Performance of commercial growing-finishing pigs fed supplemental isoquinoline alkaloids: a statistical process control analysis.

BACKGROUND: Statistical Process Control (SPC) is a powerful statistical tool that can be used in animal production to evaluate the evolution of production parameters overtime in response to the implementation of a specific strategy. The aim of this study was to evaluate the effect of supplementing growing-finishing pigs with isoquinoline alkaloids (IQ) on growth performance parameters by using the SPC method. IQ are natural secondary plant metabolites which have been extensively investigated in food animals due to their efficacy in supporting growth performance and the overall health status. Performance parameters and medication usage were collected from 1,283,880 growing-finishing pigs fed the same basal diet, 147,727 of which were supplemented with IQ from day 70 of life until slaughter. RESULTS: Supplementation with IQ improved feed conversion ratio, while feed intake and daily gain were maintained. CONCLUSION: SPC methods are useful statistical tools to evaluate the effect of using a new feed additive in the feed of pigs on growth performance at a commercial level. Additionally, IQ supplementation improved growth performance and it can be considered as a good strategy to reduce feed conversion in growing-finishing pigs.

Immunogenicity, effectiveness and safety of COVID-19 vaccine in older adults living in nursing homes: A real-life study.

INTRODUCTION: BNT162b2 (BioNTech and Pfizer) is a nucleoside-modified mRNA vaccine that provides protection against SARS-CoV-2 infection and is generally well tolerated. However, data about its efficacy, immunogenicity and safety in people of old age or with underlying chronic conditions are scarce. PURPOSE: To describe BNT162b2 (BioNTech and Pfizer) COVID-19 vaccine immunogenicity, effectiveness and reactogenicity after complete vaccination (two doses), and immunogenicity and reactogenicity after one booster, in elders residing in nursing homes (NH) and healthy NH workers in real-life conditions. METHODS: Observational, ambispective, multicenter study. Older adults and health workers were recruited from three nursing homes of a private hospital corporation located in three Spanish cities. The primary vaccination was carried out between January and March 2021. The follow-up was 13 months. Humoral immunity, adverse events, SARS-CoV-2 infections, hospitalizations and deaths were evaluated. Cellular immunity was assessed in a participant subset. RESULTS: A total of 181 residents (mean age 84.1 years; 89.9% females, Charlson index ≥2: 45%) and 148 members of staff (mean age 45.2 years; 70.2% females) were surveyed (n:329). After primary vaccination of 327 participants, vaccine response in both groups was similar; ≈70% of participants, regardless of the group, had an antibody titer above the cut-off considered currently protective (260BAU/ml). This proportion increased significantly to ≈ 98% after the booster (p<0.0001 in both groups). Immunogenicity was largely determined by a prior history of COVID-19 infection. Twenty residents and 3 workers were tested for cellular immunity. There was evidence of cellular immunity after primary vaccination and after booster. During the study, one resident was hospitalized for SARS-CoV-2. No SARS-CoV-2-related deaths were reported and most adverse events were mild. CONCLUSIONS: Our results suggest that the BNT162b2 mRNA COVID-19 vaccine is immunogenic, effective and safe in elderly NH residents with underlying chronic conditions.

Sensor Data Analytics: Challenges and Methods for Data-Intensive Applications.

Sensors have become a key element for the development of the Information Society [...].

Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection.

In this paper, a method to classify behavioural patterns of cattle on farms is presented. Animals were equipped with low-cost 3-D accelerometers and GPS sensors, embedded in a commercial device attached to the neck. Accelerometer signals were sampled at 10 Hz, and data from each axis was independently processed to extract 108 features in the time and frequency domains. A total of 238 activity patterns, corresponding to four different classes (grazing, ruminating, laying and steady standing), with duration ranging from few seconds to several minutes, were recorded on video and matched to accelerometer raw data to train a random forest machine learning classifier. GPS location was sampled every 5 min, to reduce battery consumption, and analysed via the k-medoids unsupervised machine learning algorithm to track location and spatial scatter of herds. Results indicate good accuracy for classification from accelerometer records, with best accuracy (0.93) for grazing. The complementary application of both methods to monitor activities of interest, such as sustainable pasture consumption in small and mid-size farms, and to detect anomalous events is also explored. Results encourage replicating the experiment in other farms, to consolidate the proposed strategy.

Taking Particle Tracking into Practice by Novel Software and Screening Approach: Case-Study of Oral Lipid Nanocarriers.

The success on the design of new oral nanocarriers greatly depends on the identification of the best physicochemical properties that would allow their diffusion across the mucus layer that protects the intestinal epithelium. In this context, particle tracking (PT) has arisen in the pharmaceutical field as an excellent tool to evaluate the diffusion of individual particles across the intestinal mucus. In PT, the trajectories of individual particles are characterized by the mean square displacement (MSD), which is used to calculate the coefficient of diffusion (D) and the anomalous diffusion parameter (α) as MSD=4Dτα. Unfortunately, there is no stablished criteria to evaluate the goodness-of-fit of the experimental data to the mathematical model. This work shows that the commonly used R2 parameter may lead to an overestimation of the diffusion capacity of oral nanocarriers. We propose a screening approach based on a combination of R2 with further statistical parameters. We have analyzed the effect of this approach to study the intestinal mucodiffusion of lipid oral nanocarriers, compared to the conventional screening approach. Last, we have developed software able to perform the whole PT analysis in a time-saving, user-friendly, and rational fashion.

Optimization instances for deterministic and stochastic problems on energy efficient investments planning at the building level.

Optimization instances relate to the input and output data stemming from optimization problems in general. Typically, an optimization problem consists of an objective function to be optimized (either minimized or maximized) and a set of constraints. Thus, objective and constraints are jointly a set of equations in the optimization model. Such equations are a combination of decision variables and known parameters, which are usually related to a set domain. When this combination is a linear combination, we are facing a classical Linear Programming (LP) problem. An optimization instance is related to an optimization model. We refer to that model as the Symbolic Model Specification (SMS) containing all the sets, variables, and parameters symbols and relations. Thus, a whole instance is composed by the SMS, the elements in each set, the data values for all the parameters, and, eventually, the optimal decisions resulting from the optimization solution. This data article contains several optimization instances from a real-world optimization problem relating to investment planning on energy efficient technologies at the building level.