified checks. Combined analyses across environments showed moderate to high heritability for both Zn (0.48–0.81) and Fe (0.46–0.79) except a low heritability for Fe observed for 7th HPYT (0.15). Grain yield was positively correlated with the number of tillers per m2, while negatively correlated with days to heading and maturity, grain iron, grain weight per spike, and thousand grain weight. The grain zinc and iron concentration were positively correlated, suggesting that the simultaneous improvement of both micronutrients is possible through wheat breeding. Extensive testing of CIMMYT derived high Zn wheat lines in Nepal led to the release of five biofortified wheat varieties in 2020 with superior yield, better disease resistance, and 30–40% increased grain Zn and adaptable to a range of wheat growing regions in the country – from the hotter lowland, or Terai, regions to the dry mid- and high-elevation areas

Mixture modelling of multiresolution 0-1 data

Biological systems are complex and measurements in biology are made with high throughput and high resolution techniques often resulting in data in multiple resolutions. Furthermore, ISCN [1] has defined five different resolutions of the chromosome band. Currently, available standard algorithms can only handle data in one resolution at a time. Hence, transformation of the data to the same resolution is inevitable before the data can be fed to the algorithm. Furthermore comparing the results of an algorithm on data in different resolutions can produce interesting results which aids in determining suitable resolution of data. In addition, experiments in different, resolutions can be helpful in determining the appropriate resolution for computational methods. In this thesis, one method for up sampling and three different methods of down sampling 0-1 data are proposed, implemented and experiments are performed on different resolutions. Suitability of the proposed methods is validated and the results are compared across different resolutions. The proposed methods produce plausible results showing that the significant patterns in the data are retained in the transformed resolution. Thereafter, the mixture models are trained on the data original data and the results are analyzed. However, machine learning methods such as mixture models require high amounts of data to produce plausible results. Therefore, the major aim of the data transformation procedure was the integration of databases. Hence, two different datasets available in two different resolutions were integrated after transforming them to a single resolution and mixture models were trained on them. Trained models can be used to classify cancers and cluster the data. The results on integrated data showed significant improvements compared with the data in the original resolution

Probabilistic Modelling of Multiresolution Biological Data

When the measurements from the ever improving measurement technology are accumulated over a period of time, the result is the collection of data in different representations. However, most machine learning and data mining algorithms, in their standard form, are designed to operate on data in single representation. This thesis proposes machine learning and data mining algorithms to analyze data in different representation with respect to the resolution within a single analysis. The novel algorithms proposed to analyze multiresolution data are in the field of probabilistic modelling and semantic data mining. First, three different deterministic data transformation methods are proposed to transform data across different resolutions. After the data transformation, the resulting data in same resolution are integrated and modeled using mixture models. Second, similar mixture components in a mixture model are merged one by one repetitively to generate a chain of mixture models. A new fast approximation of the KL-divergence is derived to determine the similarity of the mixture components. The chain of generated mixture models are useful for comparison, for example, in model selection. Third, mixture components in different resolutions are iteratively merged to model multiresolution data generating models in each modeled resolution that incorporate information from data in other resolution. Fourth, a single multiresolution mixture model with multiresolution mixture components is proposed whose mixture components independently have the capabilities of a Bayesian network. Finally, three--part methodology consisting of clustering using mixture models, rule learning using semantic subgroup discovery, and pattern visualization using banded matrices is developed for comprehensive analysis of multiresolution data. The multiresolution data analysis methods presented in this thesis improves the performance of the methods in comparison with the their single resolution counterparts. Furthermore, developed methods aims to make the results understandable to the domain experts. Therefore, the developed methods are useful addition in the analysis of chromosomal aberration patterns and the cancer research in general

Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada, October 4-6, 2015. Proceedings

In the current scientific age, the measurement technology has considerably improved and diversified producing data in different representations. Traditional machine learning and data mining algorithms can handle data only in a single representation in their standard form. In this contribution, we address an important challenge encountered in data analysis: what to do when the data to be analyzed are represented differently with regards to the resolution? Specifically, in classification, how to train a classifier when class labels are available only in one resolution and missing in the other resolutions? The proposed methodology learns a classifier in one data resolution and transfers it to learn the class labels in a different resolution. Furthermore, the methodology intuitively works as a dimensionality reduction method. The methodology is evaluated on a simulated dataset and finally used to classify cancers in a real–world multiresolution chromosomal aberration dataset producing plausible results.</p

Hearing loss: an unusual presentation of neurobrucellosis: a case report

Abstract Introduction Brucellosis is a zoonotic disease, caused by a Gram-negative coccobacillus of Brucella genus, transmitted to humans by animals, especially cattle. It rarely involves the nervous system (neurobrucellosis); only a few cases present with hearing loss. We report a case of neurobrucellosis, that presented with bilateral sensorineural hearing loss and mild to moderate persistent headache. To the best of our knowledge, this is the first well-documented case from Nepal. Case presentation The patient was a 40-year-old Asian male shepherd from the western mountainous region of Nepal who came to the emergency department of Manipal Teaching Hospital, Pokhara in May, 2018 and did a follow-up for 6 months. He presented with high-grade fever, profuse sweating, headache, myalgia, and bilateral sensorineural hearing loss. His history of consuming raw milk of cattle, symptoms including persistent mild to moderate headache, bilateral hearing loss, and serological findings were suggestive of neurobrucellosis. Following treatment, the symptoms improved, including the complete recovery of hearing loss. Conclusion Hearing loss may be the manifestation of neurobrucellosis. Physicians should know about such presentations in brucella endemic areas