A fresh look at the (non-)Abelian Landau-Khalatnikov-Fradkin transformations

The Landau-Khalatnikov-Fradkin transformations (LKFTs) allow to interpolate $n$-point functions between different gauges. We first offer an alternative derivation of these LKFTs for the gauge and fermions field in the Abelian (QED) case when working in the class of linear covariant gauges. Our derivation is based on the introduction of a gauge invariant transversal gauge field, which allows a natural generalization to the non-Abelian (QCD) case of the LKFTs. To our knowledge, within this rigorous formalism, this is the first construction of the LKFTs beyond QED. The renormalizability of our setup is guaranteed to all orders. We also offer a direct path integral derivation in the non-Abelian case, finding full consistency.Comment: 16 page

Methanol masers reveal the magnetic field of the high-mass protostar IRAS 18089-1732

Context. The importance of the magnetic field in high-mass-star formation is not yet fully clear and there are still many open questions concerning its role in the accretion processes and generation of jets and outflows. In the past few years, masers have been successfully used to probe the magnetic field morphology and strength at scales of a few au around massive protostars, by measuring linear polarisation angles and Zeeman splitting. The massive protostar IRAS 18089-1732 is a well studied high-mass-star forming region, showing a hot core chemistry and a disc-outflow system. Previous SMA observations of polarised dust revealed an ordered magnetic field oriented around the disc of IRAS 18089-1732. Aims. We want to determine the magnetic field in the dense region probed by 6.7 GHz methanol maser observations and compare it with observations in dust continuum polarisation, to investigate how the magnetic field in the compact maser region relates to the large-scale field around massive protostars. Methods. We reduced MERLIN observations at 6.7 GHz of IRAS 18089-1732 and we analysed the polarised emission by methanol masers. Results. Our MERLIN observations show that the magnetic field in the 6.7 GHz methanol maser region is consistent with the magnetic field constrained by the SMA dust polarisation observations. A tentative detection of circularly polarised line emission is also presented. Conclusions. We found that the magnetic field in the maser region has the same orientation as in the disk. Thus the large-scale field component, even at the au scale of the masers, dominates over any small-scale field fluctuations. We obtained, from the circular polarisation tentative detection, a field strength along the line of sight of 5.5 mG which appeared to be consistent with the previous estimates.Comment: 12 pages, 7 figures, accepted for publication in A&

A melanocortin 1 receptor (MC1R) gene polymorphism is useful for authentication of Massese sheep dairy products

Massese is an Italian sheep breed, with black or grey coat colour, mainly reared in the Tuscany and Emilia Romagna regions. Recently, the emerging interests in this breed have resulted in the production of Pecorino cheese obtained with only Massese milk. In order to be profitable, this marketing link between Massese breed and its products should be defended against fraudsters who could include milk of other sheep breeds or cow milk in Massese labelled productions. To identify the genetic factors affecting coat colour in sheep, we have recently analysed the melanocortin 1 receptor (MC1R) gene and identified several single nucleotide polymorphisms (SNPs). In this work, as a first step to set up a DNA based protocol for authentication of Massese dairy products, we further investigated the presence and distribution of one of these SNPs (c.-31G>A) in 143 Massese sheep and in another 13 sheep breeds (for a total of 351 animals). The Massese breed was fixed for allele c.-31A, whereas in all other breeds allele c.-31 G was the most frequent or with frequency of 0\ub750. At the same nucleotide position the cattle MC1R gene carries the G nucleotide. Using these data we developed a method to detect adulterating milk (from other sheep breeds or from cow) in Massese dairy products based on the analysis of the c.-31G>A SNP. We first tested the sensitivity of the protocol and then applied it to analyse DNA extracted from ricotta and Pecorino cheese obtained with only Massese milk or obtained with unrestricted sheep and cattle milk. To our knowledge, this system represents the first one that can be used for breed authentication of a sheep production and that, at the same time, can reveal frauds derived from the admixture of milk of an unreported species

Analysis of PRKAG3 gene polymorphisms in Italian autochthonous pig breeds.

The PRKAG3 gene encodes for the regulatory gamma 3 subunit of adenosine monophosfate-activated protein kinase (AMPK) protein that acts as a regulator of carbohydrate and fat metabolism. Several single nucleotide polymorphisms (SNPs) have been identified in the porcine PRKAG3 gene. Two of them (c.595A>G or p.I199V and c.599G>A or p.R200Q) have been associated with variability of meat quality and carcass traits. In this study, we investigated the frequency of the c.595A>G and c.599G>A SNPs in 372 animals of five Italian autochthonous pig breeds (Apulo-Calabrese, Casertana, Cinta Senese, Mora Romagnola and Nero Siciliano). Genomic DNA were extracted from hair roots or blood and SNPs genotyping was performed by PCR-RFLP protocols. The c.599A (p.200Q) allele was carried by only 3 Nero Siciliano pigs. All five breeds were polymorphic at the c.595A>G site. The c.595A (p.I199) allele was the less frequent in the analysed breeds with minor allele frequency ranging from 0.144 (Nero Siciliano) to 0.464 (Casertana). Based on identified allele frequencies, the c.595A>G SNP can be useful in association studies with meat, carcass and ham quality traits in the Italian local pig breeds

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) \u2013 the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking \u2013 was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results

Targeted metabolomic profiles of piglet plasma reveal physiological changes over the suckling period

The suckling phase is a critical period for the piglets due to their incomplete immune system development and their rapid growth rates. In this study, we analysed the metabolomic profiles of piglets over this period. Eighteen piglets (nine males and nine females) from three different litters were included in the study. Body weight was recorded at birth (T0), 12 (T1) and 21 (T2) days after birth. Plasma samples were collected at two critical time points of the suckling phase (T1 and T2) and about 180 metabolites of five different biochemical classes (glycerophospholipids, amino acids, biogenic amines, hexoses and acylcarnitines) were analyzed using a target metabolomics approach based on Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). Metabolites whose levels could discriminate the plasma profiles at T1 and T2 were identified using the sparse version of Multilevel Partial Least Squares Discriminant Analysis (sMLPLS-DA), coupled with a stability test based on a Leave One Out (LOO) procedure. The level of twenty-three metabolites differed significantly (P < 0.1; both for stability and the effect size) between the two time points. Higher levels of six acylcarnitine (C14:1, C14:1-OH, C16-OH, C4, C5 and C5-OH), serine, threonine and tyrosine, and one phosphatidylcholine (PC ae C42:3) were observed at T1, whereas one biogenic amine (creatinine), eight phosphatidylcholines including PC aa C30:2, PC ae C30:0, PC ae C32:1, PC ae C38:4, PC ae C40:4, PC ae C42:4, PC ae C42:5 and PC ae C44:6, and four sphingomyelins, including SM (OH) C22:1, SM C16:0, SM C16:1 and SM C18:0, were more abundant at T2. The Metabolite Set Enrichment Analysis and the Pathway Analysis modules suggested a perturbation of the \u201cglycine and serine metabolism\u201d and the \u201csphingolipid metabolism\u201d. Differences of these metabolites between these two time points might be related to the rapid growth and immunological maturation phases of the piglets in this period. Our results provided new information that could describe the biological changes of the piglets over the suckling period. The identified metabolites may be useful markers of the developmental processes occurring in the piglets over this critical pre-weaned phase

A first comparative map of copy number variations in the sheep genome

We carried out a cross species cattle–sheep array comparative genome hybridization experiment to identify copy number variations (CNVs) in the sheep genome analysing ewes of Italian dairy or dual-purpose breeds (Bagnolese, Comisana, Laticauda, Massese, Sarda, and Valle del Belice) using a tiling oligonucleotide array with ~385,000 probes designed on the bovine genome. We identified 135 CNV regions (CNVRs; 24 reported in more than one animal) covering ~10.5 Mb of the virtual sheep genome referred to the bovine genome (0.398%) with a mean and a median equal to 77.6 and 55.9 kb, respectively. A comparative analysis between the identified sheep CNVRs and those reported in cattle and goat genomes indicated that overlaps between sheep and both other species CNVRs are highly significant (Pb0.0001), suggesting that several chromosome regions might contain recurrent interspecies CNVRs. Many sheep CNVRs include genes with important biological functions. Further studies are needed to evaluate their functional relevance

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case

Follow-Up Assessment of Intracranial Aneurysms Treated with Endovascular Coiling: Comparison of Compressed Sensing and Parallel Imaging Time-of-Flight Magnetic Resonance Angiography

The aim of our study was to compare compressed sensing (CS) time-of-flight (TOF) magnetic resonance angiography (MRA) with parallel imaging (PI) TOF MRA in the evaluation of patients with intracranial aneurysms treated with coil embolization or stent-assisted coiling. We enrolled 22 patients who underwent follow-up imaging after intracranial aneurysm coil embolization. All patients underwent both PI TOF and CS TOF MRA during the same examination. Image evaluation aimed to compare the performance of CS to PI TOF MRA in determining the degree of aneurysm occlusion, as well as the depiction of parent vessel and vessels adjacent to the aneurysm dome. The reference standard for the evaluation of aneurysm occlusion was PI TOF MRA. The inter-modality agreement between CS and PI TOF MRA in the evaluation of aneurysm occlusion was almost perfect (κ = 0.98, p < 0.001) and the overall inter-rater agreement was substantial (κ = 0.70, p < 0.001). The visualization of aneurysm parent vessel in CS TOF images compared with PI TOF images was evaluated to be better in 11.4%, equal in 86.4%, and worse in 2.3%. CS TOF MRA, with almost 70% scan time reduction with respect to PI TOF MRA, yields comparable results for assessing the occlusion status of coiled intracranial aneurysms. Short scan times increase patient comfort, reduce the risk of motion artifacts, and increase patient throughput, with a resulting reduction in costs. CS TOF MRA may therefore be a potential replacement for PI TOF MRA as a first-line follow-up examination in patients with intracranial aneurysms treated with coil embolization