Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency, biofilm formation and acid susceptibility of streptococcus mutans and streptococcus sobrinus in saliva of preschool children with different levels of caries activity

Background: One of the causative factors in development of dental caries is microorganisms. Two species of Mutans streptococci including Streptococcus mutans and Streptococcus sobrinus are associated with dental caries in human beings. The aim of this study was to investigate the frequency of S. mutans and S. sobrinus in saliva of children with different caries activity and ability to form biofilm and acid susceptibility of these microorganisms. Materials and Methods: This analytical case-control study was performed on 83 preschool children, 4-6 years old. Children were divided into two groups including 41 caries-active and 42 caries-free children. Non-stimulated saliva samples were collected and culture and polymerase chain reaction techniques were used. Statistical analysis was performed using t-test, Chi-square, ANOVA, and Kappa tests. Results: S. mutans and S. sobrinus were found in 65% and 21.6% of the samples respectively. S. mutans was isolated from 75.6% of caries-active and 54.8% of caries-free children. Figures for S. sobrinus were 29.2% and 14.3% respectively. Acid susceptibility of microorganisms isolated from saliva was 87.43 in caries-active children and 94.30 for caries-free children. Biofilm formation of microorganisms in caries-active and caries-free children was 0.77 and 0.73, respectively. Conclusion: Frequency of S. mutans in caries-active children was significantly higher than caries-free children, but the difference in frequency of S. sobrinus was not significant. Acid susceptibility of microorganisms in caries-active children was significantly lower, but the ability to form biofilm was not significantly different in two groups.
Zinc availability and alkaline phosphatase activity in Emiliania huxleyi: Implications for Zn‐P co‐limitation in the ocean

Zinc (Zn) serves as a cofactor in several extracellular phosphatases, which allow microorganisms to acquire phosphorus from organic P compounds. In oligotrophic ocean water, where both phosphate and Zn concentrations are low, orthophosphate regeneration through enzymatic hydrolysis of organic compounds may be restricted by Zn availability. We examined the possibility of co‐limitation by P and Zn in batch cultures of the coccolithophore Emiliania huxleyi grown at very low biomass. Both growth rates and extracellular phosphatase activity were inhibited by low Zn. Cultures grown at nanomolar P and subpicomolar, unchelated Zn concentrations had higher phosphatase activity and slower growth rates when grown on organic P than when grown on inorganic P. We calculated that the additional Zn demand for phosphatase activity in the culture with organic P amounted to 16% of the cellular Zn quota. This percentage would be lower at higher organic P concentrations. Extrapolating from our data, we surmise that Zn‐P co‐limitation may prevail in highly oligotrophic systems such as the Sargasso Sea, but it is not likely to be widespread in the ocean. Nonetheless, the observation of a significant enhancement of extracellular phosphatase activity in Zn‐amended water samples from the Bering Sea demonstrates the potential for Zn‐P colimitation during phytoplankton blooms.
Color correction between gray world and white patch

Color equalization algorithms exhibit a variety of behaviors described in two differing types of models: Gray World and White Patch. These two models are considered alternatives to each other in methods of color correction. They are the basis for two human visual adaptation mechanisms: Lightness Constancy and Color Constancy. The Gray World approach is typical of the Lightness Constancy adaptation because it centers the histogram dynamic, working the same way as the exposure control on a camera. Alternatively, the White Patch approach is typical of the Color Constancy adaptation, searching for the lightest patch to use as a white reference similar to how the human visual system does. The Retinex algorithm basically belongs to the White Patch family due to its reset mechanism. Searching for a way to merge these two approaches, we have developed a new chromatic correction algorithm, called Automatic Color Equalization (ACE), which is able to perform Color Constancy even if based on Gray World approach. It maintains the main Retinex idea that the color sensation derives from the comparison of the spectral lightness values across the image. We tested different performance measures on ACE, Retinex and other equalization algorithms. The results of this comparison are presented.
Information Rates of ASK-Based Molecular Communication in Fluid Media

This paper studies the capacity of molecular communications in fluid media, where the information is encoded in the number of transmitted molecules in a time-slot (amplitude shift keying). The propagation of molecules is governed by random Brownian motion and the communication is in general subject to intersymbol interference (ISI). We first consider the case where ISI is negligible and analyze the capacity and the capacity per unit cost of the resulting discrete memoryless molecular channel and the effect of possible practical constraints, such as limitations on peak and/or average number of transmitted molecules per transmission. In the case with a constrained peak molecular emission, we show that as the time-slot duration increases, the input distribution achieving the capacity per channel use transitions from binary inputs to a discrete uniform distribution. In this paper, we also analyze the impact of ISI. Crucially, we account for the correlation that ISI induces between channel output symbols. We derive an upper bound and two lower bounds on the capacity in this setting. Using the input distribution obtained by an extended Blahut-Arimoto algorithm, we maximize the lower bounds. Our results show that, over a wide range of parameter values, the bounds are close.
The Association of Albuminuria With Tubular Reabsorption of Uric Acid: Results From a General Population Cohort

Background Elevated albuminuria as well as an increased serum uric acid concentration is associated with poor cardiovascular outcome. We questioned whether these 2 variables (albuminuria and serum uric concentration) may be interrelated via tubular uric acid reabsorption. Methods and Results Included were 7688 participants of the PREVEND Study, an observational, general population‐based cohort study. Linear regression analyses were used to test associations of baseline albuminuria with baseline serum uric acid concentration and tubular uric acid reabsorption (calculated as [100−fractional uric acid excretion]%). Cox regression analyses were used to study the association of baseline serum uric acid and albuminuria with incident cardiovascular morbidity and mortality. In cross‐sectional analyses, albuminuria was associated positively with serum uric acid concentration, both crude and after adjustment for potential confounders (both P<0.001). Albuminuria was found to be associated positively with tubular uric acid reabsorption, again both crude and after adjustment for potential confounders (both P<0.001). In longitudinal analyses during a median follow‐up of 10.5 years, 702 cardiovascular events occurred. After adjusting for cardiovascular risk factors, both albuminuria and serum uric acid were associated with incident cardiovascular events (Hazard Ratios 1.09 [1.03 to 1.17], P=0.01 and 1.19 [1.09 to 1.30], P<0.001, respectively). A significant interaction between these variables was present (P<0.001), consistent with high serum uric acid being less predictive for cardiovascular morbidity and mortality in the presence of high albuminuria and vice versa. Conclusions Albuminuria is strongly associated with tubular uric acid reabsorption, and consequently with serum uric acid concentration. This phenomenon may explain in part why albuminuria is associated with cardiovascular outcome.
Improved Metaheuristic Based on the R2 Indicator for Many-Objective Optimization

In recent years, performance indicators were introduced as a selection mechanism in multi-objective evolutionary algorithms (MOEAs). A very attractive option is the R2 indicator due to its low computational cost and weak-Pareto compatibility. This indicator requires a set of utility functions, which map each objective to a single value. However, not all the utility functions available in the literature scale properly for more than four objectives and the diversity of the approximation sets is sensitive to the choice of the reference points during normalization. In this paper, we present an improved version of a MOEA based on the $R2$ indicator, which takes into account these two key aspects, using the achievement scalarizing function and statistical information about the population's proximity to the true Pareto optimal front. Moreover, we present a comparative study with respect to some other emerging approaches, such as NSGA-III (based on Pareto dominance), Δp-DDE (based on the Δp indicator) and some other MOEAs based on the R2 indicator, using the DTLZ and WFG test problems. Experimental results indicate that our approach outperforms the original algorithm as well as the other MOEAs in the majority of the test instances, making it a suitable alternative for solving many-objective optimization problems.
Clinical properties of a novel, glistening-free, single-piece, hydrophobic acrylic IOL

A new, single-piece, hydrophobic acrylic lens – the first constructed with a lens optic and haptics comprised of a hydroxyethyl methacrylate-polyethylene glycol phenyl ether acrylate–styrene copolymer, cross-linked with ethylene glycol dimethacrylate, and labeled as “glistening-free” – was recently introduced globally. Glistenings have been a significant source of clinical concern with previous hydrophobic lens designs. This new monofocal lens provides enhanced, clear optics for lens-based surgery. The superior optical clarity of this lens is achieved through the elimination of glistenings, enhanced surface durability, high refractive index, a high Abbe number, and an aspheric design. Additionally, the lens design reduces the risk of developing posterior capsule opacification.
Analysis of Leaf Proteome after UV-B Irradiation in Maize Lines Differing in Sensitivity*S

UV-B radiation causes diverse morphological and physiological responses in plants, but the underlying mechanisms governing these integrated responses are unknown. In this study, we systematically surveyed responses of maize leaves to UV-B radiation using DIGE 2D gels and identified selected proteins by mass spectrometry and immunodetection analysis. To identify changes in protein accumulation in response to UV-B radiation, a line (b, pl W23) deficient in flavonoid sunscreen compounds and hence similar to commercial corn was used. In addition, its proteome in natural UV-B conditions was compared with that of two maize landraces from high altitudes (Cacahuacintle and Confite Puneño) that have improved UV-B tolerance. Protein patterns in adult maize leaves (Zea mays) were documented after growth for 21 days in sunlight depleted of UV-B radiation or growth in sunlight including an 8-h UV-B supplementation during 1 day in the field. We found that there is a very high correlation between previously documented mRNA accumulation assessed by microarray hybridization and quantitative real time reverse transcription-PCR and protein expression after UV-B irradiation in leaves of W23. Multiple isoforms were confirmed for some proteins; at least one protein, pyruvate, phosphate dikinase, is regulated post-translationally by phosphorylation by UV-B exposure. Proteins differentially regulated by UV-B radiation in W23 with higher levels under similar UV-B conditions in high altitude plants were also identified. These could be genetically fixed traits conferring UV-B tolerance and offer clues to specific adaptations to living in high ambient UV-B conditions.
Risk factors of diarrhea in children under 5 years in Al-Mukalla, Yemen

Objectives: To assess the risk factors associated with diarrhea among children below 5-years-old in Mukalla, Yemen, and compare with other studies. Methods: We conducted a case-control study on 200 children (100 cases and 100 controls) who attended the Maternity and Child Hospital, outpatient-clinics, and the Primary Health Care Centers in Al-Mukalla, Hadhramout, Yemen between February and April 2013. Results: We found that the significant risk factors associated with diarrhea were crowded housing (odds ratio [OR] 2.02; p=0.02; confidence interval [CI] 1.03-4.01), incomplete vaccination of the child (OR=2.02; p=0.027; CI: 1.08-3.8), and recurrent diarrheal illness during the last 3 months (OR=6.61; p=0.001; CI: 3.41-12.90). Conclusion: Diarrheal diseases are strongly associated with incomplete vaccination, recurrent diarrheal disease, and crowded housing.
Are There High-Tech Industries or Only High-Tech Firms? Evidence from New Technology-Based Firms

Considerable attention has been directed at understanding the structural changes that are generating an increased need for skilled workers. These changes are perceived to be the result of developments associated with the emergence of the new knowledge economy, whose potential is often linked to the growth of new technology-based firms (NTBFs). Where are these firms to be found? Related work on changes in technology and innovativeness has been accompanied by the creation of taxonomies that classify industries as high-tech or high-knowledge, based primarily on the characteristics of large firms. There is a temptation to use these taxonomies to identify new technology-based firms only within certain sectors. This paper uses a special survey that collected data on new firms to argue that this would be unwise. The paper investigates the limitations of existing classification schemes that might be used to classify industries as high- or low-tech, as advanced or otherwise. Characteristically unidimensional in scope, many of these taxonomies employ conceptual and operational measures that are narrow and incomplete. Consequently, previous rankings that identify sectors as high- or low-tech using these measures obscure the degree of innovativeness and human capital formation exhibited by certain industries. In a policy environment wherein emotive 'scoreboard' classifications have direct effects on resource allocation, the social costs of misclassification are potentially significant. Using a comparative methodology, this study investigates the role that conceptualization plays in devising taxonomies of high- and low-tech industries. Far from producing definitive classifications, existing measures of technological advancement are found to be wanting when their underpinnings are examined closely. Our objective in the current analysis is to examine the limitations of standard classification schemes, particularly when applied to new small firms, and to suggest an alternative framework based on a competency-model of the firm.
Revisiting Tversky's diagnosticity principle

Similarity is a fundamental concept in cognition. In 1977, Amos Tversky published a highly influential feature-based model of how people judge the similarity between objects. The model highlights the context-dependence of similarity judgments, and challenged geometric models of similarity. One of the context-dependent effects Tversky describes is the diagnosticity principle. The diagnosticity principle determines which features are used to cluster multiple objects into subgroups. Perceived similarity between items within clusters is expected to increase, while similarity between items in different clusters decreases. Here, we present two pre-registered replications of the studies on the diagnosticity effect reported in Tversky (1977). Additionally, one alternative mechanism that has been proposed to play a role in the original studies, an increase in the choice for distractor items (a substitution effect, see Medin et al., 1995), is examined. Our results replicate those found by Tversky (1977), revealing an average diagnosticity-effect of 4.75%. However, when we eliminate the possibility of substitution effects confounding the results, a meta-analysis of the data provides no indication of any remaining effect of diagnosticity.
All Hail Suboptimal Choice! Now, Can We "Fix" It?

Zentall (2019) describes cases in which nonhuman animals show interesting failures in some kinds of choice tests. The failures are particularly valuable, he argues, for understanding the nature of choice behavior and why it may be adaptive in some contexts but then necessarily may look suboptimal in others. I agree that these are interesting test cases. I discuss some of the ways in which the presented results converge among themselves, and with some other choice tasks, on the idea that choices are made in contexts, and those contexts play as large a role as do the actual choice options themselves. Framing effects, temporal discounting, and motivation levels of choosers all lead to choice behavior that reflects bounded rationality, just as is true for humans. In this way, suboptimal choice is natural to expect in some instances, and potentially can be offset by manipulations to the environment in which the choice is made.
Mixture models for linkage analysis of affected sibling pairs and covariates

To determine the genetic etiology of complex diseases, a common study design is to recruit affected sib/relative pairs (ASP/ARP) and evaluate their genome‐wide distribution of identical by descent (IBD) sharing using a set of highly polymorphic markers. Other attributes or environmental exposures of the ASP/ARP, which are thought to affect liability to disease, are sometimes collected. Conceivably, these covariates could refine the linkage analysis. Most published methods for ASP/ARP linkage with covariates can be conceptualized as logistic models in which IBD status of the ASP is predicted by pair‐specific covariates. We develop a different approach to the problem of ASP analysis in the presence of covariates, one that extends naturally to ARP under certain conditions. For ASP linkage analysis, we formulate a mixture model in which a disease mutation is segregating in only a fraction α of the sibships, with 1 – α sibships being unlinked. Covariate information is used to predict membership within groups; in this report, the two groups correspond to the linked and unlinked sibships. For an ASP with covariate(s) Z = z and multilocus genotype X = x, the mixture model is α(z)g(x; λ) + [1 – α(z)]g0(x), in which g0(x) follows the distribution of genotypes under the null IBD distribution and g(x; λ) allows for increased IBD sharing. Two mixture models are developed. The pre‐clustering model uses covariate information to form probabilistic clusters and then tests for excess IBD sharing independent of the covariates. The Cov‐IBD model determines probabilistic group membership by joint consideration of covariate and IBD values. Simulations show that incorporating covariates into linkage analysis can enhance power substantially. A feature of our conceptualization of ASP linkage analysis, with covariates, is that it is apparent how data analysis might evaluate covariates prior to the linkage analysis, thus avoiding the loss of power described by Leal and Ott [2000] when data are stratified. Genet. Epidemiol. 22:52–65, 2002. © 2002 Wiley‐Liss, Inc.
Dopamine neuronal responses in monkeys performing visually cued reward schedules

Dopamine neurons are important for reward‐related behaviours. They have been recorded during classical conditioning and operant tasks with stochastic reward delivery. However, daily behaviour, although frequently complex in the number of steps, is often very predictable. We studied the responses of 75 dopamine neurons during schedules of trials in which the events and related reward contingencies could be well‐predicted, within and across trials. In this visually cued reward schedule task, a visual cue tells the monkeys exactly how many trials, 1, 2, 3, or 4, must be performed to obtain a reward. The number of errors became larger as the number of trials remaining before the reward increased. Dopamine neurons frequently responded to the cues at the beginning and end of the schedules. Approximately 75% of the first‐cue responsive neurons did not distinguish among the schedules that were beginning even though the cues were different. Approximately half of the last‐cue responsive neurons depended on which schedule was ending, even though the cue signalling the last trial was the same in all schedules. Thus, the responses were related to what the monkey knew about the relation between the cues and the schedules, not the identity of the cues. These neurons also frequently responded to the go signal and/or to the OK signal indicating the end of a correctly performed trial whether a reward was forthcoming or not, and to the reward itself. Thus, dopamine neurons seem to respond to behaviourally important, i.e. salient, events even when the events have been well‐predicted.
Memory-Based Weighted-Majority Prediction for Recommender Systems

Recommender Systems are learning systems that make use of data representing multi-user preferences over items (e.g. Vote [user, item] matrix), to try to predict the preference towards new items or products regarding a particular user. User preferences are in fact the learning target functions. The main objective of the system is to filter items according to the predicted preferences and present to the user the options that are most attractive to him; i.e. he would probably like the most. We study Recommender Systems viewed as a pool of independent prediction algorithms, one per every user, in situations in which each learner faces a sequence of trials, with a prediction to make in each step. The goal is to make as few mistakes as possible. We are interested in the case that each learner has reasons to believe that there exists some other target functions in the pool that consistently behaves similar, neutral or opposite to the target function it is trying to learn. The learner doesn’t know a priori which ones, but is willing to discover and use this information for the benefit of its own prediction. A simple, yet effective method is introduced for constructing a compound algorithm for each learner. Prediction is based on transformations over the original preferences, recorded as votes over the same items by other users, according to similarity patterns and updated weights, combining memory-based individual prediction and on-line weighted-majority voting. We prove mistake bounds for this algorithm that are closely related to the total loss of the best predictor in the pool.