Bounds on Energy and Laplacian Energy of Graphs

Let G be simple graph with n vertices and m edges. The energy E(G) of G, denotedby E(G), is dened to be the sum of the absolute values of the eigenvalues of G. Inthis paper, we present two new upper bounds for energy of a graph, one in terms ofm,n and another in terms of largest absolute eigenvalue and the smallest absoluteeigenvalue. The paper also contains upper bounds for Laplacian energy of graph

Effect of Integrated Nutrient Management on Onion Yield and Soil Properties under Chromic haplusterts of Karnataka

A field experiment was conducted during the Kharif season of 2002 and 2003 under Chromic Haplusterts (medium black soils) at Zonal Agricultural Research Station, Hiriyur to study the effect of Coir Pith Based Compost (CPBC) along with organic manures and inorganic fertilizers on yield of Onion. The study revealed that combined application of CPBC @ 15 t/ha along with press mud (PM) and half the recommended dose of fertilizer (RDF) gave significant higher bulb yield of onion (14.70 t/ha) as compared to RDF along with FYM (9.55 t/ha). The bulb yields were on par with the combined application of CPBC along with FYM and 50% RDF or combined application of CPBC and Green manure (GM) along with 100% RDF, indicating the utility of CPBC in onion cultivation. Higher net and gross returns were recorded with application of CPBC and PM @ 15 t/ha each along with 50% RDF with better benefit cost ratio. The yield and quality parameters also differed significantly among the various combinations of CPBC with PM or FYM in conjunction with inorganic fertilizers. Analysis of the soil after the harvest of onion crop did not show any significant difference in pH and EC among the treatments. However, organic carbon, available phosphorus and available potash were significantly higher due to application of CPBC, PM, FYM and GM along with 50% or 100% RDF

Randomized Phase II trial assessing estramustine and vinblastine combination chemotherapy vs estramustine alone in patients with progressive hormone-escaped metastatic prostate cancer

Abstract Based on the results of combined data from three North American Phase II studies, a randomised Phase II study in the same patient population was performed, using combination chemotherapy with estramustine phosphate (EMP) and vinblastine (VBL) in hormone refractory prostate cancer patients. In all, 92 patients were randomised into a Phase II study of oral EMP (10 mg kg day continuously) or oral EMP in combination with intravenous VBL (4 mg m(2) week for 6 weeks, followed by 2 weeks rest). The end points were toxicity and PSA response in both groups, with the option to continue the trial as a Phase III study with time to progression and survival as end points, if sufficient responses were observed. Toxicity was unexpectedly high in both treatment arms and led to treatment withdrawal or refusal in 49% of all patients, predominantly already during the first treatment cycle. The mean treatment duration was 10 and 14 weeks, median time to PSA progression was 27.2 and 30.8 weeks, median survival time was 44 and 50.9 weeks, and PSA response rate was only 24.6 and 28.9% in the EMP/VBL and EMP arms, respectively. There was no correlation between PSA response and survival. While the PSA response in the patients tested was less than half that recorded in the North American studies, the toxicity of EMP monotherapy or in combination with VBL was much higher than expected. Further research on more effective and less toxic treatment strategies for hormone refractory prostate cancer is mandatory

High activity Rhenium-186 HEDP with autologous peripheral blood stem cell rescue: a phase I study in progressive hormone refractory prostate cancer metastatic to bone

We tested the feasibility and toxicity of high activities Rhenium-186 hydroxyethylidene diphosphonate, with peripheral blood stem cell rescue in patients with progressive hormone refractory prostate cancer metastatic to bone. Twenty-five patients received between 2500 and 5000 MBq of Rhenium-186 hydroxyethylidene diphosphonate followed 14 days later by the return of peripheral blood peripheral blood stem cells. Activity limiting toxicity was defined as grade III haematological toxicity, lasting at least 7 days, or grade IV haematological toxicity of any duration or any serious unexpected toxicity. Activity limiting toxicity occurred in two of six who received activities of 5000 MBq and maximum tolerated activity was defined at this activity level. Prostate specific antigen reductions of 50% or more lasting at least 4 weeks were seen in five of the 25 patients (20%) all of whom received more than 3500 MBq of Rhenium-186 hydroxyethylidene diphosphonate. The actuarial survival at 1 year is 54%. Administered activities of 5000 MBq of Rhenium-186 hydroxyethylidene diphosphonate are feasible using autologous peripheral blood peripheral blood stem cell rescue in patients with progressive hormone refractory prostate cancer metastatic to bone. The main toxicity is thrombocytopaenia, which is short lasting. A statistically significant activity/prostate specific antigen response was seen. We have now commenced a Phase II trial to further evaluate response rates

Quantum well state of self-forming 3C-SiC inclusions in 4H SiC determined by ballistic electron emission microscopy

High-temperature-processing-induced double-stacking-fault 3C-SiC inclusions in 4H SiC were studied with ballistic electron emission microscopy in ultrahigh vacuum. Distinctive quantum well structures corresponding to individual inclusions were found and the quantum well two-dimensional conduction band minimum was determined to be approximately 0.53 ?? 0.06 eV below the conduction band minimum of bulk 4H SiC. Macroscopic diode I-V measurements indicate no significant evidence of metal/semiconductor interface state variation across the inclusions.open292

Suggesting Accurate Method and Class Names

Descriptive names are a vital part of readable, and hence maintain-able, code. Recent progress on automatically suggesting names for local variables tantalizes with the prospect of replicating that success with method and class names. However, suggesting names for meth-ods and classes is much more difficult. This is because good method and class names need to be functionally descriptive, but suggesting such names requires that the model goes beyond local context. We introduce a neural probabilistic language model for source code that is specifically designed for the method naming problem. Our model learns which names are semantically similar by assigning them to locations, called embeddings, in a high-dimensional contin-uous space, in such a way that names with similar embeddings tend to be used in similar contexts. These embeddings seem to contain semantic information about tokens, even though they are learned only from statistical co-occurrences of tokens. Furthermore, we introduce a variant of our model that is, to our knowledge, the first that can propose neologisms, names that have not appeared in the training corpus. We obtain state of the art results on the method, class, and even the simpler variable naming tasks. More broadly, the continuous embeddings that are learned by our model have the potential for wide application within software engineering

Space Charge at Nanoscale: Probing Injection and Dynamic Phenomena Under Dark/Light Configurations by Using KPFM and C-AFM

International audienc

An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions