High field level crossing studies on spin dimers in the low dimensional quantum spin system Na2_2T2_2(C2_2O4_4)3_3(H2_2O)2_2 with T=Ni,Co,Fe,Mn

In this paper we demonstrate the application of high magnetic fields to study the magnetic properties of low dimensional spin systems. We present a case study on the series of 2-leg spin-ladder compounds Na$_2$T$_2$(C$_2$O$_4$)$_3$(H$_2$O)$_2$ with T = Ni, Co, Fe and Mn. In all compounds the transition metal is in the $T^{2+}$ high spin configuation. The localized spin varies from S=1 to 3/2, 2 and 5/2 within this series. The magnetic properties were examined experimentally by magnetic susceptibility, pulsed high field magnetization and specific heat measurements. The data are analysed using a spin hamiltonian description. Although the transition metal ions form structurally a 2-leg ladder, an isolated dimer model consistently describes the observations very well. This behaviour can be understood in terms of the different coordination and superexchange angles of the oxalate ligands along the rungs and legs of the 2-leg spin ladder. All compounds exhibit magnetic field driven ground state changes which at very low temperatures lead to a multistep behaviour in the magnetization curves. In the Co and Fe compounds a strong axial anisotropy induced by the orbital magnetism leads to a nearly degenerate ground state and a strongly reduced critical field. We find a monotonous decrease of the intradimer magnetic exchange if the spin quantum number is increased

A comparison of four clustering methods for brain expression microarray data

Background DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed. Results k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia. Conclusion Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice

Genomewide Analysis of Inherited Variation Associated with Phosphorylation of PI3K/AKT/mTOR Signaling Proteins

While there exists a wealth of information about genetic influences on gene expression, less is known about how inherited variation influences the expression and post-translational modifications of proteins, especially those involved in intracellular signaling. The PI3K/AKT/mTOR signaling pathway contains several such proteins that have been implicated in a number of diseases, including a variety of cancers and some psychiatric disorders. To assess whether the activation of this pathway is influenced by genetic factors, we measured phosphorylated and total levels of three key proteins in the pathway (AKT1, p70S6K, 4E-BP1) by ELISA in 122 lymphoblastoid cell lines from 14 families. Interestingly, the phenotypes with the highest proportion of genetic influence were the ratios of phosphorylated to total protein for two of the pathway members: AKT1 and p70S6K. Genomewide linkage analysis suggested several loci of interest for these phenotypes, including a linkage peak for the AKT1 phenotype that contained the AKT1 gene on chromosome 14. Linkage peaks for the phosphorylated:total protein ratios of AKT1 and p70S6K also overlapped on chromosome 3. We selected and genotyped candidate genes from under the linkage peaks, and several statistically significant associations were found. One polymorphism in HSP90AA1 was associated with the ratio of phosphorylated to total AKT1, and polymorphisms in RAF1 and GRM7 were associated with the ratio of phosphorylated to total p70S6K. These findings, representing the first genomewide search for variants influencing human protein phosphorylation, provide useful information about the PI3K/AKT/mTOR pathway and serve as a valuable proof of concept for studies integrating human genomics and proteomics

Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis

The authors would like to thank members of OBD Reference Facility Benjamin Foulkes, Chloe Bird, Emily Corfeld and Matthew Salter for expedient processing of clinical samples on the EpiSwitch™ platform and Magdalena Jeznach and Willem Westra for help with preparation of the manuscript. The study employed samples from the SERA Biobank used with permission and approval of the SERA Approval Group. We gratefully acknowledge the invaluable contribution of the clinicians and operating team in SERA. We would also like to thank Prof. Raju Kucherlapati (Harvard Medical School), and Prof. Jane Mellor (Oxford Univ.), Prof. John O’Shea (National Institute of Health) and Prof. John Isaacs (New Castle Univ.) for their independent and critical review of our study. A list of Scottish Early Rheumatoid Arthritis (SERA) inception cohort investigators is provided in Additional fle 1: Additional Note. Funding This work was funded by Oxford BioDynamics.Peer reviewedPublisher PD

A Multi Criteria Decision Making Approach for Suppliers Selection

AbstractThe study deals with the factors affecting the supplier selection process, to study the interaction between them and thus empirically assess which factors are most influencing one in supply chain operations which must be given careful attention. For studying the interaction between the factors and prioritizing the factors, we have used ISM (Interpretive Structural Modeling) technique, by which we got the weights for the performance factors which are examined and ranked. AHP (Analytical Hierarchy Process) is used to rank the supplier and found out the best supplier from the group of supplier. This method is applied to an automotive component manufacturing industry in the southern part of India. Qualification and final selection of the supplier are done based on the method proposed. We have collected the data from the mentioned industry and the results are implemented by the industr

Choroidal melanoma: A short review with an Indian perspective

Choroidal melanoma (CM), the most common intraocular tumor in adults, is still a rarity in Asia. Having a high propensity for metastasis with a poor survival, recognizing it early is essential. Although it has typical clinical features, there are instances of simulating lesions. Fine-needle aspiration biopsy can be a valuable tool not only to confirm our clinical suspicion but also aid in prognosticating it. From days of histopathological prognostic markers, we are moving on to genetic markers which are reliably providing insights, helping us in providing a better care for our patients. Eye preservation has taken an all new important meaning in CM with many centers opting for different modalities of radiation. Herein, we try to provide a short synopsis of CM looking into its epidemiology, clinical features, diagnosis, and management. We also look briefly into the role of fine-needle biopsy in managing CM. Being a tertiary referral ocular center in India, we do come across CM; we have shared the preliminary reports of our analysis of managing CM over a 9-year period

To Study and Analyse the Impact of Smart Artificial Irrigation System on Agriculture Industry Using the Concepts of Artificial Intelligence as a CSR for Farmers

Agriculture performs a key role in the economy and its contribution is based on the potential crop yields that are highly dependent on irrigation. The history of England gives us a clear evidence that the revolution in Agriculture preceded the Industrial Revolution in United Kingdom. Also, In U.S.A. and Japan, agricultural development has contributed to a great extent in the process of their industrialisation. Similarly, many under-developed countries who got involved in the process of economic development by now have learnt the inadequacies of putting too much stress on industrialisation to attain greater per capita real income. Thus, we can say that industrial and agricultural advancements are not substitutes, they are complementary and are mutually supporting with respect to both inputs and outputs. It is seen that enhanced agricultural output and efficiency tend to contribute significantly to an overall economic development of the country, it will be logical and suitable to place greater emphasis on additional development of the agricultural sector. In a nation like India, where agriculture remains heavily reliant on the informal sector, irrigation methods and procedures are ineffective and often result in unnecessary water spills. This calls for the need for a system that can provide an effective and reliable solution. Agriculture is dependent on the monsoons, therefore this new smart irrigation system has been introduced for agriculture by Texanium. It is a web development company. In this system, based on the soil form, the water will be supplied to the agricultural field. The present paper demonstrate and conclude that an automatic Irrigation System supported by Artificial Intelligence and the Internet of Things, can independently irrigate fields using soil moisture data. The program is based on prediction algorithms that use historical weather data to identify and predict rainfall patterns and climate change thus, a smart system that irrigates crop fields

Reproducible Clusters from Microarray Research: Whither?

Abstract Motivation In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets. Methods We considered four commonly used iterative partitioning algorithms (Self Organizing Maps (SOM), K-means, Clutsering LARge Applications (CLARA), and Fuzzy C-means) and evaluated their performances on 37 microarray datasets, with sample sizes ranging from 12 to 172. We assessed reproducibility of the clustering algorithm by measuring the strength of relationship between clustering outputs of subsamples of 37 datasets. Cluster stability was quantified using Cramer's v2 from a kXk table. Cramer's v2 is equivalent to the squared canonical correlation coefficient between two sets of nominal variables. Potential scores range from 0 to 1, with 1 denoting perfect reproducibility. Results All four clustering routines show increased stability with larger sample sizes. K-means and SOM showed a gradual increase in stability with increasing sample size. CLARA and Fuzzy C-means, however, yielded low stability scores until sample sizes approached 30 and then gradually increased thereafter. Average stability never exceeded 0.55 for the four clustering routines, even at a sample size of 50. These findings suggest several plausible scenarios: (1) microarray datasets lack natural clustering structure thereby producing low stability scores on all four methods; (2) the algorithms studied do not produce reliable results and/or (3) sample sizes typically used in microarray research may be too small to support derivation of reliable clustering results. Further research should be directed towards evaluating stability performances of more clustering algorithms on more datasets specially having larger sample sizes with larger numbers of clusters considered.</p