Efficient Two-Level Swarm Intelligence Approach for Multiple Sequence Alignment

This paper proposes two-level particle swarm optimization (TL-PSO), an efficient PSO variant that addresses two levels of optimization problem. Level one works on optimizing dimension for entire swarm, whereas level two works for optimizing each particle's position. The issue addressed here is one of the most challenging multiple sequence alignment (MSA) problem. TL-PSO deals with the arduous task of determination of exact sequence length with most suitable gap positions in MSA. The two levels considered here are: to obtain optimal sequence length in level one and to attain optimum gap positions for maximal alignment score in level two. The performance of TL-PSO has been assessed through a comparative study with two kinds of benchmark dataset of DNA and RNA. The efficiency of the proposed approach is evaluated with four popular scoring schemes at specific parameters. TL-PSO alignments are compared with four PSO variants, i.e. S-PSO, M-PSO, ED-MPSO and CPSO-Sk, and two leading alignment software, i.e. ClustalW and T-Coffee, at different alignment scores. Hence obtained results prove the competence of TL-PSO at accuracy aspects and conclude better score scheme

G × E interactions in QTL introgression lines of Spanish-type groundnut (Arachis hypogaea L.)

Multi-environment testing at five locations for rust and late leaf spot (LLS) resistance with 41 introgressed lines (ILs) bred using marker-assisted backcross breeding in the genetic background Spanish- type groundnut varieties identified significant genotype, and genotype 9 environment interactions (GEI) for LLS disease resistance and yield parameters. Significant GEI effects suggest the need to identify location specific breeding lines to achieve gains in pod yield and LLS resistance. The observed variable LLS disease reaction among the ILs in part suggests influence of background genotype on the level of resistance. A breeding scheme with early generation selection using molecular markers followed by phenotyping for LLS, and multi-location testing of fixed breeding lines was optimized to enhance selection intensity and accuracy in groundnut breeding. The ILs, ICGVs 14431, 14436 and 14438 with pooled LLS score at 90 DAS of 3.5–3.7 were superior to respective recurrent parent for pod yield, with early maturing similar to recurrent parents. The pod yield advantage in ILs is attributed by more number of pods, besides resistance to LLS that contributes to better filling

Molecular breeding tools improved drought tolerant groundnut variety for resistance to foliar fungal diseases

A largely rainfed crop in India, drought tolerance, particularly mid- and end-season tolerance, is a key trait in groundnut varieties. A combination of both empirical and trait-based approaches was used in breeding programs of ICAR and ICRISAT, resulting in release of few tolerant varieties that have superior pod yield under drought stress and/or have enhanced water-use-efficiency. There is a need to breed varieties with drought tolerance, disease resistance and quality traits that suit different production ecologies as well as meet the needs of the farmers, consumers and industries. ICRISAT has released an early-maturing (90-95 d) and drought- tolerant variety ICGV 91114 for the drought-prone Ananthapur district of Andhra Pradesh, India, where about 0.7 m ha area is under groundnut cultivation and has low (300 mm) and erratic (30-40 rainy days) rainfall. On-farm studies conducted with ICGV 91114 during 2008-10 showed 30% reduction in yield variability over the years. Following screening in hot-spots of both rust and LLS disease during 2014 rainy season, a total of 27 introgression lines derived from ICGV 91114 were selected and advanced for evaluation in multi-location trials at six locations in 2015 under rainfed conditions. Based on the pod yield under rainfed conditions and disease resistance, three superior introgression lines (ICGV 14410, ICGV 13189, ICGV 14421) were proposed for the first-ever NILs trial (near-isogenic lines trial) along with eight others conducted under All India Coordinated Research Project on Groundnut (AICRP-G) at national level

Power programming with Gupta SQL Windows

New Jerseyxix, 379 p.: illus.; 23 c

CAA-PPI: A Computational Feature Design to Predict Protein–Protein Interactions Using Different Encoding Strategies

Protein–protein interactions (PPIs) are involved in an extensive variety of biological procedures, including cell-to-cell interactions, and metabolic and developmental control. PPIs are becoming one of the most important aims of system biology. PPIs act as a fundamental part in predicting the protein function of the target protein and the drug ability of molecules. An abundance of work has been performed to develop methods to computationally predict PPIs as this supplements laboratory trials and offers a cost-effective way of predicting the most likely set of interactions at the entire proteome scale. This article presents an innovative feature representation method (CAA-PPI) to extract features from protein sequences using two different encoding strategies followed by an ensemble learning method. The random forest methodwas used as a classifier for PPI prediction. CAA-PPI considers the role of the trigram and bond of a given amino acid with its nearby ones. The proposed PPI model achieved more than a 98% prediction accuracy with one encoding scheme and more than a 95% prediction accuracy with another encoding scheme for the two diverse PPI datasets, i.e., H. pylori and Yeast. Further, investigations were performed to compare the CAA-PPI approach with existing sequence-based methods and revealed the proficiency of the proposed method with both encoding strategies. To further assess the practical prediction competence, a blind test was implemented on five other species’ datasets independent of the training set, and the obtained results ascertained the productivity of CAA-PPI with both encoding schemes

Assessment of drug-related problems in depressive patients

Background: Drug-related problems (DRPs) frequently occur in modern medical practice, increasing the morbidity and mortality as well as increasing cost of care. Objective: The study is to evaluate the incidence of DRPs in patients admitted to a psychiatric department. Materials and Methods: A prospective observational study was conducted for a period of 4 months at Baliga psychiatric hospital. All prescriptions of the study population were screened for DRPs such as adverse drug reactions (ADRs) and potential drug-drug interactions (pDDIs) by using computerized database system. Results: Out of 120 patients, 19 patients had observed 26 DRPs. Out of 33 patients, 19 patients had observed 26 ADRs and 14 patients had observed 24 pDDIs. The overall incidence of DRPs was 15.83%. Female patients outnumbered the male patients, in which 12 women constitute 10% followed by men 7 (5.83%). The common ADRs observed were hyponatremia and headache. Considering the outcomes, 20 (76.9%) cases recovered from ADRs and 20 (76.9%) of the ADRs were definitely preventable. Majority of ADRs were probable and were found to be mild to moderately severe. Conclusions: Age, female gender and polypharmacy were the risk factors for the developing DRPs

Pathological Spectrum and β-APP Immunoreactivity as a Diagnostic Tool of Diffuse Axonal Injury following Traumatic Brain Injury: A Novel Classification

Aim Different deposition patterns and grading systems used to define and identify DAI remain discordant and to date these are a challenge in clinical practice. Our main objective was to study the post-mortem axonal changes and develop a grading system to identify DAI on the basis of histopathological and immunoreactive β-amyloid precursor protein (β-APP) observations in severe TBI cases. Methods Prospective study with 35 decedents with sTBI (GCS score ≤ 8) was conducted and samples were collected from three different sites–corpus callosum, thalamus and brain stem. Serial sections from each site were stained with hematoxylin and eosin (H&E), and immunohistochemistry (IHC) of β-APP. Results We developed a grading system based on histopathological characteristics to assess the overall damage of axonal injury. We found maximum histopathological changes in cases with prolonged stay. Corpus callosum showed maximum changes in both gradings. Curiously, we also detected axonal swellings with H&E staining. Usually neglected, the thalamus also showed significant histopathological and immunoreactive changes for sTBI. Conclusion Our study based on histopathological and β-APP scoring system to define and identify DAI thus facilitates accurate diagnosis of DAI post mortem, which has forensic implications, and may further contribute toward survival and improvement of quality of life of sTBI patients

Myelin degradation, axonal changes and expression trajectories of glial cells stimulated by rapid head insult in humans to estimate approximate time elapsed since trauma

Abstract Background Post severe traumatic brain injury (sTBI), axonal alterations lead to myelin loss and its degeneration. In the recovery phase, numerous intermingled biochemical pathways involving complex inflammatory reactions cloud the understanding of this yet undiscerned process that also varies with agonal period. In cases with dubious histories, approximating the survival time can be challenging, and expression levels of characteristic markers may aid forensic experts in the same. Methods This exploratory study recruited 100 samples—68 sTBI, 22 non-TBI and 10 age- and sex-matched control samples. Male:female ratio was 87:13. Histochemical staining using H&E was used to characterize myelination pattern, and IHC of GFAP and CD-68 were performed to assess astroglial and microglial reactions with respect to survival time in specific sites. Result Among sTBI, non-TBI and control recruits, sTBI patients depicted significant myelination abnormalities, astroglial proliferation and microglial reaction and varying with survival time. Non-TBI and control samples depicted nearly similar profiles. Conclusion In order to untangle the complex mesh of biochemical responses, nuanced research on individual factors (both pre- and post mortem) with regard to specific site and survival time are warranted. Standardizing experimental data and converting it into empirical data shall aid forensic experts in suggesting approximate agonal period

Molecular breeding tools improved drought tolerant groundnut variety for resistance to foliar fungal diseases

A largely rainfed crop in India, drought tolerance, particularly mid- and end-season tolerance, is a key trait in groundnut varieties. A combination of both empirical and trait-based approaches was used in breeding programs of ICAR and ICRISAT, resulting in release of few tolerant varieties that have superior pod yield under drought stress and/or have enhanced water-use-efficiency. There is a need to breed varieties with drought tolerance, disease resistance and quality traits that suit different production ecologies as well as meet the needs of the farmers, consumers and industries. ICRISAT has released an early-maturing (90-95 d) and drought- tolerant variety ICGV 91114 for the drought-prone Ananthapur district of Andhra Pradesh, India, where about 0.7 m ha area is under groundnut cultivation and has low (300 mm) and erratic (30-40 rainy days) rainfall. On-farm studies conducted with ICGV 91114 during 2008-10 showed 30% reduction in yield variability over the years. Following screening in hot-spots of both rust and LLS disease during 2014 rainy season, a total of 27 introgression lines derived from ICGV 91114 were selected and advanced for evaluation in multi-location trials at six locations in 2015 under rainfed conditions. Based on the pod yield under rainfed conditions and disease resistance, three superior introgression lines (ICGV 14410, ICGV 13189, ICGV 14421) were proposed for the first-ever NILs trial (near-isogenic lines trial) along with eight others conducted under All India Coordinated Research Project on Groundnut (AICRP-G) at national level