Many results had been recently published regarding the development of new ligand strategies to minimize interparticle spacing. Zhang et al. reported that optical absorption of NCs could be effectively improved after ligand removal [19]. Lauth et al. reported that 3 orders of magnitude conductivity increase of CIGS NC films could be achieved after ligand removal and conductivity enhancement depends on the NC size accentuating MEK162 datasheet the role

of trap states and internal grain boundaries in ligand-free NC solids for electrical transport [20]. Carrete et al. and Stolle et al. performed ligand exchange on CZTSe nanoparticles, finding that crystallization of NCs and cell performances could be promoted [21, 22]. Their works focused on improving the optical and electrical properties of CZTSe

films to increase the photocurrent of the device, but there is no detailed study clarifying the band alignment between the CdS layer and the absorption layer after ligand exchange. Herein, we employed click here a convenient one-step method to synthesize CZTSe NCs. The key feature of this synthesis was to use excess Se relative to Cu, Zn, and Sn and conduct the reaction at a relatively low temperature. All-inorganic CZTSe NCs were obtained by ligand exchange strategy using a simple metal-free chalcogenide compound [(NH4)2S] as the inorganic ligand. We showed the energy level movement of CZTSe films before and after ID-8 ligand exchange. Using cyclic voltammetry (CV) measurements, we found that the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO)

energy levels of CZTSe films shifted down after ligand exchange. Utilizing energy level alignment at the CdS/CZTSe interface, we constructed an energy level diagram to explain the physical mechanism of reducing recombination in CZTSe solar cells. This provides a different approach to the design of the absorption layer, which is generally not afforded by previous reports applying interface see more passivation and the control of trap states, focuses on the problem of recombination, and holds for a more convenient way to optimize interface properties. Methods Cupric(II) acetylacetonate [Cu(acac)2], zinc(II) acetylacetonate [Zn(acac)2], tin(IV) chloride tetrahydrate (SnCl4 · 4H2O), 2,4-pentanedione, triethylamine, perchlorethylene 1-dodecanethiol (DDT), and oleylamine (OLA) were purchased from Alfa Aesar (Ward Hill, MA, USA). Tetrabutylammonium hexafluorophosphate (TBAPF6) and sodium hydroxide (NaOH) were purchased from Aldrich (St. Louis, MO, USA). Toluene, N,N-dimethylformamide (DMF), and ethanol are of analytical grade. All water used was obtained from a Millipore Milli-Q purification system (Darmstadt, Germany). The chemicals were used in an as-received condition without further purification.

More recently, the triplet state of electron donors in photosynthesis became amenable to investigation (van Gastel 2009). In this state, the HOMO and the

LUMO coefficients of the electron donor are obtained, revealing the distribution of the MO from which the electron leaves the cofactor (LUMO) and the MO which will accept the electron in the eventual charge recombination event. The relation between the light-induced reactions and the orbitals mentioned are discussed elsewhere in this issue (Carbonera 2009). Electronic structure from EPR and NMR Information from the hyperfine and the G-tensors Advanced methods, XAV-939 solubility dmso such as solid-state NMR (Alia et al. 2009; Matysik et al. 2009), pulsed EPR (van Gastel 2009), and ENDOR (Kulik and Lubitz 2009), yield magnetic resonance parameters

with high accuracy. To link these parameters to the electronic structure, quantum chemistry is used, and in many cases further method development in this area was driven by the desire to interpret magnetic resonance parameters. To describe the development in the interpretation of magnetic resonance parameters is beyond PD-1/PD-L1 inhibitor drugs the scope of this account, but as above we will illustrate the essence using the nitroxide spin labels. Their π-electron system comprises only two atoms, the nitrogen and the oxygen atom, substantially simplifying the discussion compared to a molecule such as the chlorophyll, for example. Hyperfine interaction

The spin-density distribution can be obtained from the hyperfine interaction of the unpaired electron with the nitrogen nuclear spin (I = 1). The interaction gives click here rise to the three lines separated by A zz in Fig. 2. Overlap of the N and O pz-orbitals results in the doubly occupied π-orbital and the singly occupied π*-orbital (MO scheme, Fig. 3). The energy of the N versus the O pz-orbital determines the magnitude of the MO coefficient on N, and thereby the hyperfine coupling of N. If the polarity in the vicinity of the NO group increases, the energy of the pz-orbital on oxygen will decrease relative to the energy of the nitrogen pz-orbital. As a result, the π*-orbital will have a larger N character or, in other words, the MO coefficient on N will be larger, resulting in a larger nitrogen hyperfine coupling. Fig. 3 Top: Schematic representation of the frontier orbitals of the nitroxide group. Left: pz-type orbital on nitrogen; right: pz- and non-bonding (n-) orbitals on oxygen. Polarity changes in the environment will shift the energy of the nitrogen pz relative to the oxygen pz-orbital, selleck kinase inhibitor shifting spin density from nitrogen to oxygen. The spin density at nitrogen determines the electron-nitrogen hyperfine splitting, which therefore is a measure for polarity.

Participants were instructed to maintain their habitual dietary and fluid intake prior to both the familiarisation and experimental trials. All participants were provided with a food diary to record food and fluids consumed 24 hours prior to entering the laboratory, and in order to replicate dietary

intake for subsequent trials. Participants were also instructed to abstain from alcohol and caffeine for 24 hours prior to all visits and none were known to be consuming any prescription medications, or other ergogenic substances that may have affected energy transfer [22]. Participants EPZ004777 supplier were instructed to maintain the same training frequency, volume and intensity at the initiation of the study for the duration of the investigation, but to refrain from exercise during the 24 hours prior to entering the laboratory. Experimental protocol The study followed a randomised, double blind crossover design. Initial testing consisted of an assessment of maximal oxygen uptake (VO2max) and maximal power output (Wmax) utilizing an incremental cycle GSK1838705A mw test to exhaustion.

Participants then returned to the laboratory on a further four occasions (7–10 days apart) to complete firstly a familiarisation and subsequently the experimental trials. All trials consisted of a 90 https://www.selleckchem.com/products/mi-503.html minute (min) cycle task at 50% Wmax followed by a 5 km time trial. Participants arrived at the laboratory approximately 12 hours post prandial and all testing was initiated at 0900 to minimize any influence of circadian variation. All procedures were conducted at sea level in a thermo-neutral laboratory environment (temperature:

21.0 ± 1.2°C; humidity: 40 ± 6 %; barometric pressure: 761 ± 8 mmHg). Maximal oxygen consumption & maximal power output assessment During their initial visit to the laboratory, body mass (SECA digital weighing scales, SECA, Birmingham, UK) and height (Holtain stadiometer, Holtain, Crymych, Dyfed) were recorded prior to testing along with each participant’s desired ergometer orientation, which was replicated during subsequent visits. VO2max and Wmax were determined utilizing a step-incremented protocol to exhaustion on G protein-coupled receptor kinase an electromagnetically braked cycle ergometer (Lode Sport Excalibur, Lode B.V. Medical Technology, Groningen, Netherlands) and following the methods of Currell and Jeukendrup [23]. Briefly, the protocol consisted of a three minute warm-up at 95 W proceeded by an increase of 35 W every three minutes until fatigue with the ergometer set in cadence independent (hyperbolic) mode [23]. Pulmonary oxygen uptake (VO2), carbon dioxide production (VCO2) and respiratory exchange ratio (RER) were determined continuously during exercise via an automated metabolic gas analyzer (Cortex Metalyzer 3B-R2, Cortex Biophysic, Leipzig, Germany). The modular gas analyzers were calibrated with gases of known concentrations (17.05% O2, 4.98% CO2, Cranlea, Birmingham, UK) and ambient air.

41 ± 0.77 1.47 ± 0.28 25 ± 6 38 ± 9 GP 111 ± 62 95 ± 49 1.03 ± 0.57 1.25 ± 0.23 26 ± 9 38 ± 11 COT 129 ± 71 121 ± 78 1.10 ± 0.88 1.27 ± 0.23 24 ± 5 35 ± 9 Values are expressed as mean ± SD; GC= creatine ABT-263 chemical structure supplemented athletes; GP= placebo (malthodextrin) AZD2014 datasheet supplemented athletes;

COT= non-supplemented control athletes. A significant 61% increase on the post-training mean value of uric acid was found for GC, when compared to GP and COT (7.4 ±1.6 mg/dL, 6.7 ± 2.3 mg/dL and 6.7 ± 1.2 mg/dL, respectively; p = 0.025), whereas no differences were seen for TBARS. Nevertheless, TAS values were significantly reduced for GC, in comparison to GP or COT (0.60 ± 0.19 mmol/L, 0.75 ± 0.22 mmol/L and 0.87 ± 0.42 mmol/L, respectively; p = 0.001). Furthermore, GC showed a significant 46% decrease Foretinib molecular weight for TAS, when comparing pre- and post-supplementation time (1.11 ± 0.34 mmol/L for pre- vs. 0.60 ± 0.19 mmol/L for post-supplementation time; p=0.025). Table 5 Effect of creatine supplementation and resistance training on oxidative stress markers Group Uric Acid (mg/dl) TBARS (ng/dl) TAS (mmol/l)   Pre Post Pre

Post Pre Post GC 4.6 ± 1.0 7.4 ± 1.6 a 216 ± 79 271 ± 92 1.11 ± 0.34 0.60 ± 0.19 b GP 4.4 ± 1.1 6.7 ± 2.3 209 ± 104 255 ± 77 0.91 ± 0.28 0.75 ± 0.22 COT 5.1 ± 0.9 6.7 ± 1.2 211 ± 96 264 ± 109 0.89 ± 0.15 0.87 ± 0.42 Values are expressed as mean ± SD; GC= creatine supplemented athletes; GP= placebo (malthodextrin) supplemented athletes; COT= non-supplemented control athletes; TBARS= Thiobarbituric Acid Reactive Substances; TAS= Total Antioxidant Status; a P value = 0.025 vs. Pre; b P value = 0.001 vs. Pre. Additionally, the differences between post- and pre-supplementation values were calculated and revealed that GC group displayed significant higher levels than GP and COT of uric acid (2.77 ±1.70 mg/dL, 2.26 ± 2.38 mg/dL and 1.00 ± 1.03 mg/dL, respectively; p = 0.0276) and strength (8.30 ± 2.26 kg, 5.29 ± 3.77 kg, and 5.29 ± 2.36 kg, respectively; p = 0,0209), and lower levels of TAS (−0.51 ± 0.36

Fludarabine nmr mmol/L, -0.11 ± 0.37 mmol/L and −0.02 ± 0.50 mmol/L, respectively; p = 0.0268). On the other hand, no differences were found for TBARS (Table 6). Table 6 Differences (post- vs. pre-training) on oxidative stress markers and strength Group Uric Acid (mg/dl) TBARS (ng/dl) TAS (mmol/l) Strength (kg) GC 2.77 ± 1.70 a 55 ± 98 −0.51 ± 0.36 b,c 8.30 ± 2,26 d,e GP 2.26 ± 2.38 40 ± 118 −0.11 ± 0.37 5.29 ± 3.77 COT 1.00 ± 1.03 48 ± 130 −0.02 ± 0.50 5.29 ± 2.36 Values are expressed as mean ± SD; GC= creatine supplemented athletes; GP= placebo (malthodextrin) supplemented athletes; COT= non-supplemented control athletes; TBARS= Thiobarbituric Acid Reactive Substances; TAS= Total Antioxidant Status; a P value = 0.0276 vs.

CrossRef 14. Keskin S, Culha M: Label-free detection of proteins from dried-suspended droplets using surface enhanced Raman scattering. Analyst 2012, 137:2651–2657.CrossRef 15. Zhou W, Hu A, Ying SB, Ma Y, Su Q: Surface-enhanced Raman spectra of medicines with large-scale self-assembled silver nanoparticle films based on the BI 10773 cost modified coffee ring effect. Nanoscale Res Lett 2014, 9:87.CrossRef 16. Campion A, Kambhampati P: Surface-enhanced Raman scattering. Chem Soc Rev 1998, 27:241–250.CrossRef

17. Naja G, Bouvrette P, Hrapovic S, Luong JHT: Raman-based detection of bacteria using silver nanoparticles conjugated with antibodies. Analyst 2007, 132:679–686.CrossRef 18. Huang X, El-Sayed IH, Qian W, El-Sayed MA: Cancer cells assemble and align gold nanorods conjugated to antibodies to produce highly enhanced, sharp, and polarized Inhibitor Library research buy surface Raman spectra: a potential cancer diagnostic marker. Nano Lett 2007, 7:1591–1597.CrossRef 19. Liu TY, Tsai KT, Wang HH, Chen Y, Chen YH, Chao YC, Chang HH, Lin CH, Wang JK, Wang YL: Functionalized arrays of Raman-enhancing nanoparticles for capture and culture-free analysis of bacteria in human blood. Nat Commun 2011, 2:538.CrossRef 20. Khoshmanesh K, Nahavandi S, Baratchi S, Mitchell A, Kalantar-zadeh K: Dielectrophoretic platforms for bio-microfluidic systems. Biosens Bioelectron 2011, 26:1800–1814.CrossRef

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L, Zhang Y: Theoretical simulation on the assembly of carbon nanotubes between electrodes by AC dielectrophoresis. Nanoscale Res Lett 2009, 4:157–164.CrossRef 24. Chung CC, Cheng IF, Chen HM, Kan HC, Yang WH, Chang HC: Screening of the antibiotic susceptibility to β-lactam-induced elongation of Gram-negative bacteria based on dielectrophoresis. Anal Chem 2012, 84:3347–3354.CrossRef 25. Thamida SK, Chang HC: Nonlinear electrokinetic ejection and entrainment due to polarization at nearly insulated wedges. Phys Fluids Temsirolimus concentration 2002, 14:4315–4328.CrossRef 26. Blanca HLE, Rafael VD, Blake AS, Eric BC, Yolanda F: An insulator-based (electrodeless) dielectrophoretic concentrator for microbes in water. J Microbiol Methods 2005, 62:317–326.CrossRef 27. Basuray S, Chang HC: Induced dipoles and dielectrophoresis of nano-colloids in electrolytes. Phys Rev E 2007, 75:60501.CrossRef 28. Honegger T, Lecarme O, Berton K, Peyrade D: 4-D dielectrophoretic handling of Janus particles in a microfluidic chip. Microelectronic Engineering 2010, 87:756–759.CrossRef 29. Velev OD, Gangwal S, Petsev DN: Particle-localized AC and DC manipulation and electrokinetics. Annu Rep Prog Chem, Sect C 2009, 105:213–246.CrossRef 30.

Nonattached cells were removed by PBS washings for three times. Attached cells were analyzed by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium

(MTS; Promega, Madison, WI) assay according to the user manual. The mean absorbance values for statistical analysis represent the average of three independent experiments. Western blot analysis learn more Whole-cell lysates of EC9706 cells were prepared by incubating cells in RIPA buffer (1% NP-40; 0.5% sodium deoxycholate; 0.1% SDS; 50 mM Tris-HCl [pH 7.5]) containing protease inhibitors. Cell lysates were centrifuged at 10,000 g for 10 minutes at 4°C. The supernatant was collected, and the protein concentration was measured using the BCA ™ Protein Selonsertib Assay Kit (Pierce). Proteins (40 ug) in cell lysates or culture media were separated by 10-15% SDS-polyacrylamide gel electrophoresis and transferred onto PVDF membrane. The membranes were blocked in TBST (0.2 M NaCl; 10 mM Tris pH7.4; 0.2% Tween20)/5% skim milk for 2 hours at room temperature and then incubated with primary antibodies in TBST/5% skim milk. The primary antibodies used for Western blot analysis were polyclonal rabbit anti-ECRG4 (1:2000) [8], polyclonal rabbit anti-p21(1:4000; Santa Cruz, CA),

polyclonal rabbit anti-p53 (1:4000; Santa Cruz, CA), and monoclonal mouse anti-β-actin (1:4000; Santa Cruz, CA). The membranes were then washed three times with TBST, followed by incubation with horseradish peroxidase-conjugated secondary antibody (1:4000) in TBST/5% skim milk. Bound antibody was visualized using ECL detection reagent. RT-PCR analysis Cells were

washed with PBS and collected for RT-PCR. The primers designed for ECRG4 were 5′-GGT TCT CCC TCG CAG CAC CT-3′ as forward and 5′-CAG CGT GTG GCA AGT CAT GGT TAG-3′ as reverse. Thermal find more cycles were: at 95°C for 2 min, then 30 cycles at 95°C for 30 sec, at 62°C for 30 sec, at 72°C for 1 min followed by extension at 72°C for 7 min [7]. Flow cytometric analysis of cell cycle The transfected cells (pcDNA3.1-ECRG4 and pcDNA3.1) were seeded at a density of 106 cells/100-mm dish in RPMI-1640 medium with 10% FBS for 48 hours. Then cells were washed with ice-cold PBS, harvested and next fixed in 70% ethanol for 30 minutes. Cells were treated with RNase A and stained with 25 μg/ml propidium iodide (PI). Samples were analyzed using a FACScan flow cytometer (Becton Dickinson), according to the manufacturer’s protocol. Experiments were performed three times in triplicate. The mean values for statistical analysis represent the average of three independent experiments. Statistical analysis All statistical analysis was performed with the SPSS statistical program (version 13.0). Statistical significance was determined using Student’s t -tests and analysis of variance. P < 0.05 was considered statistically significant. Results ECRG4 overexpression suppressed cell migration and invasion The stable-transfected EC9706/pcDNA3.

Copper content went up

after treatment by copper nanoparticles in roots (by 94%); however, in selleck compound leaves, it Dorsomorphin ic50 decreased (by 38%). The content of manganese increased (by 30%) in leaves of treated plants and remained at control level in the roots. Figure 1 Content of metal elements in wheat seedling tissues after treatment with individual metal nanoparticles. 1 – roots, control; 2 – roots, experiment; 3 leaves, control; 4 – leaves, experiment. Thus, the results indicate the ability of metal nanoparticles to penetrate through the seed coat. The distribution of elements in plant tissues is determined by their ability to penetrate and peculiarities of transporting in the plant. Concerning the mechanism of processes, we could assume that nanoparticles with diameter less than the pore diameter of the cell wall could easily pass through and reach the plasma membrane [9]. After entering the cells, the nanoparticles transport from one cell to another through plasmadesmata. Major cell wall components are carbohydrates which are linked to form a rigid complex network and proteins [10]. The functional groups, such as carboxylate, phosphate, hydroxyl, amine, sulfhydryl, and imidazole, contained in these biomolecules offer a range of distinct active sites [11]. We investigated both the

mixtures of nanoparticle solutions and the way of their application (pre-sowing treatment and spraying of aboveground plant parts) impact upon metal contents in plant roots and leaves (aboveground parts) (Figures 2,3,4 and 5). selleck screening library Figure 2 Content of iron in wheat seedling tissues. Iron content in tissues after treatment of seeds (a) and leaves (b) with the mixture of metal nanoparticles: 1 – roots, control; 2 – roots, experiment; 3 – leaves, control; 4 – leaves, experiment. Figure 3 Content of copper in wheat seedling tissues. Copper content in tissues after treatment of seeds (a) and leaves (b) with the mixture of metal nanoparticles: 1 – roots, control; 2 – roots, experiment;

3 – leaves, control; 4 – leaves, experiment. Figure 4 Content of manganese in wheat seedling tissues. Manganese Coproporphyrinogen III oxidase content in tissues after treatment of seeds (a) and leaves (b) with the mixture of metal nanoparticles: 1 – roots, control; 2 – roots, experiment; 3 – leaves, control; 4 – leaves, experiment. Figure 5 Content of zinc in wheat seedling tissues. Zinc content in tissues after treatment of seeds (a) and leaves (b) with the mixture of metal nanoparticles: 1 – roots, control; 2 – roots, experiment; 3 – leaves, control; 4 – leaves. After seed treatment with a mixture of metal nanoparticles with subsequent determination of the content of certain metals in the leaves and roots, we found that the iron content decreased in the roots (44%) and in the leaves (27%), copper content decreased in the roots (17.5%) while in the leaves increased by 12.

001 peptidyl-Asp metallopetidases is underlined; (4) the three conserved histidines (aa 167, 171, and 177), residues for zinc binding, and glutamate (aa 168), the catalytic residue, are in green; (5) two carbohydrate binding modules of the CBM_4_9 family, aa 302 to aa 432 and aa 461 to aa 586, in blue. (B) The P. aeruginosa predicted PA2783 is homologous to metalloendopeptidases from other bacteria. Interrogation of the non-redundant databases at NCBI (http://​www.​ncbi.​nlm.​nih.​gov/​; AG-881 solubility dmso accessed 10/18/2013) was done using BLASTP and the selleck inhibitor Peptidase Database MEROPS (http://​merops.​sanger.​ac.​uk/​index.​shtml; accessed 10/18/2013) was done

using BLAST. Identical aa are shown in red, similar aa in blue, and non-similar 3-Methyladenine cell line aa in black. PA2783 is homologous to the Pseudomonas mendocina ymp (Pmendo) carbohydrate-binding CenC domain-containing protein and the Ni,Fe-hydrogenase I small subunit of Hahella chejuensis KCTC 2396 (Hcheju) across the entire endopeptidase domain. Other proteins contain the HEXXHXXGXXH motif only (highlighted by a yellow box). Amacle, Alteromonas macleodii; Ahydro,

Aeromonas hydrophila; Vchole, Vibrio cholerae; Vmimic, V. mimicus; Vvulni, V. vulnificus; Xfraga, Xanthomonas fragariae; Xcampe, X. campestris; Xvesic, X. vesicatoria. Percentages of aa identity and similarity may be found in Additional file 2. PA2782 encodes a putative 22.7-kDa protein of 219 aa that contains no specific motifs, except for the presence of an alanine-rich region Pregnenolone within

its amino terminus (23 of the first 60 aa), and that has no functional homology with other known proteins (data not shown). Characterization of PA2783, a putative metalloendopeptidase The predicted protein PA2783 contains all the features of a potential endopeptidase including the putative glutamic acid catalytic residue and the three zinc-binding histidine residues within its amino terminus (Figure 5A) [39]. We tried to assess the proteolytic activity produced by PA2783 using dialyzed brain heart infusion skim milk agar. However, this approach proved unfeasible due to the production by P. aeruginosa of several proteases with strong proteolytic activities. Both PAO1/pUCP19 and PAO1/pAB2 produced identical clearing zones of protease activity (data not shown). We faced the same problem when we utilized strain PAO-R1 (Table 1), which produces a considerably reduced level of proteolytic activity due to the mutation of lasR[33]. Despite the reduction in the extracellular proteolytic activity of this strain, PAO-R1/pUCP19 and PAO-R1/pAB2 produced identical clearing zones on skim milk agar (data not shown). As an alternative, we assessed the potential proteolytic activity of PA2783 using the E. coli strain DH5α (Table 1).

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The 2,026 human persistent strains and 1,018 avian strains were grouped by time, location and subtype, with representative samples chosen at random to yield 281 distinct human strains and 560 distinct avian strains. Classifier AZD3965 in vivo accuracy was estimated by randomly dividing BVD-523 supplier the data set into 5 non-overlapping partitions. The classifier was trained on 4 of the partitions and accuracy was measured by the percentage of correct classifications on the fifth partition, with the percentage of correct classifications calculated separately for each

class to account for the difference in class size. The average of all 5 tested non-overlapping partitions was calculated giving two accuracy values (one for each class) and the final accuracy measure was the average of these two values. The 34 pandemic conserved markers given in this report were required to be positively identified in every sequenced strain in each of the three pandemic outbreaks without deviation from the majority consensus. This led to three markers reported in [11] that were excluded from this report for lack of conservation or positive identification (when an ambiguous sequence code was present) in one of the sequenced strains associated with the pandemic outbreaks. The host specificity classifier misclassified 2 human and 2 avian strains for a classification accuracy of 99.5%. The

classification errors appeared to be due to recent reassortment events that suggest the presence of influenza genomes that are a mix of both human and avian strains [29]. The high 3-deazaneplanocin A mortality rate data set was constructed using the same procedure as the host type dataset and the same 5-fold cross validation procedure was used to estimate accuracy. A total of 111 influenza Ponatinib in vitro genomes were classified as high-mortality rate strains and 2,001 were classified as low-mortality rate strains, with a non-redundant subset taken for training (35 high mortality rate, and 255 low mortality rate). The percentage of high

and low mortality rate strains that were correctly classified was 96.2% and 96.9% respectively (an average of 96.6%). The lower accuracy for the high mortality rate classifier compared to the host type classifier likely highlights the genetic complexity associated with high mortality rate and the influence of other important factors such as host interaction. Newly generated classifiers using only a small subset of the aligned proteomes as input were required to match the original classifier accuracy (99.5% for host type and 96.6% for high mortality rate type) within a margin of error defined by a confidence threshold. The confidence thresholds were defined by confidence intervals assuming 1 sided t-test comparisons using the standard deviation in the cross validation tests.