Mixed-effects high-dimensional multivariate regression via group-lasso regularization

Linear mixed modeling is a well-established technique widely employed when observations possess a grouping structure. Nonetheless, this standard methodology is no longer applicable when the learning framework encompasses a multivariate response and high-dimensional predictors. To overcome these issues, in the present paper a penalized estimation procedure for multivariate linear mixed-effects models (MLMM) is introduced. In details, we propose to regularize the likelihood via a group-lasso penalty, forcing only a subset of the estimated parameters to be preserved across all components of the multivariate response. The methodology is employed to develop novel surrogate biomarkers for cardiovascular risk factors, such as lipids and blood pressure, from whole-genome DNA methylation data in a multi-center study. The described methodology performs better than current stateof- art alternatives in predicting a multivariate continuous outcome

On Gram-positive-and Gram-negative-bacteria-associated canine and feline skin infections: a 4-year retrospective study of the university veterinary microbiology diagnostic laboratory of Naples, Italy

A 4-year retrospective study (2016–2019) of selected routine bacteriological examinations of the veterinary microbiology laboratory of the University Veterinary Teaching Hospital of Naples (Italy) was carried out. A total of 189 bacteriological samples were collected from 171 dogs and 18 cats suffering from skin infections. In dogs, the most common cutaneous infection was otitis externa, while pyoderma was found to be prevalent in cats. The number of recorded Gram-positive strains over the study period did not vary considerably from year to year and was always significantly higher (p-value = 0.0007) in comparison with Gram-negative bacterial isolations. In dogs, Staphylococcus pseudintermedius was the most common identified Gram-positive bacterium (65%), while Pseudomonas aeruginosa (36%) was the one among the isolated Gram-negative bacteria. In cats, coagulase-negative staphylococci were the most predominant isolated bacteria (47%). The phenotypic profiles of antibiotic resistance showed that most of the strains were resistant to amoxicillin–clavulanate, penicillin, clindamycin, and trimethoprim–sulfamethoxazole. Several multi-drug-resistant strains (35%) were detected in canine isolates. An updating of antibiotic resistance profiles of the main Gram-positive and Gram-negative bacteria principally associated with skin infections of pet animals is necessary to improve stewardship programs of veterinary hospitals and clinics

Age validation in common octopus Octopus vulgaris using stylet increment analysis

6 páginas, 1 tabla, 3 figurasDaily periodicity of growth increments in stylets was validated in wild-caught Octopus vulgaris maintained under controlled conditions. It was corroborated by staining the stylets either with oxytetracycline (OTC) or tetracycline (TC), and comparing the number of rings produced with the number of days elapsed. In all, 19 animals (10 males and 9 females; 680–1470 g body weight, BW) were injected with OTC in Vigo (mean 124 mg kg−1), and another six animals (1 male and 5 females; 248–570 g BW) with TC at Naples (mean 120 mg kg−1). The animals were successfully maintained in captivity until sacrificed for up to 6 (one animal), 9 (one animal), 18 (the six animals from Naples), and 21 (17 animals) days. The number of increments counted in transverse stylet sections was 18.9 ± 1.4 and 20.5 ± 1.5 for octopuses maintained for 18 and 21 d, respectively. The mean rate of increment formation was 1.02 increments per day, suggesting a periodicity of 1 increment per day in the stylet. Consequently, the results successfully validate daily increment deposition in O. vulgaris stylets in the size range analysedThe research was supported by ECOSUMMER (European Union), and CAH received early training ECOSUMMER (Marie Curie Action) supportPeer reviewe

Occurrence and Antimicrobial Susceptibility Profiles of Streptococcus equi subsp. zooepidemicus Strains Isolated from Mares with Fertility Problems

Streptococcus equi subsp. zooepidemicus (S. zooepidemicus), is a β-hemolytic Streptococcus belonging to the Lancefield group C; it is a rare human pathogen, but in horses, it is frequently associated with endometritis. This study aimed to isolate S. zooepidemicus strains, associated with bacterial endometritis in mares, and to define their antimicrobial resistance profile. Twenty-three isolates were recovered from one hundred ninety-six equine uterine swabs (11.7%). Bacterial identification was carried out by Api 20 Strep and confirmed by matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), while antimicrobial susceptibility testing was performed by disk diffusion method on Muller Hinton agar plates. The antibiotic resistance profiles of the isolates revealed a high percentage of resistance to amikacin (95.6%), ampicillin (73.9%) and tetracycline (69.6%), while ceftiofur and ceftriaxone were highly effective with 82.6% and 78.3% of the isolates inhibited, respectively. An intriguing value of resistance to penicillin (34.8%), which represents the first-choice antibiotic in equine S. zooepidemicus infections, was observed. Furthermore, a high prevalence of multidrug-resistant strains (82.6%) was recorded. Continuous surveillance of this potential zoonotic pathogen and an appropriate antimicrobial stewardship program with the promotion of correct use of antimicrobials, after a proper diagnosis, are needed to allow an effective therapy

Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup

Nuclear data uncertainty analysis on the spent nuclear fuel inventory was performed on the Takahama-3 NT3G23 assembly, where the sample SF95-4 was irradiated up to a burnup of approximately 36 GWd/ t according to the SFCOMPO benchmark. The cross-section covariance matrices stored in the ENDF/B-VIII.0, JEFF-3.3 and JENDL-4.0u evaluated nuclear data libraries were propagated with the stochastic sampling algorithms implemented in the SANDY code. A comparison of the concentration uncertainty differences obtained using data from the three libraries is reported. Similarities were found with the fuel composition uncertainty results obtained for the Calvert Cliffs MKP109 sample P SFCOMPO benchmark. Such a similarity was also found when comparing concentration uncertainties along the sample irradiation. Therefore, the main contributors to the concentration uncertainty of a number of nuclides were identified at different burnup levels in the two samples. To complement the similarity analysis, a correlation study of the concentration distributions predicted by the two models was performed. The reported results hint a dominance of the common uncertainty propagation mechanisms over the model differences in the determination of concentration uncertainty