TEXTURE OF EXTRA VIRGIN OLIVE OIL-ENRICHED MASHED POTATOES: SENSORY, INSTRUMENTAL AND STRUCTURAL RELATIONSHIPS

The definitive version is available at www3.interscience.wiley.comThe aim of this work was to study the effect of the addition of extra virgin olive oil (EVOO) on instrumental textural properties, sensory texture profile analysis, and microstructure of fresh and frozen/thawed mashed potatoes formulated without and with added cryoprotectants (kappa-carrageenan [k-C] and xanthan gum [XG]). EVOO behaves as soft filler due to droplet aggregates, whereas addition of cryoprotectants led to more structured mashed potatoes (MP) thanks to the gelling properties of k-C. Both the percentage of added EVOO and processing had a much less significant effect on the texture of the MP containing k-C and XG, evidencing the ability of this biopolymer blend to impart freeze/thaw stability. All samples with added EVOO were perceived as significantly softer and creamier than the samples without EVOO, whereas all MP samples with added cryoprotectants were perceived as significantly thicker and creamier than those without hydrocolloids.Spanish Ministry of Science and Innovation financial support (AGL2007-62851)Peer Reviewe

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed

Update on Extended Treatment for Venous Thromboembolism

The importance of assessing the probability of venous thromboembolism recurrence, a condition that includes deep vein thrombosis and pulmonary embolism, lies in the fact that it is the most important factor in deciding the duration of anticoagulant treatment. Risk of recurrence depends mostly on the presence of a risk factor for developing venous thromboembolism, with patients with unprovoked events being at the higher risk of recurrence. The risk of recurrence needs to be balanced with the risk of bleeding and the potential severity of these thrombotic and hemorrhagic events. In patients with an unprovoked venous thromboembolism who complete treatment for the acute (first 10 days) and post-acute phase of the disease (from day 10 to 3-6 months), decision has to be made regarding prolonged antithrombotic therapy to prevent recurrences. The main goal of extended treatment is preventing recurrences with a safe profile in terms of bleeding risk. Many therapeutic options are now available for these patients, including antiplatelet therapy with aspirin or direct oral anticoagulants. Moreover, apixaban and rivaroxaban at prophylactic doses have demonstrated efficacy in preventing recurrences with a low risk of bleeding

Glottal-Source Spectral Biometry for Voice Characterization

The biometric signature derived from the estimation of the power spectral density singularities of a speaker’s glottal source is described in the present work. This consists in the collection of peak-trough profiles found in the spectral density, as related to the biomechanics of the vocal folds. Samples of parameter estimations from a set of 100 normophonic (pathology-free) speakers are produced. Mapping the set of speaker’s samples to a manifold defined by Principal Component Analysis and clustering them by k-means in terms of the most relevant principal components shows the separation of speakers by gender. This means that the proposed signature conveys relevant speaker’s metainformation, which may be useful in security and forensic applications for which contextual side information is considered relevant

Bio-inspired Dynamic Formant Tracking for Phonetic Labelling

It is a known fact that phonetic labeling may be relevant in helping current Automatic Speech Recognition (ASR) when combined with classical parsing systems as HMM's by reducing the search space. Through the present paper a method for Phonetic Broad-Class Labeling (PCL) based on speech perception in the high auditory centers is described. The methodology is based in the operation of CF (Characteristic Frequency) and FM (Frequency Modulation) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus in the automatic detection of formants and formant dynamics on speech. Results obtained informant detection and dynamic formant tracking are given and the applicability of the method to Speech Processing is discussed

The Leishmania HSP20 Is Antigenic during Natural Infections, but, as DNA Vaccine, It does not Protect BALB/c Mice against Experimental L. amazonensis Infection

Protozoa of the genus Leishmania are causative agents of leishmaniasis, an important health problem in both human and veterinary medicine. Here, we describe a new heat shock protein (HSP) in Leishmania, belonging to the small HSP (sHSP) family in kinetoplastids. The protein is highly conserved in different Leishmania species, showing instead significant divergence with sHSP's from other organisms. The humoral response elicited against this protein during Leishmania infection has been investigated in natural infected humans and dogs, and in experimentally infected hamsters. Leishmania HSP20 is a prominent antigen for canine hosts; on the contrary, the protein seems to be a poor antigen for human immune system. Time-course analysis of appearance of anti-HSP20 antibodies in golden hamsters indicated that these antibodies are produced at late stages of the infection, when clinical symptoms of disease are patent. Finally, the protective efficacy of HSP20 was assessed in mice using a DNA vaccine approach prior to challenge with Leishmania amazonensis

A Hybrid Parameterization Technique for Speaker Identification

Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. The separation methodology is based in Joint Process Estimation under the un-correlation hypothesis between vocal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency domains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speakers are shown comparing different parameterization strategies. The results confirm the better performance of decoupled parameterization compared against approaches based on plain speech parameterization