Porosity of temporary denture soft liners containing antifungal agents

Incorporation of antifungals in temporary denture soft liners has been recommended for denture stomatitis treatment; however, it may affect their properties. Objective: To evaluate the porosity of a tissue conditioner (Softone) and a temporary resilient liner (Trusoft) modified by minimum inhibitory concentrations (MICs) of antifungal agents for Candida albicans biofilm. Material and Methods: The porosity was measured by water absorption, based on exclusion of the plasticizer effect. Initially, it was determined by sorption isotherms that the adequate storage solution for specimens (65×10×3.3 mm) of both materials was 50% anhydrous calcium chloride (S50). Then, the porosity factor (PF) was calculated for the study groups (n=10) formed by specimens without (control) or with drug incorporation at MICs (nystatin: Ny-0.032 g, chlorhexidine diacetate: Chx-0.064 g, or ketoconazole: Ke-0.128 g each per gram of soft liner powder) after storage in distilled water or S50 for 24 h, seven and 14 d. Data were statistically analyzed by 4-way repeated measures ANOVA and Tukey's test (α=.05). Results: Ke resulted in no significant changes in PF for both liners in water over 14 days (p>;0.05). Compared with the controls, Softone and Trusoft PFs were increased at 14-day water immersion only after addition of Ny and Chx, and Chx, respectively (p;0.05). In all experimental conditions, Softone and Trusoft PFs were significantly lower when immersed in S50 compared with distilled water (

Caracterização molecular de cultivares de soja por meio de marcadores microssatélites com sequência de cauda universal

The objective of this work was to standardize a semiautomated method for genotyping soybean, based on universal tail sequence primers (UTSP), and to compare it with the conventional genotyping method that uses electrophoresis in polyacrylamide gels. Thirty soybean cultivars were genotypically characterized by both methods, using 13 microsatellite loci. For the UTSP method, the number of alleles (NA) was 50 (2–7 per marker) and the polymorphic information content (PIC) ranged from 0.40 to 0.74. For the conventional method, the NA was 38 (2–5 per marker) and the PIC varied from 0.39 to 0.67. The genetic dissimilarity matrices obtained by the two methods were highly correlated with each other (0.8026), and the formed groups were coherent with the phenotypic data used for varietal registration. The 13 markers allowed the distinction of all analyzed cultivars. The low cost of the UTSP method, associated with its high accuracy, makes it ideal for the characterization of soybean cultivars and for the determination of genetic purity.O objetivo deste trabalho foi padronizar um método semi‑automatizado para genotipagem de soja, baseado na metodologia de iniciadores com sequências de cauda universal (PSCU), e compará‑lo ao método de genotipagem convencional de eletroforese em gel de poliacrilamida. Trinta cultivares de soja foram caracterizadas genotipicamente por ambos os métodos, com o uso de 13 locos microssatélites. Para o método PSCU, o número de alelos (NA) foi de 50 (2–7 por marcador) e o conteúdo de informação polimórfica (PIC) variou de 0,40 a 0,74. Para o método convencional, o NA foi de 38 (2–5 por marcador) e o PIC variou de 0,39 a 0,67. As matrizes de dissimilaridade genética obtidas pelos dois métodos apresentaram alta correlação entre si (0,8026), e os grupos formados foram coerentes com dados fenotípicos utilizados para o registro varietal. Os 13 marcadores permitiram a distinção de todas as cultivares analisadas. O baixo custo do método PSCU, associado a sua alta acurácia, torna‑o ideal para a caracterização de cultivares de soja e a determinação de pureza genética

Porosity of temporary denture soft liners containing antifungal agents

ABSTRACT Incorporation of antifungals in temporary denture soft liners has been recommended for denture stomatitis treatment; however, it may affect their properties. Objective: To evaluate the porosity of a tissue conditioner (Softone) and a temporary resilient liner (Trusoft) modified by minimum inhibitory concentrations (MICs) of antifungal agents for Candida albicans biofilm. Material and Methods: The porosity was measured by water absorption, based on exclusion of the plasticizer effect. Initially, it was determined by sorption isotherms that the adequate storage solution for specimens (65×10×3.3 mm) of both materials was 50% anhydrous calcium chloride (S50). Then, the porosity factor (PF) was calculated for the study groups (n=10) formed by specimens without (control) or with drug incorporation at MICs (nystatin: Ny-0.032 g, chlorhexidine diacetate: Chx-0.064 g, or ketoconazole: Ke-0.128 g each per gram of soft liner powder) after storage in distilled water or S50 for 24 h, seven and 14 d. Data were statistically analyzed by 4-way repeated measures ANOVA and Tukey's test (&#945;=.05). Results: Ke resulted in no significant changes in PF for both liners in water over 14 days (p>0.05). Compared with the controls, Softone and Trusoft PFs were increased at 14-day water immersion only after addition of Ny and Chx, and Chx, respectively (p<0.05). Both materials showed no significant changes in PF in up to 14 days of S50 immersion, compared with the controls (p>0.05). In all experimental conditions, Softone and Trusoft PFs were significantly lower when immersed in S50 compared with distilled water (p<0.05). Conclusions: The addition of antifungals at MICs resulted in no harmful effects for the porosity of both temporary soft liners in different periods of water immersion, except for Chx and Ny in Softone and Chx in Trusoft at 14 days. No deleterious effect was observed for the porosity of both soft liners modified by the drugs at MICs over 14 days of S50 immersion

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Molecular characterization of soybean cultivars by microsatellite markers with universal tail sequence

The objective of this work was to standardize a semiautomated method for genotyping soybean, based on universal tail sequence primers (UTSP), and to compare it with the conventional genotyping method that uses electrophoresis in polyacrylamide gels. Thirty soybean cultivars were genotypically characterized by both methods, using 13 microsatellite loci. For the UTSP method, the number of alleles (NA) was 50 (2-7 per marker) and the polymorphic information content (PIC) ranged from 0.40 to 0.74. For the conventional method, the NA was 38 (2-5 per marker) and the PIC varied from 0.39 to 0.67. The genetic dissimilarity matrices obtained by the two methods were highly correlated with each other (0.8026), and the formed groups were coherent with the phenotypic data used for varietal registration. The 13 markers allowed the distinction of all analyzed cultivars. The low cost of the UTSP method, associated with its high accuracy, makes it ideal for the characterization of soybean cultivars and for the determination of genetic purity