ENHANCED ALGORITHMS FOR MINING OPTIMIZED POSITIVE AND NEGATIVE ASSOCIATION RULE FROM CANCER DATASET

The most important research aspect nowadays is the data. Association rule mining is vital mining used in data which mines many eventual informations and associations from enormous databases. Recently researchers focus many research challenges to association rule mining. The first challenge is the generation of the frequent and infrequent itemsets from a large dataset more accurately. Secondly how effectively the positive and negative association rule can be mined from both the frequent and infrequent itemsets with high confidence, good quality, and high comprehensibility with reduced time. Predominantly in existing algorithms the infrequent itemsets is not taken into account or rejected. In recent times it is said that useful information are hidden in this itemsets in the case of medical field. The third challenge are to generate is optimised positive and negative association rule. Several existing algorithms have been implemented in order to assure these challenges but many such algorithms produces data losses, lack of efficiency and accuracy which also results in redundant rules. The major issue in using this analytic optimizing method are specifying the activist initialization limit was the quality of the association rule relays on. The proposed work has three methods which mine an optimized PAR and NAR. The first method is the Apriori_AMLMS (Accurate multi-level minimum support) this algorithm derives the frequent and the infrequent itemsets very accurately based on the user-defined threshold minimum support value. The next method is the GPNAR (Generating Positive and Negative Association Rule) algorithm to mine the PAR and NAR from frequent itemsets and PAR and NAR from infrequent itemsets. The third method are to obtain an optimized PAR and NAR using the decidedly efficient swarm intelligence algorithm called the Advance ABC (Artificial Bee Colony) algorithm which proves that an efficient optimized Positive and negative rule can be mined. The Advance ABC is a Meta heuristic technique stimulated through the natural food foraging behaviour of the honey bee creature. The experimental analysis shows that the proposed algorithm can mine exceedingly high confidence non redundant positive and negative association rule with less time

Selective Sirt2 inhibition by ligand-induced rearrangement of the active site

Sirtuins are a highly conserved class of NAD+-dependent lysine deacylases. The human isotype Sirt2 has been implicated in the pathogenesis of cancer, inflammation and neurodegeneration, which makes the modulation of Sirt2 activity a promising strategy for pharmaceutical intervention. A rational basis for the development of optimized Sirt2 inhibitors is lacking so far. Here we present high-resolution structures of human Sirt2 in complex with highly selective drug-like inhibitors that show a unique inhibitory mechanism. Potency and the unprecedented Sirt2 selectivity are based on a ligand-induced structural rearrangement of the active site unveiling a yet-unexploited binding pocket. Application of the most potent Sirtuin-rearranging ligand, termed SirReal2, leads to tubulin hyperacetylation in HeLa cells and induces destabilization of the checkpoint protein BubR1, consistent with Sirt2 inhibition in vivo. Our structural insights into this unique mechanism of selective sirtuin inhibition provide the basis for further inhibitor development and selective tools for sirtuin biology

Allergic sensitization: screening methods

Experimental in silico, in vitro, and rodent models for screening and predicting protein sensitizing potential are discussed, including whether there is evidence of new sensitizations and allergies since the introduction of genetically modified crops in 1996, the importance of linear versus conformational epitopes, and protein families that become allergens. Some common challenges for predicting protein sensitization are addressed: (a) exposure routes; (b) frequency and dose of exposure; (c) dose-response relationships; (d) role of digestion, food processing, and the food matrix; (e) role of infection; (f) role of the gut microbiota; (g) influence of the structure and physicochemical properties of the protein; and (h) the genetic background and physiology of consumers. The consensus view is that sensitization screening models are not yet validated to definitively predict the de novo sensitizing potential of a novel protein. However, they would be extremely useful in the discovery and research phases of understanding the mechanisms of food allergy development, and may prove fruitful to provide information regarding potential allergenicity risk assessment of future products on a case by case basis. These data and findings were presented at a 2012 international symposium in Prague organized by the Protein Allergenicity Technical Committee of the International Life Sciences Institute’s Health and Environmental Sciences Institute

Current challenges facing the assessment of the allergenic capacity of food allergens in animal models

Food allergy is a major health problem of increasing concern. The insufficiency of protein sources for human nutrition in a world with a growing population is also a significant problem. The introduction of new protein sources into the diet, such as newly developed innovative foods or foods produced using new technologies and production processes, insects, algae, duckweed, or agricultural products from third countries, creates the opportunity for development of new food allergies, and this in turn has driven the need to develop test methods capable of characterizing the allergenic potential of novel food proteins. There is no doubt that robust and reliable animal models for the identification and characterization of food allergens would be valuable tools for safety assessment. However, although various animal models have been proposed for this purpose, to date, none have been formally validated as predictive and none are currently suitable to test the allergenic potential of new foods. Here, the design of various animal models are reviewed, including among others considerations of species and strain, diet, route of administration, dose and formulation of the test protein, relevant controls and endpoints measured

Expression of toll-like receptors 2 and 4 in subjects with asthma by total serum IgE level

Emerging data suggest that innate immunity may play a role in asthma, particularly the toll-like receptors (TLRs). Some studies pointed to an involvement of TLRs 2 and 4 in the pathogenesis of allergic asthma, and other studies related TLRs to IgE. However, there are not any studies that have comprehensively evaluated the expression of TLRs 2 and 4 in inflammatory cells, in peripheral blood and induced sputum specimens from asthmatic patients, according to their total serum IgE. We studied 44 asthmatic patients (15 with high total serum IgE and 29 with normal total serum IgE). On a single visit, all patients underwent: induced sputum, pulmonary function tests, determination of exhaled nitric oxide fraction, venipuncture for blood analysis and skin prick allergy tests. The induced sputum cellularity was analyzed by flow cytometry, where expression of TLRs 2 and 4 was studied using fluorochrome-conjugated monoclonal antibodies. Asthmatic patients with high total serum IgE showed, a higher percentage of macrophages expressing TLR4 (42.99 % ± 22.49) versus asthmatic patients with normal total serum IgE (28.84 % ± 15.16) (P = 0.048). Furthermore, we observed a correlation (but weak) between the percentage of macrophages expressing TLR4 in induced sputum and the total serum IgE level (R = 0.314; P = 0.040). Asthmatic subjects with high total serum IgE show increased macrophage expression of TLR4 in induced sputum. This outcome may result from a link between innate immunity and IgE-mediated, adaptive immune responses in asthma, and point to TLR4 as a potential therapeutic target

PRA Desain Pabrik Triple Superphosphate (TSP) dari Batuan Fosfat

Fosfat adalah salah satu unsur hara yang sangat dibutuhkan oleh semua jenis tanaman untuk memacu perkembangan akar, batang, bunga, dan buah menjadi lebih cepat. Kekurangan fosfat dapat menyebabkan tanaman akan tumbuh kerdil, daun berwarna hijau tua, anakan sedikit, pemasakan lambat dan sering tidak menghasilkan buah. Pupuk TSP (Triple Superposphate) merupakan jenis pupuk anorganik multi-komponen yang memiliki kandungan komponen hara N atau P secara parsial yang lebih besar jika dibanding dengan pupuk NPK. Bahan baku utama yang digunakan untuk membuat pupuk TSP ini adalah batuan fosfat. Pemilihan proses untuk memproduksi pupuk TSP perlu dianalisis agar produksi yang dihasilkan lebih optimal. Pupuk TSP dapat diprodiksi melalui dua macam proses, yaitu proses Odda dan Dorr-Oliver. Pada proses Odda, digunakan bahan baku berupa batuan fosfat dan asam nitrat atau asam klorida. Sedangkan pada proses Dorr-Oliver, digunakan bahan baku berupa batuan fosfat dan asam fosfat. Dari studi yang telah dilakukan, proses Odda lebih dipilih karena ditinjau dari aspek bahan baku, konversi, kondisi operasi, dan ekonomi, proses Odda lebih baik daripada proses Dorr-Oliver. Dengan desain umur pabrik selama 30 tahun, didapatkan Internal Rate of Return (IRR) sebesar 18.6% yang dimana nilainya lebih besar dari bunga pinjaman bank sebesar 9.18%. Kemudian didapatkan Pay Out Time (POT) sebesar 5.1 tahun dan Break Even Point (BEP) sebesar 26%