Optimization of cholesterol removal, growth and fermentation patterns of Lactobacillus acidophilus ATCC 4962 in the presence of mannitol, fructo-oligosaccharide and inulin: a response surface methodology approach

Aims: To optimize cholesterol removal by Lactobacillus acidophilus ATCC 4962 in the presence of prebiotics, and study the growth and fermentation patterns of the prebiotics. Methods and Results: Lactobacillus acidophilus ATCC 4962 was screened in the presence of six prebiotics, namely sorbitol, mannitol, maltodextrin, hi-amylose maize, fructo-oligosaccharide (FOS) and inulin in order to determine the best combination for highest level of cholesterol removal. The first-order model showed that the combination of inoculum size, mannitol, FOS and inulin was best for removal of cholesterol. The second-order polynomial regression model estimated the optimum condition of the factors for cholesterol removal by L. acidophilus ATCC 4962 to be 2.64% w/v inoculum size, 4.13% w/v mannitol, 3.29% w/v FOS and 5.81% w/v inulin. Analyses of growth, mean doubling time and short-chain fatty acid (SCFA) production using quadratic models indicated that cholesterol removal and the production of SCFA were growth associated. Conclusions: Optimum cholesterol removal was obtained from the fermentation of L. acidophilus ATCC 4962 in the presence of mannitol, FOS and inulin. Cholesterol removal and the production of SCFA appeared to be growth associated and highly influenced by the prebiotics. Significance and Impact of the Study: Response surface methodology proved reliable in developing the model, optimizing factors and analysing interaction effects. The results provide better understanding on the interactions between probiotic and prebiotics for the removal of cholesterol

Bioactive dairy ingredients for food and non-food applications

Lactobacilli and bifidobacteria are most commonly encountered in the dairy industries, either existing naturally in milk or inoculated as starters in fermented dairy products. Recent research suggests that fermented dairy products are a cocktail of bioactive ingredients. The objective of our study was to evaluate the bioactivity of cell wall fractions of Lactobacillus and Bifidobacterium grown in reconstituted skimmed milk, and the possibility of intra- and extracellular extracts of these bacteria for applications in foods and beyond. Intracellular and extracellular extracts of Lactobacillus and Bifidobacterium showed inhibitory activities against food and dermal pathogens. All strains were able to produce inhibitors, such as organic acids, antimicrobial peptides, diacetyl, and hydrogen peroxide. Most strains showed higher production of extracellular than intracellular inhibitors (P<0.05). Meanwhile, all strains were able to produce hyaluronic acid, lipoteichoic acid, peptidoglycan, neutral sphingomyelinase and acid sphingomyelinase at concentrations applicable for cosmeceutical application. Findings from our study demonstrated that inhibitors and bioactives from lactobacilli and bifidobacteria have the potential to be developed into formulations for food and non-food applications

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

Dinamika Akumulasi Kadmium Pada Tanaman Kangkung Darat (Ipomoae reptans Poir)

J. Akta Kimia Indonesia 2(1) 2009One heavy metal, which is potential as pollutant, is cadmium that has been\ud accumulated in soil and sediment. Although, cadmium is non essential element for plants, it\ud is easily adsorbed and accumulated by various plants. The negative effect of cadmium on\ud plants is that it can prevent the absorption of nutrition so that the plant growth will be\ud inhibited and then the plant will die. Therefore, it is necessary to reduce the concentration of\ud cadmium to be used as good growth media. Several methods of heavy metal accumulation,\ud such as physical, chemical and biological methods, have been used, but the three methods\ud have been considered as less effective methods. The use of plants to accumulate heavy metals\ud in polluted soil is considered as a good method because the method is a safe method and can\ud increase the soil fertility. In this research, accumulation of cadmium has been conducted by\ud using Ipomeae reptans Poir. Result showed that the highest concentration that can be\ud accumulated by I. reptans Poir was 3317.68 mg/kg of dried mass with the plantation time of\ud 21 days. The increase of concentration in the growth media increased the cadmium\ud concentration accumulated. The high accumulation of cadmium showed that I. reptans Poir is\ud a hyperaccumulator plant for cadmium. The bioconcentration value was higher than 1,\ud whereas the translocation factor was lower than 1 indicating that the accumulation\ud mechanism was phytostabilization

Efek Proteksi Dekokta Kulit Alpukat Pada Hepar Tikus Terinduksi Karbon Tetraklorida

The decoction of avocado peels was tested for their hepatoprotective activity against carbon tetrachloride-induced hepatotoxicity in rat. The rats were treated with the decoction of avocado peels at doses of 363; 762, 1600 mg/kg per oral once in a day for 6 days and carbon tetrachloride (2 mL/kg) was given on the 7th day. Different groups of rats were given water decoction of avocado peels at a dose of 363; 762, 1600 mg/kg and after 6 hours received carbon tetrachloride (2 mL/kg), respectively. The degree of protection was measured by using biochemical parameters like serum transaminase, alkaline phosphatase and albumin. From these results it suggested that the decoction of avocado peels 762, 1600 mg/kg for 6 hours has a potent hepatoprotective action upon carbon tetrachloride-induced hepatic damage in rats

PEMANFAATAN KARBON AKTIF TEMPURUNG KENARI SEBAGAI ADSORBEN FENOL DAN KLOROFENOL DALAM PERAIRAN

Marina Chimica Acta 6(1) 2005Artikel ini menyajikan hasil investigasi yang dilakukan pada penghilangan fenol dan 4-klorofenol dari\ud lingkungan air dengan menggunakan karbon aktif tempurung kenari. adsorben dibuat dengan mengarangkan\ud tempurung kenari menggunakan sekam padi dan arang yang terbentuk dipanaskan pada temperatur 200 oC .\ud hasil yang diperoleh dengan ukuran 100 ??? 200 mesh diaktivasi pada suhu 500 oC. penentuan konsentrasi fenol\ud dan 4-klorofenol yang teradsorpsi pada waktu kesetimbangan adsorpsi dijadikan dasar untuk mempelajari\ud pengaruh ph dan konsentrasi awal terhadap efektivitas adsorpsi senyawa-senyawa fenol oleh karbon aktif\ud tempurung kenari. Persamaan Freundlich dan Langmuir digunakan untuk mempelajari isotermal adsorpsi.\ud hasil penelitian menunjukkan bahwa waktu optimum adsorpsi fenol dan 4-klorofenol oleh karbon aktif\ud berturut-turut adalah 135 dan 150 menit, adsorpsi kedua senyawa fenol memenuhi persamaan laju orde dua\ud semu dengan tetapan laju 0,082 dan 0,127 g mg-1 menit-1 berturut-turut untuk adsorpsi fenol dan 4-klorofenol.\ud isotermal adsorpsi kedua senyawa fenol memenuhi persamaan langmuir dan freundlich. kapasitas adsorpsi\ud fenol lebih besar dari kapasitas adsorpsi 4-klorofenol

A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. 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Enhanced antibacterial activity of silver nanoparticles/halloysite nanotubes/graphene nanocomposites with sandwich-like structure

A sandwich-like antibacterial reagent (Ag/HNTs/rGO) was constructed through the direct growth of silver nanoparticles on the surface graphene-based HNTs nanosheets. Herein, various nanomaterials were combined by adhesion effect of DOPA after self-polymerization. Ag/HNTs/rGO posses enhanced antibacterial ability against E. coli and S. aureus compared with individual silver nanoparticles, rGO nanosheets or their nanocomposites