In rats fed high-energy diets, taste, rather than fat content, is the key factor increasing food intake: a comparison of a cafeteria and a lipid-supplemented standard diet
Background Food selection and ingestion both in humans and rodents, often is a critical factor in determining excess energy intake and its related disorders. Methods Two different concepts of high-fat diets were tested for their obesogenic effects in rats; in both cases, lipids constituted about 40% of their energy intake. The main difference with controls fed standard lab chow, was, precisely, the lipid content. Cafeteria diets (K) were self-selected diets devised to be desirable to the rats, mainly because of its diverse mix of tastes, particularly salty and sweet. This diet was compared with another, more classical high-fat (HF) diet, devised not to be as tasty as K, and prepared by supplementing standard chow pellets with fat. We also analysed the influence of sex on the effects of the diets. Results K rats grew faster because of a high lipid, sugar and protein intake, especially the males, while females showed lower weight but higher proportion of body lipid. In contrast, the weight of HF groups were not different from controls. Individual nutrient’s intake were analysed, and we found that K rats ingested large amounts of both disaccharides and salt, with scant differences of other nutrients’ proportion between the three groups. The results suggest that the key differential factor of the diet eliciting excess energy intake was the massive presence of sweet and salty tasting food. Conclusions The significant presence of sugar and salt appears as a powerful inducer of excess food intake, more effective than a simple (albeit large) increase in the diet’s lipid content. These effects appeared already after a relatively short treatment. The differential effects of sex agree with their different hedonic and obesogenic response to diet.
Oliva et al. (2017) In rats fed high-energy diets, taste, rather than fat content, is the key factor increasing food intake: a comparison of a cafeteria and a lipid-supplemented standard diet. PeerJ 5:e3697 https://doi.org/10.7717/peerj.3697
Awareness about antibiotic resistance in a self-medication user group from Eastern Romania: a pilot study
Background Awareness about antibiotic resistance depends on the attitudes and information about antibiotic resistance of both patients and physicians. Persons who practice self-medication are at high risk of also self-medicating with antibiotics. The purpose of the present study was to evaluate the awareness about antibiotic resistance by investigating the practice in a group of self-medication users in a sample of adults in Romania and the variables associated with such practice. Material and Methods A cross-sectional self-filled questionnaire based study was conducted from December 2016 through January 2017 amongst 218 self-medication users (SMUG). The attitudes, the level of knowledge, the perceptions, about antibiotic use (ABU) and about antibiotic resistance (ABR) were compared to a reference group represented by medical residents group in their specialty training (MRG) considered to have a higher level of knowledge and awareness about ABU and ABR. Results The response rate was 87.2% in the SMUG group and 100% in the MRG group. The SMUG group reported self-medication practices for antibiotics with a high frequency at any time in life (72%), but with a very low frequency from the month previous to the date of the study (12%), comparative with the MRG group (75% and 7%, respectively). The multivariate logistic regression analysis showed that self-medication with antibiotics at any time in life in the SMUG group could be predicted by the answers to two questions regarding the practices and knowledge about ABU (Q13 and Q20). On the other hand, in the MRG group, a question about ABR perception (Q23), could be predictor for self-medication with antibiotics. Self-medication with antibiotics in the month previous to the date of the study in the SMUG group could be predicted with three questions: one about ABU practice (Q14), one about ABR perception (Q26) and one referring to ABR knowledge (Q28). On the other hand, in the MRG group, a question about ABR knowledge (Q32) could be predictor for self-medication with antibiotics. The reduced awareness about ABR in the SMUG group, is revealed by the reduced number of subjects (38%), who did not know that missing an antibiotic dose during a medical treatment contributes to ABR, comparative with the MRG group (84%). Indirectly, low ABR awareness in the SMUG group is revealed by the confusion about the appropriate use of antibiotics in bacterial or viral infections (that antibiotics are not used against viruses). Conclusions The findings from our study on the awareness about antibiotic resistance in the SMUG group might help the policy makers and regulatory authorities to develop educational programs directed to change the perceptions and attitudes about the appropriate use of antibiotics in order to diminish self-medication practices with antibiotics.
Topor et al. (2017) Awareness about antibiotic resistance in a self-medication user group from Eastern Romania: a pilot study. PeerJ 5:e3803 https://doi.org/10.7717/peerj.3803
Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach
Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson’s coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.
Murat et al. (2017) Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach. PeerJ 5:e3792 https://doi.org/10.7717/peerj.3792
Greater reproductive investment, but shorter lifespan, in agrosystem than in natural-habitat toads
Global amphibian decline is due to several factors: habitat loss, anthropization, pollution, emerging diseases, and global warming. Amphibians, with complex life cycles, are particularly susceptible to habitat alterations, and their survival may be impaired in anthropized habitats. Increased mortality is a well-known consequence of anthropization. Life-history theory predicts higher reproductive investment when mortality is increased. In this work, we compared age, body size, and different indicators of reproductive investment, as well as prey availability, in natterjack toads (Epidalea calamita) from agrosystems and adjacent natural pine groves in Southwestern Spain. Mean age was lower in agrosystems than in pine groves, possibly as a consequence of increased mortality due to agrosystem environmental stressors. Remarkably, agrosystem toads were larger despite being younger, suggesting accelerated growth rate. Although we detected no differences in prey availability between habitats, artificial irrigation could shorten aestivation in agrosystems, thus increasing energy trade. Moreover, agrosystem toads exhibited increased indicators of reproductive investment. In the light of life-history theory, agrosystem toads might compensate for lesser reproductive events—due to shorter lives—with a higher reproductive investment in each attempt. Our results show that agrosystems may alter demography, which may have complex consequences on both individual fitness and population stability.
Zamora-Camacho et al. (2017) Greater reproductive investment, but shorter lifespan, in agrosystem than in natural-habitat toads. PeerJ 5:e3791 https://doi.org/10.7717/peerj.3791
Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey
Background Respondent fatigue, also known as survey fatigue, is a common problem in the collection of survey data. Factors that are known to influence respondent fatigue include survey length, survey topic, question complexity, and open-ended question type. There is a great deal of interest in understanding the drivers of physician survey responsiveness due to the value of information received from these practitioners. With the recent explosion of mobile smartphone technology, it has been possible to obtain survey data from users of mobile applications (apps) on a question-by-question basis. The author obtained basic demographic survey data as well as survey data related to an anesthesiology-specific drug called sugammadex and leveraged nonresponse rates to examine factors that influenced respondent fatigue. Methods Primary data were collected between December 2015 and February 2017. Surveys and in-app analytics were collected from global users of a mobile anesthesia calculator app. Key independent variables were user country, healthcare provider role, rating of importance of the app to personal practice, length of time in practice, and frequency of app use. Key dependent variable was the metric of respondent fatigue. Results Provider role and World Bank country income level were predictive of the rate of respondent fatigue for this in-app survey. Importance of the app to the provider and length of time in practice were moderately associated with fatigue. Frequency of app use was not associated. This study focused on a survey with a topic closely related to the subject area of the app. Respondent fatigue rates will likely change dramatically if the topic does not align closely. Discussion Although apps may serve as powerful platforms for data collection, responses rates to in-app surveys may differ on the basis of important respondent characteristics. Studies should be carefully designed to mitigate fatigue as well as powered with the understanding of the respondent characteristics that may have higher rates of respondent fatigue.
O’Reilly-Shah (2017) Factors influencing healthcare provider respondent fatigue answering a globally administered in-app survey. PeerJ 5:e3785 https://doi.org/10.7717/peerj.3785
Identification and analysis of CYP450 genes from transcriptome of Lonicera japonica and expression analysis of chlorogenic acid biosynthesis related CYP450s
Background Lonicera japonica is an important medicinal plant that has been widely used in traditional Chinese medicine for thousands of years. The pharmacological activities of L. japonica are mainly due to its rich natural active ingredients, most of which are secondary metabolites. CYP450s are a large, complex, and widespread superfamily of proteins that participate in many endogenous and exogenous metabolic reactions, especially secondary metabolism. Here, we identified CYP450s in L. japonica transcriptome and analyzed CYP450s that may be involved in chlorogenic acid (CGA) biosynthesis. Methods The recent availability of L. japonica transcriptome provided opportunity to identify CYP450s in this herb. BLAST based method and HMM based method were used to identify CYP450s in L. japonica transcriptome. Then, phylogenetic analysis, conserved motifs analysis, GO annotation, and KEGG annotation analyses were conducted to characterize the identified CYP450s. qRT-PCR was used to explore expression patterns of five CGA biosynthesis related CYP450s. Results In this study, 151 putative CYP450s with complete cytochrome P450 domain, which belonged to 10 clans, 45 families and 76 subfamilies, were identified in L. japonica transcriptome. Phylogenetic analysis classified these CYP450s into two major branches, A-type (47%) and non-A type (53%). Both types of CYP450s had conserved motifs in L. japonica. The differences of typical motif sequences between A-type and non-A type CYP450s in L. japonica were similar with other plants. GO classification indicated that non-A type CYP450s participated in more molecular functions and biological processes than A-type. KEGG pathway annotation totally assigned 47 CYP450s to 25 KEGG pathways. From these data, we cloned two LjC3Hs (CYP98A subfamily) and three LjC4Hs (CYP73A subfamily) that may be involved in biosynthesis of CGA, the major ingredient for pharmacological activities of L. japonica. qRT-PCR results indicated that two LjC3Hs exhibited oppositing expression patterns during the flower development and LjC3H2 exhibited a similar expression pattern with CGA concentration measured by HPLC. The expression patterns of three LjC4Hs were quite different and the expression pattern of LjC4H3 was quite similar with that of LjC3H1. Discussion Our results provide a comprehensive identification and characterization of CYP450s in L. japonica. Five CGA biosynthesis related CYP450s were cloned and their expression patterns were explored. The different expression patterns of two LjC3Hs and three LjC4Hs may be due to functional divergence of both substrate and catalytic specificity during plant evolution. The co-expression pattern of LjC3H1 and LjC4H3 strongly suggested that they were under coordinated regulation by the same transcription factors due to same cis elements in their promoters. In conclusion, this study provides insight into CYP450s and will effectively facilitate the research of biosynthesis of CGA in L. japonica.
Qi et al. (2017) Identification and analysis of CYP450 genes from transcriptome of Lonicera japonica and expression analysis of chlorogenic acid biosynthesis related CYP450s. PeerJ 5:e3781 https://doi.org/10.7717/peerj.3781
Comparison of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in the serum of hypothyroxinemic and euthyroid dogs
Objective To determine the profile of 14 polybrominated diphenyl ethers (PBDEs) and 23 polychlorinated biphenyls (PCBs) in serum of domestic canines and whether this was predictive of thyroid hormone status. Samples Serum samples were collected from 51 client-owned dogs visiting the University of California Davis William R. Pritchard Veterinary Medical Teaching Hospital during 2012 to 2016 for routine appointments. Fifteen dogs were diagnosed with hypothyroxinemia while 36 were euthyroid. Procedures Concentrations of PBDEs and PCBs in canine serum samples were measured by gas chromatography mass spectrometry. Logistic regression analysis was used to determine the association between the presence/absence of canine hypothyroxinemia and the serum concentration of individual PBDE or PCB congeners. Results The median concentrations of total PBDE and PCB congeners in the hypothyroxinemic group were 660 and 1,371 ng/g lipid, respectively, which were higher than concentrations detected in the control group. However, logistic regression analysis determined that current concentrations of PBDEs and PCBs in canines were not significantly associated with hypothyroxinemia. BDE 183 was the only congener showing near significance (p = 0.068). Conclusions PBDE and PCB congeners were detected in all canine samples confirming ongoing exposure to these pollutants. Because household dogs share the human environment, they may serve as biosentinels of human exposure to these contaminants.
Environmental Contamination and Remediation
Lau et al. (2017) Comparison of polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in the serum of hypothyroxinemic and euthyroid dogs. PeerJ 5:e3780 https://doi.org/10.7717/peerj.3780
The landscape of fear conceptual framework: definition and review of current applications and misuses
Landscapes of Fear (LOF), the spatially explicit distribution of perceived predation risk as seen by a population, is increasingly cited in ecological literature and has become a frequently used “buzz-word”. With the increase in popularity, it became necessary to clarify the definition for the term, suggest boundaries and propose a common framework for its use. The LOF, as a progeny of the “ecology of fear” conceptual framework, defines fear as the strategic manifestation of the cost-benefit analysis of food and safety tradeoffs. In addition to direct predation risk, the LOF is affected by individuals’ energetic-state, inter- and intra-specific competition and is constrained by the evolutionary history of each species. Herein, based on current applications of the LOF conceptual framework, I suggest the future research in this framework will be directed towards: (1) finding applied management uses as a trait defining a population’s habitat-use and habitat-suitability; (2) studying multi-dimensional distribution of risk-assessment through time and space; (3) studying variability between individuals within a population; (4) measuring eco-neurological implications of risk as a feature of environmental heterogeneity and (5) expanding temporal and spatial scales of empirical studies.
Bleicher (2017) The landscape of fear conceptual framework: definition and review of current applications and misuses. PeerJ 5:e3772 https://doi.org/10.7717/peerj.3772
Habitat suitability—density relationship in an endangered woodland species: the case of the Blue Chaffinch (Fringilla polatzeki)
Background Understanding constraints to the distribution of threatened species may help to ascertain whether there are other suitable sectors for reducing the risks associated with species that are recorded in only one protected locality, and to inform about the suitability of other areas for reintroduction or translocation programs. Methods We studied the Gran Canaria blue chaffinch (Fringilla polatzeki), a habitat specialist endemic of the Canary Islands restricted to the pine forest of Inagua, the only area where the species has been naturally present as a regular breeder in the last 25 years. A suitability distribution model using occurrences with demographic relevance (i.e., nest locations of successful breeding attempts analysed using boosted classification trees) was built considering orographic, climatic and habitat structure predictors. By means of a standardized survey program we monitored the yearly abundance of the species in 100 sectors since the declaration of Inagua as a Strict Nature Reserve in 1994. Results The variables with the highest relative importance in blue chaffinch habitat preferences were pine height, tree cover, altitude, and rainfall during the driest trimester (July–September). The observed local abundance of the blue chaffinch in Inagua (survey data) was significantly correlated with habitat suitability derived from modelling the location of successful nesting attempts (using linear and quantile regressions). The outcomes of the habitat suitability model were used to quantify the suitability of other natural, historic, pine forests of Gran Canaria. Tamadaba is the forest with most suitable woodland patches for the species. We estimated a population size of 195–430 blue chaffinches in Inagua since 2011 (95% CI), the smallest population size of a woodland passerine in the Western Palearctic. Discussion Habitat suitability obtained from modelling the location of successful breeding attempts is a good surrogate of the observed local abundance during the reproductive season. The outcomes of these models can be used for the identification of potential areas for the reintroduction of the species in other suitable pine forests and to inform forest management practices.
Carrascal et al. (2017) Habitat suitability—density relationship in an endangered woodland species: the case of the Blue Chaffinch (Fringilla polatzeki). PeerJ 5:e3771 https://doi.org/10.7717/peerj.3771
Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data
High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells, though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.
Olson et al. (2017) Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data. PeerJ 5:e3729 https://doi.org/10.7717/peerj.3729