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Ostrya carpinifolia is an interesting and suitable species for reforestation and landscaping. For the first time ever, phenolic profile, antioxidative and antimicrobial activity of O. carpinifolia was done in this study. Aqueous and methanol extracts of the aerial parts were analyzed using either fast screening methods of secondary metabolites, and UV/VIS spectrophotometry for determination of polyphenolic contents and antioxidant activity (DPPH). Antimicrobial activity of methanol extracts was investigated using the disc diffusion method against a selected nine microorganisms. Phytochemical tests confirm the presence of cardiac glycosides, coumarins, emodins, flavonoids, tannins, terpenes, terpenoids and steroids, while anthocyanins, fatty acids and saponins were absent in all aqueous extracts. Leucoanthocyanins were observed only in the stem extract. Methanol extracts of leaves contain the highest level of total phenolics and flavonoids (35.574 and 30.908 mg CE g-1 DW, respectively), while the inflorescences extracts were the richest with total proanthocyanidins and phenolic acids (19.165 mg CE and 9.342 mg CAE g-1 DW, respectively). All methanol extracts showed very strong antioxidative activity, where the lowest activity was recorded for inflorescences (IC50: 0.242 mg mL-1) and the highest for stem (IC50: 0.107 mg mL-1). Analyzed extracts showed no antimicrobial activity against the test organisms. ANOVA indicated the presence of significant differences between the total phenolics and flavonoids and DPPH (p<0.05). Duncan's test confirmed the presence of statistically significant and very high positive correlation (R=0.989) between total phenolics and phenolic acids contents. Obtained results indicate the necessity of further research of European hop-hornbeam. [ABSTRACT FROM AUTHOR]

Tannins are a large group of polyphenolic compounds that are concentrated in roots, wood, bark, seeds, leaves and fruit of different plants. Tannins have the capability to tan proteins. Beer has a considerable amount of tannins because they are made from different crop types. An excess of tannins in beer results in astringency, but beer deprived of tannins do not taste right. The objective of this research was to determine the tannins content in domestic beer samples by a spectrophotometric method. The method is based on the reduction of Fe(III) to Fe(II) by tannins. The iron (II) reacts with 1,10-phenantroline to form a color complex. The absorbance was measured at 540 nm. Background correction is needed to minimize the interferences due to other reducing substances, and that was done by precipitating tannins in the sample solution with gelatin and kaolin. The solid matrix of gelatin and kaolin gave a tannin free solution. The calibration curve of tannic acid was linear from 0.5 to 4 μgml-1 with a linearity coefficient R2=0.9943. The tannins content was determined in twelve beer samples and it was in range 15.49 - 1722.05 μgml-1. [ABSTRACT FROM AUTHOR]

Honey is a natural food product produced when the nectar and sweet deposits from plants are gathered, modified and stored in the honeycomb by honey bees. It has multiple physiological effects, and one of the most important is antioxidant activity which is attributed to the content of phenolic compounds. In this study, forest bee honey samples (33) from Bosnia and Herzegovina were analyzed to determine their total phenolic contents (TPC). These samples consisted of acacia (9), mixed forest (9), chestnut (7), mountain (5), and linden (3) honeys. The TPC was determined by the Folin-Ciocalteu method. The results of the study showed that TPC differ widely among different honey types. Phenolic content ranged from 4.57 mgGAE/100 g (linden honey from Cazin, 2014) to 270.41 mgGAE/100 g (mountain honey from Bjelašnica, 2013). The highest TPC had dark samples of honey, while the lowest TPC were found in pale honeys. It was also confirmed that TPC of honey is affected by climate changes during the year, so analyzed chestnut honey (Cazin-Brezova Kosa) collected in 2013, had 98.26 mgGAE/100 g, while the same sort of honey (from the same location) collected in 2014 (which was abundant rainfall), had 69.27 mgGAE/100 g. Bulletin of the Chemists and Technologists of Bosnia and Herzegovina Print ISSN: 0367-4444 Online ISSN: 2232-7266 2016 Special Issue PP-BB-10 UDC: __________________________ Abstract 66 [ABSTRACT FROM AUTHOR]

Honey has been used as a food and medical product since the earliest times. Honey is rich in phenolic acids and flavonoids, which exhibit a wide range of biological effects and act as natural antioxidants. In this study, the total phenolic contents of forty-three (43) meadow bee honeys from Bosnia and Herzegovina were evaluated [sage honey (7), honey from winter savory (7), and mixed meadow honey (29)] by the Folin-Ciocalteu method. Analyzed samples were collected in the period 2013-2015. Total phenolic contents (TPC) of honey samples varied from 5.26 (sage honey from Stolac) to 84.45 mgGAE/100 g honey (winter savory honey from Konjic). The present study confirms that Bosnian honey contains significant content of phenolic antioxidants that may have therapeutic potential. It was confirmed that darkcolored honeys had higher TPC than those light-colored. Overall, our results indicated that there were significant seasonal variations in the TPC over the three-year period. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to develop a deep learning algorithm capable of diagnosing radicular cysts in the lower jaw on panoramic radiographs., Materials and Methods: In this study, we conducted a comprehensive analysis of 138 radicular cysts and 100 normal panoramic radiographs collected from 2013 to 2023 at Clinical Hospital Dubrava. The images were annotated by a team comprising a radiologist and a maxillofacial surgeon, utilizing the GNU Image Manipulation Program. Furthermore, the dataset was enriched through the application of various augmentation techniques to improve its robustness. The evaluation of the algorithm's performance and a deep dive into its mechanics were achieved using performance metrics and EigenCAM maps., Results: In the task of diagnosing radicular cysts, the initial algorithm performance-without the use of augmentation techniques-yielded the following scores: precision at 85.8%, recall at 66.7%, mean average precision (mAP)@50 threshold at 70.9%, and mAP@50-95 thresholds at 60.2%. The introduction of image augmentation techniques led to the precision of 74%, recall of 77.8%, mAP@50 threshold to 89.6%, and mAP@50-95 thresholds of 71.7, respectively. Also, the precision and recall were transformed into F1 scores to provide a balanced evaluation of model performance. The weighted function of these metrics determined the overall efficacy of our models. In our evaluation, non-augmented data achieved F1 scores of 0.750, while augmented data achieved slightly higher scores of 0.758., Conclusion: Our study underscores the pivotal role that deep learning is poised to play in the future of oral and maxillofacial radiology. Furthermore, the algorithm developed through this research demonstrates a capability to diagnose radicular cysts accurately, heralding a significant advancement in the field.

The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the loss of loyal customers. In this paper, we propose a novel deep-learning (DL)-based OOS-detection method that utilizes a two-stage training process and a post-processing technique designed for the removal of inaccurate detections. To develop the method, we utilized an OOS detection dataset that contains a commonly used fully empty OOS class and a novel class that represents the frontal OOS. We present a new image augmentation procedure in which some existing OOS instances are enlarged by duplicating and mirroring themselves over nearby products. An object-detection model is first pre-trained using only augmented shelf images and, then, fine-tuned on the original data. During the inference, the detected OOS instances are post-processed based on their aspect ratio. In particular, the detected instances are discarded if their aspect ratio is higher than the maximum or lower than the minimum instance aspect ratio found in the dataset. The experimental results showed that the proposed method outperforms the existing DL-based OOS-detection methods and detects fully empty and frontal OOS instances with 86.3% and 83.7% of the average precision, respectively.

Background and Objectives : The purpose of this study was to develop and evaluate a deep learning model capable of autonomously detecting and segmenting radiolucent lesions in the lower jaw by utilizing You Only Look Once (YOLO) v8. Materials and Methods : This study involved the analysis of 226 lesions present in panoramic radiographs captured between 2013 and 2023 at the Clinical Hospital Dubrava and the School of Dental Medicine, University of Zagreb. Panoramic radiographs included radiolucent lesions such as radicular cysts, ameloblastomas, odontogenic keratocysts (OKC), dentigerous cysts and residual cysts. To enhance the database, we applied techniques such as translation, scaling, rotation, horizontal flipping and mosaic effects. We have employed the deep neural network to tackle our detection and segmentation objectives. Also, to improve our model's generalization capabilities, we conducted five-fold cross-validation. The assessment of the model's performance was carried out through metrics like Intersection over Union (IoU), precision, recall and mean average precision (mAP)@50 and mAP@50-95. Results : In the detection task, the precision, recall, mAP@50 and mAP@50-95 scores without augmentation were recorded at 91.8%, 57.1%, 75.8% and 47.3%, while, with augmentation, were 95.2%, 94.4%, 97.5% and 68.7%, respectively. Similarly, in the segmentation task, the precision, recall, mAP@50 and mAP@50-95 values achieved without augmentation were 76%, 75.5%, 75.1% and 48.3%, respectively. Augmentation techniques led to an improvement of these scores to 100%, 94.5%, 96.6% and 72.2%. Conclusions : Our study confirmed that the model developed using the advanced YOLOv8 has the remarkable capability to automatically detect and segment radiolucent lesions in the mandible. With its continual evolution and integration into various medical fields, the deep learning model holds the potential to revolutionize patient care.

Many heavy metals (HMs) are essential micronutrients for the growth and development of plants. However, human activities such as mining, smelting, waste disposal, and industrial processes have led to toxic levels of HMs in soil. Fortunately, many plant species have developed incredible adaptive mechanisms to survive and thrive in such harsh environments. As a widespread and ruderal species, Geranium robertianum L. inhabits versatile soil types, both polluted and unpolluted. Considering the ubiquity of G. robertianum, the study aimed to determine whether geographically distant populations can tolerate HMs. We collected soil and plant samples from serpentine, an anthropogenic heavy metal contaminated, and a non-metalliferous site to study the physiological state of G. robertianum. HMs in soil and plants were determined using flame atomic absorption spectrometry. Spectrophotometric methods were used to measure the total content of chlorophylls a and b, total phenolics, phenolic acids, flavonoids, and proline. Principal component analysis (PCA) was used to investigate the potential correlation between HMs concentrations gathered from various soil types and plant samples and biochemical data acquired for plant material. A statistically significant difference was observed for all localities regarding secondary metabolite parameters. A positive correlation between Ni and Zn in soil and Ni and Zn in plant matter was observed (p<0.0005) indicating higher absorption. Regardless of high concentrations of heavy metals in investigated soils, G. robertianum displayed resilience and was capable of thriving. These results may be ascribed to several protective mechanisms that allow G. robertianum to express normal growth and development and act as a pioneer species., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.

Introduction: Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry., Objective: Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages., Conclusion: The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes., Competing Interests: Conflict of interest None declared

Purpose: The aim of the study was to test the efficacy, reliability, and applicability of the Kvaal and Tooth Coronal Index (TCI) age determination techniques, and then to compare them with each other, as well as with the conventional anthropological age and sex determination techniques., Methods: The analyzed material originates from the medieval necropolis of the Vinča-Belo brdo site. During the research, 60 periapical (PA) and 30 orthopantomographic (OPT) images were analyzed. On each analyzed tooth, age assessment was performed using both TCI and Kvaal techniques. The obtained values of dental estimated age were compared with age estimated by anthropological analysis, and the deviations between the estimated and chronological age were analyzed in relation to the assessment technique, type of dental radiograph, tooth group, sex, and age., Results: The mean error between TCI and the osteological method was 8.44 (SD = 7.56, Min = 0.169, Max = 36.4) and between Kvaal and the osteological method was 7.71 (SD = 5.57, Min = 0.133, Max = 26.7). The average value of age recorded by TCI method was 32.5 years and by Kvaal method was 34.7 years. There was no statistically significant difference based on the two radiographic methods, gender, individual teeth, or tooth group pairs. There was a statistically significant positive correlation between age and the error present., Conclusion: Gender determination based only on the mandible has a high correlation with the anthropological gender determination. The Kvaal method and the TCI method have proven their efficiency, reliability, and applicability. Significant correlation has been observed between dental and anthropological age and sex determination methods., (© 2022. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

Non-destructive testing is a group of methods for evaluating the integrity of components. Among them, ultrasonic inspection stands out due to its ability to visualize both shallow and deep sections of the material in the search for flaws. Testing of the critical components can be a tiring and time-consuming task. Therefore, human experts in analyzing inspection data could use a hand in discarding anomaly-free data and reviewing only suspicious data. Using such a tool, errors would be less common, inspection times would shorten and non-destructive testing would be more efficient. In this work, we evaluate multiple state-of-the-art deep-learning anomaly detection methods on the ultrasonic non-destructive testing dataset. We achieved an average performance of almost 82% of ROC AUC. We discuss in detail the advantages and disadvantages of the presented methods., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Color constancy is an essential component of the human visual system. It enables us to discern the color of objects invariant to the illumination that is present. This ability is difficult to reproduce in software, as the underlying problem is ill posed, i.e., for each pixel in the image, we know only the RGB values, which are a product of the spectral characteristics of the illumination and the reflectance of objects, as well as the sensitivity of the sensor. To combat this, additional assumptions about the scene have to be made. These assumptions can be either handcrafted or learned using some deep learning technique. Nonetheless, they mostly work only for single illuminant images. In this work, we propose a method for learning these assumptions for multi-illuminant scenes using an autoencoder trained to reconstruct the original image by splitting it into its illumination and reflectance components. We then show that the estimation can be used as is or can be used alongside a clustering method to create a segmentation map of illuminations. We show that our method performs the best out of all tested methods in multi-illuminant scenes while being completely invariant to the number of illuminants.

Ultrasonic imaging is widely used for non-destructive evaluation in various industry applications. Early detection of defects in materials is the key to keeping the integrity of inspected structures. Currently, there have been some attempts to develop models for automated defect detection on ultrasonic data. To push the performance of these models even further more data is needed to train deep convolutional neural networks. A lot of data is also needed for training human experts. However, gathering a sufficient amount of data for training is a challenge due to the rare occurrence of defects in real inspection scenarios. This is why inspection results heavily depend on the inspector's previous experience. To overcome these challenges, we propose the use of Generative Adversarial Networks for generating realistic ultrasonic images. To the best of our knowledge, this work is the first one to show that a Generative Adversarial Network is able to generate images indistinguishable from real ultrasonic images. The most thorough statistical quality analysis to date of generated ultrasonic images has been conducted with the participation of human expert inspectors. The experimental results show that images generated using our Generative Adversarial Network provide the highest quality images compared to other published methods., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Kefir is a well-known health-promoting beverage that can be produced by using kefir grains (traditional method) or by using natural starter cultures from kefir (backslopping method). The aim of this study was to elucidate the microbial dynamics and volatilome profile occurring during kefir production through traditional and backslopping methods by using five kefir grains that were collected in Bosnia and Herzegovina. The results from conventional pour plating techniques and amplicon-based sequencing were combined. The kefir drinks have also been characterized in terms of their physico-chemical and colorimetric parameters. A bacterial shift from Lactobacillus kefiranofaciens to Acetobacter syzygii, Lactococcus lactis and Leuconostoc pseudomesenteroides from kefir grains in traditional kefir to backslopped kefir was generally observed. Despite some differences within samples, the dominant mycobiota of backslopped kefir samples remained quite similar to that of the kefir grain samples. However, unlike the lactic acid and acetic acid bacteria, the yeast counts decreased progressively from the grains to the backslopped kefir. The backslopped kefir samples showed higher protein, lactose and ash content and lower ethanol content compared to traditional kefir samples, coupled with optimal pH values that contribute to a pleasant sensory profile. Concerning the volatilome, backslopped kefir samples were correlated with cheesy, buttery, floral and fermented odors, whereas the traditional kefir samples were correlated with alcoholic, fruity, fatty and acid odors. Overall, the data obtained in the present study provided evidence that different kefir production methods (traditional vs backslopping) affect the quality characteristics of the final product. Hence, the functional traits of backslopped kefir should be further investigated in order to verify the suitability of a potential scale-up methodology for backslopping., (Copyright © 2020 Elsevier Ltd. All rights reserved.)