MAGGnet : an international network to foster mitigation of agricultural greenhouse gases

Liebig, M. A. USDA-ARS, Mandan, ND, USA.Franzluebbers, A. J. USDA-ARS, Raleigh, NC, USA.Alvarez, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi (EEA Manfredi). Córdoba, Argentina.Chiesa, T. D. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.Lewczuk, N. Instituto Nacional de Tecnología Agropecuaria (INTA). Buenos Aires, Argentina.Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.Posse, Graciela Noemí. Instituto Nacional de Tecnología Agropecuaria (INTA). Buenos Aires, Argentina.Yahdjian, María Laura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina.8Research networks provide a framework for review, synthesis and systematic testing of theories by multiple scientists across international borders critical for addressing global-scale issues. In 2012, a GHG research network referred to as MAGGnet (Managing Agricultural Greenhouse Gases Network) was established within the Croplands Research Group of the Global Research Alliance on Agricultural Greenhouse Gases (GRA). With involvement from 46 alliance member countries, MAGGnet seeks to provide a platform for the inventory and analysis of agricultural GHG mitigation research throughout the world. To date, metadata from 315 experimental studies in 20 countries have been compiled using a standardized spreadsheet. Most studies were completed (74%) and conducted within a 1-3-year duration (68%). Soil carbon and nitrous oxide emissions were measured in over 80% of the studies. Among plant variables, grain yield was assessed across studies most frequently (56%), followed by stover (35%) and root (9%) biomass. MAGGnet has contributed to modeling efforts and has spurred other research groups in the GRA to collect experimental site metadata using an adapted spreadsheet. With continued growth and investment, MAGGnet will leverage limited-resource investments by any one country to produce an inclusive, globally shared meta-database focused on the science of GHG mitigation

Cardiac Surgery is Associated with Biomarker Evidence of Neuronal Damage

BACKGROUND: Anesthesia and surgery is commonly associated with central nervous system sequelae and cognitive symptoms, which may be caused by neuronal injury. Neuronal injury can be monitored by plasma concentrations of the neuronal biomarkers tau and neurofilament light protein (NFL). Currently, there are no studies examining whether neuronal injury varies between surgical procedures. OBJECTIVE: Our aim was to investigate if neuronal damage is more frequent after cardiac than after otolaryngeal surgery, as estimated by tau and NFL concentrations in plasma. METHODS: Blood samples were drawn before, during, and after surgery and concentrations of tau, NFL, Aβ40, and Aβ42 were measured in 25 patients undergoing cardiac surgery (9 off-pump and 16 on-pump) and 26 patients undergoing otolaryngeal surgery. RESULTS: Tau increased during surgery (1752%, p = 0.0001) and NFL rose seven days post-surgery (1090%, p < 0.0001) in patients undergoing cardiac surgery; even more in patients on-pump than off-pump. No changes were observed in patients undergoing otolaryngeal surgery and only minor fluctuations were observed for Aβ40 and Aβ42. CONCLUSION: Cardiac surgery is associated with neuronal injury, which is aggravated by extracorporeal circulation. Analyses of NFL and tau in blood may guide development of surgical procedures to minimize neuronal damage, and may also be used in longitudinal clinical studies to assess the relationship of surgery with future neurocognitive impairment or dementia

Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer’s dementia

Background The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia. Methods We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. Results Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four- parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone. Conclusion A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials

A Specific Reduction in A beta(1-42) vs. a Universal Loss of A beta Peptides in CSF Differentiates Alzheimer's Disease From Meningitis and Multiple Sclerosis

A reduced concentration of A beta(1-42) in CSF is one of the established biomarkers of Alzheimer's disease Reduced CSF concentrations of A beta(1-42) have also been shown in multiple sclerosis, viral encephalitis and bacterial meningitis As neuroinflammation is one of the neuropathological hallmarks of Alzheimer's disease, an infectious origin of the disease has been proposed According to this hypothesis, amyloid pathology is a consequence of a microbial infection and the resulting immune defense Accordingly, changes in CSF levels of amyloid-beta peptides should be similar in AD and inflammatory brain diseases A beta(1-42) and A beta(1-40) levels were measured in cerebrospinal fluid by ELISA and Western blotting in 34 patients with bacterial meningitis (n = 9), multiple sclerosis (n = 5) or Alzheimer's disease (n = 9) and in suitable controls (n = 11) Reduced concentrations of A beta(1-42) were detected in patients with bacterial meningitis, multiple sclerosis and Alzheimer's disease However, due to a concurrent reduction in A beta(1-40) in multiple sclerosis and meningitis patients, the ratio of A beta(1-42)/A beta(1-40) was reduced only in the CSF of Alzheimer's disease patients Urea-SDS-PAGE followed by Western blotting revealed that all A beta peptide variants are reduced in bacterial meningitis, whereas in Alzheimer's disease, only A beta(1-42) is reduced These results have two implications First, they confirm the discriminatory diagnostic power of the A beta(1-42)/A beta(1-40) ratio Second, the differential pattern of A beta peptide reductions suggests that the amyloid pathology in meningitis and multiple sclerosis differs from that in AD and does not support the notion of AD as an infection-triggered immunopathology

Evidence for the η_b(1S) Meson in Radiative Υ(2S) Decay

We have performed a search for the η_b(1S) meson in the radiative decay of the Υ(2S) resonance using a sample of 91.6 × 10^6 Υ(2S) events recorded with the BABAR detector at the PEP-II B factory at the SLAC National Accelerator Laboratory. We observe a peak in the photon energy spectrum at E_γ = 609.3^(+4.6)_(-4.5)(stat)±1.9(syst) MeV, corresponding to an η_b(1S) mass of 9394.2^(+4.8)_(-4.9)(stat) ± 2.0(syst) MeV/c^2. The branching fraction for the decay Υ(2S) → γη_b(1S) is determined to be [3.9 ± 1.1(stat)^(+1.1)_(-0.9)(syst)] × 10^(-4). We find the ratio of branching fractions B[Υ(2S) → γη_b(1S)]/B[Υ(3S) → γη_b(1S)]= 0.82 ± 0.24(stat)^(+0.20)_(-0.19)(syst)

Measurement of B(B-->X_s {\gamma}), the B-->X_s {\gamma} photon energy spectrum, and the direct CP asymmetry in B-->X_{s+d} {\gamma} decays

The photon spectrum in B --> X_s {\gamma} decay, where X_s is any strange hadronic state, is studied using a data sample of (382.8\pm 4.2) \times 10^6 e^+ e^- --> \Upsilon(4S) --> BBbar events collected by the BABAR experiment at the PEP-II collider. The spectrum is used to measure the branching fraction B(B --> X_s \gamma) = (3.21 \pm 0.15 \pm 0.29 \pm 0.08)\times 10^{-4} and the first, second, and third moments = 2.267 \pm 0.019 \pm 0.032 \pm 0.003 GeV,, )^2> = 0.0484 \pm 0.0053 \pm 0.0077 \pm 0.0005 GeV^2, and )^3> = -0.0048 \pm 0.0011 \pm 0.0011 \pm 0.0004 GeV^3, for the range E_\gamma > 1.8 GeV, where E_{\gamma} is the photon energy in the B-meson rest frame. Results are also presented for narrower E_{\gamma} ranges. In addition, the direct CP asymmetry A_{CP}(B --> X_{s+d} \gamma) is measured to be 0.057 \pm 0.063. The spectrum itself is also unfolded to the B-meson rest frame; that is the frame in which theoretical predictions for its shape are made.Comment: 37 pages, 19 postscript figures, submitted to Phys. Rev. D. No analysis or results have changed from previous version. Some changes to improve clarity based on interactions with Phys. Rev. D referees, including one new Figure (Fig. 13), and some minor wording/punctuation/spelling mistakes fixe

Measurement of ISR-FSR interference in the processes e+ e- --> mu+ mu- gamma and e+ e- --> pi+ pi- gamma

Charge asymmetry in processes e+ e- --> mu+ mu- gamma and e+ e- --> pi+ pi- gamma is measured using 232 fb-1 of data collected with the BABAR detector at center-of-mass energies near 10.58 GeV. An observable is introduced and shown to be very robust against detector asymmetries while keeping a large sensitivity to the physical charge asymmetry that results from the interference between initial and final state radiation. The asymmetry is determined as afunction of the invariant mass of the final-state tracks from production threshold to a few GeV/c2. It is compared to the expectation from QED for e+ e- --> mu+ mu- gamma and from theoretical models for e+ e- --> pi+ pi- gamma. A clear interference pattern is observed in e+ e- --> pi+ pi- gamma, particularly in the vicinity of the f_2(1270) resonance. The inferred rate of lowest order FSR production is consistent with the QED expectation for e+ e- --> mu+ mu- gamma, and is negligibly small for e+ e- --> pi+ pi- gamma.Comment: 32 pages,29 figures, to be submitted to Phys. Rev.

Cross Sections for the Reactions e+e- --> K+ K- pi+pi-, K+ K- pi0pi0, and K+ K- K+ K- Measured Using Initial-State Radiation Events

We study the processes e+e- --> K+ K- pi+pi-gamma, K+ K- pi0pi0gamma, and K+ K- K+ K-gamma, where the photon is radiated from the initial state. About 84000, 8000, and 4200 fully reconstructed events, respectively, are selected from 454 fb-1 of BaBar data. The invariant mass of the hadronic final state defines the \epem center-of-mass energy, so that the K+ K- pi+pi- data can be compared with direct measurements of the e+e- --> K+ K- pi+pi- reaction. No direct measurements exist for the e+e- --> K+ K-pi0pi0 or e+e- --> K+ K-K+ K- reactions, and we present an update of our previous result with doubled statistics. Studying the structure of these events, we find contributions from a number of intermediate states, and extract their cross sections. In particular, we perform a more detailed study of the e+e- --> phi(1020)pipigamma reaction, and confirm the presence of the Y(2175) resonance in the phi(1020) f0(980) and K+K-f0(980) modes. In the charmonium region, we observe the J/psi in all three final states and in several intermediate states, as well as the psi(2S) in some modes, and measure the corresponding product of branching fraction and electron width.Comment: 35 pages, 42 figure

Study of Upsilon(3S,2S) -> eta Upsilon(1S) and Upsilon(3S,2S) -> pi+pi- Upsilon(1S) hadronic trasitions

We study the Upsilon(3S,2S)->eta Upsilon(1S) and Upsilon(3S,2S)->pi+pi- Upsilon(1S) transitions with 122 million Upsilon(3S) and 100 million Upsilon(2S) mesons collected by the BaBar detector at the PEP-II asymmetric energy e+e- collider. We measure B[Upsilon(2S)->eta Upsilon(1S)]=(2.39+/-0.31(stat.)+/-0.14(syst.))10^-4 and Gamma[Upsilon(2S)->eta Upsilon(1S)]/Gamma[Upsilon(2S)-> pi+pi- Upsilon(1S)]=(1.35+/-0.17(stat.)+/-0.08(syst.))10^-3. We find no evidence for Upsilon(3S)->eta Upsilon(1S) and obtain B[Upsilon(3S)->eta Upsilon(1S)]<1.0 10^-4 and Gamma[Upsilon(3S)->eta Upsilon(1S)]/Gamma[Upsilon(3S)->pi+pi- Upsilon(1S)]<2.3 10^-3 as upper limits at the 90% confidence level. We also provide improved measurements of the Upsilon(2S) - Upsilon(1S) and Upsilon(3S) - Upsilon(1S) mass differences, 562.170+/-0.007(stat.)+/-0.088(syst.) MeV/c^2 and 893.813+/-0.015(stat.)+/-0.107(syst.) MeV/c^2 respectively.Comment: 8 pages, 16 encapsulated postscript figures, submitted to Phys.Rev.