HUPA-UCM diabetes dataset.

This dataset provides a collection of Continuous Glucose Monitoring (CGM) data, insulin dose administration, meal ingestion counted in carbohydrate grams, steps, calories burned, heart rate, and sleep quality and quantity assessment ac- quired from 25 people with type 1 diabetes mellitus (T1DM). CGM data was acquired by FreeStyle Libre 2 CGMs, and Fitbit Ionic smartwatches were used to obtain steps, calories, heart rate, and sleep data for at least 14 days. This dataset could be utilized to obtain glucose prediction models, hypoglycemia and hyperglycemia prediction models, and research on the relationships among sleep, CGM values, and the rest of the mentioned variables. This dataset could be used directly from the preprocessed version or customized from raw data. The data set has been used previously with different machine learning algorithms to predict glucose values, hypo, and hyperglycemia and to analyze influences among the features and the quality and quantity of sleep in people with T1DM.

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes.

Learning Difference Equations With Structured Grammatical Evolution for Postprandial Glycaemia Prediction.

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose management requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, and also artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. This study proposes a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the two-hour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency.

Effect of transgenerational environmental condition on genetics parameters of Italian Brown Swiss.

The aim of this study was to infer the effects of heat stress (HS) of dams during late gestation on direct and maternal genetic parameters for traits related to milk production and milk quality parameters (90,558 records) in Italian Brown Swiss cattle (12,072 cows in 617 herds). Daily average temperature-humidity indices (THI) during the last 56 d of pregnancy were calculated, using the climate data from the nearest public weather station for each herd. Heat load effects were considered as the average across the entire periods considering a thermoneutrality condition for data below the THI 60. For parameter estimation a random regression model using the second-order Legendre polynomial regression coefficient for THI considering both animal and maternal effect for heat load. Direct heritability increased sharply from THI 60 to 65, then decreased gradually up to THI ∼72, and sharply thereafter. Maternal heritability showed a different trend, with values close to 0 up until to THI 65 and slightly increasing toward extreme THI values. The study suggests a lower threshold of THI 60 for the onset of HS. Higher heritability values indicate greater selective efficiency in the THI range of 65 to 70, even if a higher standard deviation value have been detected. The effects of high THI during intrauterine life varied among traits with different heritability levels. Genetic correlations for milk, fat, and protein content at 60 THI with increasing value of environmental variable, remained constant (∼0.90) until THI >75, where they slightly decreased (∼0.85). Fat and protein yields, as well as milk and energy-corrected milk, showed correlations dropping to 0.80 around THI 67 to 68 and stabilizing between 0.75 and 0.85 at extreme THI values. Maternal component correlations dropped close to zero, with negative values for protein content at THI 65 to 70. Antagonism between direct and maternal components was stronger for intermediate THI values but less divergent for extremes. Genotype by environment interaction was observed, indicating the selection of resilient animals would be theoretically possible. In the future, the application of climate variables in selection schemes first should take into account the dimensions of the genetic correlations to be able to decide between the simple inclusion of the environmental effect in the statistical models, rather than a real parallel genetic evaluation.

An LSTM-based Neural Network Wearable System for Blood Glucose Prediction in People with Diabetes.

This article proposes the first hardware implemen-tation of a low-power LSTM neural network targeting a wearable medical device designed to predict blood glucose at a 30-minute horizon. This work aims to reduce energy consumption by propos-ing new activation functions that target hardware implementation. On top of this proposal, we also prove there is room for improve-ment in energy consumption by applying neural network optimiza-tions at the algorithmic, such as quantization, and architecture level, LSTM hyperparameters, that consider the target hardware. To validate our proposal, we devise an optimized version of the neural network aimed to be wearable and, therefore, to reduce its energy consumption while preserving its accuracy as much as possible. The hardware is implemented on a Xilinx Virtex-7 FPGA VC707 Evaluation Kit. It is compared with (i) a faithful design of the original neural network implemented on the same evaluation kit, (ii) three state-of-the-art LSTM-based FPGA implementations, and (iii) software implementations running in cutting-edge smartphones:OnePlus NordTM and an Apple iPhone 13 ProTM with artificial in-telligence hardware accelerators. Our proposal consumes between ×1020 and ×7 less energy than the software implementations, being the most efficient system compared to the smartphones. On the other hand, its energy efficiency, measured in GFLOP/J, is between ×2.84 and ×7.82 greater than other state-of-the-art LSTM implementations, proving to be the most suitable implementation for a wearable system for blood glucose prediction.

Customized palatal guide and splint for maxillary expansion.

Customization in orthognathic surgery allows better precision and a reduced surgical time. In Le Fort I osteotomy surgery, the maxillary segmentation is considered one of the most unstable procedures due to transverse instability. Various different types of palatal device have been proposed to address this instability. This note describes a customized bone-borne palatal guide and splint that may help surgeons shorten the surgical time and achieve better three-dimensional repositioning, with more postoperative comfort for the patient and occlusal control for the surgeon.

Establishment of a desirable dose using neutral argon plasma to eradicate miliary peritoneal implants: A phase I/II controlled trial.

INTRODUCTION: Neutral argon plasma (NAP) system could meet the requirements to achieve oncological cytoreduction of peritoneal carcinomatosis with miliary lesions, minimizing the associated morbidity. This phase I/II trial aims to establish the desirable dose that is safe and effective in eliminating tumor cells with lower penetration. METHODS: Patients diagnosed with different origins for peritoneal carcinomatosis and miliary implants were selected for the study. The safe and potentially effective dose (desirability) of NAP was evaluated according to three factors: distance (mm), application time (s) and power (%), to evaluate the response variables such as the presence of tumor cells (Y/N) and the depth of penetration. RESULTS: Ten patients and 120 samples were evaluated and treated with NAP. There was no vascular or organ injury intraoperative using a pre-established dose of 100% (coagulation mode) at a distance of 2-3 cm. The distance was found to be correlated with the presence of the tumor cells in ex-vivo analysis, with an OR of 15.4 (4.0-111.4). The time and energy used were protective factors to eliminate tumor cells with an OR of 0.4 (0.1-0.9) and 0.8 (0.8-0.9), respectively. The safest and most effective desirability results were as follows i) energy 80% during 2-4 s with a distance of 2 cm (0.89), and ii) energy 100% during 2-4 s with a distance of 3 cm (0.90). CONCLUSIONS: The use of NAP during a CRS and HIPEC is safe and effective for eradicating tumor cells on the peritoneal surface at suggested doses of energy, distance and duration. TRIAL IDENTIFICATION: ClinicalTrials.gov Identifier: NCT04904042.

Dataset of a thermal model for the prediction of temperature fields during the creation of austenite/martensite mesostructured materials by localized laser treatments in a Fe-Ni-C alloy.

This work presents constitutive equations and a dataset of a thermal model for the prediction of temperature fields and heating rates during the application of localized laser treatments to a Fe-C-Ni alloy. The model considers transient material properties and the coupling between temperature and microstructure, with emphasis on the phase dependence of the thermal parameters and the hysteresis in the phase change. The model can predict temperature fields that are in agreement with the experimental microstructures at the laser-affected zones. This model can be applied to other materials exhibiting solid-state transformations upon the application of laser treatments.

Evaluating the effect of phenolic compounds as hydrogen acceptors when ruminal methanogenesis is inhibited in vitro - Part 2. Dairy goats.

Most mitigation strategies to reduce enteric methane (CH4) production in the rumen induce an excess of rumen dihydrogen (H2) that is expelled and consequently not redirected to the synthesis of metabolites that can be utilised by the ruminant. We hypothesised that phenolic compounds can be potential H2 acceptors when added to the diet, as they can be degraded to compounds that may be beneficial for the animal, using part of the H2 available when ruminal methanogenesis is inhibited. We performed four in vitro incubation experiments using rumen inoculum from Murciano-Granadina adult goats: Experiment 1 examined the inhibitory potential of Asparagopsis taxiformis (AT) at different concentrations (0, 1, 2, 3, 4 and 5% of the substrate on a DM basis) in 24 h incubations; Experiment 2 investigated the effect of a wide range of phenolic compounds (phenol, catechol, resorcinol, hydroquinone, pyrogallol, phloroglucinol, gallic acid and formic acid) at different doses (0, 2, 4, and 6 mM) on rumen fermentation for 24 h; Experiment 3 evaluated the combined effect of each phenolic compound at 6 mM with AT at 2% DM in sequential batch cultures for 5 days; and Experiment 4 examined the dose-response effect of phloroglucinol at different concentrations (0, 6, 16, 26 and 36 mM) combined with AT in sequential batch cultures for 5 days. Results from Experiment 1 confirmed that AT at 2% DM substantially inhibited CH4 production while significantly increasing H2 accumulation and decreasing the acetate:propionate ratio. Results from Experiment 2 showed that phenolic compounds did not negatively affect rumen fermentation at any dose. In Experiment 3, each phenolic compound at 6 mM combined with AT at 2% DM inhibited CH4 production. Phloroglucinol numerically decreased H2 accumulation and significantly increased total gas production (TGP), volatile fatty acid (VFA) production and the acetate:propionate ratio. In Experiment 4, phloroglucinol at increasing doses supplemented with AT at 2% DM significantly decreased H2 accumulation and the abundances of archaea, protozoa and fungi abundances, and increased TGP, total VFA production and the acetate:propionate ratio in a dose-dependent way. In conclusion, combined treatment with AT and phloroglucinol was successful to mitigate CH4 production while preventing the accumulation of H2, leading to an increase in acetate and total VFA production and therefore an improvement in rumen fermentation in goats.

Evaluating the influence of sleep quality and quantity on glycemic control in adults with type 1 diabetes.

Background: Sleep quality disturbances are frequent in adults with type 1 diabetes. However, the possible influence of sleep problems on glycemic variability has yet to be studied in depth. This study aims to assess the influence of sleep quality on glycemic control. Materials and methods: An observational study of 25 adults with type 1 diabetes, with simultaneous recording, for 14 days, of continuous glucose monitoring (Abbott FreeStyle Libre system) and a sleep study by wrist actigraphy (Fitbit Ionic device). The study analyzes, using artificial intelligence techniques, the relationship between the quality and structure of sleep with time in normo-, hypo-, and hyperglycemia ranges and with glycemic variability. The patients were also studied as a group, comparing patients with good and poor sleep quality. Results: A total of 243 days/nights were analyzed, of which 77% (n = 189) were categorized as poor quality and 33% (n = 54) as good quality. Linear regression methods were used to find a correlation (r =0.8) between the variability of sleep efficiency and the variability of mean blood glucose. With clustering techniques, patients were grouped according to their sleep structure (characterizing this structure by the number of transitions between the different sleep phases). These clusters showed a relationship between time in range and sleep structure. Conclusions: This study suggests that poor sleep quality is associated with lower time in range and greater glycemic variability, so improving sleep quality in patients with type 1 diabetes could improve their glycemic control.

Haematological Alterations Associated with Selected Vector-Borne Infections and Exposure in Dogs from Pereira, Risaralda, Colombia.

Infections due to Ehrlichia, Anaplasma, Dirofilaria, Mycoplasma, Babesia and Hepatozoon continue to be highly prevalent in dogs, especially in tropical and subtropical areas, where vectors of many of them are present. However, many clinical aspects of dogs have not been characterized in detail, including assessing the haematological alterations associated with them, particularly in Colombia and Latin America. A group of 100 dogs with Ehrlichia, Anaplasma, Dirofilaria, Mycoplasma, Babesia and Hepatozoon infections/exposure were assessed by blood smear serology (SNAP4DX) and PCR in Pereira, Colombia. We performed blood counts to evaluate anaemia, leukopenia/leukocytosis, neutropenia, neutrophilia, lymphopenia/lymphocytosis, monocytosis, eosinophilia, and thrombocytopenia, among other alterations. Bivariate analyses were performed on Stata®14, with significant p < 0.05. From the total, 85% presented ≥1 infection (past or present), 66% with coinfections (≥2 pathogens) (Ehrlichia 75%), and 89% presented clinical alterations. A total of 100% showed anaemia, 70% thrombocytopenia, 61% monocytosis, and 47% neutropenia, among other alterations. Additionally, 11% presented pancytopenia and 59% bicytopenia. The median platelet count was lower in infected dogs (126,000 cells/µL) versus non-infected (221,000 cells/µL) (p = 0.003). Thrombocytopenia was higher among infected dogs (75%) versus non-infected (40%) (p = 0.006), with a 91% positive predictive value for infection. Median neutrophil count was lower in infected dogs (6591 cells/µL) versus non-infected (8804 cells/µL) (p = 0.013). Lymphocytosis occurred only among those infected (27%) (p = 0.022). Leukopenia was only observed among infected dogs (13%). Pancytopenia was only observed among infected dogs. Ehrlichiosis and other hematic infections have led to a significant burden of haematological alterations on infected dogs, including pancytopenia in a tenth of them, most with thrombocytopenia and all anemic.

Aberrant perineuronal nets alter spinal circuits, impair motor function, and increase plasticity.

Perineuronal nets (PNNs) are a specialized extracellular matrix that have been extensively studied in the brain. Cortical PNNs are implicated in synaptic stabilization, plasticity inhibition, neuroprotection, and ionic buffering. However, the role of spinal PNNs, mainly found around motoneurons, is still unclear. Thus, the goal of this study is to elucidate the role of spinal PNNs on motor function and plasticity in both intact and spinal cord injured mice. We used transgenic mice lacking the cartilage link protein 1 (Crtl1 KO mice), which is implicated in PNN assembly. Crtl1 KO mice showed disorganized PNNs with an altered proportion of their components in both motor cortex and spinal cord. Behavioral and electrophysiological tests revealed motor impairments and hyperexcitability of spinal reflexes in Crtl1 KO compared to WT mice. These functional outcomes were accompanied by an increase in excitatory synapses around spinal motoneurons. Moreover, following spinal lesions of the corticospinal tract, Crtl1 KO mice showed increased contralateral sprouting compared to WT mice. Altogether, the lack of Crtl1 generates aberrant PNNs that alter excitatory synapses and change the physiological properties of motoneurons, overall altering spinal circuits and producing motor impairment. This disorganization generates a permissive scenario for contralateral axons to sprout after injury.

Simplified risk-prediction for benchmarking and quality improvement in emergency general surgery. Prospective, multicenter, observational cohort study.

BACKGROUND AND AIMS: Emergency General Surgery (EGS) conditions account for millions of deaths worldwide, yet it is practiced without benchmarking-based quality improvement programs. The aim of this observational, prospective, multicenter, nationwide study was to determine the best benchmark cutoff points in EGS, as a reference to guide improvement measures. METHODS: Over a 6-month period, 38 centers (5% of all public hospitals) attending EGS patients on a 24-h, 7-days a week basis, enrolled consecutive patients requiring an emergent/urgent surgical procedure. Patients were stratified into cohorts of low (i.e., expected morbidity risk <33%), middle and high risk using the novel m-LUCENTUM calculator. RESULTS: A total of 7258 patients were included; age (mean ± SD) was 51.1 ± 21.5 years, 43.2% were female. Benchmark cutoffs in the low-risk cohort (5639 patients, 77.7% of total) were: use of laparoscopy ≥40.9%, length of hospital stays ≤3 days, any complication within 30 days ≤ 17.7%, and 30-day mortality ≤1.1%. The variables with the greatest impact were septicemia on length of hospital stay (21 days; adjusted beta coefficient 16.8; 95% CI: 15.3 to 18.3; P < .001), and respiratory failure on mortality (risk-adjusted population attributable fraction 44.6%, 95% CI 29.6 to 59.6, P < .001). Use of laparoscopy (odds ratio 0.764, 95% CI 0.678 to 0.861; P < .001), and intraoperative blood loss (101-500 mL: odds ratio 2.699, 95% CI 2.152 to 3.380; P < .001; and 500-1000 mL: odds ratio 2.875, 95% CI 1.403 to 5.858; P = .013) were associated with increased morbidity. CONCLUSIONS: This study offers, for the first time, clinically-based benchmark values in EGS and identifies measures for improvement.

Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers.

Background: The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records. Objective: This study presents the application of machine learning techniques to these records to predict the blood culture's outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes. Methods: Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture. Results: The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93. Conclusions: Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers' importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time.

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024-0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from -0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from -0.42 to -0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from -0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.

Short communication: Saliva and salivary components affect goat rumen fermentation in short-term batch incubations.

The research about the role of saliva in ruminants has been mainly focused on its buffering capacity together with facilitation of the rumination process. However, the role of salivary bioactive components on modulating the activity of the rumen microbiota has been neglected until recently. This study developed an in vitro approach to assess the impact of different components in saliva on rumen microbial fermentation. Four different salivary fractions were prepared from four goats: (i) non-filtrated saliva (NFS), (ii) filtrated through 0.25 µm to remove microorganisms and large particles (FS1), (iii) centrifuged through a 30 kDa filter to remove large proteins, (FS2), and (iv) autoclaved saliva (AS) to keep only the minerals. Two experiments were conducted in 24 h batch culture incubations with 6 ml of total volume consisting of 2 ml of rumen fluid and 4 ml of saliva/buffer mix. In Experiment 1, the effect of increasing the proportion of saliva (either NFS or FS1) in the solution (0%, 16%, 33% and 50% of the total volume) was evaluated. Treatment FS1 promoted greater total volatile fatty acids (VFA) (+8.4%) and butyrate molar proportion (+2.8%) but lower NH3-N concentrations than NFS fraction. Replacing the bicarbonate buffer solution by increasing proportions of saliva resulted in higher NH3-N, total VFA (+8.0%) and propionate molar proportion (+11%). Experiment 2 addressed the effect of the different fractions of saliva (NFS, FS1, FS2 and AS). Saliva fractions led to higher total VFA and NH3-N concentrations than non-saliva incubations, which suggests that the presence of some salivary elements enhanced rumen microbial activity. Fraction FS1 promoted a higher concentration of total VFA (+7.8%) than the other three fractions, and higher propionate (+26%) than NFS and AS. This agrees with findings from Experiment 1 and supports that 'microbe-free saliva', in which large salivary proteins are maintained, boosts rumen fermentation. Our results show the usefulness of this in vitro approach and suggest that different salivary components can modulate rumen microbial fermentation, although the specific metabolites and effects they cause need further research.

Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle.

Milkability is a trait related to the milking efficiency of an animal, and it is a component of the herd profitability. Due to its economic importance, milkability is currently included in the selection index of the Italian Simmental cattle breed with a weight of 7.5%. This lowly heritable trait is measured on a subjective scale from 1 to 3 (1 = slow, 3 = fast), and genetic evaluations are performed by pedigree-based BLUP. Genomic information is now available for some animals in the Italian Simmental population, and its inclusion in the genetic evaluation system could increase accuracy of breeding values and genetic progress for milkability. The aim of this study was to test the feasibility and advantages of having a genomic evaluation for this trait in the Italian Simmental population. Phenotypes were available for 131,308 cows. A total of 9,526 animals had genotypes for 42,152 loci; among the genotyped animals, 2,455 were cows with phenotypes, and the other were their relatives. The youngest cows with both phenotypes and genotypes (n = 900) were identified as selection candidates. Variance components and heritability were estimated using pedigree information, whereas genetic and genomic evaluations were carried out using BLUP and single-step genomic BLUP (ssGBLUP), respectively. In addition, a weighted ssGBLUP was assessed using genomic regions from a genome-wide association study. Evaluation models were validated using theoretical and realized accuracies. The estimated heritability for milkability was 0.12 ± 0.01. The mean theoretical accuracies for selection candidates were 0.43 ± 0.08 (BLUP) and 0.53 ± 0.06 (ssGBLUP). The mean realized accuracies based on linear regression statistics were 0.29 (BLUP) and 0.40 (ssGBLUP). No genomic regions were significantly associated with milkability, thus no improvements in accuracy were observed when using weighted ssGBLUP. Results indicated that genomic information could improve the accuracy of breeding values and increase genetic progress for milkability in Italian Simmental.

Gender influence on work satisfaction and leadership for medical oncologists: a survey of the Spanish Society of Medical Oncology (SEOM).

BACKGROUND: Women represent an increasing proportion of the oncology workforce; however, globally this does not translate into leadership roles, reflecting disparities in career opportunities between men and women. The Spanish Society of Medical Oncology (SEOM) undertook a survey to investigate gender disparity in the Spanish oncology context. DESIGN: An online survey was made available to SEOM medical oncologists between February and May 2019. It included demographics, professional context and achievements, parenthood and family conciliation issues, workplace gender bias, and approaches to address disparities. RESULTS: Of the 316 eligible respondents, 71.5% were women, 59.5% were aged 45 or younger, and 66.1% had children. Among women, 12.4% were division or unit heads, compared with 45.5% of men, with most women (74.3%) being attending medical oncologists, compared with 45.5% of men. More males were professors (34.4% versus 14.2% of females), had a PhD (46.7% versus 28.8%), and/or had led clinical research groups (41.1% versus 9.7%). Spending time overseas after completing a residency was also more common for men than women (34.4% versus 20.4%). Professional satisfaction was similar between genders, driven primarily by patient care and intellectual stimulation. More women (40.7%) considered parenthood to have a strong negative impact on their career, compared with men (9.0%). Main perceived barriers to gender equality included a lack of work-life balance (72.6% women, 44.4% men), bias of peers and superiors (50.0% women, 18.9% men), and different career goals (41.2% women, 24.4% men). Preferred solutions included educational programs and scholarships (52.9%), communication and leadership training (35.8%), childcare at conferences (33.2%), and postmaternity return-to-work incentives (32.0%). CONCLUSION: There is a clear paucity of equal opportunities for female oncologists in Spain. This can be addressed by encouraging professional development and merit recognition particularly for younger female oncologists, and empowering women to be involved in management and leadership of institutions and professional societies.