Thermal treatment of the sorbents and liquid TEPA at 100 °C for 12 h changed their color from white to yellow. The CO2 capture capacity of the TEPA/SiO2 sorbents  decreased by more than 60 %. IR and NMR spectroscopy studies showed that the yellow color of the degraded sorbents resulted from the formation of imide species. The imide species, consisting of NH associated with two C—O functional groups, were produced from the oxidation of methylene groups in TEPA. Imide species on the degraded sorbent are not capable of binding CO2 due to its weak basicity. The addition of P200 and P600 to the supported amine sorbents improved both their CO2 capture capacities and oxidative degradation resistance. IR spectroscopy results also showed that TEPA was immobilized on the SiO2 surface through hydrogen bonding between amine groups and the silanol groups of SiO2. The OH groups of PEG interact with NH2/NH of TEPA through hydrogen bonding. Hydrogen bonds disperse TEPA on SiO2 and block O2 from accessing TEPA for oxidation. Oxidative degradation resistance and CO2 capture capacity of the supported amine sorbents can be optimized through adjusting the ratio of hydroxyl to amine groups in the TEPA/PEG mixture.Oxidative degradation characteristics of silica-supported amine sorbents with varying amounts of tetraethylenepentamine (TEPA) and polyethylene glycol (PEG; P The use of commercially available aqueous amine technology for capturing CO2 from flue gas could result in more than an 85 % increase in the cost of electricity.2 in the flue gas. The solid amine sorbents could potentially decrease the cost of CO2 capture by 1) reducing the energy needed for sorbent regeneration, 2) avoiding equipment corrosion, and 3) increasing the rate of CO2 adsorption/absorption and desorption.CO2 capture capacity of 2.0 mmol per gram of sorbent to provide a performance comparable to that of a large-scale liquid amine process.2, using aminosilanes, and 2) physical adsorption of amines on the support surface.2 adsorption sites that can be easily regenerated.Solid amine sorbents should possess a minimum CO2 to the highly dispersed amines relative to limited access of O2 in liquid amines.The major concern associated with amine-based sorbents is that the amines gradually degrade during thermal regeneration.2 at 105 °C led to conversion of amine to form urea and the presence of moisture in the CO2 gas stream slowed down the degradation.2 slowed down the formation of a thermally degraded product—carboxylate species—during CO2 adsorption in the presence of air and desorption in an argon gas environment.2 capture capacity of the amine-based sorbents2 capture.2 on the sorbent. A better understanding of the oxidative degradation process and the effect of PEG on the process could help in the development of more effective sorbents. In the present work, we used IR and NMR spectroscopy to study the structure of tetraethylenepentamine (TEPA) and PEG immobilized on SiO2 and to determine the oxidative degradation products. TEPA, which is a small molecule with a well-defined structure, serves as an excellent model compound to study the interaction of amines with the OH (surface silanol) groups of silica, the OH groups of PEG, and O2 in an oxidative environment. The results presented herein showed that oxidative degradation of the sorbent and liquid amines resulted from oxidation of methylene groups of TEPA to form imide/amide. The presence of PEG in the sorbents not only increased the CO2 capture capacity, but also slowed down the degradation of SiO2-supported amine sorbents in the oxidative  environment. The results of this study show that the degradation of SiO2-supported amine strongly depends on its environment. A degradation study should be carried out in an oxygen-containing environment to emulate the practical conditions of CO2 capture processes.Oxidative degradation products of liquid amines, such as ethanolamines and ethylenediamines, have been identified as aldehydes and carboxylic acids.2 and NH), the number of moles of hydroxyl groups from PEG present on each sorbent, and the sorbent color changes observed after the oxidative degradation studies. The extent of the color change may reflect the degree of degradation of the sorbent. All of the fresh sorbents were initially white. After oxidative degradation, the TEPA/SiO2 (TS 25/75) sorbent became intense yellow, whereas the PEG/SiO2 (P200S 25/75 or P600S 25/75) sorbents remained white. The change in the color of the TPS (TEPA/PEG/SiO2) sorbents varied from white to intense yellow, as shown in Table 200 showed less color change than P600. This observation could be attributed to the higher ratio of OH to amine on P200  than P600  sorbents. The sorbents with ethylene glycol (E) and glycerol (G) changed color to intense yellow and brown, respectively. Treating liquid TEPA, PEG, and TEPA/PEG at 100 °C for 12 h also resulted in color changes and followed the same trend as those observed on TP200S and TP600S sorbents. These results showed that the presence of PEG in TS sorbents and in liquid samples slowed down the color change.An oxidative degradation study on silica-supported amine sorbents and TEPA/PEG liquid samples was carried out at 100 °C for 12 h. Table 2 flowing at 50 cm3min−1 for 10 min over the sorbents at 25 °C. The CO2 capture capacity of the sorbents was measured by determining the change in weight of the sorbents before and after saturation. Table 2 capture capacities and corresponding amine efficiencies  of the fresh and degraded TS 25/75, TP200S, TP600S, TGS, and TES sorbents; sorbents with E and G ], and in the presence of water one mole of CO2 reacts with one mole of amine to form bicarbonate ,2 adsorption in this study was carried out in the absence of H2O vapor, it is expected that CO2 is adsorbed in the form of carbamate on two amine sites, as shown in Equation (1). The maximum achievable amine efficiency would be 0.5. Recent studies suggested that the presence of PEG in solid amine sorbents increased the CO2 capture capacity by changing the rate of CO2 adsorption/desorption.2 capture results reported herein showed that the addition of PEG increased the CO2 capture capacities and the amine efficiencies of the sorbents; this is in good agreement with the literature.2 surface by hydrogen bonding through primary amines and hydroxyl groups with the surface silanol groups, as shown in Scheme 200S 5/5/90 would occupy 30 % of the SiO2 surface (specific surface area 160 m2 g−1) and TP200S 15/15/70 would occupy 90 %. Increasing the TEPA and PEG loading beyond this value resulted in 100 % monolayer coverage and eliminated the isolated OH band at 3750 cm−1  values of 0.37 and 0.27 for TP200S 25/25/50 and TP600S 25/25/50, respectively  showed that the degradation was fractional per cycle and TS 25/75 reached its steady state after 10 h, whereas TP200S sorbents reached a steady state after 24 h of oxidative degradation. These results suggest that the presence of PEG in the sorbents prevent the degradation of amines on the sorbents.Table 2, fresh and degraded TS 25/75  sorbents, and urea obtained by DRIFTS at 100 °C. Urea is included for comparison purposes. Pure SiO2 exhibited isolated O—H stretching at 3750 cm−1, hydrogen-bonded O—H at 3660 cm−1, hydrogen-bonded water at 2700–3600 cm−1, and a water bending vibration at 1630 cm−1.2 followed by drying produced the IR spectra of TS 25/75, which showed symmetric and asymmetric NH2 stretching vibrations at 3290 and 3360 cm−1, NH2 deformation of primary amines at 1601 cm−1, CH2 stretching vibrations at 2931 and 2810 cm−1, and bending at 1458 cm−1. The absence of the broad water band at 2700–3600 cm−1 indicates that the impregnation process displaced H2O from the SiO2 surface; the absence of the isolated O—H band at 3750 cm−1 suggests that these surface OH groups are bound with N in NH/NH2 functional groups. The IR band assignments in this study are presented in Table Figure 2 stream,2 with amines was not only associated with production of C—O, observed at 1670 cm−1, but should also give a peak at 1410 cm−1 for C—N, which was not observed in degraded sorbents of the present study. Oxidative degradation decreased the intensity of the CH2 bands more than the NH2 stretching bands. A decrease in the intensities of these bands was accompanied by the emergence of a prominent C—O band at 1670 cm−1 and a broad N—H band at 3290 cm−1. These two bands coincide with the IR spectra of an imide species consisting of NH associated with two C—O groups. Assignment of these IR bands for the yellowish degraded sorbents to imide is further supported by the yellow color of polyimide species.−1, overlapping with the imide C—O band.2 bands at 3360 and 3290 cm−1 became a broad band centered at 3290 cm−1, indicating the occurrence of oxidation of these primary amine species. The spectra of the degraded sorbent also showed an increase in the intensity of the isolated OH at 3750 cm−1. The above IR observations suggested that TEPA on SiO2 was degraded through 1) oxidation of both C—H next to the secondary amines (N—H) and 2) oxidation of primary amines (NH2) to nitrite species. The latter appears to occur at a lesser extent than the former because the decrease in NH intensity is significantly less than C—H intensity. The NH group of the imide or amide and nitrite species is known to be a significantly weaker base than the primary amine functional group in TEPA. The weak basicity of these spices prevents them from adsorbing CO2. These amide/imide species are not expected to interact with isolated OH at 3750 cm−1.Degraded TS 25/75 sorbent showed 60 % less capacity than that of the fresh sorbent. The spectra of these degraded sorbents exhibited bands that were significantly different from those of urea Figure . Althoug200S sorbents, exhibiting the characteristic bands of TEPA and TP200 on SiO2. A comparison of the IR spectra of TS 25/75 and TP200S sorbents shows that the addition of P200 broadens the bands in the 3000–3600 cm−1 region. The amine stretching bands on TP200S sorbents are not as distinct as those observed on TS 25/75 sorbents. The emergence of a small hump at 3160 cm−1 for TP200S 20/20/60 suggests the formation of hydrogen bonding between the amines and OH groups of PEG. This vague band on the SiO2-supported sorbent became prominent in the mixture of TEPA and PEG  due to evaporation. The presence of hydrogen bonding between amine and hydroxyl groups could change the reactivity of the amines toward oxygen molecules. In addition, PEG served as an oxygen-barrier enhancer, reflecting its low permeability to O2.Figure 13C NMR spectra of fresh and degraded TS 25/75 and TP200S sorbents. All of the sorbents exhibited a signal at δ=111.9 ppm due to the background of the system and does not represent any functional group in the sample. Carbon bonded to a primary or secondary amine appeared at δ=46.6 and 39.7 ppm,2O in PEG resulted in the signals at δ=74.7 and 71.0 ppm.δ=61.8 ppm in the TP200 sorbents. The spectra of the fresh sorbents did not show any signals of carbamate,−1 for 30 min) prior to NMR spectroscopy analysis to remove CO2 and water adsorbed from the atmosphere. Both degraded sorbents of TS and TP200S showed an increase in the intensity of the signals at δ=164.6 and 160 ppm assigned to the formation of C—O imides/amides due to the degradation of amines. These C—O signals are not likely to be associated with urea,−1 due to prolonged scanning to get a better signal-to-noise ratio. The increase in the intensities of CH2NH at δ=44.6 and 39.7 ppm in the degraded sorbents needs further investigation. The carbon signal associated with nitrite was not observed in the NMR spectra. The 13C NMR spectroscopy results confirm those of the DRIFT and ATR studies, which supported the proposal that amines were degraded by oxidation of CH2 groups in TEPA to form imide/amide C—O groups at 100 °C in air.Figure Oxidative degradation of liquid monoethanolamine and ethylenediamines at 140 °C produced dealkylation products and small amounts of carboxylic acids.2. Hydroxyl groups of polyethylene glycol (PEG) are also expected to interact with the surface hydroxyl groups. These interactions are evidenced by the decrease in the intensity of isolated OH groups with increasing TEPA/PEG loading. Oxidative degradation of TEPA in liquids and on SiO2 supports occurs through oxidation of methylene groups in TEPA to C—O, converting an amine species into imide/amide species, which leads to a color change to yellow. The weak basicity of the N—H group in imide/amide species prevents them from interacting with CO2 and lowers the CO2 capture capacities. The addition of PEG improved the CO2 capture capacity and oxidative resistance. The positive effect of PEG was attributed to the formation of hydrogen bonds between NH2 groups of TEPA and OH groups of PEG. These hydrogen-bonding interactions between amines and OH groups increased the dispersion of TEPA and slowed down the oxidation of the amine to imide species by blocking the amine sites from access to oxygen. P200 was more effective at slowing down the oxidative degradation of the amines than P600 due to more OH groups. The extent of degradation, color change, and decrease in the CO2 capture capacity of the sorbents are in agreement with each other.The results of this study summarize in Scheme 200) and 600 (P600), ethylene glycol (E), and glycerol (G), obtained from Aldrich Chemicals, USA. Silica (Brunauer–Emmett–Teller surface area 160 m2 g−1) was used as a support. Sorbents with varying weight ratios of TEPA/PEG/SiO2 (TPS) were prepared by impregnating 10 cm3 of TEPA/ethanol solution onto SiO2 followed by impregnating 10 cm3 of PEG/ethanol solution onto TEPA/SiO2. For example, the TP200S 5/5/90 sorbent was prepared by dissolving TEPA (0.25 g) or P200 (0.25 g) in ethanol to make a 10 cm3 solution for impregnation over SiO2 (4.5 g). The sorbents were heated at 100 °C for 15–20 min to evaporate the excess ethanol after each impregnation. TEPA/PEG liquid samples with varying compositions were prepared by mixing pure TEPA and PEG. For example, the compositions of the liquid sample TP200 30/70 was obtained by mixing TEPA (0.3 g) and P200 (0.7 g).Preparation of TPS sorbents: The specific chemicals used in the present study are tetraethylenepentamine (TEPA: T), poly(ethylene glycol) (PEG) with molecular weights 200 (P2 capture and oxidative degradation: CO2 capture measurements for the solid sorbents were measured as follows: 1) pretreatment: sorbents were heated at 100 °C for 7 min to remove CO2 and H2O pre-adsorbed from the atmosphere; 2) CO2 adsorption: CO2 (50 cm3 min−1) was flowed for 10 min over the sorbents at 25 °C for CO2 adsorption; and 3) regeneration: sorbents were heated at 100 °C for 10 min for the removal of adsorbed CO2. The CO2 capture capacity of the sorbent was determined by the weight change before and after CO2 saturation. Short adsorption and desorption periods were used to determine the working capacity of the sorbent. The working capacity determined herein reflects the amount of captured CO2 in a 20 min thermal swing cycle. After regeneration the sorbents were subjected to oxidative degradation by heating the sorbent at 100 °C for 12 h in air. After oxidative degradation treatments the sorbents were described as degraded samples. The CO2 capture capacity of the degraded sorbents was measured in a similar way to the fresh sorbents. The liquid samples were also subjected to the same treatment to produce degraded liquid samples.CO−1) at 100 °C for 10 min to remove preadsorbed water and CO2 from the atmosphere. The IR study of the fresh and degraded liquid samples  was performed using ATR by coating a 0.1 mm liquid layer on the ZnSe  window. The single beam spectra of both solid and liquid samples were collected by 32 co-added scans and a resolution of 4 cm−1. The absorbance spectrum was obtained by the equation Abs=log(1/I), in which I is the single beam spectrum of interest. Solid-state magic-angle spinning (MAS) 13C NMR spectra of the fresh and degraded sorbents were collected by using multiple-pulse direct polarization (DP) experiments at 400 MHz . Sorbents (≍50 mg) were packed in a Zirconia pencil rotor and spun at magic angle at 10 kHz. A relaxation time of 8 s was applied for single-pulse experiments to allow thermal equilibrium and at least 1536 scans were utilized to achieve a proper signal-to-noise ratio. Prior to NMR spectroscopy analysis the sorbents were degassed at 100 °C to remove any adsorbed CO2 or water from the atmosphere.IR and NMR spectral analysis: The IR study of the fresh and degraded solid sorbents was performed ex situ by placing 50 mg of the sorbent in a DRIFT (Harrick) cell on a Nicolet-6700 FTIR spectrometer. The IR spectra of the sorbents were collected after pretreatment in flowing argon (150 cc min


O-β-D-glucopyranosyl-D-glucose) in solution has been determined via neutron diffraction with isotopic substitution (NDIS), computer modeling and nuclear magnetic resonance (NMR) spectroscopic studies. This study shows direct evidence for an intramolecular hydrogen bond between the reducing ring HO3 hydroxyl group and the non-reducing ring oxygen (O5′) that has been previously predicted by computation and NMR analysis. Moreover, this work shows that hydrogen bonding to the non-reducing ring O5′ oxygen is shared between water and the HO3 hydroxyl group with an average of 50% occupancy by each hydrogen-bond donor. The glycosidic torsion angles Hφ and Hψ from the neutron diffraction-based model show a fairly tight distribution of angles around approximately 22° and −40°, respectively, in solution, consistent with the NMR measurements. Similarly, the hydroxymethyl torsional angles for both reducing and non-reducing rings are broadly consistent with the NMR measurements in this study, as well as with those from previous measurements for cellobiose in solution.The structure of the disaccharide cellulose subunit cellobiose (4- Conversion of plant cellulose into ethanol has been industrially achievable since the late Nineteenth Century. However, the near-insolubility of cellulose in aqueous solvents initially required physical separation of pure cellulose from plant material and harsh acid hydrolysis to produce the glucose used in fermentation.Unlike higher cello-oligosaccharides, cellobiose with two glucose subunits exhibits considerable solubility in water, making it an ideal model molecule for investigations in solution. Cellobiose and methyl cellobioside in aqueous solutions have been studied extensively by NMR spectroscopy where various measures of coupling constantsDespite these extensive studies, there is still debate about the structure of cellobiose in solution–specifically with respect to its conformation about the glycosidic linkage as well as its hydrogen-bonding structure. There is a particular question concerning whether or not an internal hydrogen bond between the non-reducing ring oxygen (O5′) and the adjacent reducing ring hydroxyl group HO3; see [17]–[19]In this work, NDIS augmented by computer simulation has been used in combination with NMR spectroscopy to determine the structure of cellobiose in aqueous solution. NDIS can measure the hydrogen bonding of molecules in aqueous solutions on the atomic-length scale and has been one of the premier techniques for structural determinations of hydrogen-containing liquids due to the ‘sensitivity’ of the neutron to hydrogen and deuterium. NDIS also has the advantage of being a direct structural technique, analogous to crystallography, which does not rely on structural interpretation of dynamical data as is the case with spectroscopy. By combining neutron scattering and computation with NMR spectroscopy, which provides an assessment of the cellobiose conformations, a rigorous structural assessment of cellobiose hydrogen bonding in water can be realized.unique yet chemically equivalent samples yielding multiple distinct measurements of the same chemical system.NDIS has been used to investigate the structure of hydrogen-containing liquids such as anhydrous HF,F(Q),ic and ib are the relative concentration and scattering length of atom i,αβδ is the Kroneker delta function introduced to avoid double counting of like-atom pairs, and αβS(Q) is the partial structure factor for the α–β atom pair. Q is the change in magnitude of the incident wave vector of the neutrons when they are scattered from the sample where Q = 4πsin(θ)/λ with 2θ being the scattering angle relative to the incident neutron beam and λ the incident neutron wavelength.The quantity measured in a neutron diffraction experiment is the total scattering structure factor, m(m+1)/2 for m number of structurally unique atoms in the system. The Fourier transform of αβS(Q) gives the distribution of atomic separations (distances) in real space on an atomic scale (Å) as a radial distribution function (RDF)ρ is the atomic number density of the sample and αβg(r) is the RDF for the α–β atom pair.Eq. 1 describes the sum of all the partial structure factors, for which there are β around atoms α, 1r to 2r, namelyTo characterize the average local structure of a liquid, the RDF for an atom pair can be integrated yielding the average coordination number of atoms α at the origin (1r = 0) to the distance of the first minimum (2r) after the first obvious maximum in the RDF.The coordination number is usually calculated from atom With NDIS it is only possible to extract all of the RDFs experimentally from systems with a small number of unique atoms such as HFempirical potentials in the Monte Carlo simulation. This refinement process proceeds iteratively and results in a model that is consistent with a set of measured diffraction data. It should be noted that, while EPSR provides a model consistent with the data, it is not necessarily a definitive model of the systems in question. As is true with any modeling technique which determines the structure present in a liquid, as much knowledge as possible about the solution must be introduced into the EPSR model such as charges, molecular structures and overlap restrictions (as is done in the present case), in order to provide a physically realistic model of the physical structure. RDFs can be calculated for each unique atom pair from the resulting model. Additionally, spherical harmonic expansion of the calculated RDFs can be performed to generate spatial density functions (SDFs) that describe the location of molecules or portions of molecules relative to one another in three dimensions. A more detailed description of EPSR is given elsewhere,The EPSR method begins with a standard Monte Carlo simulation of the sample structure based on a set of reference potentials. EPSR perturbs these initial reference potentials thus creating new potentials whose magnitudes are proportional to the difference between the measured total structure factors and those calculated from the model. EPSR then uses these new d-glucopyranosyl-(1→4)-d-glucopyranose, 99%, Sigma–Aldrich) and ultra-pure water (Milipore) or from a sample of cellobiose previously lyophilized from deuterium oxide (D2O)  and fresh D2O . Seven isotopically unique cellobiose–water solutions were studied; the isotopic composition of each sample is listed in the Supporting Information Neutron diffraction measurements were performed using the Small Angle Neutron Diffractometer for Amorphous and Liquid Samples (SANDALS) located at the ISIS facility  on an isotopomeric series of cellobiose–water solutions each at a molecular ratio of 1∶63 cellobiose:water (∼0.88 M) at standard ambient temperature and pressure . Isotopic H/D substitution was performed on the cellobiose hydroxyl groups and on the water solvent. Samples were prepared from cellobiose (β-F(Q) after appropriate corrections for neutron absorption, multiple scattering, and inelasticity effects using the program GUDRUN, which is based on the ATLAS suite of programs available at the ISIS facility.The solutions were prepared by weight and transferred to sample cans constructed from a Ti/Zr alloy with a flat plate geometry and 1 mm sample thickness. Ti/Zr cans give very little scattering, thus simplifying data analysis, due to cancellation from the positive and negative coherent neutron scattering lengths of zirconium and titanium, respectively.1H NMR spectroscopy and two-dimensional (2D) NMR correlation spectroscopy were performed using a Varian Inova spectrometer operating at 600 MHz for 1H and 150 MHz for 13C. The instrument was equipped with a triple-channel cryoprobe and z-axis gradients. 1D 13C NMR spectroscopy was performed using a Bruker AMX spectrometer operating at 100 MHz for 13C and equipped with a broadband X-channel inverse geometry probe. The sample temperature was regulated at 298±0.5 K for all measurements. 1H and 13C resonances were interpreted according to Roslund et al.NMR studies were conducted to probe the molecular conformation of cellobiose in aqueous solution. One-dimensional (1D) 13C spectroscopy. Samples of cellobiose lyophilized from D2O were prepared in fresh D2O at concentrations of 15 mM and 0.88 M. These two concentrations were chosen to represent a “typical” NMR measurement (15 mM) and the concentration measured by NDIS (0.88 M), thus allowing a direct comparison of results from the two methods. 13C NMR spectra were acquired using inverse-gated 1H decoupling over six hours  to obtain signal-to-noise ratios exceeding 30∶1, thus allowing for accurate integration of the signals arising from the anomeric carbons. Anomeric ratios of ∼40∶60 α-:β-cellobiose were observed at both concentrations.Cellobiose exists in two anomeric forms in aqueous solution at an equilibrium ratio of 38∶62 α-:β-cellobiose .[54] Pos1H, 13C gHMBC (J-mod gHMBC) experiments were performed to determine the magnitudes of the interglycosidic coupling constants 3JH1’,C4 and 3JH4,C1'. The J-mod gHMBC experiment,nJH,Xτπ) where τ represents a variable mixing time for polarization transfer. Performing a series of J-mod gHMBC experiments with varied mixing times and integrating the 1D projections of the cross-peaks of interest from each spectrum produces a set of intensity values as a function of τ that can be fit by non-linear regression to extract the value of nJH,X. J-mod gHMBC spectra of 15 mM and 0.88 M cellobisoe in D2O were recorded for τ values ranging from 20–200 ms. Coupling constants derived by non-linear regression were related to the torsion angles Hφ (H1′–C1′–O4–C4) and Hψ (C1′–O4–C4–H4), illustrated in J-coupling-modulated 2OH) rotamer populations were made using the method reported by Serianni and co-workers3JH5,H6R and 3JH5,H6S. This method assumes that the measured coupling constants are weighted averages resulting from the population of three preferred conformations of the ω dihedral angle . The following Karplus-type relationships for 3JH5,H6R and 3JH5,H6S are solved for each of the preferred conformations, and the calculated coupling constants are used to factor out the contributions of the preferred conformations to the measured, average coupling constant (Eq. 's 5 and 6).Determinations of the cellobiose hydroxymethyl  and 1260 water molecules for a 1∶63 cellobiose:water molecular ratio at a density of 0.103018 atoms ÅThe EPSR reference potentials used were Single Point Charge/Extended (SPC/E) model for water molecules (Ow and Hw),H ψHφ and ω('), are illustrated in Hφ and Hψ obtained via crystallography,Flexibility in cellobiose primarily arises from rotations about the β-(1→4) glycosidic linkage and the orientation of the exocyclic hydroxymethyl groups about the C5(')–C6(') bonds. The torsion angles related to these flexibilities, Hφ and Hψ , gauche–gauche  and trans–gauche  due to the orientations of O5 and C4 relative to O6. Previous NMR studies of glucose,gt≈Pgg>Ptg in aqueous solution, which is opposite to the Ptg conformation in the crystal structure of cellobiosetg >> Pgg≈Pgt distribution observed for native cellulose polymorphs.gt, gg and tg conformers compared with current NMR measurements and previous investigations of glucoseCellobiose also has preferred hydroxymethyl orientations (ω(′); tg rotamer being the least populated and the gg and gt rotamers showing proportionally higher populations. This is consistent with prior NDIS/MD investigations of glucose in aqueous solution where a predominance of the gt and gg conformations were also observed.gg and gt rotamers where specifically the relative concentrations from EPSR are Pgt>Pgg>>Ptg compared with Pgg>Pgt>>Ptg from NMR measurements for ω and the order Pgg>Pgt>>Ptg for ω′ compared with Pgt>Pgg>>Ptg observed by NMR spectroscopy. For ω, the higher population of gt compared with gg is evident in The EPSR rotamer populations are in broad agreement with those from NMR studies, with the tg rotamer being slightly more populated in the EPSR model, as Pgg is similar from both the NMR and EPSR (tg rotamer for the ω′ torsion in The variance between the NMR and the neutron diffraction data in ω′ appears to be due to the and EPSR . HoweverHydrogen-bonding interactions between cellobiose hydroxyl groups and water are easily distinguished via the RDFs from the hydroxyl group–water pairs . The RDFH(')-Owg(r) from 0–2.46 Å shows an average of ∼0.8 hydrogen bonds being donated from cellobiose to water. Similar integration of O(')-Hwg(r) over the same distance range (0–2.46 Å) gives OH bands and water υOH shifts in infrared spectra, it has been suggested that water is both a donor and acceptor of hydrogen bonds from the hydroxyl group.The cellobiose hydroxyl groups show similar hydration with respect to both oxygen and hydrogen atoms with the exception of the HO3 group where fewer hydrogen bonds are donated to water (O4-Hwg(r) (0–2.46 Å) gives ∼0.6–0.7 hydrogen bonds, and similar integration of O5'Hw(r)g gives roughly the same number of bonds. This is similar to previous studies that have shown similar under-hydration of X–O–X (X ≠ H) when compared to hydroxyl, carbonyl or carboxylate groups in water.The glycosidic linkage oxygen atom–water RDFs for both α-cellobiose and β-cellobiose oxygens –Hw and O5(′)–Ow atom pairs  for the distributions of water around O5 and O5′ were calculated from the EPSR modeling box. SDFs  give theyz-plane and show that the distribution of waters around the O5′  curve (Eq. 3) in location of HO3 atoms around O5′ with the O5′ on the central axis, where in this figure the yellow shell represents the most probable 25% of HO3 locations around O5′–HO3 at a distance of 1.5–3.0 Å—corresponding to the minimum and maximum distance in the RDF peak from xy plane in a distribution consistent with rotations about the C3–O3 bond and conformational flexibility about the glycosidic linkage. Analogous to yz plane (yz plane  clearly yz plane  thus conin solution has been determined that most notably gives firm evidence of the intramolecular hydrogen bond between the non-reducing ring O5′ and the reducing ring HO3 group. The existence of this O5′···HO3 hydrogen bond has been previously predicted by NMR spectroscopic studies on methyl α-cellobioside3 group (fixed respectively in the β- or α- position on C1) instead of an -OH on the C1 carbon atom, which is free to mutarotate Click here for additional data file.


The amyloid hypothesis proposes that Aβ plaques precede tangle formation but there is still much controversy concerning the order of events and the linkage between Aβ and tau alterations is still unknown. Mathematical modelling has become an essential tool for generating and evaluating hypotheses involving complex systems. We have therefore used this approach to discover the most probable pathway linking Aβ and tau. The model supports a complex pathway linking Aβ and tau via GSK3β, p53, and oxidative stress. Importantly, the pathway contains a cycle with multiple points of entry. It is this property of the pathway which enables the model to be consistent with both the amyloid hypothesis for familial AD and a more complex pathway for sporadic forms.Alzheimer's disease (AD) is characterised by the aggregation of two quite different proteins, namely, amyloid-beta (A The connection between these events is still not clear although it has been proposed that the formation of plaques precedes the appearance of tangles which in turn precedes cell death [β may be a better correlate of disease than the insoluble plaques [β is a damage response protein [β and tau is linear for early-onset AD but hypothesize that a dual pathway links the two in late-onset disease [β and tau aggregates. One important candidate is glycogen synthase kinase-3β (GSK3β). It is well established that GSK3β activity leads to hyperphosphorylation of tau and there is also evidence that it accounts for increased production of Aβ [β provides the link between Aβ and tau [β behaves like an antagonist of insulin and prevents activation of Akt [β which inhibits its activity; Aβ therefore indirectly increases the activity of GSK3β. There is also a link between p53 and GSK3β and we recently modelled this to show that this interaction might explain the link between protein aggregation and neuronal loss in AD [β overactivity leads to an increase in levels of Aβ plaques and tau tangles by independent processes supporting the idea of a dual pathway.Alzheimer's disease (AD) is characterised by the presence of extracellular amyloid-beta  removal. By doing so it is possible to test whether there is a linear or a dual pathway. If the pathway is linear, then the model should predict that increasing clearance of Aβ will also reduce the formation of tau tangles  conditions, both p53 and Mdm2 are kept at low basal levels. However, when cells are stressed and DNA damage occurs, p53 is phosphorylated and is then unable to bind to Mdm2 and so is no longer degraded. Therefore p53 levels increase. In addition, phosphorylation of p53 increases its activity. Full details of this module are already published [β and so we assume that GSK3β activity is low when cells are not stressed.We previously built a stochastic dynamic model of p53 regulation  which wa and tau . The mod and tau . SBML al and tau . The extublished . Under nβ, and the phosphorylation/dephosphorylation and aggregation of tau. In addition we assume that Aβ results in increased generation of ROS and increased transcription of p53. The full details of the model are available in an open access journal and the SBML code is available from Biomodels (BioModels ID:BIOMD0000000286)[The model also includes reactions for the production, clearance and aggregation of A00000286). The sim00000286)–21. The β at levels so that if there was an increase in tau phosphorylation or an increase in Aβ production, the formation of aggregates would appear within 2 or 3 days. In reality, the aggregation process is likely to have much longer lag periods. Acceleration of the aggregation process in our computer model is merely a device to increase the throughput of simulations. With normal rates of Aβ clearance, our model predicts that a small percentage of cells do not accumulate any plaques or tangles by 12 days  leads to a much lower level of Aβ so that no plaques form and there are also much lower levels of tau tangles and p53 . β bound to p53, Aβ plaques, tau tangles, and damaged DNA over a 12-day period. It can be seen that with the exception of p53, the levels of the all species shown are close to zero. So the model predicts that this intervention completely prevents the increase in DNA damage, the elevation of p53, the increase in GSK3β activity, and the formation of plaques and tangles producing results similar to increased clearance of Aβ at day 0 (see To confirm whether the increase in p53 is due to Aay 0 see .β/p53 binding on the aggregation process we inhibited the interaction between GSK3β and p53 (by setting the parameter for GSK3β/p53 binding to zero). We ran 100 simulations with increased clearance of Aβ on day 8 (with ROS production via Aβ restored). This additional intervention also prevented the formation of plaques and tangles even though p53 levels rose during the simulation  for each simulation run. For each simulation the level of p53 was tracked over time, starting at time zero. If the level of p53 exceeded the chosen threshold, the time at which this occurred was recorded and the simulated cell was considered to have undergone cell death at this time. The percentage of viable cells at each time point was calculated for each of the intervention times and plotted . The modβ reduces levels of ROS, which leads to less DNA damage, lower p53 activity, lower GSK3β activity, and reduced tau phosphorylation. If Aβ clearance is increased at early time points, there is a decrease in plaques and also a reduction in tau tangles. The model therefore does not support a dual pathway , more detail of tau regulation, the insulin pathway, and wnt signalling pathways. It would be of particular interest to include the insulin signalling pathway in order to explore the connection between AD and type 2 diabetes since GSK3β has been implicated in both diseases. Mitochondria also play an important role in the disease process. For example, damaged mitochondria may accumulate in postmitotic neurons and cause an increase in ROS which could start the vicious cycle shown in β binds to Aβ-binding alcohol dehydrogenase (ABAD) which leads to increase ROS via mitochondrial dysfunction [β act cooperatively to impair mitochondrial function and reduce mitochondrial transport in neurons [The model is a simplification of the system but as the model is encoded in SBML, it can easily be extended to include further details. Other important components which could be added are chaperones (GSK3function , 26. Rec neurons . A modelβ immunotherapy works by either active immunization with Aβ aggregates or by passive transfer of anti-Aβ antibodies. Both approaches have been shown to prevent Aβ deposition and to clear already existing plaques. Wilcock et al. showed two phases in the clearance of plaques [β and since these may be toxic due to their interaction with mitochondria and their involvement in ROS production, our model may show that such an intervention would be less beneficial than the increased clearance of soluble Aβ. Therefore, the model could prove very useful for testing the consequences of different interventions.A plaques . First,  plaques . Our curβ and tau via GSK3β, p53, and oxidative stress. Importantly, the pathway contains a cycle with multiple points of entry. It is this property of the pathway which enables the model to be consistent with both the amyloid hypothesis for familial AD and a more complex pathway for sporadic forms.Our mathematical model supports a complex pathway linking A


Plant fossil record suggests that plant adaptation to changing atmospheric CO2 involved correlated evolution of stomata density (d) and size (s), and related maximal aperture, maxa. We interpreted the fossil record of s and d correlated evolution during the Phanerozoic to quantify impacts on gas conductance affecting plant transpiration, E, and CO2 uptake, A, independently, and consequently, on plant WUE. A shift in stomata configuration from large s-low d to small s-high d in response to decreasing atmospheric CO2 resulted in large changes in plant gas exchange characteristics. The relationships between gas conductance, wsg, A and E and maximal relative transpiring leaf area, (maxa⋅d), exhibited hysteretic-like behavior. The new WUE trend derived from independent estimates of A and E differs from established WUE-CO2 trends for atmospheric CO2 concentrations exceeding 1,200 ppm. In contrast with a nearly-linear decrease in WUE with decreasing CO2 obtained by standard methods, the newly estimated WUE trend exhibits remarkably stable values for an extended geologic period during which atmospheric CO2 dropped from 3,500 to 1,200 ppm. Pending additional tests, the findings may affect projected impacts of increased atmospheric CO2 on components of the global hydrological cycle.Plant gas exchange is a key process shaping global hydrological and carbon cycles and is often characterized by plant water use efficiency (WUE - the ratio of CO The intertwined control of CO2 uptake and water loss obscures the primary driving process, prompting a century-long debate concerning the primary regulating factor Stomata and impervious leaf cuticle have originated some 400 Myr ago as crucial adaptations enabling plant proliferation in water-limited terrestrial habitats wg [mol m−2 s−1], considering diffusion through an ensemble of non-interacting individual pores wg, and CO2 conductance, cgA widely-used approach for the evaluation of plant WUE in 2 and water vapor in air. The gas fluxes based on Fickian diffusion are expressed as ip and ap are the leaf interior and atmospheric partial pressures of CO2; and similarly ie and ae are the leaf interior and atmospheric water vapor pressures, with Δe = (ae–ie) often expressed in units of [mol gas mol−1 air]. For low respiration rates relative to A, ap.The factor 1.6 is the ratio of binary diffusivities of COessed as 4)with with 2 an2 that deviates from the simple linear expression in Noting that information concerning plant stomatal configuration cancels out in 2 concentrations (2)CO decreased by one order of magnitude from 4,000 ppm to present day levels d [mm−2], and size , s [µm2] s, estimated by multiplying guard cell length by the width of guard cell pair maxa, approximated as s and d is in line with theoretical and experimental evidence demonstrating that stomata maxa and d jointly determine gas diffusive resistance, a nonlinear function of these two variables During the 400 million years (Myr) of plant evolution (the Phanerozoic eon), atmospheric COA and E to variations in atmospheric CO2 during the Phanerozoic. The primary objectives of this study were to: (i) analyze the impact of independent estimates of A and E on historical trends in plant WUE relative to the coupled estimates based on 2CO during the Phanerozoic is deduced from the fossil record of s [µm2] and d [mm−2] evolution 2.We hypothesize that improved description of diffusive resistances by consideration of specific stomatal configurations deduced from plant fossil record could provide new insights on the independent responses of 2CO time series for the Phanerozoic provided by Berner and Kothvala 2CO during plant evolutionwgevidence suggests that fluctuations in solar radiation and air temperature during the Phanerozoic were relatively minor, with the estimated mean air temperature varying only between 15°C and 23°C pE, during plant evolution remained relatively moderate as noted by Sperry pEpE would vary by 15%. For simplicity, we assumed a constant pE value of 7,500 [µmol m−2 s−1]  [−], deduced from average values of s and d for each time interval determines the maximal relative leaf transpiring area, (maxa⋅d) [−], and thus strongly influences plant gas exchange. The resulting (s⋅d) evolution during the Phanerozoic is depicted in s⋅d) characterize both the beginning of the eon with high 2CO , and present day values with low 2CO (∼350 ppm). Plant adaptation to decreasing 2CO may be reflected by increasing (s⋅d) from initial  to mid-range 2CO levels , followed by decreasing (s⋅d) to present day values s⋅d) variation during the Phanerozoic is depicted in the inset in 2CO between 1500 to 2000 ppm) the trend in (s⋅d) was reversed, and decreased with decreasing 2CO. Statistical analysis including fitting a general additive model (GAM) showed that the trend (solid curve) was highly significant.Fossil record data on the evolution of s and d individually as functions of 2CO (Eqs. S3 and S4 in s⋅d) computed according to their regression equations (Eqs. S3 and S4) is depicted by the dashed line in s and d vs. 2CO that yield a monotonously increasing (s⋅d) with 2CO decrease  to a present day small s-high d configuration  trajectory (as depicted in The correlated evolution of plant stomata guration . The ranmaxa⋅d), is proportional to (s⋅d)  that represents the potential evaporation, pE. Several diffusion models have been proposed to quantify these resistances E, across a diffusion barrier perforated by identical pores of radius r relative to a potential evaporation rate from uncovered free water surface (under similar conditions), pE. The first diffusion resistance term, R1, is defined as:l is the thickness of the diffusion barrier or membrane [mm], approximated in the case where it represents the stomatal depth by k, the ratio of water vapor diffusivity to air molar volume; 2]; and d, the pore density [mm−2]. In terms of conductance, wsgmaxa is the stomata maximum aperture. The second diffusive resistance term, 2R, represents the effect of interactions between neighboring pores or stomata, and it is defined in the model of Bange r is the pore radius [L] and h, half mean distance between pores or stomata [L], approximated by 2R proposed by Cooke max⋅da) values of plant leaves (results not shown).Diffusion theory predicts formation of a three-dimensional water vapor concentration field over individual stomata as depicted in R3, is expressed byFinally, the third resistance term, ressed bywhere δ iThe resulting relative transpiration rate, Zebrina pendula leaves  measured by Bange a⋅d). The corresponding boundary layer thickness δ was set to 7.5 mm, well within the range of viscous sub-layer thicknesses expected for air flows according to the Blasius relationship δ∼2v∞−1/2 with mean air velocity v∞ in [m/s], and δ in [mm] 2 and R3. The results demonstrate that the resistance R3 due to the boundary layer is the most important component of overall resistance to gas diffusion. An important aspect from plant gas exchange point of view, is that consideration of stomatal resistance only, 1R (a⋅d) values exceeding 0.001. Neglecting the contribution of interactions between neighboring stomata expressed by R2 resulted in overestimation of gas fluxes by 65% for low (a⋅d) values down to 15% for higher (a⋅d) values. This suggests that including stomatal interactions systematically reduces gas fluxes with significant effects for certain a and d combinations, hence such interactions cannot be generally disregarded as suggested by Waggoner and Zetlich The expression in only, R1  does notpE remained constant as discussed above). For estimation of absolute values of E, we have used a constant pE = 7,500 [µmol m−2 s−1] equivalent to mean daily potential evaporation rate of 3 mm d−1 and boundary layer thickness δ = 2.0 mm that corresponds to mean wind speed of 1 m/s, in accordance with the assumptions in Beerling et al. pE related to variations in mean air temperature (results not shown).The model in 2 assimilation rate, A [µmol m−2 s−1], is more difficult to estimate than E, due to additional assumptions concerning uncertain biochemical processes. We considered two different methods for estimation of A as a function of 2CO during the Phanerozoic. Both methods rely on the widely-used photosynthesis model of Farquhar et al. A is expressed as:1a [µmol m−2 s−1] and 2a [µmol mol−1] whose values depend on light or Rubisco limitations; pc [µmol mol−1] is the CO2 compensation point, and ic the intercellular CO2 concentration. For light-saturated conditions, 1a represents the maximum carboxylation capacity, and 2a accounts for both carboxylation rate and the impact of air oxygen concentration on CO2 fixation Katul et al.A is expressed in terms of leaf conductance to CO2, cg, and atmospheric CO2 concentration, ac:Plant CO2 uptake and water vapor loss , Franks and Beerling cmaxV as a function of gas diffusion conductance wg:wg is expressed in [mmol m−2 s−1]. The second method for estimating A was based on Beerling and Woodward tance gw:where gw A for the Phanerozoic, we considered A estimates from the reconstruction of Franks and Beerling A-2CO relationships was obtained for the following parameter values: RV = 120 µmol m−2 s−1; κ = 2.8 10−4 ppm−1; α = 82 µmol m−2 s−1; β = 0.974. The A-2CO relationships resulting from the two methods applied are depicted in 2 concentrations, along with the estimates of A as reported in figure 7A (symbols) of Franks and Beerling A estimates is similar to that described in that figure in Franks and Beerling Within the unavoidable uncertainties of reconstructing plant A and E (2CO in A estimation described above). The resulting WUE-2CO trends were compared with the conventional approach . This trend lends support to an alternate interpretation whereby regulation of water losses and not improving CO2 uptake could have served as the primary driver for plant gas exchange evolution, especially during a period where plants have gradually adapted to life in dryer terrestrial environments At first glance, the interpretation of the conventional nearly-linear decreasing WUE with declining E, was estimated based on max–da configurations at each 2CO level corresponding to the s-d patterns in E-2CO relationships are depicted in E values deduced from the ratio of WUE and A values (represented by the symbols in E-2CO relationship for the skewed (s⋅d)-2CO function based on Franks and Beerling E with decreasing 2CO. In contrast, the E-2CO relationship based on s⋅d)-2CO line in 2CO changes during the Phanerozoic. The initial steep increase of E is associated with increasing (s⋅d) as 2CO decreased early in the Phanerozoic  with decreasing 2CO for the rising limb of the skewed (s⋅d)-2CO relationship -2CO relationships exerted only a small impact on the E-2CO trend. The largest influence on plant gas exchange seems to be associated with the early increase in (s⋅d) reflecting a gradual shift from large s-low d to small s-high d configuration .The results in guration , that coA for 2CO concentrations below 1,200 ppm  as seen in A and E suggest that total resistances for plant CO2 uptake and water loss are different. Part of this difference are attributed to additional biochemical/photosynthetic resistance that affect CO2 uptake but not water loss, and to other factors not explored here such as mesophyll resistance A may be different and decoupled from the response in plant E. Additional factors involving asymmetry in diffusion fields for water vapor and CO2 have been discussed by Parkhurst We note that in contrast with the steep decrease in ,200 ppm , E remais and d evolution on maximal conductance to water vapor, wsg [mol m−2 s−1], was estimated based on maxa and d values corresponding to the s and d values in Eqs. S1 and S2, and those of Franks and Beerling wsg as a function of (maxa⋅d) variations during the Phanerozoic is depicted in wsg-(maxa⋅d) reflects the parabolic (s⋅d)-2CO relationship (wsg-(maxa⋅d) relation resulting from the monotonous (s⋅d)-2CO relationship (max-ahigh d resulted in higher wsg for similar (maxa⋅d) values. The two trends in max-ahigh d configuration not only enabled lower diffusive gas resistance with lower (maxa⋅d), it also reduced gas exchange sensitivity to leaf evaporating area (as evidenced by maintenance of nearly constant transpiration rates). It may have also contributed to improved leaf thermal management The impact of 2 assimilation rates (Eq. 12) as functions of leaf partial evaporative area (maxa⋅d) for the parabolic (s⋅d)-2CO relationships estimated in this study (wsg-(maxa⋅d) (E-(maxa⋅d) and A-(maxa⋅d) (E-(maxa⋅d) and A-(maxa⋅d) differ significantly after the hysteretic apex. Following the joint initial increase in E and A with increasing plant (maxa⋅d) (see maxa⋅d) resulted in a virtually stable E whereas A exhibited a consistent decrease. The decrease in (maxa⋅d) was accompanied by the selection for smaller and denser stomata (E remained constant as (maxa⋅d) decreased to present day values. These improvements in diffusive gas exchange were insufficient to compensate for the decrease in 2CO, hence A continually decreased with decreasing (maxa⋅d).(amax⋅d)  is refle(amax⋅d) . Surprisx⋅d) see  and 6, a stomata , which r stomata . Consequs and d data from plant fossil record spanning the Phanerozoic eon to investigate effects of stomatal configuration on plant CO2 uptake, A, on transpiration, E, rates, and on plant water use efficiency, WUE. Plant transpiration rates were estimated based on the diffusion resistance model of Bange 2 uptake rates were estimated based on the model of Farquhar et al. s⋅d)-2CO patterns resulting from different analyzes of the fossil record were considered. Stomata configuration, expressed in terms of maximal relative leaf evaporating area, (maxa⋅d), was found to affect mostly the gas exchange rates during the first phase of the Phanerozoic when large s-low d configuration prevailed. It is likely that the drastic drop in atmospheric CO2 induced a shift from large s-low d configuration to small s-high d configuration. This shift enabled lower diffusive gas resistance with lower (maxa⋅d), decreased gas exchange sensitivity to leaf evaporating area and may has also contributed to improved leaf thermal management.We have considered 2 concentration. In contrast with the linear decrease in WUE with decreasing 2CO, the estimated WUE was relatively stable during a significant part of the eon. This new trend results from decoupled response of plant A and E to the correlated evolution of s and d and their related product (maxa⋅d). We observed that the shape of A exerts a stronger influence on the WUE pattern than the shape of E. Uncertainties in past plant biochemical photosynthetic response introduces complexities into the estimation of A, and thus in estimates of WUE. Nevertheless, the new WUE trend was relatively robust using two variations in the model for A. This trend suggests that regulation of water losses could have served as the primary driver for plant gas exchange evolution, thus contributing to the invasion of dryer terrestrial environments The estimated plant WUE during the Phanerozoic reveals a significantly different trend than the conventional WUE postulating proportionality with atmospheric COAppendix S1Detailed presentation of the three different methods used to quantify the changes in stomatal density, d, and size, s2 concentrations during the Phanerozoic eon based on fossil record. with atmospheric CO These three methods led to the three curves depicted in s⋅d) evolution during the Phanerozoic.(DOCX)Click here for additional data file.


Rationale: Adoptive T cell therapy depends on the harvesting of the cells from the host, their activation in vitro, and their infusion back to the same host. The way of activating the T cells in vitro is a critical factor for their homing, survival and function in vivo. Sustaining T cell homing molecules, particularly CD62L, is benefic for the trafficking of the adoptive transferred cells.Objective: The aim of the present study is to test whether insulin–like growth factor–1 (IGF–1), thymosin– α1 (T–α1) as well as all–trans retinoid acid (ATRA) alone or in combination with IL–2, IL–12, IL–15 can enhance the activation and survival phenotypes of antigen-activated T cells in vitro.Methods & Results: To this end, OT–1 transgenic T cells were used as a model. These CD8+ T cells recognize OVA peptide presented by MHC class–I. The results showed that antigen stimulation of OT1 cells resulted in their activation as evidenced by the decrease in surface expression of CD62L, analyzed for 3 days after antigen stimulation and was more pronounced on day 5. The addition of IL–12 or IGF–1 alone but not of IL–2, IL–15 augmented OT–1 cell activation measured on day 5. Interestingly, the combination of IL–12 with IGF–1 sustained the expression of CD62L on OT1 cells. Although the addition of ATRA alone or in combination with IL–12 resulted in decreases in CD62L expression on day 3, they showed a dose–dependent effect on the restoration of CD62L expression on day 5. The analysis of the activation–induced cell death (apoptosis) of OT1 cells showed an increased rate of death on day 5 than on day 3–post antigen stimulation. The addition of only IL–12 or IGF–1 alone, but not of IL–2, IL–15 or T– α1, decreased OT1 cell apoptosis on day 3. These anti–apoptotic effects of IL–12 and IGF– 1, however, were recovered on day 5–post stimulation.Discussion: In conclusion, these results indicate that the activation phenotype and the survival of antigen–specific T cells can be differently modulated by immunomodulatory factors, where, interleukin–12 and IGF–1 induced the favorable effect. These results have a significant implication for T cell adoptive immunotherapy in different settings. Upon antigen (Ag) recognition, early effector CD8+ T cells are characterized by the expression of high levels of the lymphoid homing molecule CD62L or L–selectin. The expression of CD62L decreases together with the activation progresses, with a re–expression almost restricted to central memory cells [3]. It is believed that the enhanced anti–Ag activity associated with T cells expressing high levels of CD62L, related to their ability to home the secondary lymph nodes, where effective stimulation can take place [4]. Thus, approaches aimed at the generation and maintenance of early effector populations could be valuable in the development of effective ACT–based immunotherapies for cancer. Because cytokines are known to play fundamental roles in the differentiation and maturation of Ag reactive T cells, cytokine conditioning prior to cell transfer has been considered as a means of increasing the proliferation and function of these cells [5]. Interleukin–2 and interleukin–15  are key participants in T and NK cell activation and function [6]. Sharing the beta and gamma–receptor subunits, such as the promotion of T cell proliferation, results in several common functions. On the other hand, due to their distinct alpha–receptor subunits, they also play opposing roles in immune processes such as activation–inducing cell death [6].Adoptive cell transfer (ACT) represents an important advance in cancer immunotherapy. The success of ACT depends on the functional quality of cells transferred [7]. Of significant interest is the role of IL–12. In addition to its role in polarizing Th1 responses and in the maturation of cytotoxic function of NK cells [8], IL–12 acts as a third signal along with Ag and IL–2 on naïve CD8+ T cells, increasing the pool of effector and memory cells, two crucial components of a successful ACT approach [10]. Evidence for this has been provided in recent reports demonstrating increased anti–tumor capabilities of CD8+ T cells after ex vivo and in vitro culture in the presence of IL–12 [12].However, IL–2 and IL–15 could both induce apoptosis and survival; the outcome depends on the cytokine concentration [13].Increasing evidence indicated that insulin–like growth factor–1 (IGF–1) is involved in the function and development of the immune system. IGF–1 may alter homeostasis in the immune system by modulating lymphocyte generation and survival [In vivo treatment with IGF–1 enhanced thymic reconstitution in steroid–treated [14], aged [15], and diabetic [16] animals. Hettmer et al. [17] suggested a role for IGF binding protein as a local growth factor contributing to the proliferation and activation of mononuclear cells. The role of IGF–1 in the regulation of apoptosis has also been suggested both in vivo and in vitro [18]. The production of IGF–1 by thymic epithelial cells [19] and the increased IGF–1 receptor expression on T cells after in vitro activation with anti–CD3 antibody [20] suggested that IGF–1 may play a role in the T cell selection process.21], primed dendritic cells for antifungal T–helper type 1 resistance through Toll–like receptor signaling [22], reduced pancreatic inflammation by regulating differentiation of CD3/CD4+ T cells [23] and increased cytokine production. Vitamin A and its metabolite all transretinoic acid (ATRA) have attracted considerable attention as compounds that have a broad range of immune modulating effects on both humoral and cellular immune responses. It was demonstrated that the topical retinoids have significant anti–inflammatory effects in experimental trials [24]. While ATRA downregulated the proinflammatory cytokines, the production of immune modulating cytokines was enhanced by ATRA [25]. ATRA induced a "priming" of the immune system by increasing the number of T lymphocytes and LPS binding protein expression [26], and stimulated T cell proliferation by modulating IL–2–mediated signaling [27]. ATRA has been used as monotherapy for treatment of cutaneous T–cell lymphomas for years [28]. The combination of ATRA and IFN–gamma could become an efficacious chemoimmunotherapy for the treatment of human glioblastoma [29]. ATRA also showed potent effects on hemopoietic stem cell integrity, inhibiting the expansion of human progenitor cells and accelerating their differentiation to B lineage cells [30].Thymosin–α1 (T–α1), initially isolated from thymus, is now proved to be effective in inhibiting tumoral growth and in controlling infective diseases. Different studies evaluated its immunomodulating effects and showed that T– α1 increased major histocompatibility complex (MHC) class–1, antagonized dexamethasone–induced apoptosis of CD4+CD8+ thymocytes . In brief, one or 2 OT–1 mice were sacrificed and spleens and peripheral  and mesenteric lymph nodes were harvested and pooled. Single cell suspension was prepared in PBS by grinding these tissues between the 2 rough ends of microscopic slides. Cells were washed twice and re–suspended in RPMI medium. Unfractionated OT–1 cells (1 × 105) were cultured in triplicate in 6–well plate for 3 days in RPMI medium containing 1 μg/ml OVAp and the tested growth factors at the dose indicated in each experiment. Cells were harvested and washed twice and were freshly processed for the activation phenotype and apoptosis measured by flow cytometry.AMeasuring T cell activation by flow cytometry: Staining for flow cytometry was performed as previously described by Salem et al. [31]. Fresh cells (0.5–1 × 106) were treated with anti–CD16/CD32 for 5 minutes on ice and then stained with the indicated conjugated mAbs, and incubated for 30 min on ice. The cells were washed twice and resuspended in 0.3 ml of 0.5% BSA, 0.02% sodium azide solution. Cells were washed, and analyzed by flow cytometry using the Cell Quest software package .Measuring apoptosis by flow cytometry: To measure apoptosis, cells were stained for the surface markers as described above and cells were then washed twice with FCAS buffer and twice with Annexin–V binding buffer (Pharmingen). Cells were incubated at room temperature for 15 minutes and then washed and analyzed immediately by flow cytometry.Fig. 1).CD8+ T cells stimulated with Ag showed higher levels of CD69 and lower levels of CD62L expression, compared to the CD69 and CD62L expression on unstimulated CD8+ T cells, on day 3 indicating their simulation. Of Note, the decrease in CD62L expression after Ag stimulation was further augmented on day 5 post–stimulation. Addition of IL–2, IL–12 or IL– 15 to CD8+ T cells culture during Ag stimulation did not alter the phenotypic activation of CD8+ T cells when cells were harvested on day 3. The expression of CD62L assessed on CD8+ T cells on day 5 was much lower than on day 3. However, only CD8+ T cells that had conditioned with IL–12 showed higher levels of CD62L .Addition of IGF–1 alone or in combination with IL–2, IL–12 or IL–15 to CD8+ T cells culture during Ag stimulation did not significantly alter the expression of either CD69 or CD62L on CD8+ T cells on day 3. The expression of CD62L assessed on CD8+ T cells on day 5 was much lower than on day 3. However, only CD8+ T cells that had been conditioned with combined IGF–1 and IL–12 showed higher levels of CD62L expression. The magnitude of this increase reached about 6–folds of the corresponding values of Ag stimulated CD8+ T cells .Addition of T–α1 alone or in combination with IL–2, IL–12 or IL–15 to CD8+ T cells culture during Ag stimulation did not alter the expression of either CD69 or CD62L on CD8+ T cells on day 3. The expression of CD62L assessed on CD8+ T cells on day 5 was much lower than on day 3. However, only CD8+ T cells that had been conditioned with T–α1 and IL–12 in combination showed higher levels of CD62L expression. The magnitude of this increase reached about 3–folds of the corresponding values of Ag–stimulated CD8+ T cells .Addition of ATRA with doses 1, 5, 10 or 15 μM to CD8+ T cells culture during Ag stimulation did not alter the expression of CD62L on CD8+ T cells on day 3. The expression of CD62L assessed on CD8+ T cells on day 5 was much lower than on day 3. However, only CD8+ T cells that had been conditioned with ATRA with doses 10 and 15 μM showed higher levels of CD62L expression. The magnitude of this increase reached about 2.8– and 3–folds of the corresponding values of Ag–stimulated CD8+ T cells, respectively .Addition of ATRA with doses 1, 5, 10 or 15 μM in combination with IL–12 to CD8+ T cells culture during Ag stimulation did not alter the expression of CD62L on CD8+ T cells on day 3. The expression of CD62L assessed on CD8+ T cells on day 5 was much lower than on day 3. However, only CD8+ T cells that had been conditioned with ATRA with doses 5, 10 and 15 μM showed higher levels of CD62L expression. The magnitude of this increase reached about 2.7–, 3– and 3.7–folds of the corresponding values of Ag–stimulated CD8+ T cells, respectively .To test whether conditioning CD8+ T cells with IL–2, IL–12 or IL–15 impacts on Ag–induced T cells apoptosis, expression of Annexin V, as a marker of apoptosis, was measured by flow cytometry. It was found that stimulation of cells with Ag alone induced about 40% of the cultured cells to undergo apoptosis on day 3, which further augmented on day 5. Treatment of CD8+ T cells with only IL–12, but not IL–2 or IL–15 decreased the apoptosis level of CD8+ T cells by about 41% of the corresponding value of Ag–stimulated CD8+ T cells when measured on day 3. This anti–apoptotic effect of IL–12, however, was lost by day 5 .Treatment of CD8+ T cells with IGF–1 alone or in combination with IL–12 or IL–15, but not IL–2, decreased the percentage of apoptosis of these cells by about 51.1 and 62.9% of the corresponding value of Ag–stimulated CD8+ T cells, respectively, when measured on day 3. However, the anti–apoptotic effect of IGF–1 alone or in combination with IL–12 or IL–15 was less pronounced on day 5 .Combined treatment of CD8+ T cells with only T–α1 and IL–12, but not T–α1 alone or in combination with IL–2 or IL–15, decreased the percentage of apoptosis of these cells by about 47.5% of the corresponding value of Agstimulated CD8+ T cells when measured on day 3. However, the anti–apoptotic effect of a combined treatment with T–α1 and IL–12 was less pronounced by day 5 . Combined treatment of CD8+ T cells with ATRA  and IL–12 decreased the percentage of apoptosis of these cells by about 67.0, 70.1, 66.5 and 65.8% of the corresponding value of Ag–stimulated CD8+ T cells, respectively when measured on day 3. However, the anti–apoptotic effect of a combined treatment of ATRA with doses 1, 5, 10 and 15 μM and IL–12 was recovered by day 5 .Treatment of CD8+ T cells with ATRA (1 and 10 μM), but not 5 or 15 μg, decreased the percentage of apoptosis of these cells by about 49.2 and 44.7% of the corresponding value of Ag#x2013;stimulated CD8+ T cells, respectively when measured on day 3. However, the anti–apoptotic effect of ATRA treatment with doses 1 and 10 μg was abrogated by day 5  of OT1 cells showed that addition of only IL–12 or IGF–1 alone, but not IL–2, IL–15 or T–α1, to Ag–stimulated CD8+ T cells decreased the percentage of apoptosis of these cells.CD8+ T cells are a critical component of the cellular immune response. They play an important role in the control of viral infection and eliminating cells with malignant potential [In vitro or in vivo treatment with IL–12 resulted in enhanced activity of T and NK cells which is characterized by increased secretion of IFN–γ [31]. Such enhanced immune response is, in part, the result of the direct stimulation of antigen presenting cells. A previous study by Salem et al. [35] showed that paracrine administration of IL–12 enhanced the functional capability of spleen dendritic cells to present peptide to naïve CD8+ T cells. IL–12 can also directly affect the development of CD8+ T cell–mediated responses [32]. By signaling directly through its receptor on CD8 T cells, IL–12 influences their differentiation to favor the generation of fully activated effectors [34]. It is known that optimal clonal expansion and acquisition of effector function requires a third signal that can be provided by IL–12 and that, in the absence of third signal, tolerance can occur [10]. In support of these findings, Chang et al. [36] reported that IL–12 priming at the time of antigenic stimulation increased the primary expansion of CD8+ T cells by reducing cell death, rather that by increasing cell proliferation, resulting in a larger CD8+ T cell memory pool. The ability of IL–12 to enhance proliferation of activated CD8+ T cells has been associated with enhanced expression of IL–2Rα in response to IL–12R engagement [10]. IL–12R expression is upregulated upon T cell receptor (TCR) activation [37], however low levels of expression were reported in resting NK cells, dendritic cells and B cell lines [39]. Historically, the expression of IL–12R was confined to activated Th1 cells [41], however the pivotal role of IL–12 in providing third signal for the clonal expansion of naïve CD8+ T cells, demonstrates a similar pattern of expression on activated CD8+ T cells [10].42]. In vivo activated T lymphocytes exhibit altered expression of IGF– 1 receptor compared to the naïve T lymphocyte pool [43]. IGF–1 receptors were readily detectable on a wide variety of the immune cells, including T cells, B cells and monocytes, but the binding capacity for IGF–1 was monocytes, B cells and T cells. The level of IGF–1 receptor expression was increased several–fold after stimulation of both CD4+ and CD8+ T–cell subsets. IGF–1 is also expressed in many tumor cell lines and had a role in both normal cell proliferation and in the growth of cancers. Several approaches to target IGF–1 signaling resulted in the inhibition of the growth of a broad range of tumor cells [44]. IGF–1 was shown to protect cells from apoptosis by different stimuli and conditions [45]. It may alter homeostasis in the immune system by modulating lymphocyte generation and survival [13].IGF–1 has been implicated to play a regulatory role in T cell development and in T cell function [46]. T cell death–associated gene 51 plays an important role in the anti–apoptotic effects of IGF–I. Thus, effective strategies to generate competent CD8+ T cells for adoptive transfer should promote not only the generation of key subpopulations with superior anti–tumor activity, but also the maintenance of their viability in vivo. The present results suggest that antigen priming in the presence of both IL– 12 and IGF–1 accomplishes both ends. It not only directly modulates the expression of CD62L on activated CD8+ T cells, but also selectively promotes the survival of activated CD8+ T cells expressing high levels of CD62L. In conclusion, the present results indicate that the activation phenotype and the survival of antigen–specific T cells can be differentially modulated by different immunomodulatory factors, where, interleukin– 12 and IGF–1 induced the favorable effect. These results have a significant implication for T cell adoptive immunotherapy in different settings.Mice deficient in dendritic epidermal T cells had a notable increase in epidermal apoptosis that was abrogated by the addition of IGF–1 [


Saccharomyces cerevisiae, association of telomeres with the nuclear envelope is thought to promote TPE by increasing the local concentration of histone deacetylase complexes at chromosome ends. Importantly, our understanding of TPE stems primarily from studies that employed marker genes inserted within yeast subtelomeres. In particular, the prototrophic marker URA3 is commonly used to assay TPE by negative selection on media supplemented with 5-fluoro-orotic acid (5FOA). Recent findings suggested that decreased growth on 5FOA-containing media may not always indicate increased expression of a telomeric URA3 reporter, but can rather reflect an increase in ribonucleotide reductase (RNR) function and nucleotide metabolism. Thus, we set out to test if the 5FOA sensitivity of subtelomeric URA3-harboring cells in which we deleted various factors implicated in perinuclear telomere tethering reflects changes to TPE and/or RNR. We report that RNR inhibition restores 5FOA resistance to cells lacking RNR regulatory factors but not any of the major telomere tethering and silencing factors, including Sir2, cohibin, Mps3, Heh1, and Esc1. In addition, we find that the disruption of tethering pathways in which these factors participate increases the level of URA3 transcripts originating from the telomeric reporter gene and abrogates silencing of subtelomeric HIS3 reporter genes without altering RNR gene expression. Thus, increased 5FOA sensitivity of telomeric URA3-harboring cells deficient in telomere tethers reflects the dysregulation of TPE but not RNR. This is key to understanding relationships between telomere positioning, chromatin silencing, and lifespan.Telomeres are repetitive DNA sequences that protect the ends of linear chromosomes. Telomeres also recruit histone deacetylase complexes that can then spread along chromosome arms and repress the expression of subtelomeric genes in a process known as telomere position effect (TPE). In the budding yeast  Saccharomyces cerevisiae, telomeres mediate the recruitment of the Silent Information Regulator (SIR) complex, which is composed of the histone deacetylase Sir2 as well as the adapter proteins Sir3 and Sir4  constitutes a highly sensitive assay with a wide dynamic range for the assessment of changes to gene expression  can grow on 5FOA-containing media  and it is thought that perinuclear telomere anchoring and clustering maintains a high local concentration of SIR complexes to ensure efficient telomeric silencing , which is a competitive inhibitor of the HIS3 gene product  and negligible for heh1Δ cells, but the 5FOA sensitivity of mps3Δ75-150 heh1Δ cells is relatively higher and is closer to the sensitivity of lrs4Δ cells  were subjected to bead beating with an equal volume of silica beads and lysis buffer  for 2 × 30 s with an intermittent 2 min incubation on ice. Lysates were clarified by two consecutive rounds of centrifugation at 16,000 rcf for 5 and 15 min. Samples were sheared through a 26G1/2 needle and boiled at 95°C for 5 min prior to SDS-PAGE.Whole cell lysates were prepared as previously described . After which, 20 ml of culture was taken for RNA extraction, while the remainder of the culture was split and treated with 100X 5FOA solution to a final concentration of 1 g/l or the equivalent amount of DMSO. For HU rescue experiments, HU was added to the corresponding cell cultures to a final concentration of 10 mM.Treatment of cells were conducted as previously described  via hot phenol extraction. Cells were centrifuged and resuspended in 400 μl of AE buffer (50 mM NaOAc pH 5.3 and 10 mM EDTA in 0.1% DEPC). Forty microliters of 10% SDS and 440 μl of acidic phenol (pH 4.5) was added to each sample and incubated at 65°C for 5 min. The samples were rapidly chilled in a dry ice/EtOH bath until phenol crystals appeared. The samples were then centrifuged for 2 min at max speed at 4°C, and the upper phase was transferred to fresh tubes. One volume of phenol:chloroform (pH 4.5) was added to each sample, followed by centrifugation, and transferring of the upper phase to a fresh tube. Forty microliters of 3 M NaOAc (pH 5.3) and 2.5 volumes of cold 100% EtOH was added to each tube prior to centrifugation to precipitate the RNA. The resultant RNA pellet was washed with 2.5 volumes of cold 80% EtOH. The pellet was left to dry and then resuspended in 0.1% DEPC and quantified. Subsequently, 100 μg of the precipitated RNA was cleaned-up using RNeasy Mini Kit (Qiagen) with on-column DNase digestion. 1 μg of total RNA was treated with 1 U DNase I (Invitrogen) to further remove genomic DNA contaminations.Total RNA was prepared from logarithmically growing cells , 500 ng total RNA, 5× First-Strand Buffer (Invitrogen), 100 mM DTT, 40 U/μl RNase OUT (Invitrogen), and 200 U/μl M-MLV reverse transcriptase (Invitrogen) at 23°C for 10 min, 37°C for 60 min, and 70°C for 15 min. A 10 μl qPCR reaction using 2× Power SYBR Green PCR Master Mix (Applied Biosystems), 1 μM each forward and reverse primer, and 1 μl of cDNA prepared from the RT reaction. The primers used in this study are listed in Table The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Wheat supplied with 2 concentration. Plants exposed to subambient and ambient CO2 concentrations typically had the greatest biomass and nutrient accumulation under both N forms. In general 2 concentration.Inorganic N is available to plants from the soil as ammonium  Plants usually acquire N from the soil in the forms of ammonium  and two forms of N nutrition  2 enrichment than 2 inhibition of shoot 2. Therefore, we used the well supported Miller equation  were surface sterilized for one minute in 2.6% sodium hypochlorite solution and thoroughly rinsed with DDI water. The seeds were then rolled up in germination paper saturated with 10 mM CaSO4. The germination paper was placed in a 400 mL beaker with approximately 75 mL of 10 mM CaSO4 solution, covered with a plastic bag and placed in an incubator (23°C) for four days. Seedlings were transplanted into 20 L tubs filled with an aerated nutrient solution that contained 1 mM CaSO4, 1 mM K2HPO4, 1 mM KH2PO4, 2 mM MgSO4, and 0.2 g L−1 Fe-NaEDTA and micronutrients (20% of a modified Hoagland’s solution with either 0.2 mM KNO3 or 0.1 mM (NH4)2HPO4 as the N source, Epstein and Bloom, Wheat seeds (−2 s−1 at plant height. Plants were subjected to one of three CO2 concentrations: “subambient” (310 ± 30 ppm), “ambient” (410 ± 30 ppm), and “elevated” (720 ± 5 ppm). Subambient CO2 concentrations were maintained by passing air that entered the growth chamber through wet soda lime, a mixture of KOH, NaOH, and Ca(OH)2 that was replaced as needed. The elevated CO2 conditions were maintained in an environmental chamber equipped with non-dispersive infrared analyzers for CO2 (Horiba model APBA-250E) and valves that added pure CO2 to the incoming air stream to hold the chamber concentration at 720 ppm.The plants were grown in controlled environment chambers  set at 23/20°C day/night at 60–70% relative humidity with a photoperiod of 15 h. The photosynthetic flux density was 375 μmol m−1, and HI were collected prior to sample preparation for nutrient analysis. A portion of the grain was analyzed for phytate using a modification of the method as described by Haug and Lantzsch , kernel mass, number of heads, kernels headLantzsch . The rem2 factors were treated as fixed independent variables. We used the Tukey–Kramer Honestly Significant Difference test for mean separation. Probabilities less than 0.05 were considered significant. Because some of the transformed variables did not meet the assumption of homogeneity of variances, but one-way ANOVAs met the ANOVA assumptions, we analyzed the results via one-way ANOVAs to gain some information on the interactions between CO2 and N form.Data were analyzed using PROC MIXED . Nitrogen form and COWe used a database derived from the United Nation’s Food and Agriculture Organization (FAO)’s national food balance sheets (FBS) to estimate the average daily per capita dietary intake of zinc and phytate from 95 different food commodities in each of 176 countries. This database combines FAO data on per capita intake of food commodities with USDA data on the nutrient or phytate content of each of these commodities. More detailed discussion of the creation of this database for the International Zinc Collaborative Group may be found in Wuehler et al. . Using tTo determine the proportion of a population at risk for zinc deficiency from a hypothetical least developed country (LDC), we first calculated TDP and TDZ values for a set of 44 countries defined by the United Nations as being least developed. We took the mean TDP and TDZ values for these countries to represent a hypothetical “less developed country.” To calculate the bioavailable zinc portion  we used the Miller equation , and yields and biomass have been found to be greater under mixed N nutrition than under either T. aestivum cv. Veery 10) is a short-statured variety that has rarely been used in other studies and may have accounted for some of the differences between our study and other published data.The decrease in yield and biomass measures at elevated CO2 have been observed previously  has been found to activate Rubisco in place of Mg2+ and the Rubisco-Mn complex has been observed to decrease Rubisco carboxylase activity while minimally affecting or even enhancing oxygenase activity  or addition of nitrification inhibitors that increase the amount of available In summary, both COThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper presents new simple approaches for evaluating determinant and inverse of a matrix. The choice of pivot selection has been kept arbitrary thus they reduce the error while solving an ill conditioned system. Computation of determinant of a matrix has been made more efficient by saving unnecessary data storage and also by reducing the order of the matrix at each iteration, while dictionary notation  The term determinant is originally associated with the system of linear equations. It provides us with a foresight about the nature of solution of a given system of linear equations.Maclaurin The existence of matrix inverse also depends on its determinant. The method of finding the inverse by Gaussian method is discussed later in this paper. Ahmed & Khan A, denoted by det(A), is basically a real valued function. Because of its useful relationship with the matrix A and the solution to the system of equations of the form Ax = b, it becomes essential to have a knowledge about determinants while studying matrices. Evaluation of a determinant through its cofactor expansion  is famous for lower order Determinant of a square matrix A.Let A is opted for cofactor expansion then,If Similarly the cofactor expansion along the n, the evaluation of determinant by the above cofactor expansion requires computing n determinants of matrices of order (n−1). Therefore it can be implemented for finding determinant of a matrix of order 2 or 3 with ease but for higher orders it becomes a tedious job. To reduce the computational effort usually the following three basic row operations of matrices are incorporated to evaluate the determinant For a matrix of order A be an Let Multiply a row by a non-zero constant.Interchange two rows.Add a multiple of one row to another row.A be an Let B is the matrix that results when a single row or single column of A is multiplied by a scalar k, then det(B) = k det(A).If B is the matrix that results when a two rows or two columns of A are interchanged then det(B) = −det(A).If B is the matrix that results when a multiple of one row of A is added to another row or when a multiple of one column is added to another column, then det(B) = det(A).If A is an A) is the product of the entries on the main diagonal of the matrix; that is, If The essence of the method is to transform the given matrix into its upper/lower triangular form by applying elementary row operations. The determinant can then be computed by incorporating the properties defined above in ‘Effect of row operations’ and the lemma.n−1) applications of row operation (c) only, and can be employed to evaluate the determinants avoiding involvement of row operations (a) and (b).Approach of row reduction may involve all the elementary row operations, as illustrated in above example. Here we are defining an operation  which consists of , say Selecting a non-zero pivot element located at any arbitrary location . A repetition of the above procedure will eventually give a n non-zero pivot elements. The product of respective pivot elements gives the value of the determinant. However, if at any step there is no non-zero pivot exists, we can deduce that the determinant of the given matrix is 0.The above procedure results in a computation of determinant of a matrix with reduced order of  = d. Exit.Iteration 1:P: = {1,2,3,4}, here we take p = 1 so, L: = {1,2,3,4}. Taking k = 1Iteration 2:P: = {1,2,3}, here we take p = 1 so, L: = {2,3}. Taking k = 2Iteration 3:P: = {1,2}, here we take p = 1 so, L: = {1,2}. Taking k = 1Iteration 4:P: = {1}, here we take p = 1 so, L: = {1}. Taking k = 1The above example illustrates the efficiency of the algorithm in terms of memory storage and the number of elements computed. If a matrix of order 4 is solved by row reduction method the number of elements computed will be 12, 6 and 2 in first, second and third iterations respectively. However we have computed 9, 4 and 1 elements in the respective iterations. So the total number of element computations needed for row reduction method is 20, but our method needs only 14 element computations. Also, at each iteration the size of the matrix has been reduced. Row reduction method needs to store 16 elements for each iteration, hence on the whole method requires 48 elements to be stored in the memory, while in contrast our algorithm stores 16+9+4+1 = 30 elements, which is a noteworthy reduction in terms of storage requirement.A comparison of the number of elements computed and stored to evaluate the determinant of different orders by row reduction and our algorithm are shown in n. To evaluate the inverse of the matrix, say B, one must solve the following n system of equations, for Consider a matrix From system (1) : n) :From system (2) : A.Then the matrix I is identity matrix of order n.This procedure could be compactly preformed by expressing the above system in the augmented form,A is invertible, applying successive elementary row operations of Gaussian method yields,If B and non-basis N: (Note basic variables are shown in left most column and non basic variables in the top row of the dictionary).Now consider again the ix into basis B and jy into non-basis N,The following pivot operations Divide the pivot row by pivot element and the pivot column by negative of the pivot element (except the pivot element).n−1)2 elements are determined by the formula as defined in the new method of determinant.The remaining .Step 2: Set P: = {p:Step 3: If P = L: = {k: kxN}Otherwise, Step 4: If L = Otherwise, for any Step 5:D(A) and go to step 2.Step 6:Inv(A) = N: = {B: = {Iteration 1:H: = {1,2,3,4}, P: = {1,2,3,4}, taking p = 1 we get L: = {1,2,3,4}. Taking k = 1N: = {B: = {Iteration 2:P: = {2,3,4}, taking p = 2 we get L: = {3,4}. Taking k = 3N: = {B: = {Iteration 3:P: = {3,4}, taking p = 3 we get L: = {2,4}. Taking k = 2N: = {B: = {Iteration 4:P: = {4}, taking p = 4 we get L: = {4}. Taking k = 4N: = {B: = {B and N.Now place the elements with respect to indices of variables in H: = {1,2,3,4}, So For example Here The above example illustrates the efficiency of the algorithm in terms of memory storage and the number of elements computed. If a matrix of order 4 is solved by Gaussian method the number of elements computed will be 20 at each iteration. However our method requires computation of 16 elements at each iteration. So the total number of element computations needed for Gaussian method is 80, on the other hand for our method it is 64. Also the Gaussian method needs to store 32 elements for each iteration, hence on the whole Gaussian method requires 128 elements to be stored in the memory, while in contrast our algorithm stores 64 elements, which is a noteworthy reduction in terms of storage requirement.A comparison of the number of elements computed and stored to evaluate the inverse by Gaussian method and our algorithm are shown in p-dimensional volume of parallelepiped in Matrix determinant and inverse have applications in various fields like mathematics, economics, physics, biology etc. Solving different models like population growth involve the use of matrix determinant and inverse. Matrix inverse and determinant are also used in cryptography This paper presented easy algorithms for computations of determinant and inverse of a matrix. Since the order of the given matrix has been reduced at each step while calculating its determinant, the algorithm reduces the storage requirement (as exhibited in the example). The calculation of inverse has been done using the dictionary notation which obviates the use of permutations and makes it easier to cope with in class room teaching. Ill conditioned system can also be handled as the selection of pivots has been kept arbitrary, thus improving the numerical accuracy of the system.


Max of 2.72 nmols/min/mg and Km 23 µM. This activity is also evident in lysates from neuronal cell lines overexpressing αS. This activity is dependent on copper bound to αS as a cofactor and NADH as an electron donor. Overexpression of α-synuclein by cells significantly increases the percentage of iron (II) in cells. The common disease mutations associated with increased susceptibility to PD show differences in activity or iron (II) levels. This discovery may well provide new therapeutic targets for PD and Lewy body dementias.α-synuclein (αS) is a cellular protein mostly known for the association of its aggregated forms with a variety of diseases that include Parkinson's disease and Dementia with Lewy Bodies. While the role of αS in disease is well documented there is currently no agreement on the physiological function of the normal isoform of the protein. Here we provide strong evidence that αS is a cellular ferrireductase, responsible for reducing iron (III) to bio available iron (II). The recombinant form of the protein has a V The 140 residue presynaptic protein α-synuclein (αS) has been implicated in the pathogenesis of several neurodegenerative disorders including Parkinson's disease (PD), Lewy body variant Alzheimer's disease and dementia with Lewy bodies (DLB) It has previously been suggested that αS is a copper binding proteinThe function of αS has often been suggested to be associated with dopamine metabolism and a variety of data indicate changes to dopamine synthesis, storage and transport processesOur investigations of metal binding to αS lead us to investigate the potential of this interaction to endow function to the protein that might be relevant to the maintenance of Fe(II) levels in cells. Our findings demonstrate conclusively that αS is a ferrireductase.−1. The titration of iron (III) into αS produced an exothermic reaction suggesting two binding sites (−1 both sites. A titration of iron (III) into protein that has been allowed to equilibrate with 2 equivalents of copper produced an exothermic reaction. The reaction indicated two sites with a affinity of log 4.50 M−1. Although the affinity at the sites was reduced the data indicated that αS can bind both copper and iron simultaneously. This then led us to hypothesise that copper loaded αS may be catalysing a redox process with the iron, perhaps reducing it to the ferrous form.We first assessed the copper (II) and iron (III) binding to αS in order to gain insight into the significance of this association. Recombinant αS was cloned from human cell lines and expressed and purified by a method developed by our laboratory. Thermodynamic analysis using isothermal titration calorimetry (ITC) was used to assess the Cu(II) and Fe (III) binding dynamics of the protein. ng sites . The aff2+ against absorbance for the determination of ferric iron concentrations. The ferric iron was added anaerobically as Fe (II) sulphate. For the assessment of αS ferrireductase activity Fe (III) was added as iron citrate. Controls in the absence of copper, protein or NADH were used to ensure any observed activity was due to the copper loaded protein. max and Km can be calculated for the reaction  bound protein was subjected to a ferrozine ((3-(2-pyridyl)-5,6- bis(phenyl sulfonic acid)-1,2,4-triazine), based assay to detect the reduction of iron (III) to iron (II), using NADH as an electron donor. Ferrozine forms a bis complex which is highly specific to the ferrous form of the ionic metal reaction . CertainWith data obtained from recombinant protein, there is clearly a need to investigate whether cellular forms behave in a similar way. As such, we also transfected human neural cell lines with αS and its disease associated mutants to overexpress the protein in a cell culture based system. Lysates were then prepared and equal quantities of protein, assessed by Bradford assay and western blot, were assayed under identical conditions to those before. In order to verify that increased cellular ferrireductase activity resulted in an increase in reduced iron in cells overexpressing αS or its mutants, the levels of ratio of Fe(II) total cellular Fe was determined for the stable cell lines using a commercial kit. The ratio of Fe(II) to total Fe is shown in While the association of αS with a number of diseases has resulted in the expression “synucleinopathies” to describe them, the exact role of this protein in disease pathogenesis is poorly understoodWhile analysis of copper binding to αS has been relatively extensiveOur data is the first to show that αS can bind two different metals simultaneously. Cu(II) saturate αS was still able to bind Fe(III) with little change in affinity. This clearly indicates that the two metals occupy different sites on the protein. The majority of data suggest that copper binds to the N-terminus of αS while iron binds to the C-terminus. This finding would also suggest that there is a difference in the co-ordination of the metals. Some studies have suggested that αS can bind more copper than we have shown with ITC. Under these conditions copper binding might prevent iron binding at one or more sites. However, as previously suggest, replacement of iron by copper, and binding of copper at multiple sites on the protein might lead to aggregation of the protein. The potential of copper to enhance αS aggregation has been observed. In contrast, our suggestion is that both copper and iron can bind to the protein under normal physiological conditions and allow it catalyse the reduction of Fe(III) to Fe(II), indicative of a ferrireductase.The data presented here suggests that αS is able to function as a cellular ferrireductase. This ties in well with recent evidence showing the protein associated with cell membranes Our findings on αS activity are possibly important for understanding its cellular role. However, a key reason for studying αS is its role in diseases such as Parkinson's Disease. In Parkinson's disease the important change that occurs is the loss of dopamingeric neurons. When considering the potential role of a ferrireductase in dopaminergic cells then the generation of increased levels of Fe(II) may be important. A key enzyme in the dopamine pathway is tyrosine hydroxylase (TH) αS mutants identified from inherited forms of Parkinson's disease were also studied for their impact on Ferriductase activity and iron reduction. There was little evidence that these point mutations had any significant effect on this activity. This does not preclude the possibility that alterations in ferrireductase activity could be important for dopaminergic neuron survival. Partients with inherited forms of Parkinson's disease live a significant part of their lives without developing symptoms but have an earlier onset of symptoms than seen in sporadic. Therefore it is likely that these mutations have their impact in concert with age dependent changes. Thus a gross alteration of protein function is unlikely and has been confirmed by these findings.In summary we have identified αS as a cellular ferrireductase. This provides the first finding of a function for this protein via direct observation rather than inference. Given the importance of maintaining Fe(II) levels in dopaminergic cells, this activity is likely to be of considerable importance to understanding the changes that occur in dopaminergic cells leading to their selective loss in Parkinson's disease.All reagents were purchased from Sigma unless otherwise stated.αS and βS were produced as previously described 2 in a humidified incubator.SH-SY5Y (human neuroblastoma) cells were cultured in 50% (v/v) Dulbecco's Modified Eagle Media and 50% (v/v) Ham's F-12 supplemented with 10% (v/v) fetal bovine serum (FBS), 100 U/ml penicillin, 100 µg/ml streptomycin and 2 mM glutamine. All the media and reagents were obtained from Biowhittaker Lonza. Growth conditions were maintained at 37°C and 5% COWildtype βS, αS and its disease associated mutants were subcloned into pcDNA plasmid transfected using FuGene 6 transfection reagent (Roche applied science). For stable transfections, 6-well plates were used and cells were plated at 50% confluency the day before the transfection. To perform the transfection 50 µl of serum free media was transferred into a micro-centrifuge tube; 3 µl of the FuGene 6 reagent was then added directly to this and mixed by gentle flicking. The mixture was incubated for 5 minutes at room temperature. For each cell line, 1 µg of DNA was then added to the mixture, mixed as before, and incubated at room temperature for 15 minutes. After this the transfection mixture was added to the cells, the 6-well plate swirled and returned to the incubator overnight. Cells were selected with 400 µg/ml G418 sulfate  24 hours after transfection, and maintained in 100 µg/ml to keep the plasmid in the cells.4 prior to ITC experiments.ITC was carried out as previously described Protein solutions of standardised cell lystates or to a final concentration of 100 µM recombinant protein were mixed with final concentrations of 100 µM ferrozine ((3-(2-pyridyl)-5,6- bis(phenyl sulfonic acid)-1,2,4-triazine), 50 µM NADH, 100 mM MOPS pH 7. Protein had been exposed to 4 equivalents of copper (II) sulphate and allowed to equilibrate. The reaction was started by the addition of iron (III) citrate to final concentrations between 1 µM and 100 µM to a final reaction volume of 100 µL. Reactions were carried out in 96 well plates and monitored at 562 nm in a BMG flurostar. All conditions wee repeated at least 8 times.Levels of Fe(II) and total Fe were analysed in cell lines using the kit from Abcam and following the manufacturer's instructions. Cells were harvested from a confluent T75 for each analysis.