A simple and rapid electrochemical sensing method with high sensitivity and specificity of aptamers was developed for the detection of methylamphetamine (MAMP). A short anti-MAMP thiolated aptamer (Apt) with a methylene blue (MB) probe at 3\'-end was immobilized on the surface of a gold electrode (MB-Apt-S/GE). The electrochemical signal appeared when MAMP presenting in the sample solution competed with cDNA for binding with MB-Apt-S. Under optimized conditions, the liner range of this signal-on electrochemical aptasensor for the detection of MAMP achieved from 1.0 to 10.0 nmol/L and 10.0-400 nmol/L. LOD 0.88 nmol/L were obtained. Satisfactory spiked recoveries of saliva and urine were also obtained. In this method, only 5 min were needed to incubate before the square wave voltammetry (SWV) analysis, which was much more rapid than other electrochemical sensors, leading to a bright and broad prospect for the detection of MAMP in biological sample. This method can be used for on-site rapid detection on special occasions, such as drug driving scenes, entertainment venues suspected of drug use, etc.

The small molecule, isatin, is a well-known reversible inhibitor of the monoamine oxidase (MAO) enzymes with IC50 values of 12.3 and 4.86 μM for MAO-A and MAO-B, respectively. While the interaction of isatin with MAO-B has been characterized, only a few studies have explored structure-activity relationships (SARs) of MAO inhibition by isatin analogues. The current study therefore evaluated a series of 14 isatin analogues as in vitro inhibitors of human MAO-A and MAO-B. The results indicated good potency MAO inhibition for some isatin analogues with five compounds exhibiting IC50  < 1 μM. 4-Chloroisatin (1b) and 5-bromoisatin (1f) were the most potent inhibitors with IC50 values of 0.812 and 0.125 μM for MAO-A and MAO-B, respectively. These compounds were also found to be competitive inhibitors of MAO-A and MAO-B with Ki values of 0.311 and 0.033 μM, respectively. Among the SARs, it was interesting to note that C5-substitution was particularly beneficial for MAO-B inhibition. MAO inhibitors are established drugs for the treatment of neuropsychiatric and neurodegenerative disorders, while potential new roles in prostate cancer and cardiovascular disease are being investigated.

The vegetation layer contributes to multiple functions of green roofs including their hydrological function as plants remove water from substrates between rainfall events through evapotranspiration, restoring the green roofs storage capacity for rainfall retention. While individual traits have been related to water use strategies of green roof plants, these traits are inconsistent, suggesting the importance of trait combinations which may be reflected in CSR (competitor, stress tolerator, ruderal) strategies. Therefore, relating plant water use to leaf traits and CSR strategies could help facilitate green roof plant selection into new geographical regions where green roof technology is developing. For example, in high latitude northern European regions with long daylight during the growing season. Growth (shoot biomass, relative growth rate and leaf area), leaf traits (leaf dry matter content, specific leaf area and succulence) and CSR strategies were determined of 10 common European green roof plants and related to their water use under well-watered (WW) and water-deficit (WD) conditions. All three succulent species included in the experiment showed mostly stress tolerant traits and their water loss was less than the bare unplanted substrate, likely due to mulching of the substrate surface. Plants with greater water use under WW conditions had more ruderal and competitive strategies, and greater leaf area and shoot biomass, than species with lower WW water use. However, the four species with the highest water use under WW conditions were able to downregulate their water use under WD, indicating that they could both retain rainfall and survive periods of water limitations. This study indicates that, for optimal stormwater retention, green roof plant selection in high latitude regions like northern Europe, should focus on selecting non-succulent plants with predominantly competitive or ruderal strategies to make the most of the long daylight during the short growing season.

This study investigated the competitive adsorption and immobilization of cadmium (Cd), nickel (Ni), and copper (Cu) by biochar in unsaturated soils under single-, binary-, and ternary-metal systems. The results showed that the immobilization effects by the soil itself were in the order of Cu > Ni > Cd, and the adsorption capacities of freshly contaminated heavy metals by biochar were in the order of Cd > Ni > Cu in unsaturated soils. The adsorption and immobilization of Cd by biochars in soils was weakened by competition more in the ternary-metal system than that in the binary-metal system; the competition with Cu caused a more significant weakening effect than that with Ni. For Cd and Ni, nonmineral mechanisms preferentially adsorbed and immobilized Cd and Ni compared to mineral mechanisms, but the contributions of the mineral mechanisms to the adsorption gradually increased and became dominant with increasing concentrations (at average percentages of 62.59%-83.30% and 41.38%-74.29%, respectively). However, for Cu, the contributions of the nonmineral mechanisms to Cu adsorption were always dominant (average percentages of 60.92%-74.87%) and gradually increased with increasing concentrations. This study highlighted that the types of heavy metals and coexistence should be focused when remediating heavy metal contamination in soils.

BACKGROUND: The objective and relative competitiveness of obstetrical and gynecologic subspecialty training programs remain understudied. Traditional metrics, such as match rate or program fill rate, fail to standardize the application environment. This limits their applicability when examining demographic trends or when comparing data between different fellowship matches. The normalized competitive index was introduced to serve as a comprehensive metric of competitiveness by incorporating disparate indicators and normalizing to enable more detailed analyses.
OBJECTIVE: This study aimed to analyze trends in the competitiveness across obstetrical and gynecologic subspecialty fellowship matches during the last decade.
METHODS: The results and data reports from the National Resident Match Program fellowship for 2010 to 2019 were used to collect data on multiple metrics of competitiveness for 6 obstetrical and gynecologic subspecialties. These data were used to determine the normalized competitive index. Subanalyses were conducted to identify trends over the last decade.
RESULTS: Among fellowship programs in obstetrics and gynecology, the overall specialty match rate was 67.6%. The overall specialty program fill rate was 95.7%. According to the normalized competitive index metric, minimally invasive gynecologic surgery was the most competitive fellowship match (normalized competitive index=1.31; P=.002). Maternal-fetal medicine was the least competitive (normalized competitive index=0.94; P≤.005). When comparing the first and second half of the decade, no specialty experienced a significant decrease in match rate. The only significant increase in match rates occurred for female pelvic medicine and reconstructive surgery (P=.035). Subanalyses of the normalized competitive index metric and other indicators of competitiveness demonstrated a strong negative correlation between the normalized competitive index and the subspecialty match rate (r=-0.9444) and a moderately positive correlation between the normalized competitive index and the program fill rate (r=0.4047).
CONCLUSIONS: The normalized competitive index offers trainees a more quantitative understanding of the fellowship application environment. By incorporating multiple metrics and normalizing the result, it uniquely enables comparison between the subspecialty matches and the match process over time. The same standardization offers the potential for future comparisons of competitiveness within a single subspecialty match based on geographic region, applicant demographics, and other important determinants of a diverse and vibrant training environment.

The pandemic COVID-19 has caused severe losses in public health and economy. One of the most difficult problems in prevention of the disease spread is the emergence of new variants. In this paper, a mathematical model is formulated, which captures the main feature of COVID-19 spread with two viral strains. It is shown by analytical method that the model exhibits the competitive exclusion principle, where one viral strain with the larger basic reproduction number is dominant and the viral strain with the smaller reproduction number is excluded. The results are important for the deployment of prevention policy of COVID-19.

A pricing-decision analysis is a critical aspect of supply chain management since it directly affects manufacturers\' and retailers\' profits. The members of a supply chain all want to be treated properly during the pricing process, which means that they pay attention to the fairness of the profit distribution. Additionally, consumers are increasingly gravitating to green products as their awareness of green consumption grows. Thus, incorporating consumers\' green preferences into a supply chain, this paper investigates pricing decisions with two competitive manufacturers under horizontal and vertical fairness concerns and seeks the optimal degrees of product greenness, prices, profits, and utilities. The game-theoretical models with and without bidirectional fairness concerns are constructed and analyzed to identify the implications on pricing, profits, and utilities of competing manufacturers\' bidirectional fairness concerns and consumers\' green preferences. Then, we determine the decisional differences between the two designs using comparative analysis and numerical simulation. Finally, propositions, corollaries, and policy implications are derived. The results indicate that consumers\' green preferences and competition between manufacturers contribute to increasing the optimal pricing and retailer\'s profit while harming manufacturers\' utilities and the supply chain\'s profits under some conditions. The findings also demonstrate that horizontal and vertical fairness concerns generate different impacts on the product\'s greenness degree and pricing. Still, they are detrimental to manufacturers\' utilities and supply chain profit while possessing negligible effects on retailer\'s profit.

Kefir grains consist of complex symbiotic mixtures of bacteria and yeasts, and are reported to impart numerous health-boosting properties to milk and water kefir beverages. The objective of this work was to investigate the microbial communities in kefir grains, and explore the possibility of deriving useful probiotic strains from them. A total of 158 microbial strains, representing six fungal and 17 bacterial species, were isolated from milk and water kefir grains collected from a Singapore-based homebrewer. Based on 16S rRNA sequencing, isolated genera included Lactobacillus, Liquorilactobacillus, Lacticaseibacillus, Lentilactobacillus, Leuconostoc, Lactococcus, Acetobacter, Gluconobacter, Oenococcus, Clostridium, Zymomonas, Saccharomyces, Kluyveromyces, Pichia, Lachancea, Candida, and Brettanomyces. To characterize these isolates, a funnel approach, involving numerous phenotypic and genomic screening assays, was applied to identify kefir-derived microbial strains with the highest probiotic potential. Particular focus was placed on examining the pathogen inhibitory properties of kefir isolates toward enteric pathogens which pose a considerable global health burden. Enteric pathogens tested include species of Bacillus, Salmonella, Vibrio, Clostridium, Klebsiella, Escherichia, and Staphylococcus. Well diffusion assays were conducted to determine the propensity of kefir isolates to inhibit growth of enteric pathogens, and a competitive adhesion/exclusion assay was used to determine the ability of kefir isolates to out-compete or exclude attachment of enteric pathogens to Caco-2 cells. Seven bacterial strains of Lentilactobacillus hilgardii, Lacticaseibacillus paracasei, Liquorilactobacillus satsumensis, Lactobacillus helveticus, and Lentilactobacillus kefiri, were ultimately identified as potential probiotics, and combined to form a \"kefir probiotics blend.\" Desirable probiotic characteristics, including good survival in acid and bile environments, bile salt hydrolase activity, antioxidant activity, non-cytotoxicity and high adhesion to Caco-2 cells, and a lack of virulence or antimicrobial resistance genes. In addition, vitamin and γ-aminobutyric acid (GABA) synthesis genes, were identified in these kefir isolates. Overall, probiotic candidates derived in this study are well-characterized strains with a good safety profile which can serve as novel agents to combat enteric diseases. These kefir-derived probiotics also add diversity to the existing repertoire of probiotic strains, and may provide consumers with alternative product formats to attain the health benefits of kefir.

This study aims to investigate the effects of students\' learning motivation and learning performance in a digital game-based learning setting and the structure of competition. This study uses Social Cognitive Theory, which emphasizes the bidirectional effects between personal factors, environmental factors, and behavior. We use the emotional state as the personal factor, social support as the environmental factor, learning performance as behavior. We also use self-efficacy and learning motivation as the mediating factors in the model. Data samples were collected from approximately 600 students in junior high schools in Taiwan. The students learned via either application or conventional lectures in three groups. The Control Group (CG) learned the course through a conventional learning approach. The Experimental group 1 (EG1) learned by a digital game, while Experimental Group 2 (EG2) learned through the digital game in combination with a structure that involved competing and entrepreneurship with classmates. The result of this research shows that the emotional state negatively affects learning motivation and self-efficacy, that self-efficacy will positively affect learning motivation, social support will positively affect self-efficacy, and self-efficacy and learning motivation will both positively affect learning performance. In addition, this research certifies previous works that entrepreneurs prefer to be more aggressive in competitions, have a high demand for accomplishment motivation, and are more likely to facilitate competitive over non-competitive environments.

Molecularly imprinted polymers have been shown to be useful in competitive biomimetic binding assays. Recent developments in materials science have further enhanced the capabilities of imprinted polymers. Binding assays, biological and biomimetic alike, owe their usefulness to their selectivity. The selectivity of competitive binding assays has been characterized with the cross-reactivity, which is usually expressed as the ratio of the measured IC50 concentration values of the interferent and the analyte, respectively. Yet this cross-reactivity is only a rough estimate of analytical selectivity. The relationship between cross-reactivity and analytical selectivity has apparently not been thoroughly investigated. The present work shows that this relationship depends on the underlying model of the competitive binding assay. For the simple but widely adopted model, where analyte and interferent compete for a single kind of binding site, we provide a simple formula for analytical selectivity. For reasons of an apparent mathematical problem, this formula had not been found before. We also show the relationship between analytical selectivity and cross-reactivity. Selectivity is also shown to depend on the directly measured quantity, e.g., the bound fraction of the tracer. For those cases where the one-site competitive model is not valid, a practical procedure is adopted to estimate the analytical selectivity. This procedure is then used to analyze the example of the competitive two-site binding model, which has been the main model for describing molecularly imprinted polymer behavior. The results of this work provide a solid foundation for assay development.