Characterization of the spoilage microbiota of hake fillets packaged under a modified atmosphere (MAP) rich in CO2 (50% CO2/50% N2) and stored at different temperatures

The aim of this study was to characterize the spoilage microbiota of hake fillets stored under modified atmospheres (MAP) (50% CO2/50% N2) at different temperatures using high-throughput 16S rRNA gene sequencing and to compare the results with those obtained using traditional microbiology techniques. The results obtained indicate that, as expected, higher storage temperatures lead to shorter shelf-lives (the time of sensory rejection by panelists). Thus, the shelf-life decreased from six days to two days for Batch A when the storage temperature increased from 1 to 7 °C, and from five to two days—when the same increase in storage temperature was compared—for Batch B. In all cases, the trimethylamine (TMA) levels measured at the time of sensory rejection of hake fillets exceeded the recommended threshold of 5 mg/100 g. Photobacterium and Psychrobacter were the most abundant genera at the time of spoilage in all but one of the samples analyzed: Thus, Photobacterium represented between 19% and 46%, and Psychrobacter between 27% and 38% of the total microbiota. They were followed by Moritella, Carnobacterium, Shewanella, and Vibrio, whose relative order varied depending on the sample/batch analyzed. These results highlight the relevance of Photobacterium as a spoiler of hake stored in atmospheres rich in CO2. Further research will be required to elucidate if other microorganisms, such as Psychrobacter, Moritella, or Carnobacterium, also contribute to spoilage of hake when stored under MAP

Eco-innovative possibilities for improving the quality of thawed cod fillets using high-power ultrasound

In order to improve the quality of thawed cod fillets and minimize the impact of processing, an extended hydration phase is applied in the fishery product industry in order to recover the water lost during freezing and thawing. Such long phases not only compromise productivity, but increase the chances of microbial growth in fish. Ultrasound (US) is a technology that could reduce these long hydration times, thanks to its capacity to improve mass-transfer processes, thereby limiting the development of fish microbiota. This investigation studies the effect of different US intensities (25 kHz, 29.4 W/kg to 2.9 W/kg, 113.7 to 15.3 W) on weight gain (WG) in the hydration process of cod fillets. The influence of the hydration medium's pH (from pH 8.5 to 10.5) in combination with US was likewise evaluated. Microbiological and sensory analyses were carried out at the end of the hydration process in order to evaluate its impact. The higher the applied US power, the lower was the WG. US intensities of 2.9 W/kg produced the highest increments in WG (18.6%), reducing hydration time by 33% and thereby achieving the same hydration values as in control samples. The combination of US with a controlled pH of 8.5 permitted to shorten hydration time by an additional day, and also led to improved microbial quality in comparison with control samples. Sensorial analyses indicated that after 5 d of hydration, Quality Index Method (QIM) values were better than those obtained for control samples after 5 and 7 d. Specifically, color and gaping were the sensorial attributes of cod fillets better protected with the application of US

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Influence of the length of hospitalisation in post-discharge outcomes in patients with acute heart failure: Results of the LOHRCA study

Objective: To investigate the relationship between length of hospitalisation (LOH) and post-discharge outcomes in acute heart failure (AHF) patients and to ascertain whether there are different patterns according to department of initial hospitalisation. Methods: Consecutive AHF patients hospitalised in 41 Spanish centres were grouped based on the LOH (15 days). Outcomes were defined as 90-day post-discharge all-cause mortality, AHF readmissions, and the combination of both. Hazard ratios (HRs), adjusted by chronic conditions and severity of decompensation, were calculated for groups with LOH >6 days vs. LOH <6 days (reference), and stratified by hospitalisation in cardiology, internal medicine, geriatrics, or short-stay units. Results: We included 8563 patients (mean age: 80 (SD = 10) years, 55.5% women), with a median LOH of 7 days (IQR 4–11): 2934 (34.3%) had a LOH 15 days. The 90-day post-discharge mortality was 11.4%, readmission 32.2%, and combined endpoint 37.4%. Mortality was increased by 36.5% (95%CI = 13.0–64.9) when LOH was 11–15 days, and by 72.0% (95%CI = 42.6–107.5) when >15 days. Conversely, no differences were found in readmission risk, and the combined endpoint only increased 21.6% (95%CI = 8.4–36.4) for LOH >15 days. Stratified analysis by hospitalisation departments rendered similar post-discharge outcomes, with all exhibiting increased mortality for LOH >15 days and no significant increments in readmission risk. Conclusions: Short hospitalisations are not associated with worse outcomes. While post-discharge readmissions are not affected by LOH, mortality risk increases as the LOH lengthens. These findings were similar across hospitalisation departments

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.Comment: 24 pages, 5 figure

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Ω0T<5.58×10-8, Ω0V<6.35×10-8, and Ω0S<1.08×10-7 at a reference frequency f0=25 Hz. © 2018 American Physical Society

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral

On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×104  years. We infer the component masses of the binary to be between 0.86 and 2.26  M⊙, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17–1.60  M⊙, with the total mass of the system 2.74+0.04−0.01M⊙. The source was localized within a sky region of 28  deg2 (90% probability) and had a luminosity distance of 40+8−14  Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology