Pregnancy associated plasma protein-A (PAPP-A) as an early marker for the diagnosis of acute coronary syndrome.

Aims and objectives Pregnancy associated plasma protein-A (PAPP-A), a metalloproteinase plays a pivotal role in the pathogenesis of atherosclerosis. Recent studies have reported that elevated levels of PAPP-A, signal the onset of acute coronary syndrome (ACS). We, therefore, proposed to study the analytical competence of PAPP-A in patients admitted to the emergency department with chest pain and finally diagnosed as ACS. Methods and results Pregnancy associated plasma protein-A was measured using enzyme-linked immunosorbent assay (ELISA) in 485 patients admitted to emergency care unit, of which 89 patients were diagnosed as Non-cardiac chest pain (NCCP). Elevated levels of PAPP-A were observed in patients diagnosed as ACS on comparison with the controls. Receiver operator characteristic (ROC) curve analysis showed PAPP-A to be a good discriminator between ischaemic and non-ischaemic patients. The area under the curve was found to be 0.904, 95% CI (0.874–0.929) with 90% sensitivity and 85% specificity (P< 0.0001). The cut-off value from the ROC curve was 0.55 μg/mL above which PAPP-A was considered to be positive. Conclusion Pregnancy associated plasma protein-A seems to be a promising biomarker for identification and risk stratification for patients with ACS

Guidance Systems of Fighter Aircraft

Mission performance of a fighter aircraft is crucial for survival and strike capabilities in todays' aerial warfare scenario. The guidance functions of such an aircraft play a vital role inmeeting the requirements and accomplishing the mission success. This paper presents the requirements of precision guidance for various missions of a fighter aircraft. The concept ofguidance system as a pilot-in-loop system is pivotal in understanding and designing such a system. Methodologies of designing such a system are described

Oxidative DNA cleavage, cytotoxicity and antimicrobial studies of l-ornithine copper (II) complexes

New ternary copper (II) complexes, Cu(l-orn)(B)(Cl)(Cl·2H 2O) (1-2) where l-orn is l-ornithine, B is an N,N-donor heterocyclic base, viz. 2,2â²-bipyridine (bpy, 1) and 1,10-phenanthroline (phen, 2), were synthesized and characterized by various spectroscopic techniques. Complex 2 is characterized by the X-ray single crystallographic method. The complex shows a distorted square-pyramidal (4 + 1) CuN 3OCl coordination sphere. Binding interactions of the complexes with calf thymus DNA (CT-DNA) were investigated by UV-Vis absorption titration, ethidium bromide displacement assay, viscometric titration experiment and DNA melting studies. Complex 2 shows appreciable chemical nuclease activity in the presence of 3-mercaptopropionic acid (MPA). The complexes were subjected to in vitro cytotoxicity studies against carcinomic human alveolar basal epithelial cells (A-549) and human epithelial (HEp-2) cells. The IC 50 values of 1 and 2 are less than that of cisplatin against HEp-2 cell lines. MIC values for 1 against the bacterial strains Streptococcus mutans and Pseudomonas aeruginosa are 0.5 mM. © 2012 Elsevier Ltd. All rights reserved

Genetic variability in sugarcane (Saccharum spp. hybrid) genotypes through inter simple sequence repeats (ISSR) markers

In the present study, 14 sugarcane (Saccharum spp. hybrid) genotypes were used for genomic diversity analysis based on nineteen inter simple sequence repeat (ISSR). These nineteen sets of ISSR markers generated a total of 164 discernible and reproducible bands including 109 polymorphic and 55 monomorphic bands. The unweighted pair group method with arithmetic average (UPGMA) analysis revealed three distinct clusters: I, II and III within the 14 genotypes. The polymorphic information content (PIC) value per locus ranged from 0.14 (UBC811) to 0.53 (ISSR1) locus with an average of 0.42 for all loci. The range of genetic distance or coefficient of similarity among sugarcane genotypes varied 0.14 - 0.78. The analysis of these similarities matrix revealed that greater similarity between CoS03234 and CoSe1424 (0.78), and lowest similarity between CoS03234 and Co0118 (0.14). The knowledge gained in this study would be useful to future breeding programs for increasing genetic diversity of sugarcane varieties and cultivars to meet the increasing demand of sugarcane cultivation for sugar and bio energy uses

Observability and nonlinear filtering

This paper develops a connection between the asymptotic stability of nonlinear filters and a notion of observability. We consider a general class of hidden Markov models in continuous time with compact signal state space, and call such a model observable if no two initial measures of the signal process give rise to the same law of the observation process. We demonstrate that observability implies stability of the filter, i.e., the filtered estimates become insensitive to the initial measure at large times. For the special case where the signal is a finite-state Markov process and the observations are of the white noise type, a complete (necessary and sufficient) characterization of filter stability is obtained in terms of a slightly weaker detectability condition. In addition to observability, the role of controllability in filter stability is explored. Finally, the results are partially extended to non-compact signal state spaces

Itinerant electron metamagnetism in LaCo9_9Si4_4

The strongly exchange enhanced Pauli paramagnet LaCo$_9$Si$_4$ is found to exhibit an itinerant metamagnetic phase transition with indications for metamagnetic quantum criticality. Our investigation comprises magnetic, specific heat, and NMR measurements as well as ab-initio electronic structure calculations. The critical field is about 3.5 T for $H||c$ and 6 T for $H\bot c$, which is the lowest value ever found for rare earth intermetallic compounds. In the ferromagnetic state there appears a moment of about 0.2 $\mu_B$/Co at the $16k$ Co-sites, but sigificantly smaller moments at the 4d and $16l$ Co-sites.Comment: 11 pages, 5 figures, PRB Rapid Communication, in prin

Building automation pipeline for diagnostic classification of sporadic odontogenic keratocysts and non-keratocysts using whole-slide images

The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types