High-performance self-aligned SiGeC HBT with selectively grown Si/sub 1-x-y/Ge/sub x/C/sub y/ base by UHV/CVD

Si/sub 1-x-y/Ge/sub x/C/sub y/ selective epitaxial growth (SEG) was performed by cold-wall, ultrahigh-vacuum chemical vapor deposition, and the effects of incorporating C on the crystallinity of Si/sub 1-x-y/Ge/sub x/C/sub y/ layers and the performance of a self-aligned SiGeC heterojunction bipolar transistor (HBT) were evaluated. A Si/sub 1-x-y/Ge/sub x/C/sub y/ layer with good crystallinity was obtained by optimizing the growth conditions. Device performance was significantly improved by incorporating C, as a result of applying Si/sub 1-x-y/Ge/sub x/C/sub y/ SEG to form the base of a self-aligned HBT. Fluctuations in device performance were suppressed by alleviating the lattice strain. Furthermore, since the B out diffusion could be suppressed by incorporating C, the cutoff frequency was able to be increased with almost the same base resistance. A maximum oscillation frequency of 174 GHz and an emitter coupled logic gate-delay time of 5.65 ps were obtained at a C content of 0.4%, which shows promise for future ultrahigh-speed communication systems.     

Rates for intramolecular excimer formation of poly(α-methylstyrene) measured by using picosecond pulse radiolysis

Summary The rate constants for intramolecular excimer formation, k_DM, of poly(α-methylstyrene) with different molecular weight were determined by using picosecond pulse radiolysis. Values of k_DM for poly(α-methylstyrene) are a little smaller than those for polystyrene with nearly same molecular weight. It appears to be mainly due to steric hindrance by methyl substituent of main chain.     

Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

     

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

Basket Analysis, which is a standard method for data mining, derives frequent itemsets from database. However, its mining ability is limited to transaction data consisting of items. In reality, there are many applications where data are described in a more structural way, e.g. chemical compounds and Web browsing history. There are a few approaches that can discover characteristic patterns from graph-structured data in the field of machine learning. However, almost all of them are not suitable for such applications that require a complete search for all frequent subgraph patterns in the data. In this paper, we propose a novel principle and its algorithm that derive the characteristic patterns which frequently appear in graph-structured data. Our algorithm can derive all frequent induced subgraphs from both directed and undirected graph structured data having loops (including self-loops) with labeled or unlabeled nodes and links. Its performance is evaluated through the applications to Web browsing pattern analysis and chemical carcinogenesis analysis.     

Effect of CNTs on Activation Volume and Mobile Dislocation Exhaustion Rate of CNTs/Al under Compression Loading

In this study, stress relaxation compression tests were performed to investigate the strain rate sensitivity, activation volume and mobile dislocations in carbon nanotubes/aluminum (CNTs/Al) composites. The results reveal that, with the addition of CNTs, the strain rate sensitivity of CNTs/Al increased. Meanwhile, a smaller V^* of CNTs/Al compared with pure Al was attributed mainly to the CNT-Al interfaces and partly to the increased forest dislocations cutting activities in grain interior, which was related to the tendency of short ranges order formation during plastic deformation. The incorporation of CNTs also improved the dislocation storage capability and reduced the dislocation velocity, leading to a lower mobile dislocation exhaustion rate.     

Rewired Cellular Metabolic Profiles in Response to Metformin under Different Oxygen and Nutrient Conditions

Metformin is a metabolic disruptor, and its efficacy and effects on metabolic profiles under different oxygen and nutrient conditions remain unclear. Therefore, the present study examined the effects of metformin on cell growth, the metabolic activities and consumption of glucose, glutamine, and pyruvate, and the intracellular ratio of nicotinamide adenine dinucleotide (NAD⁺) and reduced nicotinamide adenine dinucleotide (NADH) under normoxic (21% O₂) and hypoxic (1% O₂) conditions. The efficacy of metformin with nutrient removal from culture media was also investigated. The results obtained show that the efficacy of metformin was closely associated with cell types and environmental factors. Acute exposure to metformin had no effect on lactate production from glucose, glutamine, or pyruvate, whereas long-term exposure to metformin increased the consumption of glucose and pyruvate and the production of lactate in the culture media of HeLa and HaCaT cells as well as the metabolic activity of glucose. The NAD⁺/NADH ratio decreased during growth with metformin regardless of its efficacy. Furthermore, the inhibitory effects of metformin were enhanced in all cell lines following the removal of glucose or pyruvate from culture media. Collectively, the present results reveal that metformin efficacy may be regulated by oxygen conditions and nutrient availability, and indicate the potential of the metabolic switch induced by metformin as combinational therapy.     

Gelation of gellan – A review

Gellan is an anionic extracellular bacterial polysaccharide discovered in 1978. Acyl groups present in the native polymer are removed by alkaline hydrolysis in normal commercial production, giving the charged tetrasaccharide repeating sequence: → 3)-β-d-Glcp-(1 → 4)-β-d-GlcpA-(1 → 4)-β-d-Glcp-(1 → 4)-α-l-Rhap-(1 →. Deacylated gellan converts on cooling from disordered coils to 3-fold double helices. The coil–helix transition temperature (T_m) is raised by salt in the way expected from polyelectrolyte theory: equivalent molar concentrations of different monovalent cations (Group I and Me₄N⁺) cause the same increase in T_m; there is also no selectivity between different divalent (Group II) cations, but divalent cations cause greater elevation of T_m than monovalent. Cations present as counterions to the charged groups of the polymer have the same effect as those introduced by addition of salt. Increasing polymer concentration raises T_m because of the consequent increase in concentration of the counterions, but the concentration of polymer chains themselves does not affect T_m. Gelation occurs by aggregation of double helices. Aggregation stabilises the helices to temperatures higher than those at which they form on cooling, giving thermal hysteresis between gelation and melting. Melting of aggregated and non-aggregated helices can be seen as separate thermal and rheological processes. Reduction in pH promotes aggregation and gelation by decreasing the negative charge on the polymer and thus decreasing electrostatic repulsion between the helices. Group I cations decrease repulsion by binding to the helices in specific coordination sites around the carboxylate groups of the polymer. Strength of binding increases with increasing ionic size (Li⁺ < Na⁺ < K⁺ < Rb⁺ < Cs⁺); the extent of aggregation and effectiveness in promoting gel formation increase in the same order. Me₄N⁺ cations, which cannot form coordination complexes, act solely by non-specific screening of electrostatic repulsion, and give gels only at very high concentration (above ∼0.6 M). At low concentrations of monovalent cations, ordered gellan behaves like a normal polymer solution; as salt concentration is increased there is then a region where fluid “weak gels” are formed, before the cation concentration becomes sufficient to give true, self-supporting gels. Aggregation and consequent gelation with Group II cations occurs by direct site-binding of the divalent ions between gellan double helices. High concentrations of salt or acid cause excessive aggregation, with consequent reduction in gel strength. Maximum strength with divalent cations comes at about stoichiometric equivalence to the gellan carboxylate groups. Much higher concentrations of monovalent cations are required to attain maximum gel strength. The content of divalent cations in commercial gellan is normally sufficient to give cohesive gels at polymer concentrations down to ∼0.15 wt %. Gellan gels are very brittle, and have excellent flavour release. The networks are dynamic: gellan gels release polymer chains when immersed in water and show substantial recovery from mechanical disruption or expulsion of water by slow compression. High concentrations of sugar (∼70 wt % and above) inhibit aggregation and give sparingly-crosslinked networks which vitrify on cooling. Gellan forms coupled networks with konjac glucomannan and tamarind xyloglucan, phase-separated networks with kappa carrageenan and calcium alginate, interpenetrating networks with agarose and gelling maltodextrin, and complex coacervates with gelatin under acidic conditions. Native gellan carries acetyl and l-glyceryl groups at, respectively, O(6) and O(2) of the 3-linked glucose residue in the tetrasaccharide repeat unit. The presence of these substituents does not change the overall double helix structure, but has profound effects on gelation. l-Glyceryl groups stabilise the double helix by forming additional hydrogen bonds within and between the two strands, giving higher gelation temperatures, but abolish the binding site for metal ions by changing the orientation of the adjacent glucuronate residue and its carboxyl group. The consequent loss of cation-mediated aggregation reduces gel strength and brittleness, and eliminates thermal hysteresis. Aggregation is further inhibited by acetyl groups located on the periphery of the double helix. Gellan with a high content of residual acyl groups is available commercially as “high acyl gellan”. Mixtures of high acyl and deacylated gellan form interpenetrating networks, with no double helices incorporating strands of both types. Gellan has numerous existing and potential practical applications in food, cosmetics, toiletries, pharmaceuticals and microbiology.     

Effects of fermentation conditions and soybean peptide supplementation on hyaluronic acid production by Streptococcus thermophilus strain YIT 2084 in milk

To increase the hyaluronic acid (HA) yield from Streptococcus thermophilus YIT 2084, fermentation conditions (pH, temperature, agitation, aeration) were optimized in milk-based medium, and the effects of supplemental soybean peptides, which have different molecular weight distributions, were determined. HA production was enhanced to approximately 100 mg/l at pH 6.8 and 33–40 °C. Agitation speed and aeration rate slightly affected HA production. Soybean peptides including those of high molecular weight (approximately 27 to 130 kDa) further increased HA production to 208 mg/l under the optimal condition (pH 6.8, 35 °C, 100 rpm), which was 20-fold greater than non-optimal condition. HA production was no longer related to the specific growth rate. The HA produced under the optimal condition included a large amount of high-molecular-weight fraction of 100 to 2000 kDa, compared with under the basal condition without optimization.     

Molecular structures of gellan gum imaged with atomic force microscopy (AFM) in relation to the rheological behavior in aqueous systems in the presence of sodium chloride

Aqueous solutions of gellan gum with comparable molecular mass but with different acyl contents were investigated by atomic force microscopy and rheological measurements in the presence of sodium chloride (NaCl). Results obtained were discussed in relation to our previous report using potassium chloride (KCl) as an added salt. For a low-acyl sample, continuous fibrous network structures were identified microscopically as in the case of KCl. The network structures were more heterogeneous than those formed with KCl in terms of the height distribution of molecular assemblies. Rheological thermal hysteresis between sol–gel transitions was detected as in the case of KCl. The storage modulus (G′) of the gelled system was ca. 15% of the corresponding data with KCl at 20 °C. For a high-acyl sample, no continuous network structures were identified but branches with observable ends were identified as in the case of KCl. The hysteresis was less evident than the corresponding data with KCl and for the low-acyl sample with NaCl. Also, G′ values at 20 °C were ca. 30% and 20% of the corresponding data with KCl and for the low-acyl sample with NaCl, respectively. Continuousness and homogeneity of network structures related to the hysteresis and elasticity of the system, respectively.     

Multi-structure information retrieval method based on transformation invariance

The needs of efficient and flexible information retrieval on multi-structural data stored in database and network are significantly growing. Especially, its flexibility plays one of the key roles to acquire relevant information desired by users in retrieval process. However, most of the existing approaches are dedicated to a single content and data structure respectively, e.g., relational database and natural text. In this work, we propose “Multi-Structure Information Retrieval” (MSIR) approach applicable to various types of contents and data structures by adapting a small part of the approach to data structures. The power of this approach comes from the use of the invariant feature information obtained from byte patterns in the files through some mathematical transformation. The experimental evaluation of the proposed approach for both artificial and real data indicates its high feasibility. Fuminori Adachi: He received his Master of engineering from Osaka University in ’03. He is enrolled in the doctoral course of Osaka University from ’03. His current research interest includes scientific discovery, data mining and machine learning techniques. Takashi Washio, Ph.D.: He received his Ph.D. from Tohoku University in ’88. In ’88, he became a visiting reseacher in Massachusetts Institute of Technology. In ’90, he joined Mitsubishi Research Institute Inc., and is working for Osaka University from ’96. His current research interest includes scientific discovery, data mining and machine learning techniques. Atsushi Fujimoto: He is enrolled in the master cource of Osaka University from ’03. His Current research interest includes correlation analysis, data mining and machine learning techniques. Hiroshi Motoda, Ph.D.: He received his Ph.D. from University of Tokyo in ’72. In ’67, he joined Hitachi Ltd. and has been working for Osaka University since ’96. His current research interest includes scientific discovery, data mining and machine learning. Hidemitsu Hanafusa: He received Master of Engineering from Keio University in ’83. In ’83, he joined The Kansai Electric Power Co. Ins. (KEPCO). He researched on Maintenance Support System at INSS from ’97 to ’02. Now, he is working in KEPCO.