Intelligent opinion mining and sentiment analysis using artificial neural networks

The article formulates a rigorously developed concept of opinion mining and sentiment analysis using hybrid neural networks. This conceptual method for processing natural-language text enables a variety of analyses of the subjective content of texts. It is a methodology based on hybrid neural networks for detecting subjective content and potential opinions, as well as a method which allows us to classify different opinion type and sentiment score classes. Moreover, a general processing scheme, using neural networks, for sentiment and opinion analysis has been presented. Furthermore, a methodology which allows us to determine sentiment regression has been devised. The paper proposes a method for classification of the text being examined based on the amount of positive, neutral or negative opinion it contains. The research presented here offers the possibility of motivating and inspiring further development of the methods that have been elaborated in this paper.Stuart, KDC.; Majewski, M. (2015). Intelligent opinion mining and sentiment analysis using artificial neural networks. Lecture Notes in Computer Science. 9492:103-110. doi:10.1007/978-3-319-26561-2_13S1031109492Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)Chen, H., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)Majewski, M., Zurada, J.M.: Sentence recognition using artificial neural networks. Knowl. Based Syst. 21(7), 629–635 (2008)Kacalak, W., Stuart, K.D., Majewski, M.: Intelligent natural language processing. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 584–587. Springer, Heidelberg (2006)Kacalak, W., Stuart, K., Majewski, M.: Selected problems of intelligent handwriting recognition. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. Advances in Soft Computing, vol. 41, pp. 298–305. Springer, Cancun (2007)Stuart, K.D., Majewski, M.: Selected problems of knowledge discovery using artificial neural networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part III. LNCS, vol. 4493, pp. 1049–1057. Springer, Heidelberg (2007)Stuart, K., Majewski, M.: A new method for intelligent knowledge discovery. In: Castillo, O., Melin, P., Ross, O.M., Cruz, R.S., Pedrycz, W., Kacprzyk, J. (eds.) IFSA 2007. Advances in Soft Computing, vol. 42, pp. 721–729. Springer, Heidelberg (2007)Stuart, K.D., Majewski, M.: Artificial creativity in linguistics using evolvable fuzzy neural networks. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 437–442. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M.: Evolvable neuro-fuzzy system for artificial creativity in linguistics. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 46–53. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M., Trelis, A.B.: Selected problems of intelligent corpus analysis through probabilistic neural networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part II. LNCS, vol. 6064, pp. 268–275. Springer, Heidelberg (2010)Stuart, K.D., Majewski, M., Trelis, A.B.: Intelligent semantic-based system for corpus analysis through hybrid probabilistic neural networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part I. LNCS, vol. 6675, pp. 83–92. Springer, Heidelberg (2011)Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991

Production of α1,3-galactosyltransferase-deficient pigs

The enzyme α1,3-galactosyltransferase (α1,3GT or GGTA1) synthesizes α1,3galactose (α1,3Gal) epitopes (Galα1,3Galβ1,4GlcNAc-R), which are the major xenoantigens causing hyperacute rejection in pig-to-human xenotransplantation. Complete removal of α1,3Gal from pig organs is the critical step toward the success of xenotransplantation. We reported earlier the targeted disruption of one allele of the α1,3GT gene in cloned pigs. A selection procedure based on a bacteria[toxin was used to select for cells in which the second allele of the gene was knocked out. Sequencing analysis demonstrated that knockout of the second allele of the α1,3GT gene was caused by a T-to-G single point mutation at the second base of exon 9, which resulted in inactivation of the α1,3GT protein. Four healthy α1,3GT double-knockout female piglets were produced by three consecutive rounds of cloning. The piglets carrying a point mutation in the α1,3GT gene hold significant value, as they would allow production of α1,3Gal-deficient pigs free of antibiotic-resistance genes and thus have the potential to make a safer product for human use

Small Scattered Fragments Do Not a Dwarf Make: Biological and Archaeological Data Indicate that Prehistoric Inhabitants of Palau Were Normal Sized

Current archaeological evidence from Palau in western Micronesia indicates that the archipelago was settled around 3000–3300 BP by normal sized populations; contrary to recent claims, they did not succumb to insular dwarfism

Collection of Epithelial Cells from Rodent Mammary Gland Via Laser Capture Microdissection Yielding High-Quality RNA Suitable for Microarray Analysis

Laser capture microdissection (LCM) enables collection of cell populations highly enriched for specific cell types that have the potential of yielding critical information about physiological and pathophysiological processes. One use of cells collected by LCM is for gene expression profiling. Samples intended for transcript analyses should be of the highest quality possible. RNA degradation is an ever-present concern in molecular biological assays, and LCM is no exception. This paper identifies issues related to preparation, collection, and processing in a lipid-rich tissue, rodent mammary gland, in which the epithelial to stromal cell ratio is low and the stromal component is primarily adipocytes, a situation that presents numerous technical challenges for high-quality RNA isolation. Our goal was to improve the procedure so that a greater probe set present call rate would be obtained when isolated RNA was evaluated using Affymetrix microarrays. The results showed that the quality of RNA isolated from epithelial cells of both mammary gland and mammary adenocarcinomas was high with a probe set present call rate of 65% and a high signal-to-noise ratio

Clinical relevance of DNA microarray analyses using archival formalin-fixed paraffin-embedded breast cancer specimens

Abstract Background The ability of gene profiling to predict treatment response and prognosis in breast cancers has been demonstrated in many studies using DNA microarray analyses on RNA from fresh frozen tumor specimens. In certain clinical and research situations, performing such analyses on archival formalin fixed paraffin-embedded (FFPE) surgical specimens would be advantageous as large libraries of such specimens with long-term follow-up data are widely available. However, FFPE tissue processing can cause fragmentation and chemical modifications of the RNA. A number of recent technical advances have been reported to overcome these issues. Our current study evaluates whether or not the technology is ready for clinical applications. Methods A modified RNA extraction method and a recent DNA microarray technique, cDNA-mediated annealing, selection, extension and ligation (DASL, Illumina Inc) were evaluated. The gene profiles generated from FFPE specimens were compared to those obtained from paired fresh fine needle aspiration biopsies (FNAB) of 25 breast cancers of different clinical subtypes (based on ER and Her2/neu status). Selected RNA levels were validated using RT-qPCR, and two public databases were used to demonstrate the prognostic significance of the gene profiles generated from FFPE specimens. Results Compared to FNAB, RNA isolated from FFPE samples was relatively more degraded, nonetheless, over 80% of the RNA samples were deemed suitable for subsequent DASL assay. Despite a higher noise level, a set of genes from FFPE specimens correlated very well with the gene profiles obtained from FNAB, and could differentiate breast cancer subtypes. Expression levels of these genes were validated using RT-qPCR. Finally, for the first time we correlated gene expression profiles from FFPE samples to survival using two independent microarray databases. Specifically, over-expression of ANLN and KIF2C, and under-expression of MAPT strongly correlated with poor outcomes in breast cancer patients. Conclusion We demonstrated that FFPE specimens retained important prognostic information that could be identified using a recent gene profiling technology. Our study supports the use of FFPE specimens for the development and refinement of prognostic gene signatures for breast cancer. Clinical applications of such prognostic gene profiles await future large-scale validation studies

Effect of Differential N-linked and O-linked Mannosylation on Recognition of Fungal Antigens by Dendritic Cells

BACKGROUND. An experimental approach for improving vaccine efficacy involves targeting antigens to mannose receptors (MRs) on dendritic cells (DCs) and other professional antigen presenting cells. Previously, we demonstrated that mannosylated Pichia pastoris-derived recombinant proteins exhibited increased immunogenicity compared to proteins lacking mannosylation. In order to gain insight into the mechanisms responsible for this observation, the present study examined the cellular uptake of the mannosylated and deglycosylated recombinant proteins. METHODOLOGY/PRINCIPAL FINDINGS. Utilizing transfected cell lines, roles for the macrophage mannose receptor (MMR, CD206) and DC-SIGN (CD209) in the recognition of the mannosylated, but not deglycosylated, antigens were demonstrated. The uptake of mannosylated antigens into murine bone marrow-derived DCs (BMDCs) was inhibited by yeast mannans (YMs), suggesting a mannose-specific C-type lectin receptor-dependent process, while the uptake of deglycosylated antigens remained unaffected. In particular, antigens with both N-linked and extensive O-linked mannosylation showed the highest binding and uptake by BMDCs. Finally, confocal microscopy studies revealed that both mannosylated and deglycosylated P. pastoris-derived recombinant proteins localized in MHC class II+ compartments within BMDCs. CONCLUSIONS/SIGNIFICANCE. Taken together with our previous results, these data suggest that increased uptake by mannose-specific C-type lectin receptors is the major mechanism responsible for the enhanced antigenicity seen with mannosylated proteins. These findings have important implications for vaccine design and contribute to our understanding of how glycosylation affects the immune response to eukaryotic pathogens.National Institutes of Health (RO1 AI25780, RO1 AI37532

DNA Barcoding in the Cycadales: Testing the Potential of Proposed Barcoding Markers for Species Identification of Cycads

Barcodes are short segments of DNA that can be used to uniquely identify an unknown specimen to species, particularly when diagnostic morphological features are absent. These sequences could offer a new forensic tool in plant and animal conservation—especially for endangered species such as members of the Cycadales. Ideally, barcodes could be used to positively identify illegally obtained material even in cases where diagnostic features have been purposefully removed or to release confiscated organisms into the proper breeding population. In order to be useful, a DNA barcode sequence must not only easily PCR amplify with universal or near-universal reaction conditions and primers, but also contain enough variation to generate unique identifiers at either the species or population levels. Chloroplast regions suggested by the Plant Working Group of the Consortium for the Barcode of Life (CBoL), and two alternatives, the chloroplast psbA-trnH intergenic spacer and the nuclear ribosomal internal transcribed spacer (nrITS), were tested for their utility in generating unique identifiers for members of the Cycadales. Ease of amplification and sequence generation with universal primers and reaction conditions was determined for each of the seven proposed markers. While none of the proposed markers provided unique identifiers for all species tested, nrITS showed the most promise in terms of variability, although sequencing difficulties remain a drawback. We suggest a workflow for DNA barcoding, including database generation and management, which will ultimately be necessary if we are to succeed in establishing a universal DNA barcode for plants

Decrease in thyroid adenoma associated (THADA) expression is a marker of dedifferentiation of thyroid tissue

<p>Abstract</p> <p>Background</p> <p><it>Thyroid adenoma associated (THADA) </it>has been identified as the target gene affected by chromosome 2p21 translocations in thyroid adenomas, but the role of THADA in the thyroid is still elusive. The aim of this study was to quantify <it>THADA </it>gene expression in normal tissues and in thyroid hyper- and neoplasias, using real-time PCR.</p> <p>Methods</p> <p>For the analysis <it>THADA </it>and 18S rRNA gene expression assays were performed on 34 normal tissue samples, including thyroid, salivary gland, heart, endometrium, myometrium, lung, blood, and adipose tissue as well as on 85 thyroid hyper- and neoplasias, including three adenomas with a 2p21 translocation. In addition, <it>NIS </it>(<it>sodium-iodide symporter</it>) gene expression was measured on 34 of the pathological thyroid samples.</p> <p>Results</p> <p>Results illustrated that <it>THADA </it>expression in normal thyroid tissue was significantly higher (<it>p </it>< 0.0001, exact Wilcoxon test) than in the other tissues. Significant differences were also found between non-malignant pathological thyroid samples (goiters and adenomas) and malignant tumors (<it>p </it>< 0.001, Wilcoxon test, t approximation), anaplastic carcinomas (ATCs) and all other samples and also between ATCs and all other malignant tumors (<it>p </it>< 0.05, Wilcoxon test, t approximation). Furthermore, in thyroid tumors <it>THADA </it>mRNA expression was found to be inversely correlated with <it>HMGA2 </it>mRNA. <it>HMGA2 </it>expression was recently identified as a marker revealing malignant transformation of thyroid follicular tumors. A correlation between <it>THADA </it>and <it>NIS </it>has also been found in thyroid normal tissue and malignant tumors.</p> <p>Conclusions</p> <p>The results suggest <it>THADA </it>being a marker of dedifferentiation of thyroid tissue.</p

Surface functionalisation of nanodiamonds for human neural stem cell adhesion and proliferation.

Biological systems interact with nanostructured materials on a sub-cellular level. These interactions may govern cell behaviour and the precise control of a nanomaterial's structure and surface chemistry allow for a high degree of tunability to be achieved. Cells are surrounded by an extra-cellular matrix with nano-topographical properties. Diamond based materials, and specifically nanostructured diamond has attracted much attention due to its extreme electrical and mechanical properties, chemical inertness and biocompatibility. Here the interaction of nanodiamond monolayers with human Neural Stem Cells (hNSCs) has been investigated. The effect of altering surface functionalisation of nanodiamonds on hNSC adhesion and proliferation has shown that confluent cellular attachment occurs on oxygen terminated nanodiamonds (O-NDs), but not on hydrogen terminated nanodiamonds (H-NDs). Analysis of H and O-NDs by Atomic Force Microscopy, contact angle measurements and protein adsorption suggests that differences in topography, wettability, surface charge and protein adsorption of these surfaces may underlie the difference in cellular adhesion of hNSCs reported here

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding