Genome-wide comparative analysis of the codon usage patterns in plants

Codon usage analysis has been a classical area of study for decades and is important for evolution, mRNA translation, and new gene discovery. Recently, genome sequencing has made it possible to perform studies of the entire genome in plant kingdoms. The base composition of the coding sequence, codon usage pattern, codon pairs, and related indicators of relative synonymous codon usage (RSCU), including the Fop, Nc, RSCU, CAI and GC contents, were analyzed. We found that the GC content of single-celled algae is the highest, whereas dicotyledons are the lowest. Moreover, the base composition of plants is similar within the same family. In addition, the GC content of the second base of the codon is lower than the first and third base. In conclusion, the codon usage characteristics are opposite in Gramineae, single-celled algae, fern and dicotyledon, moss, and Pinaceae. Furthermore, the degree of codon usage bias is decreasing with evolution. Therefore, we hypothesize that the lower the plants, the more that they must optimize codons and that higher plants no longer need to optimize codons.     

Functional Dentition in Brazilian Adults: An Investigation of Social Determinants of Health (SDH) Using a Multilevel Approach

Objectives

Estimate the prevalence of functional dentition among Brazilian adults using four different definitions and identify associated factors.

Methods

A cross-sectional study was conducted involving 9564 Brazilian adults aged 35–44 years who participated in the 2010 National Oral Health Survey. Data collection involved oral examinations and the administration of questionnaires. The following definitions were used: 1—WHO Functional Dentition (FDWHO: ≥ 20 teeth present); 2—well-distributed teeth (WDT: ≥ 10 teeth in each arch); 3 –Functional dentition classified by esthetics and occlusion (FD_Class5: dentitions that sequentially exhibit at least one tooth in each arch, at least 10 teeth in each arch, all maxillary and mandibular anterior teeth, three or four premolar posterior occluding pairs [POPs], and at least one molar POP bilaterally); 4—Functional dentition classified by esthetics, occlusion and periodontal status (FD_Class6: corresponds to FD_Class5 with the addition of periodontal status of all sextants in the oral cavity with, at most, shallow pockets and/or clinical attachment level of 5 mm (CPI ≤ 3 and/or CAL ≤ 1). The independent variables were individual factors (gender, self-declared skin color, schooling, monthly household income, age group, self-rated treatment need, dental pain, dental appointment in the previous 12 months and dental services) and contextual factors (Municipal Human Development Index [MHDI]), Gini coefficient, fluoridated water supply and oral health coverage). Multilevel mixed-effect Poisson regression analyses were performed.

Results

The prevalence of functional dentition based on the FDWHO, WDT, FD_Class5 and FD_Class6 definitions was 77.9%, 72.9%, 42.6% and 40.3%, respectively. Adults with ≥12 years of schooling and monthly household income from US$ 853 to 2557 had higher prevalence rates of FDWHO (PR: 1.41 and 1.10, respectively), WDT (PR: 1.58 and 1.14, respectively), FD_Class5 (PR: 2.03 and 1.27, respectively) and FD_Class6 (PR: 2.15 and 1.35, respectively). These values in the final models were adjusted for gender, self-declared skin color (FD_Class5), age group, self-rated treatment need (FDWHO, FD_Class5 and FD_Class6), dental appointment in the previous 12 months (FDWHO and WDT), dental services (FDWHO and WDT) and contextual factors. A very high MHDI and presence of fluoridated water supply were associated with higher prevalence rates of the four outcomes.

Conclusions

The incorporation of the criteria of new definitions of functional dentition led to a lower prevalence rate among Brazilian adults. Striking individual and contextual inequalities were identified with regard to the four definitions analyzed, which need to be addressed through inter-sector efforts.     

In silico Identification and Validation of a Linear and Naturally Immunogenic B-Cell Epitope of the Plasmodium vivax Malaria Vaccine Candidate Merozoite Surface Protein-9

Synthetic peptide vaccines provide the advantages of safety, stability and low cost. The success of this approach is highly dependent on efficient epitope identification and synthetic strategies for efficacious delivery. In malaria, the Merozoite Surface Protein-9 of Plasmodium vivax (PvMSP9) has been considered a vaccine candidate based on the evidence that specific antibodies were able to inhibit merozoite invasion and recombinant proteins were highly immunogenic in mice and humans. However the identities of linear B-cell epitopes within PvMSP9 as targets of functional antibodies remain undefined. We used several publicly-available algorithms for in silico analyses and prediction of relevant B cell epitopes within PMSP9. We show that the tandem repeat sequence EAAPENAEPVHENA (PvMSP9_E795-A808) present at the C-terminal region is a promising target for antibodies, given its high combined score to be a linear epitope and located in a putative intrinsically unstructured region of the native protein. To confirm the predictive value of the computational approach, plasma samples from 545 naturally exposed individuals were screened for IgG reactivity against the recombinant PvMSP9-RIRII_729-972 and a synthetic peptide representing the predicted B cell epitope PvMSP9_E795-A808. 316 individuals (58%) were responders to the full repetitive region PvMSP9-RIRII, of which 177 (56%) also presented total IgG reactivity against the synthetic peptide, confirming it validity as a B cell epitope. The reactivity indexes of anti-PvMSP9-RIRII and anti-PvMSP9_E795-A808 antibodies were correlated. Interestingly, a potential role in the acquisition of protective immunity was associated with the linear epitope, since the IgG1 subclass against PvMSP9_E795-A808 was the prevalent subclass and this directly correlated with time elapsed since the last malaria episode; however this was not observed in the antibody responses against the full PvMSP9-RIRII. In conclusion, our findings identified and experimentally confirmed the potential of PvMSP9_E795-A808 as an immunogenic linear B cell epitope within the P. vivax malaria vaccine candidate PvMSP9 and support its inclusion in future subunit vaccines.     

Early Indicators of Fatal Leptospirosis during the 2010 Epidemic in Puerto Rico

Background

Leptospirosis is a potentially fatal bacterial zoonosis that is endemic throughout the tropics and may be misdiagnosed as dengue. Delayed hospital admission of leptospirosis patients is associated with increased mortality.

Methodology/Principal Findings

During a concurrent dengue/leptospirosis epidemic in Puerto Rico in 2010, suspected dengue patients that tested dengue-negative were tested for leptospirosis. Fatal and non-fatal hospitalized leptospirosis patients were matched 1:1–3 by age. Records from all medical visits were evaluated for factors associated with fatal outcome. Among 175 leptospirosis patients identified (4.7 per 100,000 residents), 26 (15%) were fatal. Most patients were older males and had illness onset during the rainy season. Fatal case patients first sought medical care earlier than non-fatal control patients (2.5 vs. 5 days post-illness onset [DPO], p < 0.01), but less frequently first sought care at a hospital (52.4% vs. 92.2%, p < 0.01). Although fatal cases were more often diagnosed with leptospirosis at first medical visit (43.9% vs. 9.6%, p = 0.01), they were admitted to the hospital no earlier than non-fatal controls (4.5 vs. 6 DPO, p = 0.31). Cases less often developed fever (p = 0.03), but more often developed jaundice, edema, leg pain, hemoptysis, and had a seizure (p ≤ 0.03). Multivariable analysis of laboratory values from first medical visit associated with fatal outcome included increased white blood cell (WBC) count with increased creatinine (p = 0.001), and decreased bicarbonate with either increased WBC count, increased creatinine, or decreased platelet count (p < 0.001).

Conclusions/Significance

Patients with fatal leptospirosis sought care earlier, but were not admitted for care any earlier than non-fatal patients. Combinations of routine laboratory values predictive of fatal outcome should be considered in admission decision-making for patients with suspected leptospirosis.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.