José Tadeu Abreu de Oliveira from the Department of Biochemistry, Universidade Federal of Ceará, Fortaleza, Ceará, Brazil. The yeasts were maintained on Sabouraud agar (1% peptone, 2% glucose and 1.7% agar). The fungi were maintained on potato agar (PDA) at 4 °C. JBU was

hydrolyzed using different commercial enzyme: trypsin (EC 3.4.21.4 – Sigma–Aldrich, St. Louis, MO, USA), chymotrypsin (EC 3.4.21.1 – Sigma–Aldrich, St. Louis, MO, USA) papain (Merck, Darmstadt, Alemanha), selleck compound pepsin (EC 3.4.23.1 – Sigma, St. Louis, MO, USA). Different conditions of hydrolysis were tested, varying pH, incubation time and enzyme:substrate ratio. The reaction mixture after hydrolysis with papain was submitted to ultrafiltration (4000 × g, 10 min) using Navitoclax molecular weight 10,000 mw cut-off Amicon cartridges (Millipore, Billerica, MA, USA) to separate

a pass-through filtered fraction containing peptides with Mr below 10,000 d and a retained fraction, with molecules bigger than 10,000 d. The hydrolyzed fractions of JBU were visualized in SDS-Tricine gels [36]. The gels were stained with Colloidal Coomassie. The filtered fractions (<10 kDa) after hydrolysis of JBU were desalted on reverse-phase column (C-18) in a HPLC system (Shimadzu). The column was equilibrated with 0.1% TFA (trifluoroacetic acid) and the retained fraction were eluted with a gradient (0–100%) of 99.9% acetonitrile in 0.1% TFA. The eluted peptides were pooled and lyophilized. The lyophilized material was suspended in 0.1% formic acid (20 μL) and 5 μL were subjected to reversed phase chromatography (NanoAcquity UltraPerformance LC®-UPLC®, Waters, Milford,

United States chromatograph) using a Nanoease C18, 75 μm ID at 35 °C. The column was equilibrated with 0.1% TFA and the peptides were eluted in 20 min gradient, ramping from 0 to 60% acetonitrile in 0.1% TFA at 0.6 nL/min constant flow. Eluted peptides were subjected to electro spray ionization and analyzed by mass spectrometry using a Q-TOF Micro™ spectrometer (Micromass, Waters, Milford, United States). The voltage applied to the cone for the ionization was 35 V. The three most intense ions in the range of m/z 200–2000 and +2 or +3 charges were selected for fragmentation. The acquired MS/MS spectra were processed using Proteinlynx Resveratrol v.2.0 software (Waters, Milford, US) and the generated .mgf files were used to perform database searches using the MASCOT software (version 2.4.00) (Matrix Science, London, UK) against the NCBI database, restricting the organism to taxonomy “green plants_taxid 33,090.” No digestion enzyme was selected. Search parameters allowed a maximum of one missed cleavage, the carbamidomethylation of cysteine, the possible oxidation of methionine, peptide tolerance of 1.2 Da, and MS/MS tolerance of 1.2 Da. The significance threshold was set at p < 0.

, 1999), in general, ligand-bound iron can be taken up (e.g. Maldonado and Price, 1999), using a range of different uptake mechanisms (Maldonado and Price, 2001, Shaked et al., 2005 and Boukhalfa and Crumbliss, 2002). Several of these mechanisms are likely to result in a net loss of complexing capacity. In the model we thus describe the loss of ligands through uptake as Rupt = puptRFe, where pupt is a probability that iron uptake destroys a ligand molecule and RFe is the uptake of iron by phytoplankton.

Finally, part of the ligands is certainly colloidal (Cullen et al., 2006) and can aggregate with sinking particles. In the model this process is described as Rcol = pcolλL, Compound Library high throughput where pcol is the fraction of ligands that undergoes aggregation and L is the total ligand concentration. λ is an aggregation rate, which we calculate from the concentrations

of dissolved and particulate organic carbon and aggregation kernels for shear and Brownian motion ( Jackson and Burd, 1998). At the moment, we assume that aggregated ligand is lost from the system completely, unlike for iron, where PISCES allows for re-dissolution of particulate iron. The ligand model as described above contains several parameters that must be chosen, namely rL:C, kphot, τmax, τmin, pupt and pcol. While direct measurements of each are unavailable at present, we can make first order approximations of their likely range from selleckchem previous work (the sensitivity to each will be explored in additional model experiments). Concerning first the ratio of ligand to carbon rL:C, the seasonal variations in

ligand and DOC concentrations at the DYFAMED site in the Mediterranean by Wagener et al. (2008) show a good ligand:DOC correlation with a slope Inositol oxygenase of ≈ 10− 4 mol L mol− 1 C. A second constraint comes from a linear correlation between iron solubility (a proxy for organic ligands) and regenerated phosphate in the Mauritanian upwelling ( Schlosser and Croot, 2009) with a slope of ≈ 10− 3 mol L mol− 1 P. Using the Redfield ratio of 106 mol mol− 1 for C:P this translates into a ligand:C range 10− 4 < rL : C < 10− 5 mol mol− 1. The shipboard incubation experiments with particles sampled in the water column at a polar and a subantarctic site south of Australia by Boyd et al. (2010) found a release of ligands and of iron in a ratio of ≈ 5 mol mol− 1. Assuming a typical Fe:C ratio in biogenic particles of ≈ 5 − 20 ⋅ 10− 6 mol mol− 1, this translates into a ligand:carbon ratio of 2.5 − 10 ⋅ 10− 5 mol mol− 1, within the range estimated above. Hansell et al. (2012) gives a range of degradation time-scales for dissolved organic carbon from 1.5 years for semi-labile DOC to 16,000 years for refractory DOC. We assume that the ligands that we are modeling are part of the continuum between semi-labile and more refractory DOC with a minimum degradation time-scale τmin of one year and a maximum time-scale τmax of 1000 years (at a reference temperature of 0 °C).

In the first of these [26], we studied the effect of electrostatic fields on the rate DNA/RNA Synthesis inhibitor of drying of wet materials. It is well known from the study of transport phenomena that a thin layer of relatively inert air exists at the surface of most materials where the relative velocity of gas flow asymptotically drops to zero. These surface boundary layers both interfere with the diffusion of gases out of the material and limit the rate of convective heat transfer into it (e.g., [3], [6] and [7]).

It is also known that an electric or “corona” wind is generated on the surface of electrically charged objects as a result of ions leaving the surface, and this wind can cause a marked increase in heat conduction at a surface by disrupting the stagnant surface boundary layer [2], [4], [8], [27], [31] and [37]. This electrostatic effect per ion is several orders of magnitude above thermal noise. In our previous study, we found that electrostatic fields comparable to those used in CAS freezers were able to disrupt the inert surface boundary layer of air molecules, and dramatically shorten drying times [26]. We therefore argue here that

the high-voltage selleck compound electrostatic fields applied in the CAS freezers are increasing the cooling efficiency by disrupting the surface boundary layer of inert gas at the surface of their materials. The cooling enhancements shown by Owada et al. [34] are, in fact, similar in style to that we reported previously [26]. Hence, either DC or AC high-voltage electric fields would be expected to promote rapid heat removal needed for supercooling. An intrinsically more interesting question concerns the possible mechanism of action of the weak, oscillating magnetic fields on cryopreservation. There are only four possible physical coupling mechanisms that can yield interaction effects of oscillating magnetic fields with matter (electrical induction, diamagnetism, paramagnetism, and ferromagnetism). However, for low-frequency fields weaker than a few hundred uT, all except

ferromagnetism do not work, with peak interaction energies well below the thermal noise limit. We are in complete agreement with Wowk [44] on this. However, particles of ferromagnetic materials can interact hundreds to thousands of times stronger with earth-strength PI-1840 magnetic fields than the background thermal energy (see discussion by Kirschvink [19]). Owada et al. [34] and [35] and Wowk [44] did not consider the well-known presence of ferromagnetic materials, principally biologically-precipitated magnetite (Fe3O4), in a wide range of biological tissues (see [13], [20], [30], [39], [40], [41] and [43], for example). These observations have been replicated widely (e.g., [5], [9], [11], [14], [15], [16] and [36]). Brain tissues in humans have been studied extensively [5], [9], [10], [11], [16], [21], [22] and [36], and magnetite deposits in specialized cells are extensive [24] and [25].

5% for all outcomes [41]. In addition to the standard morphological analysis, we applied

a customized segmentation algorithm [37], [42] and [43] to the HR-pQCT scans to assess cortical BMD (Ct.BMD, mm HA/cm3), total cross-sectional area (Tt.Ar, mm2), cortical thickness (Ct.Th, mm) [44], and cortical porosity (Ct.Po, %) [37], [42] and [43]. This technique can reduce variation in Ct.Th measures caused by differences in degree of bone mineralization, which can be present when obtaining Ct.Th by dividing http://www.selleckchem.com/products/SB-431542.html cortical bone volume by the periosteal surface. In vivo reproducibility for these cortical measures is < 2.9%, with the exception of Ct.Po, which has a reported least significant change of 0.58% for the radius and 0.84% for the tibia [42]. One trained technician analyzed all HR-pQCT scans. To obtain accurate estimates of bone strength, we used custom finite element analysis (FEA) software to analyze each HR-pQCT

scan based on a linear, homogenous model with a mesh generated using GKT137831 in vivo the voxel conversion approach. This method incorporates the three-dimensional micro-architecture and local BMD of the scanned region of interest [45] and [46]. The models were solved using custom large-scale FEA software (Numerics88 Solutions, Calgary, Canada) [47] on a desktop workstation (Mac, OS X v10.5; 2 × 2.8 GHz Quad-Core Intel Xeon; 32 GB 800 MHz DDR2 FB-DIMM). Using this custom software, the radius and tibia models required an average of 60 min each to solve. The primary outcome was failure load (N), based on simulating axial compressive loading of the bone to 1% strain Cyclooxygenase (COX) [48]. A Biodex isokinetic dynamometer (Biodex®, System 3, New York, USA) was used to measure maximal isokinetic knee extension and flexion torque (Nm) of the dominant leg. The Biodex seat was adjusted until the popliteal crease was at the edge of the chair and the axis of rotation was at the level of the femoral condyle. The leg pad was placed just above the malleoli. Participants began each test with their leg in a flexed position and commenced

with knee extension at 90°/s. Once the participant reached the point of maximum extension they immediately reverted to knee flexion also at 90°/s. The combination of extension and flexion consisted of one practice trial followed by three experimental trials with no rest. A digital low-pass filter with a cut-off frequency of 5 Hz reduced noise. This test is highly reliable [49] and targets large muscle groups such as the quadriceps and hamstrings that insert on the proximal tibia. A grip strength dynamometer (Almedic, Quebec, Canada) was used to determine overall isometric strength (kg) of the hand and forearm muscles of the dominant arm (or non-dominant for those participants with previous forearm fractures) using the Canadian Physical Activity, Fitness, and Lifestyle Approach protocol [50].

Species of the genus Cystoseira, which dominate the Mediterranean upper sublittoral communities, are particularly sensitive to any natural or anthropogenic stress ( Bellan-Santini, 1966, Ballesteros et al., 1984, Hoffmann et al., 1988 and Soltan et al., 2001) and, therefore, their populations have experienced

profound declines over extensive areas ( Thibaut et al., 2005). However, our results show that while C. amentacea is considered a good indicator of environmental quality and may thus be used in water quality assessment, it is less useful than U. lactuca as an indicator of N input variation over short time periods. Cystoseira typically has a very low nitrogen uptake see more rate and large amounts of structural biomass, and so would require longer periods of exposure to assimilate sufficient new nitrogen to alter the average δ15N value of its fronds. The stable-isotope values in these two macroalgae could be used ERK activity to delineate the influence of sewage-derived nutrients in coastal areas ( Hobbie et al., 1990, Rogers, 1999, Costanzo et al., 2001 and Wayland and Hobson, 2001) and to map sewage dispersal over different timescales. However, while the isotopic signature of Ulva spp. has already been acknowledged to be highly responsive to pollution ( Gartner et al., 2002, Dailer et al., 2010, Dailer et al., 2012 and Barr

et al., 2013), further Dipeptidyl peptidase investigations are necessary to evaluate C. amentacea as a useful in situ long-term

indicator for N pollution episodes in the pristine habitats where it normally occurs. In conclusion, our large-scale study shows the usefulness of δ15N in U. lactuca as a proxy for locating anthropogenic sources of nitrogen in disturbed Mediterranean coastal areas. Short-term algal exposure represents an important temporal logistic advantage in such coastal areas characterized by intense tourism and commercial activities, which need to be reduced or interrupted during the assessment. This technique of mapping pulse nitrogen inputs of different origins could be thus used as a baseline for future water quality monitoring and management programmes, but only after defining the best sampling grid to exactly describe the topography of nitrogen inputs and distribution in coastal seas. The research was funded by Provincia Latina 2010, PNRA2010 and Ateneo-Costantini 2013. The authors thank ARPA-Latina for chemical data and G. Jona Lasinio for data spatial analysis. George Metcalf revised the English text. “
“Water clarity or transparency is a key factor for marine ecosystems, affecting the resource supply for photosynthetic organisms and filter feeders. Coral reefs and seagrass meadows are built by photosynthetic organisms, and are therefore highly sensitive to changes in water clarity.

There is a very slight (1.1%) decrease in the number of samples which would go to Tier 3, and matching increase in Tier 2 samples, suggesting that the consideration Buparlisib research buy of 2,3,7,8-TCDD,

tDDT, chlordane and Dieldrin only makes a very minor difference in the number of Tier 3 outcomes, given these protocols. When the full TEL analyte list is considered, but Consensus LAL and UAL are applied, there is a significant (10.1%) increase in LAL chemistry passes, and decreases in Tier 2 and Tier 3 assignments of 4.5 and 6.6%, respectively. The lower failure rates for metals due to the less conservative Consensus UAL and LAL values overwhelm the higher failure rates for the organic constituents. The consideration of the full suite of analytes reduces the LAL pass rate by 8.7%, increases the Tier 2 ABT-737 clinical trial samples by 1.3% and increases the Tier 3 rate to its

highest level, 26.5%, in spite of the less conservative metal SQGs. Not surprisingly, given the more conservative nature of the UAL values, organics dominate the UAL failures, although this division is not as clear-cut for LAL failures. In most regulatory programs, including DaS DM programs, a specific list of contaminants or substances of priority concern is identified for analysis and evaluation within a regulatory decision framework. These priority substances are subject to the establishment of action levels against which sediments to be evaluated are compared. However, more than 14 million commercially available chemicals and countless environmental transformation products and unintentionally formed compounds exist (Brack et al., 2009 and Daughton, 2002). Lahr et al. (2003) observed a poor correlation between sediment bioassay results and priority pollutant concentrations in some sites in the Netherlands, possibly due to agricultural runoff of pesticides, which are not

routinely measured in sediments, as well as to confounding factors. Brack et al. (2007) reviewed key toxicants identified in European river basins; in many cases, the compounds identified could only explain a small proportion of measured effects. Given the millions of C1GALT1 potential compounds, only a small proportion of which are even extractable or measurable, it is not possible to determine all the anthropogenic and natural toxicants that might be present in a sample, or to fully explain observed toxicity in a sample, based upon the chemicals that are identified. The questions of how best to select the chemicals to track and regulate, and whether complete chemical identification is a realistic goal, or a necessary objective in a sediment framework are yet to be resolved (Apitz, 2011). The priority pollutant lists used internationally are not necessarily the most risky or important contaminants.

Fig. 3 depicts this comparison for two healthy donors. The simultaneous measurements of 4 to 96 RBCs (depending on the model of the automated patch systems) allow for measurement of a population of RBCs with exactly the same experimental procedure, and there is no experimental bias towards choosing a (particular) cell. In contrast, classical patch-clamp allows for more (visual) control over the particular experiment/cell and at least an order of magnitude lower noise level, typically approximately 1 pA. Comparing

data from cell suspension experiments (cp. Section (4.2) “Ion fluxes”) and those issued from patch-clamp studies is a common but difficult task, Ion Channel Ligand Library in vitro which can be exemplified by the entry of Ca2 + observed in sickle cells upon deoxygenation. This entry, designated Psickle, is best characterised as a poorly selective permeability pathway for small, inorganic monovalent and divalent cations.77 Experiments in which the fraction of activated cells was studied as a function of the external Ca2 + concentration showed that sickling is a stochastic event

of random intensity among HbSS RBCs, capable Selleckchem Ku 0059436 of generating maximal Gardos channel activation in a small fraction of cells during each deoxygenation-sickling pulse. Consistent with the stochastic nature of Psickle, repeated pulses led to the progressive accumulation of dense cells, whereas a single long pulse caused only an early production of a single small fraction of dense RBCs.78 Lew et al. eventually depicted this nature clearly by writing: “When electrophysiologists finally approach the study of Psickle under patch-clamp, they ought to bear in mind the probabilistic nature of Psickle in each deoxygenation

pulse before consulting their Astemizole psychiatrist for the lack of reproducibility!”.77 One has to keep in mind that electrophysiology conclusions are drawn from results where the membrane potential is changed at will by the experimenter, meaning that they are rarely obtained at the resting membrane potential, rendering comparison with cell suspensions difficult. This is exemplified in a recent study, where it was shown that increased membrane permeability for sorbitol in malaria-infected RBCs could not easily be reconciled with data from whole-cell experiments.79 Indeed, in isosmotic sorbitol haemolysis, the membrane potential reaches values above + 50 mV due to the absence of charges at the extracellular side of the membrane. Subsequent comparison of these data to that obtained with patch-clamp (at this membrane potential, inwardly rectified currents induced by infection are almost totally abolished[62] and [65]) seems impossible. FCM is a technique that uses optical detection methods for counting and analysing particles in the size range of micrometres.

All Ct > 36, indicative of the plateau phase of qPCR, were considered non-expressed genes. The Ct values were then normalized against the selected endogenous control gene to generate ΔCt values (Ctgene of interest − Ctendogenous control gene). CP-868596 chemical structure All the experiments were repeated three times containing three replicates per condition and timepoint. GeneSpring™ GX11.5.1 (Agilent, United Kingdom) was used to perform the gene expression

graphical and statistical analysis. Principal Component Analysis (PCA) and hierarchical clustering were selected for graphical representations. For the hierarchical clustering algorithm, Euclidean distance measured with average linkage was selected for interpretation of the normalized gene expression data (ΔCt). One-way ANOVA was used to analyze the effect of the TCDD induction on the expression of each gene. The enzyme activity data are represented by the arithmetic IDH inhibitor mean of three experiments + standard deviation (SD). Minitab v.16 was used to perform Student’s t-test. Difference was significant when p < 0.05. The first stage in the metabolic characterization was to quantify the mRNAs of a panel of enzyme-encoding genes involved in oxidative (phase I) and conjugative (phase II) metabolism. The endogenous control gene RPLP0 showed the most stable expression across the different

samples and treatments (data not shown). Furthermore, RPLP0 has been reported as being highly conserved across tissues and species (Akamine et al., 2007). Therefore, RPLP0 was chosen for normalization of data, Thymidylate synthase generating ΔCt values (Ctgene of interest − CtRPLP0). PCA was used to visualize the dataset in a 3D scatter plot graph shown in Fig. 1. This analysis demonstrated the segregation of cell lines based on their gene expression profile. The graph shows a clear separation of the different cell lines (represented by colors) indicating that

the expression profile differs from cell line to cell line. In addition, only HepG2 cells show a variation between the induced (triangle shaped icon) and non-induced samples (rectangle shaped icon), while there is no apparent separation of the induced from the non-induced BEAS-2B and A549 samples. HepaRG cells were not induced so Fig. 1 only represents the basal gene expression levels. The hierarchical cluster shown in Fig. 2 was generated to visualize the gene expression and induction profiles of each individual cell line. This graphical representation contained the expression value for each individual gene normalized (ΔCt values); red, blue and yellow indicate increased (positive ΔCt), reduced (negative ΔCt) and undetectable (ΔCt close to or 0), respectively. The details contained in the hierarchical cluster allowed a gene by gene comparison between induced and non-induced treatments but also, between different cell lines. This cluster analysis confirms the observations made above by PCA.

”2 This definition remains broad, describing an “airflow limitation” that, in reality, is caused by distinct features of small-airway disease, chronic bronchitis, and emphysema that may be highly variable among patients despite identical measures of airflow limitation measured by the forced expiratory volume in 1 second (FEV1)/forced vital capacity MG 132 ratio. Research during the past few decades has begun to reveal a new understanding of the pathophysiology, public health impact, and overall complexity of COPD. This

issue of Translational Research contains an in-depth review of COPD that includes 4 articles that serve as illustrative examples of how our understanding of COPD is shifting from a physiologically defined obstructive lung disease caused by cigarette smoking to a complex systemic Selleck SAHA HDAC disease with risk that is modified by multiple factors (including genetics and the environment), has variable manifestations in different populations, is characterized by multiple disease phenotypes, and occurs, not in a vacuum, but in the context of

other common comorbid conditions ( Fig 1). COPD is the third leading cause of death in the United States and is the only leading cause of death that is increasing in prevalence.3 Between 1970 and 2002, death rates secondary to stroke and heart disease decreased by 63% and 52%, respectively, whereas death rates resulting from COPD increased by 100%.4 Currently, approximately 14 million Americans have been diagnosed with COPD, although it has been estimated that an additional 12 million individuals remain undiagnosed.5 By 2030, it is estimated that approximately 9 million people will die annually from COPD.6 COPD is also a source of significant health expenditure and societal Chlormezanone costs. Until recently, patients, clinicians, and researchers undervalued the overwhelming impact of this disease on individuals’ quality of life and society’s economic stability. In 2008, it was estimated that the cost to the United States for COPD and asthma was approximately

$68 billion, including $14.3 billion in direct costs and $53.7 billion in mortality costs.5 In a 2001 international study, it was found that 45.3% of COPD patients younger than 65 years of age had missed at least 1 day of work within the previous year secondary to COPD. In that same study, patients with COPD often minimized their own symptoms; 60.3% of patients who ranked their disease as mild or moderate reported severe breathlessness.7 In recognition of the increasing prevalence and costs associated with COPD, during the past decade there has been great progress in our understanding of the pathogenesis, manifestations, and clinical outcomes of this common disease. In this in-depth review issue, we explore and celebrate the strides made while also identifying areas that require further investigation to expand our understanding of COPD.

In contrast, hemorrhage and edema induced by jararhagin were unaffected by deletion of any of the inflammatory mediators investigated, indicating that these effects occurs independent of these pro-inflammatory mediators. Besides its relevance in snakebite, the action of jararhagin Ganetespib mw was investigated in a number of different cell systems. In fibroblasts, it presented an agonist effect leading to cellular activities similar to those induced when fibrillar collagen triggers the α2β1 integrin receptor as the expression of MMP-1, MT1-MMT and α2β1 integrin (Zigrino et al., 2002). In epithelial cells, jararhagin inhibited cellular adhesion to the substrate,

but stimulated cellular migration and phosphorylation of FAK, inducing the rearrangement of the actin cytoskeleton, increased of actin polymerization and formation of motility-associated cell processes (Costa and Santos, 2004). In neuroblastoma cells, jararhagin also stimulates spreading, actin dynamics, neurite outgrowth, and activation of Rac1 DAPT cost GTPase (Costa et al., 2008). In addition, studies have been carried out to investigate the ability of jararhagin to interfere on cancer cell functions. Treatment of Skmel-28 human melanoma cells altered morphology, viability and adhesion

to ECM components, resulting in a significant reduction of lung metastasis compared to controls (Corrêa et al., 2002). This toxin also up-regulated cell cycle and apoptosis-related genes in Skmel-28 cells (Klein et al., 2011) and was evoked as a putative model for an anti-cancer drug. Due to the importance of SVMPs in venom pathology, the neutralization of their biological effects is crucial for the efficacy of Montelukast Sodium antivenoms, the currently accepted treatment for snakebite. In this regard, commercial and experimental antivenoms are efficient in inhibiting venom-induced hemorrhagic activity (Lopes-Ferreira et al., 1992)

indicating the immunogenicity of hemorrhagic SVMPs. However, aiming the development of antibodies directed solely at specific medically-important toxins, jararhagin was used for immunization protocols to raise antibodies by hybridoma technology (Tanjoni et al., 2003a) or by DNA immunization (Harrison et al., 2000). Seven murine monoclonal antibodies raised against jararhagin have been isolated. They reacted preferentially with jararhagin-C and one monoclonal antibody (MAJar 3) inhibited jararhagin/collagen interactions and jararhagin-induced hemorrhagic activity (Tanjoni et al., 2003a). Specific antibodies were also raised by immunization of mice with the cDNA encoding for recombinant jararhagin-C using a Gene-Gun approach. The resulting antiserum partially inhibited the hemorrhage induced by whole B. jararaca venom ( Harrison et al., 2000). Jararhagin-specific antibodies showed a marked antigenic cross-reactivity with venoms from other snakes.