Vliv probiotik ve výživě telat na hmotnostní přírůstky živé hmotnosti a zdravotní stav

This paper aims to monitor the impact of Lactobacillus sporogenes (LS), Saccharomyces cerevisiae (SC), the combination thereof Lactobacillus sporogenes and Saccharomyces cerevisiae (CLS) on the health status and the live weight gain in calves compared to a control group (C). The experiment took place in the period from March 2022 to March 2023. 100 Holstein heifers in the age from 1 to 56 days were included in the experiment. The differences in live weight gain were significant when the live weight gains were compared in the first 14 days after birth between the CLS vs C group (63,36.72 ± 4.81 vs 59.55 ± 4.55, P 0.05. The impact on decrease and duration of diarrhea was not proved statistically P = 0.0634. However, a tendency to decrease the occurrence and duration thereof was proved. The impact of feed additives on the transmission of passive immunity in calves in their first week of life was not proved as statistically significant.Cílem této studie bylo sledovat vliv Lactobacillus sporogenes (LS), Saccharomyces cerevisiae (SC) a jejich kombinaci Lactobacillus sporogenes and Saccharomyces cerevisiae (CLS) na zdravotní stav a přírůstek živé hmotnosti telat oproti skupině kontrolní (C). Pokus se uskutečnil v období březen 2022 až březen 2023. Do pokusu bylo zařazeno celkem 100 holštýnských jaloviček ve stáří 1 až 56 dní. Rozdíly v přírůstku živé hmotnosti byly významné, pokud byly porovnány hmotnostní přírůstky ve 14. dech po narození mezi skupinou CLS vs C (63,36.72 ± 4.81 vs 59.55 ± 4.55, P < 0.05) a v 56 dnech po narození mezi skupinu CLS vs C, LS vs C a SC vs C (87.34 ± 4.95 kg vs 83.15 ± 5.32 kg, P < 0.01; 86.41 ± 5.34 kg vs 83.15 ± 5.32 kg, P < 0.05 a 85.92 ± 5.86 kg vs 83.15 ± 5.32 kg, P < 0.05). Rozdíly v přírůstku živé hmotnosti mezi pokusnými skupinami nebyly statisticky prokázány P > 0.05. Vliv na snížení výskytu a trvání průjmových onemocnění nebyl statisticky prokázán P = 0.0634, ovšem byla zde prokázána tendence ke snížení jejich výskytů a době trvání. Statisticky významný nebyl prokázán vliv krmných aditiv na přenos pasivní imunity u telat v prvním týdnu života

Kontrola kvality TMR pomocí Penn State separátoru u vysokoužitkových dojnic

The aim of this bachelor thesis is to focus on the control of dairy cow´s nutrition in the agricultural company because nutrition is the most important factor in the proces of milk production. The search deals with the way and technique of feeding in every single phase of lactation. This part of the bachelor thesis describes voluminous and salty feed as well as their usability in feeding dose and their nutritional qualities. The race "Holštýn", it´s characteristics and need of nutrients in nutrition is also mentioned in the thesis. The main part of the thesis is to introduce Penn state particle separator (2002), which has been used for the control of feeding in the farms. This part characterizes it´s utilization in place, the method of application, evaluation of feeding dose with it´s influence on dairy cow´s health and efficiency. The results have been compared to the standard determined for separator as well as evaluated in view of the milk yield and milk components. The thesis contents a series of trials made over the observed period,which is evaluated in the conclusion of the thesis, and the influence of feeding dose´s structure on dairy cow´s efficiency and health

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessmen

Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson’s Disease Dysgraphia in a Multilingual Dataset

Parkinson’s disease dysgraphia (PDYS), one of the earliest signs of Parkinson’s disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6%(mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN)