The research included seven autochthonous apple varieties in the area of Tomislavgrad, Stipanjići locality, during 2018, which represent the natural wealth of our country. In seven autochthonous apple varieties: Petrovača bijela, Budimka, Ljutika-Divljaka, Ledarica, Bedrica, Ljepocvjetka-Cvjetača and Jonatanka, phenological characteristics were monitored and analyzed with morphological-pomological properties of fruits. The following phenological characteristics were monitored: flowering phenophases, germination of the first fruits, fruit growth, fruit ripening and full maturity. Five characteristics for the description of apple fruits were determined using the UPOV descriptor for apples (UPOV, 2003). The results of monitoring the morphological and pomological properties of fruits show that the lowest average fruit weight was in the variety of Cvjetača (58,38 g), while the highest average of fruit weight had variety Bedrica (140,59 g). In the result is evident that the lowest value of fruit width had variety Cvjetača (53,72 mm), and the highest had variety of Jonatanka (70,83 mm). The Cvjetača variety had the lowest average fruit lenght (47,24 mm), while the Bedrica variety had the highest (59,72 mm). The Budimka variety had the highest average stem length (2,73 cm), while the Divljak variety had the lowest average stem length (1,43 cm). The Jonatanka variety had the largest average cup concavity and was (1,28 mm), while the Petrovača bijela variety (0,41 mm) had the lowest average cup concavity. Based on the this research, the examined autochthonous apple varieties can be used a valuable genetic material in breeding programs, and can be used to create new varieties, as well as for propagation in commercial plantations.

Traditional pear cultivars are increasingly in demand by consumers because of their excellent taste, the possibility of use in sustainable food production systems, convenience as raw materials for obtaining products of high nutritional quality, and perceived health benefits. In this study, individual sugars, organic acids, and polyphenols in the fruits of nine traditional and one commercial pear cultivar during two growing seasons were determined by HPLC. A significant influence of cultivars, growing years, and their interaction on the content of analyzed primary and secondary metabolites was determined. The commercial pear cultivar ‘Président Drouard’ and traditional cultivars ‘Dolokrahan’, ‘Budaljača’, and ‘Krakača’ had a lower content of all analyzed sugars. Overall, traditional pear cultivars had higher total polyphenols in the peel and pulp than ‘Président Drouard’, with the exception ‘Takiša’ and ‘Ahmetova’. High polyphenol content detected in ‘Budaljača’, ‘Dolokrahan’, and ‘Krakača’ shows the utilization value of traditional pear germplasm. The obtained data can serve as practical supporting data for the use of traditional pears in the neutraceutical, pharmaceutical, and food industries.

Aims: The aims of the study were to analyse the polyphenols of Pulicaria dysenterica (L.) Bernh. methanolic extracts from aerial and underground parts, assessment of antioxidant activity and to evaluate their cytotoxicity on HeLa cells of cervical cancer. Methodology: The total phenolic content (TPC) of extracts was determined by the Folin-Ciocalteu spectrophotometric method. The qualitative and quantitative analysis of individual polyphenolic compounds were performed by the reverse phase HPLC method. The antioxidant capacity was evaluated by both, 2,2-diphenyl1-picrylhydrazyl radical and FRAP assay, while cytotoxicity of the extracts was assessed by MTT assay. Results: TPC of the samples were 127.62±2.22 and 244.12±8.84 mg gallic acid equivalent/g extract. In the extracts chlorogenic acid in amount of 10.06±0.96 and 11.32±0.28 mg/g, flavonoid rutin in amount of 5.68±0.13 mg/g and three caffeic acid derivatives were recorded. Extract from underground parts achieved better antioxidant activity with IC50 value 55.36±0.75 µg/mL and FRAP value 2411.12±37.22 µmol Fe2+g-1 compared to the one from aerial parts. Extract from aerial parts achieved better cytotoxic activity with 50% inhibition of viability (IC50) at concentration of 0.389±0.07 mg/mL, against HeLa cells, compared to the extract from underground parts. Conclusion: Analyzed Pulicaria dysenterica extracts contained phenolic acids and flavonoids. The extracts showed good antioxidant activity and cytotoxic properties against HeLa cells in vitro.

With a significant economic value, pepper (Capsicum annuum L., Solanaceae) represents a major vegetable crop worldwide. Simple sequence repeats (SSR) markers and expressed sequence tags (EST-SSR) are powerful tools for genetic studies and the breeding of pepper. In this study, the genetic variability among local pepper species was assessed by using 8 EST/SSR markers. The pepper varieties were collected locally, based on old sorts, hybrids, and sorts found in markets, with a total of 14 pepper varieties.  Mean expected heterozygosity (He) ranged from s 0.30 to 0.83 in the populations studied, whereas the mean polymorphic information content (PIC) was 0.62. The average polymorphic number of alleles per primer was 2.5 per locus. The genetic relationships among the populations revealed by Neighbor-Joining dendrogram showed a clear clustering to three sub-populations making distinct clusters.  As expected, all local varieties make up one cluster, together with the hybrid Istra, concluding that this hybrid evolved from the cross-breeding of local varieties.  These results indicate and confirm the regional and physiological differences between the pepper sorts.

The Weibull distribution, with shape parameter $k>0$ and scale parameter $\lambda>0$, is one of the most popular parametric distributions in survival analysis with complete or censored data. Although inference of the parameters of the Weibull distribution is commonly done through maximum likelihood, it is well established that the maximum likelihood estimate of the shape parameter is inadequate due to the associated large bias when the sample size is small or the proportion of censored data is large. This manuscript demonstrates how the Bayesian information-theoretic minimum message length principle coupled with a suitable choice of weakly informative prior distributions, can be used to infer Weibull distribution parameters given complete data or data with type I censoring. Empirical experiments show that the proposed minimum message length estimate of the shape parameter is superior to the maximum likelihood estimate and appears superior to other recently proposed modified maximum likelihood estimates in terms of Kullback-Leibler risk. Lastly, we derive an extension of the proposed method to data with type II censoring.

Background: The aim of the present randomized, crossover study was to determine the physiological and cognitive function responses while wearing a facemask during a maximal running aerobic fitness test. Methods: Fourteen healthy, nonsmoking physical education students (age = 17.5 years, height = 1.72 m, body mass = 70.4 kg) volunteered to participate in this study. They carried out a 20 m multistage fitness test (MSFT) while wearing or not a cloth facemask on two separate occasions performed in random order. The “Rating of Perceived Exertion” (RPE) and the d2 test for visual attention were administered and assessed before and immediately after the MSFT for both conditions (with or without a facemask). Results: When wearing the facemask, the participants exhibited lower maximal aerobic speed (p = 0.039), VO2max (p = 0.039), distance covered during the MSFT (p = 0.057), and concentration performance (p < 0.001), when compared with the control situation (without facemask). Moreover, they made more errors compared with the control condition (p = 0.021). The use of a cloth facemask during maximal endurance running tests (such as the MSFT) reduced VO2max, and measures of cognitive performance as assessed by the test of focused visual attention (the d2 test). This data suggests avoiding using a cloth facemask during maximal aerobic fitness tests, and before any tasks that require a high level of visual attention.

The aim of this manuscript is to introduce the Bayesian minimum message length principle of inductive inference to a general statistical audience that may not be familiar with information theoretic statistics. We describe two key minimum message length inference approaches and demonstrate how the principle can be used to develop a new Bayesian alternative to the frequentist $t$-test as well as new approaches to hypothesis testing for the correlation coefficient. Lastly, we compare the minimum message length approach to the closely related minimum description length principle and discuss similarities and differences between both approaches to inference.

In this short note, we derive a new bias adjusted maximum likelihood estimate for the shape parameter of the Weibull distribution with complete data and type I censored data. The proposed estimate of the shape parameter is significantly less biased and more efficient than the corresponding maximum likelihood estimate, while being simple to compute using existing maximum likelihood software procedures.

The Weibull distribution, with shape parameter $k>0$ and scale parameter $\lambda>0$, is one of the most popular parametric distributions in survival analysis with complete or censored data. Although inference of the parameters of the Weibull distribution is commonly done through maximum likelihood, it is well established that the maximum likelihood estimate of the shape parameter is inadequate due to the associated large bias when the sample size is small or the proportion of censored data is large. This manuscript demonstrates how the Bayesian information-theoretic minimum message length principle coupled with a suitable choice of weakly informative prior distributions, can be used to infer Weibull distribution parameters given complete data or data with type I censoring. Empirical experiments show that the proposed minimum message length estimate of the shape parameter is superior to the maximum likelihood estimate and appears superior to other recently proposed modified maximum likelihood estimates in terms of Kullback-Leibler risk. Lastly, we derive an extension of the proposed method to data with type II censoring.

Principal component analysis (PCA) is perhaps the most widely used method for data dimensionality reduction. A key question in PCA is deciding how many factors to retain. This manuscript describes a new approach to automatically selecting the number of principal components based on the Bayesian minimum message length method of inductive inference. We derive a new estimate of the isotropic residual variance and demonstrate that it improves on the usual maximum likelihood approach. We also discuss extending this approach to finite mixture models of principal component analyzers.

Radiology is integral to cancer care. Compared to molecular assays, imaging has its advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by sampling error and is routinely acquired at multiple time points in oncological practice. Imaging data can be digitally post-processed for quantitative assessment. The ever-increasing application of Artificial intelligence (AI) to clinical imaging is challenging radiology to become a discipline with competence in data science, which plays an important role in modern oncology. Beyond streamlining certain clinical tasks, the power of AI lies in its ability to reveal previously undetected or even imperceptible radiographic patterns that may be difficult to ascertain by the human sensory system. Here, we provide a narrative review of the emerging AI applications relevant to the oncological imaging spectrum and elaborate on emerging paradigms and opportunities. We envision that these technical advances will change radiology in the coming years, leading to the optimization of imaging acquisition and discovery of clinically relevant biomarkers for cancer diagnosis, staging, and treatment monitoring. Together, they pave the road for future clinical translation in precision oncology.