Using qualitative behaviour assessment (QBA) to explore the emotional state of horses and its association with human-animal relationship

This study aimed to apply qualitative behaviour assessment (QBA) to horses farmed in single boxes, in order to investigate their emotional state and explore its association with indicators of human-animal relationship. A fixed list of 13 QBA descriptive terms was determined. Three assessors experienced with horses and skilled in measuring animal behaviour underwent a common training period, consisting of a theoretical phase and a practical phase on farm. Their inter-observer reliability was tested on a live scoring of 95 single stabled horses. Principal Component Analysis (PCA) was conducted to analyse QBA scores and identify perceived patterns of horse expression, both for data obtained in the training phase and from the on-farm study. Given the good level of agreement reached in the training phase (Kendall W\u202f=\u202f0.76 and 0.74 for PC1 and PC2 scores respectively), it was considered acceptable in the subsequent on-farm study to let these three observers each carry out QBA assessments on a sub-selection of a total of 355 sport and leisure horses, owned by 40 horse farms. Assessment took place immediately after entering the farms: assessors had never entered the farms before and were unaware of the different backgrounds of the farms. After concluding QBA scoring, the assessors further evaluated each horse with an avoidance distance test (AD) and a forced human approach test (FHA). A MANOVA test was used to assess the association of the AD and FHA tests with the on-farm QBA PC scores. The QBA approach described in this paper was feasible on farm and showed good acceptability by owners. In the analysis of on-farm QBA scores, the first Principal Component ranged from relaxed/at ease to uneasy/alarmed, the second Component ranged from curious/pushy to apathetic. Horses perceived as more relaxed/at ease with QBA showed less avoidance during the AD test (P\u202f=\u202f0.0376), and responded less aggressively and fearfully to human presence in the FHA test (P\u202f<\u202f0.0001). Our results support the hypothesis that QBA is sensitive to the quality of human contact in horses

Improving strain diagnosis of prion disease by diffusion MRI and biophysical modelling

Sporadic Creutzfeldt–Jakob disease (sCJD) is the most common form of prion disease, characterized by five different strains, presenting intracellular vacuoles with different diameter/distribution. Unfortunately, no reliable non-invasive method for strain identification currently exists. Here we provide the first quantitative maps of MR-measured vacuolar diameter/density in five sCJD patients, using multishell diffusion MRI and biophysical modelling. Results show distribution of small and larger vacuoles in the brain lesions of each patient, presumably corresponding to different sCJD strains, and absence of vacuoles in five age-matched healthy controls. If validated, this method would be extremely valuable for non-invasive diagnosis of sCJD strain

Brain activity underlying negative self- and other-perception in adolescents: The role of attachment-derived self-representations

One of teenagers' key developmental tasks is to engage in new and meaningful relationships with peers and adults outside the family context. Attachment-derived expectations about the self and others in terms of internal attachment working models have the potential to shape such social reorientation processes critically and thereby influence adolescents' social-emotional development and social integration. Because the neural underpinnings of this developmental task remain largely unknown, we sought to investigate them by functional magnetic resonance imaging. We asked n = 44 adolescents (ages 12.01-18.84 years) to evaluate positive and negative adjectives regarding either themselves or a close other during an adapted version of the well-established self-other trait-evaluation task. As measures of attachment, we obtained scores reflecting participants' positive versus negative attachment-derived self- and other-models by means of the Relationship Questionnaire. We controlled for possible confounding factors by also obtaining scores reflecting internalizing/externalizing problems, schizotypy, and borderline symptomatology. Our results revealed that participants with a more negative attachment-derived self-model showed increased brain activity during positive and negative adjective evaluation regarding the self, but decreased brain activity during negative adjective evaluation regarding a close other, in bilateral amygdala/parahippocampus, bilateral anterior temporal pole/anterior superior temporal gyrus, and left dorsolateral prefrontal cortex. These findings suggest that a low positivity of the self-concept characteristic for the attachment anxiety dimension may influence neural information processing, but in opposite directions when it comes to self- versus (close) other-representations. We discuss our results in the framework of attachment theory and regarding their implications especially for adolescent social-emotional development and social integration

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR &lt; 60 mL/min/1.73 m2) or eGFR reduction &gt; 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR &lt; 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR &gt; 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

A data-driven prediction method for an early warning of coccidiosis in intensive livestock systems: A preliminary study

Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials—some of these with antimicrobial action—is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis

Preliminary Study on a Data-driven Prediction Method for the Early Detection of Coccidiosis in Intensive Poultry Systems

Coccidiosis in poultry is still one of the main enteric diseases that can influence the performance of animals raised under intensive production system. Unfortunately, these enteric pathologies are not preventable, and diagnosis is available only when the disease is full-blown. The available diagnostic methods such as oocyst count and lesion scoring are extremely time-consuming and costly. For this reason, the use of a prediction method, that works in real-time, could provide valuable and rapid information for farmers with clear and suitable alerts in their daily routine. The quick reaction to any change in the health, well-being or productive states is the fundamental element for reduction of drugs usage, prevention and control of coccidiosis, and improvement of animal welfare. The main object of this research was to assess the possible relationship between the air quality data and the number of oocysts hosted in three different broiler houses. Prototypes have been developed and tested in three different experimental poultry farms based on collected data of the Volatile Organic Compounds (VOCs - organic chemicals and detectable to the human nose and to an electronic device such as electronic nose) emitted from broilers during the entire life cycle of the animals. A data-driven machine learning algorithm was built to relate VOCs data to the number of oocysts during time. For each broiler production cycle, the results showed that variations in the VOCs were related to the change of oocysts number, and specific critical VOCs values were associated to abnormal levels of oocysts count at an early stage of the disease. In conclusion, the results of this study support the feasibility of building an automatic data-driven machine learning algorithm for early warning of coccidiosis for intensive broiler production

Crack propagation modeling in functionally graded materials using Moving Mesh technique and interaction integral approach

This paper presents a novel FE modeling approach based on Moving Mesh technique to reproduce crack propagation mechanisms in Functionally Graded Materials. The moving mesh is consistent with the Arbitrary Lagrangian-Eulerian formulation, which is suited to handle growing random cracks, avoiding extensive remeshing processes. This approach is based on the Interaction Integral Method to extract the mixed-mode Stress Intensity Factors, which are necessary to establish crack onset conditions and propagation direction. Among the different available options for FGM, the incompatibility formulation is adopted. The proposed scheme reproduces the propagation mechanisms by moving the computational nodes around the crack tip, according to standard fracture criteria. Mesh regularization technique based on proper rezoning equations ensures the consistency of the motion, reducing mesh distortion. The reliability of the proposed method is evaluated through comparisons with experimental data and existing numerical approaches. The computational efficiency is checked through parametric analyses on mesh discretization and accuracy in the prediction of the crack path and fracture variables. The results show how the proposed method could represent a valid tool to simulate the propagation mechanisms in FGM, in which heterogeneous macro-properties involve complex crack paths

A detailed micro-model for brick masonry structures based on a diffuse cohesive-frictional interface fracture approach

In the past decades, the mechanical behavior of brick masonry material has been largely investigated using different modeling strategies, ranging from purely microscopic to purely macroscopic ones. The so-called simplified micro-modeling approaches, in which the behavior of mortar joints and brick/mortar interfaces is lumped in discontinuous elements, are commonly judged as very effective for accurately representing the interaction between the masonry constituents with an acceptable computational burden. However, they completely disregard the competition between brick/mortar decohesion and mortar cracking, whose role is not negligible, especially in presence of sufficiently thick joints and/or high-strength mortars. In this work, a detailed micro-modeling approach is proposed for the nonlinear analysis of brickworks subjected to in-plane loads. Such an approach allows failure to occur at the brick/mortar interface level and/or inside the mortar layer, while keeping the discrete nature of fracture phenomena. For this purpose, a novel diffuse cohesive-frictional interface approach for joints is presented, able to simulate multiple micro-crack onset and propagation along a-priori unknown paths. Suitable comparisons with a simplified micro-model are provided to validate the proposed approach. Moreover, a good agreement with the experimental outcomes is found, thereby assessing the reliability of the present fracture-based detailed micro-model in the numerical prediction of masonry strength under complex loading conditions

[Mesotherapy as a treatment of pain and disability in patients affected by neck pain in spondylartrosis]

Mesotherapy is a technique that treats locoregional pain with intradermal injection of a drug in the affected area. Its short-term efficacy was observed in patients with low back pain using both normal saline solution, if there were contraindications to drugs' use, or a cocktail of drugs (normal saline solution, lidocaine hydrochloride, and lysine acetylsalicylate), whereas only the latter provided benefit for up to three months after treatment. The aim of this study was to measure the effects of mesotherapy in patients affected by neck pain in spondylarthrosis, a common pathology in rehabilitation, associated with significant disability and increased health expenditure. One hundred patients participated in the study, of whom 50 (mean age 66.9 years) were treated with mesotherapy with a cocktail of drugs and 50 (mean age 64.7 years) with normal saline solution. Pain and disability were measured at different times (i.e. before treatment, at the end of five weeks of treatment, four weeks and 12 weeks after treatment), by using different pain scales, including a visual analogue scale, the short-form McGill pain questionnaire, the Present Pain Intensity scale and the Neck Disability Index. Mesotherapy with either normal saline solution or with a cocktail of drugs were both found to be effective in the short term in reducing pain and disability. However, only patients treated with a cocktail of drugs showed improvement at three months following treatment