The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review.

BACKGROUND: Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine. OBJECTIVE: We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies. METHODS: We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility. RESULTS: After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant. CONCLUSIONS: The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey.

BACKGROUND: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. METHODS: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist's and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents' electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. RESULTS: Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. CONCLUSIONS: Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks.

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.

Effects of Short- and Medium-Chain Fatty Acids on Production, Meat Quality, and Microbial Attributes-A Review.

The non-therapeutic use of antimicrobials in poultry production contributes to the spread of drug-resistant pathogens in both birds and humans. Antibiotics are known to enhance feed efficiency and promote the growth and weight gain of poultry. New regulatory requirements and consumer preferences have led to a reduced use of antibiotics in poultry production and to the discovery of natural alternatives to antibiotic growth promoters. This interest is not only focused on the direct removal or inhibition of causative microorganisms but also on the prevention of diseases caused by enteric pathogens using a range of feed additives. A group of promising feed additives is composed of short- and medium-chain fatty acids (SCFAs and MCFAs) and their derivatives. MCFAs possess antibacterial, anticoccidial, and antiviral effects. In addition, it has been proven that these acids act in synergy if they are used together with organic acids, essential oils, or probiotics. These fatty acids also benefit intestinal health integrity and homeostasis in broilers. Other effects have been documented as well, such as an increase in intestinal angiogenesis and the gene expression of tight junctions. The aim of this review is to provide an overview of SCFAs and MCFAs as alternatives to antibiotic growth promoters and to summarize the current findings in the literature to show their possible benefits on production, meat quality, and gut health in poultry.

Usp14 is required for spermatogenesis and ubiquitin stress responses in Drosophila melanogaster.

Deubiquitylating (DUB) enzymes free covalently linked ubiquitin moieties from ubiquitin-ubiquitin and ubiquitin-protein conjugates, and thereby maintain the equilibrium between free and conjugated ubiquitin moieties and regulate ubiquitin-mediated cellular processes. Here, we performed genetic analyses of mutant phenotypes in Drosophila melanogaster and demonstrate that loss of Usp14 function results in male sterility, with defects in spermatid individualization and reduced testicular free monoubiquitin levels. These phenotypes were rescued by germline-specific overexpression of wild-type Usp14. Synergistic genetic interactions with Ubi-p63E and cycloheximide sensitivity suggest that ubiquitin shortage is a primary cause of male sterility. In addition, Usp14 is predominantly expressed in testes in Drosophila, indicating a higher demand for this DUB in testes that is also reflected by testis-specific loss-of-function Usp14 phenotypes. Collectively, these results suggest a major role of Usp14 in maintaining normal steady state free monoubiquitin levels during the later stages of Drosophila spermatogenesis.This article has an associated First Person interview with the first author of the paper.

Validation of the Musculoskeletal Health Questionnaire in a general population sample: a cross-sectional online survey in Hungary.

BACKGROUND: The Versus Arthritis Musculoskeletal Health Questionnaire (MSK-HQ) measures symptom severity and health-related quality of life (HRQoL) of people with musculoskeletal (MSK) conditions. We aimed to test the psychometric properties of the MSK-HQ among the general adult population and identify the determinants of MSK-HQ states. In addition, we aimed to explore the relationship between MSK-HQ and standard well-being measurement tools. METHODS: The translation proccess of the MSK-HQ into Hungarian followed the standard methods provided by the developer. A cross-sectional online survey was performed in Hungary involving a population normative sample (N = 2004, women: 53.1%; mean age: 48.3, SD = 16.6 years). Socio-demographic characteristics and self-reported MSK disorders were recorded. Alongside the MSK-HQ, standard measures of HRQoL (EQ-5D-5L), physical functioning (HAQ-DI) and well-being (ICECAP-A/O, WHO-5, Happiness VAS) were applied. Clinical and convergent validity were assessed by subgroup comparisons (Mann-Whitney-U and Kruskal-Wallis tests) and Spearman's rank correlations. Internal consistency was assessed by Cronbach's alpha. Test-retest reliability (N = 50) was evaluated by intraclass correlation coefficient (ICC). Predictors of MSK-HQ were analysed by ordinary least square multiple regressions. RESULTS: The mean MSK-HQ index score was 44.1 (SD = 9.9). MSK-HQ scores were significantly lower in subgroups with self-reported MSK disorders. Correlations were strong between MSK-HQ and EQ-5D-5L (0.788), EQ VAS (0.644) and HAQ-DI (-0.698) and moderate with the well-being measures (p < 0.05). Cronbach's alpha was 0.924 and ICC was 0.936 (p < 0.05). Being a man, living in the capital, having higher income and education were positively associated with MSK-HQ scores. CONCLUSIONS: This is the first study to prove the validity and reliability of the MSK-HQ among the general public. The impact of socio-demographic characteristics on MSK-HQ scores deserves consideration in clinical studies.

Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods.

It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the two conflicting demands is to continuously monitor the animals. In this article, rumen bolus sensor techniques are reviewed, as they can provide lifelong monitoring due to their implementation. The applied sensory modalities are reviewed also using data transmission and data-processing techniques. During the processing of the literature, we have given priority to artificial intelligence methods, the application of which can represent a significant development in this field. Recommendations are also given regarding the applicable hardware and data analysis technologies. Data processing is executed on at least four levels from measurement to integrated analysis. We concluded that significant results can be achieved in this field only if the modern tools of computer science and intelligent data analysis are used at all levels.

Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals.

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate-the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.

Development of Population Tariffs for the ICECAP-A Instrument for Hungary and their Comparison With the UK Tariffs.

OBJECTIVES: The ICEpop CAPability measure for Adults (ICECAP-A) was developed to assess the capability well-being of adults for use in economic evaluations. Currently, ICECAP-A tariffs are available only for the UK population. The objectives of this study were to develop a Hungarian tariff set for the ICECAP-A instrument and to explore intercountry differences between the Hungarian and the UK value sets. METHODS: A survey was conducted by computer-assisted personal interviews on a sample representative of the Hungarian adult population (N = 1000) to elicit their preferences regarding ICECAP-A attributes with the use of a best-worst scaling choice task. A latent class multinomial logit model with continuous variance scale was used to estimate the weights for each of the 4 capability levels of all 5 ICECAP-A attributes, namely, attachment, stability, achievement, enjoyment, and autonomy. RESULTS: The model identified 2 preference classes with approximately equal share. The first class had a stronger relative preference for autonomy and achievement, whereas the second class had a strong preference for attachment. Multivariate analysis of the classes revealed that women, pensioners, people who are married or living in a partnership, and people with poorer health status are characteristics associated with the latter class membership (preference for attachment). Population tariffs were estimated from the model. Overall, attachment was found to be the most important attribute, followed by stability, enjoyment, achievement, and autonomy. CONCLUSIONS: Hungarian tariffs are largely consistent with those found for the United Kingdom; nevertheless, autonomy seems to be less important in Hungary compared with the United Kingdom.

Lower Limb Amputations and Revascularisation Procedures in the Hungarian Population: A 14 Year Retrospective Cohort Study.

OBJECTIVE: The aim of this study was to assess the long term trends of lower limb amputation and revascularisation in Hungary over 14 years. METHODS: This was a retrospective cohort study that included all patients who underwent lower limb amputation or revascularisation over a 14 year period (2004-2017) in Hungary. Inpatient administrative data claims covering the entire beneficiary population were incorporated. Lower limb amputations (both minor and major) and revascularisation procedures (both open and endovascular) were identified in the claims files. Incidence rates were calculated and time trends were assessed via a generalised additive model. RESULTS: From 2004 to 2017, a total of 121 351 lower limb amputations (61 154 minor; 60 197 major) and 149 355 revascularisation procedures (89 243 open; 60 112 endovascular) were detected in 140 581 patients. The number of minor amputations decreased moderately in the last few years of the study period, while major amputations showed a slight decline (15%) beginning after 2013, which was more marked (22%) following adjustment for age. While the crude incidence of open vascular surgery procedures decreased by 31% (from 74.5/105 to 51.4/105), endovascular procedures showed growth by 79% (from 33.7/105 to 60.4/105) over the whole observation period. CONCLUSION: Observed amputation and revascularisation trends in Hungary are similar to the international experience. The major difference is a more than one decade lag in the starting point of the decline of amputations and in the move towards endovascular procedures. The number of amputations is more than twofold higher and the number of revascularisations is close to half that reported internationally. This comprehensive report of two vascular care performance indicators reveals an east/west vascular health divide in Europe and indicates the need to improve amputation prevention.

Exploring eHealth Literacy and Patient-Reported Experiences With Outpatient Care in the Hungarian General Adult Population: Cross-Sectional Study.

BACKGROUND: Digital health, which encompasses the use of information and communications technology in support of health, is a key driving force behind the cultural transformation of medicine toward people-centeredness. Thus, eHealth literacy, assisted by innovative digital health solutions, may support better experiences of care. OBJECTIVE: The purpose of this study is to explore the relationship between eHealth literacy and patient-reported experience measures (PREMs) among users of outpatient care in Hungary. METHODS: In early 2019, we conducted a cross-sectional survey on a large representative online sample recruited from the Hungarian general population. eHealth literacy was measured with the eHealth Literacy Scale (eHEALS). PREMs with outpatient care were measured with a set of questions recommended by the Organisation for Economic Co-operation and Development (OECD) for respondents who attended outpatient visit within 12 months preceding the survey. Bivariate relationships were explored via polychoric correlation, the Kruskal-Wallis test, and chi-square test. To capture nonlinear associations, after controlling covariates, we analyzed the relationship between eHEALS quartiles and PREMs using multivariate probit, ordinary least squares, ordered logit, and logistic regression models. RESULTS: From 1000 survey respondents, 666 individuals (364 females, 54.7%) were included in the study with mean age of 48.9 (SD 17.6) years and mean eHEALS score of 29.3 (SD 4.9). Respondents with higher eHEALS scores were more likely to understand the health care professionals' (HCPs') explanations (χ29=24.2, P=.002) and to be involved in decision making about care and treatment (χ29=18.2, P=.03). In multivariate regression, respondents with lowest (first quartile) and moderately high (third quartile) eHEALS scores differed significantly, where the latter were more likely to have an overall positive experience (P=.02) and experience fewer problems (P=.02). In addition, those respondents had better experiences in terms of how easy it was to understand the HCPs' explanations (P<.001) and being able to ask questions during their last consultation (P=.04). Patient-reported experiences of individuals with highest (fourth quartile) and lowest (first quartile) eHEALS levels did not differ significantly in any items of the PREM instrument, and neither did composite PREM scores generated from the PREM items (P>.05 in all models). CONCLUSIONS: We demonstrated the association between eHealth literacy and PREMs. The potential patient-, physician-, and system-related factors explaining the negative experiences among people with highest levels of eHealth literacy warrant further investigation, which may contribute to the development of efficient eHealth literacy interventions. Further research is needed to establish causal relationship between eHealth literacy and patient-reported experiences.

Evaluation of a commercial intravaginal thermometer to predict calving in a Hungarian Holstein-Friesian dairy farm.

In this study, the utility of a commercial intravaginal thermometer was evaluated as an automated method for the prediction of calving in a total of 257 healthy pregnant Holstein-Friesian female cattle. The accuracy and the sensitivity of predicting calving within 48 hr before calving were also evaluated. The intravaginal temperature changes from 72 hr before and up to calving were significantly (p ≤ .001) affected by parity, season (summer vs. autumn), the time of day (8 a.m. or 8 p.m.) and the 6-hr time intervals (38.19°C: first interval 0 to 6 hr before calving vs. 38.78°C: twelfth interval 66 to 72 hr before calving), while the gender (p = .943), and the weight of the calf (p = .610), twinning (p = .300), gestation length (p = .186), foetal presentation (p = .123), dystocia (p = .197) and retention of foetal membranes (p = .253) did not affect it significantly. The sensitivity of the SMS of expecting calving within 48 hr and the positive predictive value were 62.4% and 75%, respectively, while the sensitivity and the positive predictive value for the SMS of expulsion reached 100%. It can be concluded that the investigated thermometer is not able to predict calving within 48 hr accurately; however, imminent calving can be accurately alerted.

Physical Validation of a Residual Impedance Rejection Method during Ultra-Low Frequency Bio-Impedance Spectral Measurements.

Accurate and reliable measurement of the electrical impedance spectrum is an essential requirement in order to draw relevant conclusions in many fields and a variety of applications; in particular, for biological processes. Even in the state-of-the-art methods developed for this purpose, the accuracy and efficacy of impedance measurements are reduced in biological systems, due to the regular occurrence of parameters causing measurement errors such as residual impedance, parasitic capacitance, generator anomalies, and so on. Recent observations have reported the necessity of decreasing such inaccuracies whenever measurements are performed in the ultra-low frequency range, as the above-mentioned errors are almost entirely absent in such cases. The current research work proposes a method which can reject the anomalies listed above when measuring in the ultra-low frequency range, facilitating data collection at the same time. To demonstrate our hypothesis, originating from the consideration of the determinant role of the measuring frequency, a physical model is proposed to examine the effectiveness of our method by measuring across the commonly used vs. ultra-low frequency ranges. Validation measurements reflect that the range of frequencies and the accuracy is much greater than in state-of-the-art methods. Using the proposed new impedance examination technique, biological system characterization can be carried out more accurately.

Regional variation of lower limb major amputations on different geographic scales - a Hungarian nationwide study over 13 years.

Background: The incidence of lower limb major amputations is an important healthcare quality indicator, as it reflects all efforts aimed to prevent limb loss. Analysis of within-country regional variations in incidence may reveal the sources of disparities in care. Materials and methods: Based on the data of the Hungarian healthcare beneficiary population from 2004 to 2016, the incidence of lower limb major amputations and its spatial variations was determined regionally on four levels of geographic resolution. Variability and autocorrelation were quantified on different resolutions. Results: A total of 56,468 lower limb major amputation procedures were identified in 49,528 patients over the observation period. Marked regional variations were detected at all geographic scale levels. In the case of county-level and local administrative level, the systematic component of variation was 0.03 and 0.09, respectively. Only half of the variation at local administrative level was explained by county. Conclusions: Lower limb major amputations show marked regional variations on the different geographic levels of resolution. The more granular the assessment, the higher the regional variation was. Assumingly, this observation is partially a mathematical necessity but may also be related to the different characteristics of care at a given level of spatial aggregation. The decomposition of the variance of amputation rates indicates that the potential explanatory factors contributing to spatial variability are multiple and may be interpreted on different levels of geographic resolution. Addressing the unwarranted variations and resolving the issues that contribute to high lower limb major amputation rates needs further explorative analysis.

Pregnancy and stillbirth losses in dairy cows with singleton and twin pregnancies.

The aim of this study was to examine the effect of twin pregnancy, fetal laterality, the number of corpora lutea (CL) and cavitary CL on pregnancy losses in Holstein-Friesian cows with a positive pregnancy diagnosis based on ultrasonography between days 29-42 after AI. Pregnancy was confirmed by transrectal palpation between days 57-70 after AI and at the time of drying-off as well. Twin pregnancy rate was 8.4% at the time of the early pregnancy examination. Pregnancy loss did not differ between singleton- and twin-carrying animals either between days 57-70 of gestation or at drying-off. More losses occurred in singletons between days 29-42 and 57-70 in cows with cavitary than in cows with noncavitary CL (12.1% vs. 3.6%; P < 0.05) and in cows with double CL than in cows with single CL (7.3% vs. 3.6% %; P < 0.05). Between days 57-70 of gestation and drying-off this difference was still significant (20.7% vs. 3.7%; P < 0.001), while it was non-significant between cows with one CL (5.7%) vs. double CL (3.7%). Cavity occurrence was not affected by hormone therapy prior to AI (either PGF2α or OvSynch; 4.4% vs. 5.4%, respectively); however, the number of CL was reduced by the treatments (11.6 vs. 19.6%; P < 0.0005). In twin pregnancies there was no difference in the pregnancy losses between bilateral and unilateral pregnancies at any time point. The length of gestation was 278.2 ± 10.5 (singleton) and 267.4 ± 31.2 (twin) days, respectively (P < 0.01). The stillbirth ratio was higher in twin carriers than in singleton carriers (19.5% vs. 5.3%; P < 0.001).

Relationships among some serum enzymes, negative energy balance parameters, parity and postparturient clinical (endo)metritis in Holstein Friesian cows - Short communication.

Activities of alkaline phosphatase, aspartate aminotransferase and alanine aminotransferase, and concentrations of serum metabolites [beta-hydroxybutyrate (BHB) and non-esterified fatty acids (NEFA)] of primiparous (n = 83) and multiparous (n = 213) Holstein cows were studied as possible predictors of retained fetal membranes (RFM), grade 2 clinical metritis (CM) and clinical endometritis (CEM). A logistic regression model was used to calculate odds ratios (OR) for the prevalence of CM diagnosed between 0-5, 6-10 and 11-20 days in milk (DIM) and for the prevalence of CEM diagnosed between 22-28 and 42-49 DIM. The activities of the examined serum enzymes did not show significant associations either with CM or with CEM. For NEFA sampled on days 0 and 5, an OR of 2.38 for CM 0-20 DIM and an OR of 2.58 for CM 11-20 DIM was found. For BHB sampled on days 0 and 5, an OR of 8.20 for CEM 22-28 and 42-49 DIM and an OR of 1.98 for CM 6-10 DIM were found. The prevalence of RFM was higher in ≥ 4 parity cows compared to primiparous cows (46.3% vs. 26.5%). BHB and NEFA levels measured between 0 and 5 DIM could have a predictive ability for postpartum uterine disorders such as RFM, CM and CEM.

Heart rate, cardiac vagal tone, respiratory rate, and rectal temperature in dairy calves exposed to heat stress in a continental region.

Changes in non-invasive stress biomarkers were studied in shaded (n = 8) and non-shaded (n = 8) Holstein bull calves exposed to extreme heat load conditions in a continental region. Ambient temperature and humidity data were recorded for the S and NS hutch and exercise pen environments. Temperature-humidity-index (THI) was also calculated. Respiratory rate, rectal temperature, heart rate, and the root mean square of successive differences between R-R intervals (RMSSD) were recorded as animal-based indicators during three periods: (1) day 1, control day, during which all calves were shaded for 24 h (shade removal from non-shaded calves at 2400 h); (2) day 2, heat stress day, with shade over shaded calves; and (3) days 3-5, post-stress period, with shade over shaded calves. On the heat stress day, the maximum temperatures were 44.3 and 46.7 °C for the non-shaded hutch and pen environments, respectively. The temperatures were with 6.2 and 6.9 °C (P = 0.015 and P = 0.008) and the THIs were with 5.9 and 4.2 units higher (P = 0.020 and P = 0.032) in the non-shaded than in the shaded environment for the hutch and exercise pen, respectively. Shaded calves had with 42.3 ± 3.2 breaths/min higher respiratory rate than non-shaded ones on the heat stress day at 1200 h (P = 0.001), which was moderated to 20.1 ± 2.4 breaths/min at 1600 h (P = 0.023). Significant differences in respiratory rate occurred earlier than in any other animal-based parameter between shaded and non-shaded calves on day 2. The only significant group difference in rectal temperature was found at 1200 h on day 2 when THI exceeded 91 units, with 0.59 °C higher values for non-shaded calves (P = 0.045). The heart rate on days 2 and 3 was higher for non-shaded calves than for shaded ones. Group differences were also significant at 0800 h (18.2 ± 1.2 beats/min, P = 0.008), 1200 h (22.3 ± 1.4 beats/min, P = 0.003), 1600 h (15.3 ± 0.8 beats/min, P = 0.012), and 2000 h (19.0 ± 1.1 beats/min, P = 0.010) on day 2. Following a rapid daytime reduction, RMSSD showed a nighttime overcompensation in non-shaded calves on day 3 (between 0000 and 0600 h) and day 4 (between 0000 and 0800 h), exceeding the levels recorded on day 1 (control) and the levels for shaded calves, thus suggesting a recovery of the autonomic nervous system from heat stress. Based on our results, shading effectively reduced heat stress as evidenced by heart rate and RMSSD in addition to traditional measures of heat stress. Respiratory rate and heart rates exhibited by non-shaded calves support that the well-being of pre-weaned calves can be impaired in continental regions during following a heat stress day without providing shade.

A Holstein-Friesian dairy farm survey of postparturient factors influencing the days to first AI and days open in Hungary.

The authors monitored the postpartum period during the first seven weeks after calving at a Holstein-Friesian dairy farm in Hungary. Calvings occurred between 2013 and 2015 in three periods from June to November each year (n = 314). Data were collected from the farm record and ultrasonographic examinations were performed between 22 to 28 and 43 to 49 days in milk (DIM), respectively. The animals were followed until successful artificial insemination (AI), i.e. until becoming pregnant or until culling, but at most at 365 DIM. The prevalence of dystocia, twin calving, stillbirth rate and retained fetal membranes (RFM) was 23.2%, 3.8%, 3.5%, and 34.4%, respectively. Altogether 38.9% of the cows (n = 122) had bacterial complications of involution in the first 49 DIM. The prevalence of Grade 2 clinical (puerperal) metritis (CM) was 20.1% within 5 DIM, 10.5% between 6 to 10 DIM and 13.1% from 11 to 20 DIM, while 9.9% of the cows had clinical endometritis (CEM) between 21 and 28 DIM and 1.3% of the cows between 42 and 49 DIM, respectively. Pyometra was diagnosed in 1% of the cows between 21 and 28 DIM and 0.3% between 42 to 49 DIM, respectively. About 80% (80.6%) of the cows were inseminated at least once (n = 253). The success rate of the first AI was 26.9% (n = 68). Dystocia, twin calving, RFM, CM, CEM and cyclicity had no significant effect on the days between calving and first AI, however, according to the Kaplan-Meier analysis stillbirth significantly increased the number of days from calving to first AI (P = 0.039). According to the Kaplan- Meier analysis dystocia, twin calving, stillbirth, RFM, and cyclicity had no effect on the days open. In cows with CM developed within 5 DIM or with CEM diagnosed between 21 and 28 DIM the number of days open significantly decreased (P = 0.009 and P = 0.007, respectively), which confirms the importance of early diagnosis and treatment of uterine diseases. Similar surveys should be conducted to discover the risk factors for reproductive diseases in order to decrease the reproductive losses in dairy farms.

Effect of physical activity on cardiac autonomic function of dairy cows on commercial dairy farms.

Interbeat interval data were collected from 219 Holstein cows in 2 smaller-scale farms and 3 larger-scale farms to investigate the effects of posture (standing vs. lying), rumination (rumination vs. no rumination) and feeding on baseline values of heart rate (HR) and heart rate variability (HRV) parameters reflecting vagal and sympathetic activity. A General Linear Model was used for detecting factors (parity, milking technology, herd size) having possible effects on HRV calculated for undisturbed lying posture. Basal values of cardiac parameters were also compared between larger and smaller-scale farms. Neither parity nor milking technology affected HRV parameters. Sympathetic activity increased in the order of lying, ruminating when lying, standing, ruminating when standing and feeding on both sizes of farms. Vagal activity decreased in that order in both lower- and larger-scale farms. Rumination caused an increase in HR and a decrease in vagal tone in both lower- and larger-scale farms and an increase in sympathetic activity during lying in both farm sizes. Basal vagal activity was lower in larger-scale farms compared to smaller-scale farms, while greater sympathetic activity was found in cows housed on larger-scale farms. Our findings demonstrate that reference values of HRV parameters in lactating dairy cows cannot be generally defined for Holstein cattle as they are affected by physical activity and herd size. Higher HR and sympathetic activity at rest in larger-scale farms compared to farms with lower cow population might be associated with higher levels of social stress and therefore should be considered as a potential welfare concern.

A robust fixed point transformation-based approach for type 1 diabetes control.

Modeling and control of diabetes mellitus (DM) are difficult due to the highly nonlinear attitude, time-delay effects, the impulse kind input signals and the lack of continuously available blood glucose (BG) level to be regulated. Regarding the mentioned problems, identification of DM model is crucial. Furthermore, due to the lack of information about the internal states (which cannot be measured in everyday life) and because the BG level is not available in every moment over time, adaptive robust control design method regardless exact model dependency would successfully handle these unfavorable effects without simplifications. The recently developed nonlinear robust fixed point transformation (RFPT)-based controller design method requires only a roughly approximate model in order to realize the controller structure. Moreover, parallel simulated approximate models-in order to provide additional internal information-can be used with the method. In this paper, the usability of the novel RFPT-based technique is demonstrated on the physiological problem of diabetes.