Professor Chernova was a highly respected teacher and scientist

Professor Chernova was a highly respected teacher and scientist. She was chairwoman, member of or expert in various governmental, social, educational and scientific councils, commissions, communities and foundations. Despite all of these duties, responsibilities and honorary

positions, Nina Chernova remained a readily approachable person – a human being with a genuine interest in people and events. She remembered all her students, followed their fortunes and helped where she could. Just a heart-to-heart talk with her in her small study was stimulating. In her everyday life she liked to cook, receive guests and grow flowers; she was a master at needlework. And – she liked to work, and could do so with self-abandonment, forgetting the amount of work but click here not how it should be done. I-BET-762 research buy I recall as if it

were yesterday, how the two of us, a beautiful woman of 34 and a young guy of 17, with a pair of shovels were building a multi-ton heap of cow manure for a giant decomposition experiment she had planned. It was my first field season in soil ecology and she was my first, most memorable Teacher of Science. “
“In most temperate terrestrial ecosystems, earthworms (Oligochaeta: Lumbricidae) represent the dominant fraction of the soil faunal biomass, often acting as ecosystem engineers (Jones et al. 1994) with substantial effects on the structure and fertility of soils. Most earthworm communities consist of different learn more functional groups comprising litter-dwellers (epigeics), soil-dwellers (endogeics) and vertical-burrowers (anecics; Bouché 1977). In temperate grasslands,

up to 1000 earthworms m−2 have been reported (Edwards et al. 1995). By producing huge amounts of nutrient rich casts – from 1.4–7.5 ton ha−1 a−1 (James 1991) up to 40 or even 80 ton ha−1 a−1 (reviewed in Edwards and Bohlen 1996) – such populations are a key component of nutrient cycling in soil (Lavelle, 1988 and Scheu, 1993). These earthworm casts contain more plant nutrients than bulk soil (McKenzie and Dexter, 1987, Schrader and Zhang, 1997, Zaller and Arnone, 1997 and Chaoui et al., 2002) and are also hot spots of microbial (Scheu, 1987 and Brown et al., 2000) and other invertebrate activity (Decaens et al. 1999). Moreover, earthworms are suggested to affect the diversity of grassland communities (Willems and Huijsmans, 1994, Zaller and Saxler, 2007 and Eisenhauer and Scheu, 2008) and are themselves affected by plant diversity (Zaller and Arnone, 1999a and Eisenhauer et al., 2008).

4A and B, the glucose conversion was not affected significantly i

4A and B, the glucose conversion was not affected significantly in the presence of the Tween 80 when the enzyme loading and hydrolysis time were varied (P = 0.05). This indicates that xylose might be the major factor limiting enzymatic hydrolysis. For the extruded corncobs with 80% xylose removal, the selleck products effect of Tween 80 was very small at 24 h ( Fig. 4C). However, when the hydrolysis time was prolonged to 72 h ( Fig. 4D), increasing Tween 80 concentration resulted in a significant increase in glucose conversion at a high level of enzyme

loading (P < 0.05). However as the hydrolysis time increases it would be expected to see a decrease of the hydrolysis rate due to cellulosic substrate decrease, increase of potentially inhibitory end- and by-products and general enzyme deactivation [13]; potentially more evident at low enzyme loadings. The plot shows that a higher hydrolysis yield was obtained in the presence of a high level of Tween 80 concentration. For example, the difference in the glucose conversion was changed from 36% to 42% when the enzyme loading was 2%, and a higher difference was obtained from 80% to 88% when the Tween 80 concentration increased to 6% at an enzyme loading of 8%. In addition,

the surfactant also could prevent the unproductive binding of cellulase to lignin by absorbing into the surface of lignin. This enabled the more active enzyme to only react with cellulose to improve the glucose conversion [10]. The combined effect of enzyme loading and hydrolysis time at fixed Tween 80 concentration (3%) is shown in Fig. 5. As can be seen from Fig. 5A, the conversion of glucose PLEK2 increased from 22% to 29% at an enzyme loading of 2% with extruded corncobs with 7% xylose removal, but increased from 51% to 68% at 8% enzyme loading when increasing hydrolysis time from 24 to 72 h. The effects of hydrolysis time on the glucose conversion of extruded corncobs with 80% xylose removal were also observed (Fig. 5B). When enzyme loading was at 2%, glucose conversion was only 28% at the hydrolysis time of 24 h. Increasing the amount of cellulase significantly

improved the glucose conversion to 59% when enzyme loading increased from 2% to 8%. Enzyme crowding on the cellulose surface, an effect that can result in lower hydrolysis rates at increasing enzyme concentrations [37], was not observed under the experimental conditions. An increase in hydrolysis time from 24 to 72 h at 2% enzyme loading only resulted in a slight increase in the glucose conversion. This might be due to not enough cellulase reaching adsorption saturation for a certain amount of cellulose hydrolysis in the reaction mixture. Further increases in the enzyme loading would slow down the glucose conversion due to more unused cellulase in the mixture solution. Thus, as expected, glucose conversion could be increased with longer hydrolysis times at a higher enzyme loading.

In PSM, the density of events is constant along the x-axis, trans

In PSM, the density of events is constant along the x-axis, transforming this axis to cumulative percentage (see the x-axis). The percent of events that are in clusters C1 (20%), C2 (25%), and C3 (20%), as well as Stages 1 (20%), 2 (40%), and selleck screening library 3 (40%), can be read directly from the x-axis. PSM accounts for population overlap and requires no gating (for details, see

the Supplementary Materials Section). It also enables the visualization of measurement variability with 95% confidence limits (CLs,see Fig. 1C), which are a function of measurement uncertainty and biologic heterogeneity. The relative widths of the expression profiles for features A and B show that the CLs of B are twice that of A. Since PSM reduces complex high-dimensional data into a relatively small number of CDPs for each measurement, an overlay or “progression plot” ubiquitin-Proteasome pathway can be created that summarizes all correlations and percentages in a progression (see Fig. 1D). The thicknesses of the bands in the progression plot are proportional to the 95% CLs. A probability state model can be projected onto any bivariate as a surface plot, where stage colors are appropriately blended and the projection direction is shown with arrows (see Fig. 1E). A single PSM progression plot can represent thousands

of dot plots with very high-dimensional data (Inokuma et al., 2010), while unambiguously showing biological changes that accompany complex cellular progressions. Fig. 2 demonstrates this important characteristic of PSM using one of this study’s Carnitine palmitoyltransferase II CD8+ T-cell samples. Fig. 2A shows the probability state model progression plot derived from a list-mode file containing the correlated measurements of CD3, SSC, CD8, CD4, CCR7 (CD197), CD28, and CD45RA. The x-axis represents CD8+ T-cell memory and effector differentiation with units of cumulative percent of events. The y-axis is the relative dynamic range of the measurement intensities between 0 and 100. The

end of the naïve stage (red) is defined as the beginning of the down-regulation of CD45RA (see the first black diamond). The end of the central memory (CM, green) stage is defined by the down-regulation of CD28 (see the black diamond), and the end of the effector memory stage (EM, blue) and the beginning of the terminal effector cell stage (EF, brown) are at the point where CD45RA ceases to up-regulate (see the second black diamond). Each CDP defines the shape of the expression profile. In an EP, the CDP is shown as a white or black diamond. Fig. 2B shows scatterplot matrix (SPLOM) plots of all combinations of CD3, SSC, CD8, CD4, CCR7 (CD197), CD28, and CD45RA (7 single and 21 two-parameter dot plots). The plot surfaces are appropriately blended with the stage colors, and the dots shown are events in the tails of the 95% confidence limits of the probability state model EPs.

6–14 7 mM), variable concentrations of Cl− (0 2–6 2 mM) and other

6–14.7 mM), variable concentrations of Cl− (0.2–6.2 mM) and other major cations, Ca2+ (1.2–4.8 mM), Mg2+ (0.5–2.6 mM), Na+ (0.2–7.3 mM) and K+ (0.01–5.7 mM). The groundwater displayed low concentrations of SO42− (0.0–1.5 mM), PO43−(0–9.7 μM), NH3+ (0–2.8 μM), NO2− (0–0.2 μM) and negligible amounts of nitrate and sulfide below detection limits. A piper plot (Fig.

3) indicates that shallow groundwater of Nawalparasi is Ca-HCO3 dominant. Palbociclib solubility dmso Anions are clearly dominated by HCO3−. Ca2+ dominated cations in the upper and lower region and a localized increase in Na+ was observed in the middle region. Bivariate plots of major ion ratios may help to identify the relative importance of processes such as silicate weathering, carbonate weathering and evaporite dissolution on the concentration of major cations and anions in groundwater (e.g. Mukherjee and Fryar, 2008). The Na normalized Ca versus HCO3− plot [after Gaillardet et al. (1999) and Mukherjee and Fryar (2008)] (Fig. 4a) suggests that the tubewell water samples range from being influenced by silicate weathering to carbonate dissolution. The ratio of Na normalized Mg:Ca [after Gaillardet et al. (1999) and Mukherjee and Fryar (2008)] (Fig. 4b) suggests that the source of Mg is mostly by carbonate dissolution and partly

by silicate weathering. A bivariate plot of Ca + Mg versus HCO3− [after Mukherjee and Fryar (2008)] (Fig. 4c) displays a broader scatter and suggests that the source of HCO3− is mostly carbonate dissolution or organic matter oxidation (Mukherjee and Fryar, 2008). Average (Ca + Mg)/HCO3− of tubewell water samples Bleomycin CYTH4 of the upper region were found to be 0.48, middle region was 0.38 and the lower region was 0.50. The molar ratio of (Na + K) to Cl was greater than 1 for 59 tubewell water samples, which suggests silicate weathering is an important process

(Mukherjee and Fryar, 2008 and Stallard and Edmond, 1983), especially in the middle region. A bivariate plot of (Na + K)/Cl and Si suggests that these cations relative to Cl increase as Si becomes >250 μM (Fig. 4d), which is an indicator of significant silicate weathering (Mukherjee and Fryar, 2008). Si also generally increased along the flow-path of the aquifer (Fig. 5). Aqueous geochemistry is summarized in Table 1 and bivariate plots of AsTot and other species are shown in Fig. 6. The concentration of AsTot in the filtered water samples from tubewells in the upper region ranged from below detection limits (BDL) to 1.7 μM with an average of 0.5 μM. Eighteen groundwater samples exceeded the WHO limit in this region. The aqueous speciation of As is dominated by As(III). The concentration of Fe(aq) varied from BDL to as high as 121.6 μM with mean of 54.9 μM. Fe aqueous speciation is dominated by Fe2+ which varied from 0.0 to 121.6 μM with an average of 59.2 μM. Manganese concentrations are also high and varied from BDL to 45.5 μM with an average of 8.3 μM.

To what extent disparities between global mortality data reflect

To what extent disparities between global mortality data reflect actual epidemiology or biases in research attention remains to be established, in part

hindered by current inadequacies in coinfection surveillance. The disparity between infections that feature highly in global mortality statistics and those receiving most attention in published coinfection studies poses a challenge to infectious disease research. A general understanding of the effects of coinfection is important for appropriate control of infectious diseases.4, 7, 8 and 35 Poor or uncertain observational data regarding coinfection hinders efforts to improve health strategies for infectious disease in at-risk populations.9 For example, global infectious disease mortality data28 report only single causes of death, even if comorbidities were identified. If health statistics better represent coinfection, published coinfection LY294002 concentration research could be better evaluated. Moreover there is a lack of coherence in coinfection literature, with a variety of synonyms being used for the same phenomenon, which is multi-species infection (see the Methods for examples). AZD6244 in vitro The term polymicrobial, while commonplace, is restricted

to coinfections involving microbes. Coinfection is a broader term encompassing all pathogen types including interactions between the same kinds of pathogens as well as cross-kingdom coinfections between, say, bacteria and helminths. Ultimately decisions over which term to prefer (if any) need to be made by a consensus of the diverse research communities concerned with this phenomenon. True patterns of coinfection remain unknown21 and our results suggest that it may be starkly different from existing data on important infectious diseases. Overall recently published reports of coinfection in humans show coinfection to be detrimental to human health. Understanding the nature and Axenfeld syndrome consequences of coinfection is vital

for accurate estimates of infectious disease burden. In particular, more holistic data on infectious diseases would help to quantify the size of the effects on coinfection on human health. Improved knowledge of the factors controlling an individual’s risk of coinfection, circumstances when coinfecting pathogens interact, and the mechanisms behind these pathogen–pathogen interactions, especially from experimental studies, will also aid the design and evaluation of infectious disease management programmes. To date, most disease control programs typically adopt a vertical approach to intervention, dealing with each pathogen infection in isolation. If coinfecting pathogens generally interact to worsen human health, as suggested here, control measures may need to be more integrated and specialist treatments developed for clinical cases of coinfection.

The aims of our work were therefore to obtain monoclonal antibodi

The aims of our work were therefore to obtain monoclonal antibodies directed to biologically significant toxin epitopes expressed on B. atrox lethal toxins. The corresponding hybridomas will be used to develop humanized or antibody fragments as nonimmunogenic in vivo biopharmaceutical endowed with superior biodistribution and blood clearance properties. This work was supported by FAPERJ, CNPq. WDS is supported by grants from the following agencies: CNPq, Bolsa de Produtividade, Nível A, Proc. No: 301836/2005 – 1; FAPERJ “Programa – Cientistas de Nosso Estado”, Proc. No: E – 26/100.628/200; FAPESP, Proc. No: 09/52804 – 0 and INCTTOX program of the CNPq and FAPESP.

The authors

are grateful to Instituto Selleck GSK126 Butantan for providing B. atrox venom and horse F(ab′)2 anti-bothropic antivenom. This manuscript Natural Product Library was reviewed by a professional science editor and by a native English-speaking copy editor to improve readability. “
“Ureases (urea amidohydrolase; EC are nickel-dependent enzymes that catalyze the hydrolysis of urea to ammonia and carbon dioxide (Dixon et al., 1975). Ureases have been isolated from a wide variety of organisms including plants, fungi and bacteria. In plants, ureases are homotrimers or homohexamers of a ∼90 kDa subunit and supposedly participate in the use of urea as nitrogen source (Carlini and Polacco, 2008). Evidences pointing to a possible involvement of ureases in the plant defense against Protirelin some insect pests and phytopathogens have been documented (Carlini and Grossi-de-Sa, 2002; Carlini and Polacco, 2008; Staniscuaski and Carlini, 2012). Thus, newly described

properties of plant and microbial ureases, such as entomotoxic and fungitoxic activities, have widened the proposed physiological roles of ureases (Real-Guerra et al., 2013). In Canavalia ensiformis (Leguminosae) three urease isoforms were identified: Jackbean urease (JBU), Jackbean urease II (JBUre-II) and canatoxin (CNTX). These proteins were shown to present several biological effects, including toxicity to insects and fungi ( Becker-Ritt et al., 2007; Follmer et al., 2004; Mulinari et al., 2011; Postal et al., 2012; Staniscuaski et al., 2005, 2009, 2010). These biological activities are completely independent from the ureolytic activity ( Follmer et al., 2004; Mulinari et al., 2011; Postal et al., 2012). Elucidation of which domain is related to each biological activity could lead to the development of several urease-based biotechnological tools. One of the biologically active domains of Jackbean ureases, the one responsible for its insecticidal activity, has been identified. It is a ∼10 kDa fragment released by cleavage promoted by insect digestive enzymes ( Carlini et al., 1997; Ferreira-DaSilva et al., 2000).

Our findings might indicate intense production and decomposition

Our findings might indicate intense production and decomposition processes in the settled material in the Bahía Blanca Estuary, even when the study was carried out in a particularly cold winter. The high chlorophyll and phytoplankton cell density observed in the settled material could be related to a combination of (1) high phytoplankton sedimentation during the growing period, (2) low predation pressure and (3) intense in situ growth inside the collectors. First, the low river runoff and high residence time of the inner zone of the estuary (Pratolongo et al., 2010) allowed net downward flow of phytoplankton. Secondly,

the phytoplankton in the pelagic habitat had to deal with high zooplankton grazing buy Capmatinib pressure, while the microalgae inside the sediment containers were released from predation by the suspension-feeder E. americana ( Berasategui et al., 2009). Thirdly, the microenvironment inside the collectors may have benefited the phytoplankton growth compared to the water column, where the cells can be highly stressed by water mixing and fluctuating light intensities. The continuous movement find more of phytoplankton up and down may imply an adaptation of the photosynthetic system to changing underwater conditions, and this

might lead to an extra energy cost in contrast to the cells settled in the collectors ( Villafañe et al., 2004 and references therein). In agreement, Popovich and Marcovecchio (2008) DNA ligase classified the phytoplankton species found in the internal zone of the Bahía Blanca Estuary as well adapted to grow under low light conditions. For instance, empirical research with the diatom Thalassiosira curviseriata isolated from the estuary ( Popovich and Gayoso, 1999) – and one of the dominant species within the collectors in the present work – showed a growth optimum at light intensities around 32–36 μE m−2 s−1, saturation growth between 60 and 80 μE m−2 s−1 and inhibition close to 150 μE m−2 s−1. In the present study, the light intensity received at the water surface I0 (10 cm depth) during the winter-spring

period was 823 ± 522 μE m−2 s−1 (mean value ± standard deviation), and light intensity in the mixed layer Im (total water column) was always over 100 μE m−2 s−1. This suggests that the further attenuated light conditions inside the sediment collectors were more suitable for Thalassiosira spp. growth than the light intensity received in both, the surface waters and the mixed zone. The analysis of the particle size distribution showed that during the blooming period the size-spectrum was notably heterogeneous due to the presence of phytoplankton and zooplanktonic organisms, as well as sediment and detritus. Conversely, during the post-bloom period, the water surface appeared dominated by smaller particles (i.e.

, 2008) However, no study has examined connectivity amongst diff

, 2008). However, no study has examined connectivity amongst different regions in the infant brain when language processing takes place. This study is the first step toward understanding how the infant brain creates networks when establishing word-referent associations. This research was supported by MEXT KAKENHI (#15300088, #22243043, Grant-in-Aid for Scientific Research on Innovative

Areas #23120003) to M.I. and H.O., MEXT KAKENHI (#21120005) and JST PRESTO to K.K., MEXT GCOE program to Tamagawa University, BBSRC Research Development Fellowship (BB/G023069/1) to S.K., Economic and Social Research Council (ES/E024556/1) and European Research Council (ERC-SG-209704) to G.T, and Grant-in-Aid for JSPS Research Fellows (#23-2872) to M.A. We thank Yumi Nakagawa,Yuji Mizuno, Junko Kanero and Mamiko Arata for help in data collection and analysis, and Marilyn Vihman for comments on an earlier version of the manuscript. A1210477 M.A. and M.I. are joint first authors. G.T. and S.K. made equal contributions. The authors declare no competing financial interests. “
“Storing and processing RAD001 cell line word

meanings involves a widely distributed network of brain regions. Investigating how elements of this network respond to different types of word can provide important insights into the functional organisation of the system. This study focused on differential activations during comprehension of concrete versus abstract words (e.g., rope vs hope). Two main classes of theory have been proposed to account for these. The first class claims that concrete and abstract words differ in terms of their representational substrate. It is often claimed that abstract words have weak or impoverished semantic representations ( Jones, 1985, Plaut and Shallice, 1993 and Wiemer-Hastings

and Xu, 2005). Jones (1985), for example, found that participants judged it easier to predicate (i.e., generate factual statements for) concrete concepts than for abstract. This representational weakness Bay 11-7085 for abstracts might come about because they lack information gained from sensory experience. The most well-known of these is dual-coding theory ( Paivio, 1986), which states that while both concrete and abstract concepts are used and experienced verbally, only concrete words are associated with sensory-perceptual information acquired through direct experience of their referents. Paivio proposed that verbal and sensory-perceptual information were represented in separate stores and that concrete words benefited from dual-coding in both stores, while abstract words were represented only in the verbal store. Recent studies have explored other aspects of experience that might be particularly salient for abstract concepts. Abstract words are more strongly associated with emotion and valence responses ( Kousta et al., 2011 and Vigliocco et al., 2014), for example and some abstract words are closely linked to spatial and temporal relationships ( Troche, Crutch, & Reilly, 2014).

In order to check the effect of pH on hydroperoxide

In order to check the effect of pH on hydroperoxide check details formation in meat, pH values from 1.5 to 7.0 were examined. Ringer’s solution was adjusted to the required pH with 2 M H2SO4 before incubation. The FOX method is based on oxidation by hydroperoxide under certain acidic conditions (pH 1.8) for a maximum response at room temperature (Bou et al., 2008 and Gay et al., 1999). Normally when the samples were incubated at pH 7, a final pH 1.8 (pH of maximum absorbance) was obtained when absorbances were read. But when the samples were incubated at pH 5.5, 3.5 and 1.5, the final pH was

the absorbance ratios at pH 7 to pH 5.5 (1.0134), pH 7 to pH 3.5 (1.0321) and pH 7 to pH 1.5 (1.124) to correct absorbances below pH 1.8 back to absorbance at pH 1.8. The ratio of endogeneous meat fatty acids to the liposome fatty acids varied with the amount of fat in the lean meat, but was always less than 1:2 (weight ratio). The initial peroxide value of the liposomes added was less than 0.037 mmol/kg of phospholipids. The amounts of

CC in water–methanol and chlorofrom produced during PV measurements were measured. Both the polar and non-polar phases were removed for CC measurements. Polar phase (100 μl) was removed and diluted 10 times by adding 900 μl of 75% methanol and 25% water solution and the non-polar phase was removed (50 μl) and diluted 20 times by adding 950 μl of chloroform. Both phases were measured

spectrophotometrically Veliparib in the UV range (240–340 nm). The obtained absorbances were multiplied by the dilution factor (×10 in polar phases and ×20 in non-polar phases) then divided by the molar absorptivity of conjugated trienes of 36,300 (1 cm pathway) at 268 nm. In order to check which phase hemin remained in during hydroperoxide analysis, 1 ml of hemin solution (0.31 mg/ml) was blended with 1 ml of 2:1 chloroform:methanol solution. The same procedure was also carried out for extraction of the three phases for hydroperoxide determination. After centrifugation, undissolved hemin particles were found to appear between polar phase and non-polar phase. The polar phase showed an average absorbance of 0.01 at 407 nm. The non-polar phase had its absorbance tested against chloroform L-gulonolactone oxidase as a blank. By using the molar absorbitivity of 36,000 (1 cm pathway) (Uc, Stokes, & Britigan, 2004), an upper limit of 1.8% of the added hemin was identified as presented in the non-polar phase if the initial solution contained 8 g/l of myoglobin. Therefore hemin, in meat homogenates during the PV assay, was distributed mainly to the interphase with the proteins. The analyses were carried out on meat samples, following the analytical method described by Ginevra et al. (2002) with some optimizations. Meat cuts were trimmed of all visible fat, frozen in lipid nitrogen and homogenised to meat powder. Meat homogenates (0.

In the last stage of the purification process, the active fractio

In the last stage of the purification process, the active fractions were eluted from a phenyl-sepharose column when the salt gradient had been exhausted. This procedure resulted in a purification factor of 99.3 with 8% recovery of the original β-glucosidase activity. The Dabrafenib clinical trial electrophoretic profile of the enzyme in SDS–PAGE confirmed the presence of a single protein band with an estimated molecular mass of

65.15 kDa ( Fig. 1). Substantial activity against pNPβGlc was observed for the purified enzyme within a pH range of 5.5–7.0 and temperature range of 30–50 °C. The optimum pH for the enzyme was 6.0 ( Fig. 2A) and the β-glucosidase achieved maximal substrate hydrolysis at 45 °C ( Fig. 2B). This optimum pH value is the same as those reported for hydrolysis of pNPβGlc by the β-glucosidase from apple seed ( Yu, Xu,

Lu, & Lin, 2007), from Pyrococcus furiosus ( Yeom et al., 2012) and from the endophytic bacterium Pseudomonas ZD-8 ( Yang, Ning, Shi, Chang, & Huan, 2004). The β-glucosidase from Termitomyces clypeatus also exhibited maximal activity against pNPβGlc at 45 °C ( Pal et al., 2010). The purified D. hansenii UFV-1 β-glucosidase maintained approximately 51% of its original activity after 6 h Erastin of pre-incubation at 45 °C and 30% after 60 min at 50 °C ( Fig. 2C). The half-life of D. hansenii UFV-1 β-glicosidase at 45 and 50 °C was 312 and 73 min, respectively. Stability of this enzyme was also evaluated at 4 °C and Histidine ammonia-lyase at room temperature (25 °C). The enzyme maintained 97% and 62% of its original activity after 30 and 90 days of incubation at 4 °C, respectively. When kept at room temperature, the enzyme maintained 67% and 47% of its original activity after 5 and 15 days, respectively. The D. hansenii UFV-1 β-glicosidase showed significant stability over a

wide pH range. This enzyme retained more than 90% of its activity after incubation for 30 min in a pH range of 5.5–8.0. About 85% and 64% of its activity was maintained after incubation at pH 4.5 and 4.0, respectively, and enzymatic activity was null after incubation at pH values below 3.5 ( Fig. 2A). The enzyme showed significant stability for a wide pH range and reasonable temperature levels which is desirable for industrial applications, especially for hydrolysis of isoflavones in soybean products. Immobilised D. hansenii UFV-1 cells containing β-glucosidase showed substantial activity within the same pH range of the free enzyme (5.5–7.0) and over an even larger temperature range (20–55 °C). In this case, the optimum pH was 5.5 ( Fig. 2D), lightly more acidic than the optimum pH of the free enzyme, which was 6.0. This decrease in optimum pH after immobilisation in calcium alginate can be partially explained by the effect of the micro-environment in the calcium alginate gel matrix, particularly due to the presence of positively charged Ca2+ ions ( Adami, Cavazzomi, Trezzi, & Craveri, 1998).