Eliciting a predatory response in the eastern corn snake (Pantherophis guttatus) using live and inanimate sensory stimuli: implications for managing invasive populations

North America's Eastern corn snake (Pantherophis guttatus) has been introduced to several islands throughout the Caribbean and Australasia where it poses a significant threat to native wildlife. Invasive snake control programs often involve trapping with live bait, a practice that, as well as being costly and labour intensive, raises welfare and ethical concerns. This study assessed corn snake response to live and inanimate sensory stimuli in an attempt to inform possible future trapping of the species and the development of alternative trap lures. We exposed nine individuals to sensory cues in the form of odour, visual, vibration and combined stimuli and measured the response (rate of tongue-flick [RTF]). RTF was significantly higher in odour and combined cues treatments, and there was no significant difference in RTF between live and inanimate cues during odour treatments. Our findings suggest chemical cues are of primary importance in initiating predation and that an inanimate odour stimulus, absent of simultaneous visual and vibratory cues, is a potential low-cost alternative trap lure for the control of invasive corn snake populations

Anticipated impacts of achieving SDG targets on forests - a review

Sustainable development requires knowledge of trade-offs and synergies between environmental and non-environmental goals and targets. Understanding the ways in which positive progress in matters of development not directly concerned with the environment can affect the natural environment, whether for better or for worse, can allow policymakers and development agencies to avoid the negative impacts of their actions, while capitalising on mutually beneficial opportunities. Through a systematic review of the literature, we consider the impacts of UN Sustainable Development Goal (SDG) targets on forest ecosystems, and identify 63 targets associated with potentially beneficial, damaging or mixed (i.e. damaging and/or beneficial depending on context or location) impacts. Types of impact are not uniform within SDGs, nor necessarily within individual targets. Targets relating to energy and infrastructure are among the most damaging and best studied, while targets expected to potentially result in beneficial outcomes, typically associated with social progress and well-being, have been investigated to a much lesser degree, especially in the context of external interventions. Thirty-eight targets have some variation in the direction of their impacts (i.e. at least one record with mixed impacts, or two or more records with different directions), suggesting the potential to achieve beneficial over damaging impacts in many cases. We provide illustrative examples of a range of impacts and use our findings to provide recommendations for researchers, development agencies and policymakers

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

Desert springs: deep phylogeographic structure in an ancient endemic crustacean (Phreatomerus latipes)

Extent: 13p.Desert mound springs of the Great Artesian Basin in central Australia maintain an endemic fauna that have historically been considered ubiquitous throughout all of the springs. Recent studies, however, have shown that several endemic invertebrate species are genetically highly structured and contain previously unrecognised species, suggesting that individuals may be geographically ‘stranded in desert islands’. Here we further tested the generality of this hypothesis by conducting genetic analyses of the obligate aquatic phreatoicid isopod Phreatomerus latipes. Phylogenetic and phylogeographic relationships amongst P. latipes individuals were examined using a multilocus approach comprising allozymes and mtDNA sequence data. From the Lake Eyre region in South Australia we collected data for 476 individuals from 69 springs for the mtDNA gene COI; in addition, allozyme electrophoresis was conducted on 331 individuals from 19 sites for 25 putative loci. Phylogenetic and population genetic analyses showed three major clades in both allozyme and mtDNA data, with a further nine mtDNA sub-clades, largely supported by the allozymes. Generally, each of these sub-clades was concordant with a traditional geographic grouping known as spring complexes. We observed a coalescent time between ~ 2–15 million years ago for haplotypes within each of the nine mtDNA sub-clades, whilst an older total time to coalescence (>15 mya) was observed for the three major clades. Overall we observed that multiple layers of phylogeographic history are exemplified by Phreatomerus, suggesting that major climate events and their impact on the landscape have shaped the observed high levels of diversity and endemism. Our results show that this genus reflects a diverse fauna that existed during the early Miocene and appears to have been regionally restricted. Subsequent aridification events have led to substantial contraction of the original habitat, possibly over repeated Pleistocene ice age cycles, with P. latipes populations becoming restricted in the distribution to desert springs.Michelle T. Guzik, Mark A. Adams, Nicholas P. Murphy, Steven J.B. Cooper and Andrew D. Austi