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Retinoblastoma is rare tumor of the retina caused by the homozygous loss of the Retinoblastoma 1 tumor suppressor gene (RB1). Loss of the RB1 protein, pRB, results in de-regulated activity of the E2F transcription factors, chromatin changes and developmental defects leading to tumor development. Extensive microarray profiles of these tumors have enabled the identification of genes sensitive to pRB disruption, however, this technology has a number of limitations in the RNA profiles that they generate. The advent of RNA-sequencing has enabled the global profiling of all of the RNA within the cell including both coding and non-coding features and the detection of aberrant RNA processing events. In this perspective, we focus on discussing how RNA-sequencing of rare Retinoblastoma tumors will build on existing data and open up new area’s to improve our understanding of the biology of these tumors. In particular, we discuss how the RB-research field may be to use this data to determine how RB1 loss results in the expression of; non-coding RNAs, causes aberrant RNA processing events and how a deeper analysis of metabolic RNA changes can be utilized to model tumor specific shifts in metabolism. Each section discusses new opportunities and challenges associated with these types of analyses and aims to provide an honest assessment of how understanding these different processes may contribute to the treatment of Retinoblastoma.

Retinoblastoma is a malignant tumour of the retina which most often occurs in children. Earlier studies on retinoblastoma have concentrated on the identification of key players in the disease and have not provided information on activated/inhibited signalling pathways. The dysregulation of protein phosphorylation in cancer provides clues about the affected signalling cascades in cancer. Phosphoproteomics is an ideal tool for the study of phosphorylation changes in proteins. Hence, global phosphoproteomics of retinoblastoma (RB) was carried out to identify signalling events associated with this cancer. Over 350 proteins showed differential phosphorylation in RB compared to control retina. Our study identified stress response proteins to be hyperphosphorylated in RB which included H2A histone family member X (H2AFX) and sirtuin 1. In particular, Ser140 of H2AFX also known as gamma-H2AX was found to be hyperphosphorylated in retinoblastoma, which indicated the activation of DNA damage response pathways. We also observed the activation of anti-apoptosis in retinoblastoma compared to control. These observations showed the activation of survival pathways in retinoblastoma. The identification of hyperphosphorylated protein kinases including Bromodomain containing 4 (BRD4), Lysine deficient protein kinase 1 (WNK1), and Cyclin-dependent kinase 1 (CDK1) in RB opens new avenues for the treatment of RB. These kinases can be considered as probable therapeutic targets for RB, as small-molecule inhibitors for some of these kinases are already in clinical trials for the treatment other cancers.

Background & Method: The aim of the study is study of digital and palmar dermatoglyphic pattern of both hands of female patients with carcinoma breast. Purpose of study was explained along with proper information about the procedure to the members of both the groups. Informed consent was taken from both groups. Dermatoglyphic pattern of palms and fingers was taken by Ink method as described by Cummins and Midlo. Result: It shows that significantly higher number of breast cancer patients i.e. 84.48% show presence of whorls in their two or more fingers as compared to controls where it was 69.80% (p value < .05), the table no. 3 also depicts that presence of two or more whorls is significantly associated positively with carcinoma breast (OR= 2.15). Conclusion: The present study was done to find any significant dermatoglyphic difference between breast carcinoma patients and healthy controls and it has been found that there is a possible positive correlation between digital ridge patterns, ridge counts and breast cancer. Hence dermatoglyphics could be helpful in screening out risk group which can be subjected to timely mammography and detecting possibility of breast cancer, so as to enable us to take preventive prophylactic measures like prophylactic surgery or chemoprevention. [ABSTRACT FROM AUTHOR]

We show that the ability to discriminate among unexploded ordnance (UXO) targets is enhanced by extending the measurement range of our continuous-wave (CW) electromagnetic induction (EMI) system down to extremely low frequencies (ELFs). In particular, we demonstrate that for a typical UXO target set, when data in the range 30 Hz to 24 kHz are extended downward toward 1-Hz discrimination performance improves by a factor of 1.5. Measurements and numerical simulations show that very low frequency magnetic fields penetrate deep into the target, enabling one to discriminate between thick- and thin-walled ferrous cylinders that are otherwise visually identical. Data acquisition at extremely low frequencies is a time-consuming process especially if data averaging is required to achieve acceptable signal-to-noise ratio. Therefore, in practice, discrimination improvement using ELF is probably best carried out using a 'cued' approach. In a 'cued' approach, target locations would be marked using another detection technique (e.g., a simple total-field magnetometer), and the low-frequency CW EMI system would be deployed to these known target locations for classification. Index Terms--Continuous-wave (CW) electromagnetic induction (EMI), discrimination, extremely low frequency (ELF) magnetic fields, unexploded ordnance (UXO).

BACKGROUND:Detection of single cell epitopes has been a mainstay of immunophenotyping for over three decades, primarily using fluorescence techniques for quantitation. Fluorescence has broad overlapping spectra, limiting multiplexing abilities. METHODOLOGY/PRINCIPAL FINDINGS:To expand upon current detection systems, we developed a novel method for multi-color immuno-detection in single cells using "Composite Organic-Inorganic Nanoparticles" (COINs) Raman nanoparticles. COINs are Surface-Enhanced Raman Scattering (SERS) nanoparticles, with unique Raman spectra. To measure Raman spectra in single cells, we constructed an automated, compact, low noise and sensitive Raman microscopy device (Integrated Raman BioAnalyzer). Using this technology, we detected proteins expressed on the surface in single cells that distinguish T-cells among human blood cells. Finally, we measured intracellular phosphorylation of Stat1 (Y701) and Stat6 (Y641), with results comparable to flow cytometry. CONCLUSIONS/SIGNIFICANCE:Thus, we have demonstrated the practicality of applying COIN nanoparticles for measuring intracellular phosphorylation, offering new possibilities to expand on the current fluorescent technology used for immunoassays in single cells.

The tobacco caterpillar, Spodoptera litura, a major pest of soybean in India is under surveillance in all soybean growing areas in Maharashtra in order to issue alerts to farmers and prevent economic losses. In this context, two linear models were fitted to developmental data of S. litura life stages reared on soybean at five constant temperatures viz. 15, 20, 25, 30 and 35°C through laboratory experiments. Optimum temperature for development (Topt) and upper temperature threshold (Tmax) were estimated from three nonlinear models by additionally including developmental response at >35°C. Topt estimates for the total immature development were 34.5°C (Lactin-2), 33.7°C (Briere-1) and 33.2°C (Simplified Beta type function) while Tmax estimates were in the range of 38 to 40°C. Application of a thermodynamic non-linear model (Optim SSI) gave estimate of intrinsic optimum temperature (Tf) for development of egg (28.3°C), larva (27.5°C) and pupal stage (30.3°C). The phenology model of S. litura on soybean based on estimated developmental threshold temperatures and thermal constants was validated using available field surveillance data to facilitate informed pest management decisions. [ABSTRACT FROM AUTHOR]

This paper presents a new method for improving the recognition accuracy of a speech recognition system in a noisy environment by using robust speech enhancement technique with the aid of noise estimation algorithm. The robustness of a speech recognition system can be improved by improving the speech quality at signal level by means of noise suppression algorithms, feature extraction level or at modelling phase. The proposed method uses robust speech enhancement technique as a pre-processing operation to improve the recognition accuracy in presence of noise. The suggested method is evaluated in terms of recognition accuracy. The suggested method yields better results in terms of recognition accuracy in presence of eight different types of non-stationary noises under different SNR levels when compared with the baseline speech recognition system. [ABSTRACT FROM AUTHOR]