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  • 1
    In: Plants, MDPI AG, Vol. 12, No. 15 ( 2023-07-25), p. 2761-
    Abstract: A huge amount of orange peel waste is annually discharged into the environment. Processing of this waste for the control of post-harvest fruit diseases can reduce environmental pollution. Essential oils (EOs) from fruit peels of Citrus reticulata × sinensis (Sanh cultivar) and Citrus sinensis (Xoan, Mat and Navel cultivar) were investigated for their ability to control anthracnose caused by Colletotrichum gloeosporioides and Colletotrichum scovillei on mangoes. EOs were extracted by hydro-distillation and analyzed by GC-MS and GC-FID. The antifungal activity of the EOs was determined by in vitro and in vivo assays. The Mat cultivar had the highest extraction yield of 3% FW, followed by Xoan (2.9%), Sanh (2.2%), and Navel (1%). The chemical composition of the EOs was similar, with limonene as the main compound (around 96%). The antifungal activity of EOs was not different, with a minimum fungicidal concentration of 16% for both fungi. The disease inhibition of EOs increased with their concentration. The highest inhibition of anthracnose caused by both fungi on mangoes was achieved at 16% EO. EOs had no adverse effect on mango quality (pH, total soluble solids, total acidity, color and brightness of mangoes), except firmness and weight loss at high concentrations (16%). Orange EOs can be used as bio-fungicides to control mango anthracnose at high concentrations.
    Type of Medium: Online Resource
    ISSN: 2223-7747
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704341-1
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  • 2
    In: Diagnostics, MDPI AG, Vol. 12, No. 8 ( 2022-08-03), p. 1879-
    Abstract: Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems to help acne patients better understand their conditions and support doctors in acne diagnosis.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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  • 3
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 17, No. 5 ( 2020-03-03), p. 1638-
    Abstract: Aim: It is important to meet the aesthetic expectation regarding the smile concept of both dentists and non-professionals after treatment is complete. Therefore, the study aims to evaluate the effects of altered displays in incisors, gingival margin, and other smile-related-factors on dentists’ vs. non-professionals’ aesthetics perceptions. Materials and method: We altered the features of 42 digital smile photographs to generate the changed displays in incisors, gingival margin, and other smile-related-factors. Then, these altered photographs were presented to 51 dentists and 51 non-professionals, and each picture was rated by each participant with a visual analog scale ranging from 0 (very ugly) to 100 (very beautiful). Results: We found that the alterations in incisors, gingival margin, and other factors affected studied groups’ aesthetic perception of smile. The ugly smile threshold rated by both groups for crown length of maxillary central incisors was 2.0 mm. This threshold was 2.5 mm for dentists, with moving the gingival margin of maxillary lateral incisors to the incisal ridge. The ugly thresholds for other smile-related-factors were different between studied groups; for example, the ugly thresholds for gingival exposure levels were 3 and 4mm for dentists and non-professionals, respectively. Thus, our data indicate that altered displays in incisors, gingival margin, and other smile-related-factors affected perceptions of both studied groups on smile aesthetics, but dentists tended to feel more refined than non-professionals. Dentists and non-professionals had significantly different aesthetic perceptions of the alteration of the gingival exposure level. Conclusion: Both dentists and non-professionals’ perceptions should be fully considered during orthodontic and prosthodontic treatment to achieve optimum aesthetic results.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2175195-X
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  • 4
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 16, No. 18 ( 2019-09-04), p. 3240-
    Abstract: Although HIV (human immunodeficiency virus) testing for all women has been promoted by Vietnam’s Ministry of Health since 2000, test acceptance rates in this country were reported to be less than 30% in the community. This country has been facing the barriers to approach the national services towards transmission prevention from mother to child including HIV testing during antenatal care (ANC) towards mothers. Here, we aim to assess the socioeconomic inequalities in HIV testing during ANC among Vietnamese women. This study used available data from the Vietnam Multiple Indicator Cluster Survey 2014. Overall, the prevalence of HIV testing during antenatal care was 30% and the concentrate index (CCI) was 0.1926. There was significant inequality between women classified as poor and rich, and when stratified by social characteristics, inequality was found in women aged 15–49 years (CCI: 0.4), living in rural areas (CCI: 0.3), belonging to ethnic minorities (CCI: 0.5) and having primary or less education (CCI: 0.4). In the multivariate logistic regression analysis, ethnicity and socioeconomic status were significant factors associated with HIV testing during ANC. We found the prevalence of HIV testing during ANC was low, and its inequalities were associated with age, living area, ethnicity, education, and economic status.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2175195-X
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