A
bstract
This report details the capabilities of LHCb and its upgrades towards the study of kaons and hyperons. The analyses performed so far are reviewed, elaborating on the prospects for some key ...decay channels, while proposing some new measurements in LHCb to expand its strangeness research program.
We use MasterCode to perform a frequentist analysis of the constraints on a phenomenological MSSM model with 11 parameters, the pMSSM11, including constraints from
∼
36
/fb of LHC data at 13 TeV and ...PICO, XENON1T and PandaX-II searches for dark matter scattering, as well as previous accelerator and astrophysical measurements, presenting fits both with and without the
(
g
-
2
)
μ
constraint. The pMSSM11 is specified by the following parameters: 3 gaugino masses
M
1
,
2
,
3
, a common mass for the first-and second-generation squarks
m
q
~
and a distinct third-generation squark mass
m
q
~
3
, a common mass for the first-and second-generation sleptons
m
ℓ
~
and a distinct third-generation slepton mass
m
τ
~
, a common trilinear mixing parameter
A
, the Higgs mixing parameter
μ
, the pseudoscalar Higgs mass
M
A
and
tan
β
. In the fit including
(
g
-
2
)
μ
, a Bino-like
χ
~
1
0
is preferred, whereas a Higgsino-like
χ
~
1
0
is mildly favoured when the
(
g
-
2
)
μ
constraint is dropped. We identify the mechanisms that operate in different regions of the pMSSM11 parameter space to bring the relic density of the lightest neutralino,
χ
~
1
0
, into the range indicated by cosmological data. In the fit including
(
g
-
2
)
μ
, coannihilations with
χ
~
2
0
and the Wino-like
χ
~
1
±
or with nearly-degenerate first- and second-generation sleptons are active, whereas coannihilations with the
χ
~
2
0
and the Higgsino-like
χ
~
1
±
or with first- and second-generation squarks may be important when the
(
g
-
2
)
μ
constraint is dropped. In the two cases, we present
χ
2
functions in two-dimensional mass planes as well as their one-dimensional profile projections and best-fit spectra. Prospects remain for discovering strongly-interacting sparticles at the LHC, in both the scenarios with and without the
(
g
-
2
)
μ
constraint, as well as for discovering electroweakly-interacting sparticles at a future linear
e
+
e
-
collider such as the ILC or CLIC.
Resveratrol is a polyphenol that among other sources occurs in grapes and for this reason, red wines also contain considerable amounts of this compound. Resveratrol is thought to be responsible for ...the "French Paradox" which associates red wine consumption to the low incidence of cardiovascular diseases. The interest in resveratrol has increased due to its pharmacological effects that include cardio and neuroprotection and several other benefic actions (e.g. antioxidant, anti-inflammatory, anti-carcinogenic and anti-aging). Despite the therapeutic effects of resveratrol, its pharmacokinetic properties are not favorable since this compound has poor bioavailability being rapidly and extensively metabolized and excreted. To overcome this problem, drug delivery systems have been developed to protect and stabilize resveratrol and to enhance its bioavailability. Herein is presented an up-to-date revision covering the literature reported for nano and microformulations for resveratrol encapsulation that include liposomes, polymeric nanoparticles, solid lipid nanoparticles, lipospheres, cyclodextrins, polymeric microspheres, yeast cells carriers and calcium or zinc pectinate beads. Regarding the interaction of resveratrol with cell membranes, only few studies have been published so far. However, it is believed that this interaction can be implied in the biological activities of resveratrol since transmembranar proteins are one of its cellular targets. Indeed, resveratrol presents the capacity to modulate the membrane organization which may consequently affect the protein functionality. Therefore, the intracellular effects of resveratrol and the effects of this compound at the membrane level were also revised since their knowledge is essential for understanding the pharmacological and therapeutic activities of this bioactive compound.
In weakly collisional plasma environments with sufficiently low electron beta, Alfvénic turbulence transforms into inertial Alfvénic turbulence at scales below the electron skin depth, k_{⊥}d_{e}≳1. ...We argue that, in inertial Alfvénic turbulence, both energy and generalized kinetic helicity exhibit direct cascades. We demonstrate that the two cascades are compatible due to the existence of a strong scale dependence of the phase alignment angle between velocity and magnetic field fluctuations, with the phase alignment angle scaling as cosα_{k}∝k_{⊥}^{-1}. The kinetic and magnetic energy spectra scale as ∝k_{⊥}^{-5/3} and ∝k_{⊥}^{-11/3}, respectively. As a result of the dual direct cascade, the generalized helicity spectrum scales as ∝k_{⊥}^{-5/3}, implying progressive balancing of the turbulence as the cascade proceeds to smaller scales in the k_{⊥}d_{e}≫1 range. Turbulent eddies exhibit a phase-space anisotropy k_{∥}∝k_{⊥}^{5/3}, consistent with critically balanced inertial Alfvén fluctuations. Our results may be applicable to a variety of geophysical, space, and astrophysical environments, including the Earth's magnetosheath and ionosphere, solar corona, and nonrelativistic pair plasmas, as well as to strongly rotating nonionized fluids.
In Navier-Stokes turbulence, energy and helicity injected at large scales are subject to a joint direct cascade, with both quantities exhibiting a spectral scaling ∝k^{-5/3}. We demonstrate via ...direct numerical simulations that the two cascades are compatible due to the existence of a strong scale-dependent phase alignment between velocity and vorticity fluctuations, with the phase alignment angle scaling as cosα_{k}∝k^{-1}.
Aim
The present investigation was aimed at isolating and identifying bacterial strains from cured vanilla beans. Additionally, the study focused on evaluating bacterial processes pertaining to the ...aromatic compounds production (ACP).
Methods and Results
Three bacteria were isolated from Vanilla planifolia beans, previously subjected to the curing process. According to morphological, biochemical and 16S rRNA analysis, the strains were identified as Citrobacter sp., Enterobacter sp. and Pseudomonas sp. The polygalacturonase activity (PGA) was determined using the drop, cup‐plate and DNS methods. Aromatic compounds production was analysed by cup‐plate method using FA as substrate and quantified by high performance liquid chromatography (ppm), the functional groups of vanillic acid (VA) were identified by FT‐IR and the aromatic compounds (AC) resistance was determined and reported as minimum inhibitory concentration. Citrobacter sp., Enterobacter sp. and Pseudomonas showed PGA (70·31 ± 364, 76·07 ± 12·47 and 51 ± 10·92 U ml−1 respectively), were producers of VA (3·23 ± 0·49, 324 ± 41 and 265·99 ± 11·61 ppm respectively) and were resistant to AC.
Conclusions
The Gram‐negative bacteria isolated from V. planifolia beans were responsible for ACP.
Significance and Impact of the Study
This is the first evidence for the role of Gram‐negative bacterial isolates from cured Mexican V. planifolia beans in the process related to ACP.
Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the ...cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios.
The predictive ability of these approaches was measured by the coefficient of determination (R
). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R
for blood metabolites across the three CV scenarios, especially with the herd-out CV: R
values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators.
Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.
Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental ...sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.
Feed efficiency (FE) related traits play a key role in the economy and sustainability of beef cattle production systems. The accurate knowledge of the physiologic background for FE-related traits can ...help the development of more efficient selection strategies for them. Hence, multi-trait weighted GWAS (MTwGWAS) and meta-analyze were used to find genomic regions associated with average daily gain (ADG), dry matter intake (DMI), feed conversion ratio (FCR), feed efficiency (FE), and residual feed intake (RFI). The FE-related traits and genomic information belong to two breeding programs that perform the FE test at different ages: post-weaning (1,024 animals IZ population) and post-yearling (918 animals for the QLT population).
The meta-analyze MTwGWAS identified 14 genomic regions (-log10(p -value) > 5) regions mapped on BTA 1, 2, 3, 4, 7, 8, 11, 14, 15, 18, 21, and 29. These regions explained a large proportion of the total genetic variance for FE-related traits across-population ranging from 20% (FCR) to 36% (DMI) in the IZ population and from 22% (RFI) to 28% (ADG) in the QLT population. Relevant candidate genes within these regions (LIPE, LPL, IGF1R, IGF1, IGFBP5, IGF2, INS, INSR, LEPR, LEPROT, POMC, NPY, AGRP, TGFB1, GHSR, JAK1, LYN, MOS, PLAG1, CHCD7, LCAT, and PLA2G15) highlighted that the physiological mechanisms related to neuropeptides and the metabolic signals controlling the body's energy balance are responsible for leading to greater feed efficiency. Integrated meta-analysis results and functional pathway enrichment analysis highlighted the major effect of biological functions linked to energy, lipid metabolism, and hormone signaling that mediates the effects of peptide signals in the hypothalamus and whole-body energy homeostasis affecting the genetic control of FE-related traits in Nellore cattle.
Genes and pathways associated with common signals for feed efficiency-related traits provide better knowledge about regions with biological relevance in physiological mechanisms associated with differences in energy metabolism and hypothalamus signaling. These pleiotropic regions would support the selection for feed efficiency-related traits, incorporating and pondering causal variations assigning prior weights in genomic selection approaches.
Age at first calving (AFC) plays an important role in the economic efficiency of beef cattle production. This trait can be affected by a combination of genetic and environmental factors, leading to ...physiological changes in response to heifers' adaptation to a wide range of environments. Genome-wide association studies through the reaction norm model were carried out to identify genomic regions associated with AFC in Nellore heifers, raised under different environmental conditions (EC). The SNP effects for AFC were estimated in three EC levels (Low, Medium, and High, corresponding to average contemporary group effects on yearling body weight equal to 159.40, 228.6 and 297.6 kg, respectively), which unraveled shared and unique genomic regions for AFC in Low, Medium, and High EC levels, that varied according to the genetic correlation between AFC in different EC levels. The significant genomic regions harbored key genes that might play an important biological role in controlling hormone signaling and metabolism. Shared genomic regions among EC levels were identified on BTA 2 and 14, harboring candidate genes associated with energy metabolism (IGFBP2, IGFBP5, SHOX, SMARCAL1, LYN, RPS20, MOS, PLAG1, CHCD7, and SDR16C6). Gene set enrichment analyses identified important biological functions related to growth, hormone levels affecting female fertility, physiological processes involved in female pregnancy, gamete generation, ovulation cycle, and age at puberty. The genomic regions highlighted differences in the physiological processes linked to AFC in different EC levels and metabolic processes that support complex interactions between the gonadotropic axes and sexual precocity in Nellore heifers.
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