Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Predicting the occurrence of alate aphids in Brassicaceae

The objective of this work was to predict the occurrence of alates of Brevicoryne brassicae, Lipaphis erysimi, and Myzus persicae (Hemiptera, Aphididae) in Brassicaceae. The alate aphids were collected in yellow water traps from July 1997 to August 2005. Aphid population peaks were predicted using a degree‑day model. The meteorological factors, temperature, air relative humidity, rainfall, and sunshine hours, were used to provide precision indexes to evaluate the best predictor for the date of the first capture of alate aphids by the traps. The degree‑day model indicated that the peak population of the evaluated aphid species can be predicted using one of the following biofix dates: January 1st, June 1st, and the date of the first capture of the alate aphid species by the yellow water traps. The best predictor of B. brassicae occurrence is the number of days with minimum temperature >15°C, and of L. erysimi and M. persicae, the number of days with rainfall occurrence.

TBS (Trabecular Bone Score) is More Sensitive Than BMD to Diabetes-Related Fracture Risk

Background:Type 2 diabetes (T2D) is associated with increased fracture risk but paradoxically greater BMD. TBS (trabecular bone score), a novel grey-level texture measurement extracted from DXA images, correlates with 3D parameters of bone micro-architecture. We evaluated the ability of lumbar spine (LS) TBS to account for the increased fracture risk in diabetes. Methods:29,407 women ≥50 years at the time of baseline hip and spine DXA were identified from a database containing all clinical BMD results for the Province of Manitoba, Canada. 2,356 of the women satisfied a well-validated definition for diabetes, the vast majority of whom (>90%) would have T2D. LS L14 TBS was derived for each spine DXA examination blinded to clinical parameters and outcomes. Health service records were assessed for incident non-traumatic major osteoporotic fracture codes (mean follow-up 4.7 years). Results:In linear regression adjusted for FRAX risk factors (age,BMI, glucocorticoids, prior major fracture, rheumatoid arthritis, COPD as a smoking proxy, alcohol abuse) and osteoporosis therapy, diabetes was associated with higher BMD for LS, femoral neck and total hip but lower LS TBS (all p<0.001). Similar results were seen after excluding obese subjects withBMI>30. In logistic regression (Figure), the adjusted odds ratio (OR) for a skeletal measurement in the lowest vs highest tertile was less than 1 for all BMD measurements but increased for LS TBS (adjusted OR 2.61, 95%CI 2.30-2.97). Major osteoporotic fractures were identified in 175 (7.4%) with and 1,493 (5.5%) without diabetes (p < 0.001). LS TBS predicted fractures in those with diabetes (adjusted HR 1.27, 95%CI 1.10-1.46) and without diabetes (HR 1.31, 95%CI 1.24-1.38). LS TBS was an independent predictor of fracture (p<0.05) when further adjusted for BMD (LS, femoral neck or total hip). The explanatory effect of diabetes in the fracture prediction model was greatly reduced when LS TBS was added to the model (indicating that TBS captured a large portion of the diabetes-associated risk), but was paradoxically increased from adding any of the BMD measurements. Conclusions:Lumbar spine TBS is sensitive to skeletal deterioration in postmenopausal women with diabetes, whereas BMD is paradoxically greater. LS TBS predicts osteoporotic fractures in those with diabetes, and captures a large portion of the diabetes-associated fracture risk. Combining LS TBS with BMD incrementally improves fracture prediction.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Continuous assessment of electrical epileptic activity in acute stroke.

OBJECTIVE: To determine the incidence and risk factors of electrical seizures and other electrical epileptic activity using continuous EEG (cEEG) in patients with acute stroke. METHODS: One hundred consecutive patients with acute stroke admitted to our stroke unit underwent cEEG using 10 electrodes. In addition to electrical seizures, repetitive focal sharp waves (RSHWs), repetitive focal spikes (RSPs), and periodic lateralized epileptic discharges (PLEDs) were recorded. RESULTS: In the 100 patients, cEEG was recorded for a mean duration of 17 hours 34 minutes (range 1 hour 12 minutes to 37 hours 10 minutes). Epileptic activity occurred in 17 patients and consisted of RSHWs in seven, RSPs in seven, and PLEDs in three. Electrical seizures occurred in two patients. On univariate Cox regression analysis, predictors for electrical epileptic activity were stroke severity (high score on the National Institutes of Health Stroke Scale) (hazard ratio [HR] 1.12; p = 0.002), cortical involvement (HR 5.71; p = 0.021), and thrombolysis (HR 3.27; p = 0.040). Age, sex, stroke type, use of EEG-modifying medication, and cardiovascular risk factors were not predictors of electrical epileptic activity. On multivariate analysis, stroke severity was the only independent predictor (HR 1.09; p = 0.016). CONCLUSION: In patients with acute stroke, electrical epileptic activity occurs more frequently than previously suspected.

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

Compliance with medication among outpatients with uncontrolled hypertension in the Seychelles.

Owing to increasing rates of hypertension and cardiovascular-related diseases in developing countries, compliance with antihypertensive medication is major public health importance. Few studies have reported on compliance in developing countries. We determined the compliance of 187 patients with uncontrolled hypertension in the Seychelles (Indian Ocean), by assessing the presence of a biologic marker (riboflavin) in the urine. The urine tested positive in 56% of the cases. Compliance varied from one physician to another (highest 72% versus lowest 33%, P = 0.003), improved with the level of literacy (62% versus 45%, P = 0.024), and depended on the presence absence of diuretics in the medication (respectively, 45% versus 66%, P = 0.005). The ability of patients to report correctly the number of antihypertensive pills to be taken daily was a predictor of compliance (62% of the patients who gave appropriate answers had positive urine for the marker versus 31% for those giving inappropriate answers).

Conductivity test in seeds of different passion flower species

The objective of this work was to evaluate the use of the conductivity test as a means of predicting seed viability in seven Passiflora species: P. alata, P. cincinnata, P. edulis f. edulis, P. edulis f. flavicarpa, P. morifolia, P. mucronata, and P. nitida. Conductivity of non-desiccated (control), desiccated, and non-desiccated cryopreserved seeds was determined and related to their germination percentage. The obtained results suggest that the electrical conductivity test has potential as a germination predictor for P. edulis f. flavicarpa seed lots, but not for the other tested species.

Significant decrease in in-hospital mortality and major adverse cardiac events in Swiss STEMI patients between 2000 and December 2007.

To evaluate the in-hospital outcome of STEMI (ST elevation myocardial infarction) patients admitted to Swiss hospitals between 2000 and December 2007, and to identify the predictors of in-hospital mortality and major cardiac events. Data from the Swiss national registry AMIS Plus (Acute Myocardial Infarction and Unstable Angina in Switzerland) were used. All patients admitted between January 2000 and December 2007 with STEMI or a new LBBB (left bundle branch block) were included in the registry. We studied 12 026 STEMI patients admitted to 68 hospitals. The mean age was 64 +/- 13 years and 73% of the patients were male. Incidence of in-hospital death was 7.6% in 2000 and 6% in 2007. Reinfarction fell from 3.7% in 2000 to 0.9% in 2007. Thrombolysis decreased from 40.2% in 2000 to 2% in 2007. Clinical predictors of mortality were: age >65 years, Killips class III or IV, diabetes, Q wave myocardial infarction (at presentation). Patients undergoing percutaneous coronary intervention (PCI) had lower mortality and reinfarction rates (3.9% versus 11.2% and 1.1% versus 3.1% respectively, p <0.001) over time, although their numbers increased from 43% in 2000 to 85% in 2007. Patients admitted to hospitals with PCI facilities had lower mortality than patients hospitalised in hospitals without it, but the demographic characteristics differ widely between the two groups. Both in-hospital mortality and reinfarction decreased significantly over the time, parallel to an increased number of PCI. PCI was also the strongest predictor of survival. In-hospital mortality and reinfarction rate have decreased significantly in Swiss STEMI patients in the last seven years, parallel to a significant increase in the number of percutaneous coronary interventions in addition to medical therapy. Outcome is not related to the site of admission but to PCI access.

What makes one person paranoid and another person anxious? The differential prediction of social anxiety and persecutory ideation in an experimental situation

The study shows that social anxiety and persecutory ideation share many of the same predictive factors. Non-clinical paranoia may be a type of anxious fear. However, perceptual anomalies are a distinct predictor of paranoia. In the context of an individual feeling anxious, the occurrence of odd internal feelings in social situations may lead to delusional ideas through a sense of" things not seeming right". The study illustrates the approach of focusing on experiences such as paranoid thinking rather than diagnoses such as schizophrenia.

Sydänpuuvaltaisen erikoiskuivatun mäntysahatavaran kosteuspitoisuus kuivausjännitykset eri puuaineen tiheyksissä

Puualan lisääntyvä tarve tuottaa jatkojalosteita on asettanut kuivauksen laatutasolle uusia haasteita ja laadukkaan kuivauksen tulos on perusta jatkojalosteiden toimivuudelle. Yhä enemmän tarvitaan kuivausprosessin ja kuivattavan puumateriaalin kontrollointia läpi koko kuivausprosessin. Näillä toimenpiteillä varmistetaan haluttu kuivauslaatu sisäisille ja ulkoisille asiakkaille yrityksessä. Tämän tutkimuksen tarkoituksena oli selvittää männyn sydänpuuhun muodostuvaa kosteuspitoisuutta kamarikuivausprosessissa. Keinokuivauksen tavoitteellinen loppukosteus oli 12 % kuivapainosta puumateriaalia. Jo aikaisempien tutkimusten perusteella on voitu osoittaa, että loppukosteuden arvo vaihtelee kuivauserässä eri _kappaleiden välillä johtuen puumateriaalin epähomogeenisuudesta. Sydänpuuvaltainen ja tiheä mäntysahatavara, joka sahataan tyvitukeista läheltä juuriosaa sisältää tämän tutkimuksen mukaan runsaasti pihkaa verrattuna latvaosassaan samaa sahetta. Kosteusgradientti on myös suurempi sahatavarakappaleiden tyviosassa. Vuosikasvun ja tiheyden korrelaatio on heikko männyn sydänpuulla. Kesäpuuprosentin korrelaatio tiheyteen on erittäin merkittävä. Pihkapitoisuus lisää puuaineen tiheyttä tyviosassa sahetta. Pintakovuus on puumateriaalin muodonmuutos potentiaali, kun kosteuspitoisuudet tasaantuvat poikkileikkauksessa. Kosteusgradientilla on selvä yhteyspintakovuuteen eli muodonmuutospotentiaaliin.

Assessing catchment-scale erosion and yields of suspended solids from improved temperate grassland.

This paper quantifies the yields of suspended solids (SS) from a headwater catchment managed as improved temperate grassland, providing the first direct, catchment-scale evidence of the rates of erosion from this land-use in the UK and assessing the threat posed to aquatic ecosystems. High-resolution monitoring of catchment hydrology and the concentrations of SS and volatile organic matter (VOM) were carried out in the first-order channel of the Den Brook headwater catchment in Devon (UK) during the 2006-2007 hydrological season. The widely used 'rating curve' (discharge-concentration) approach was employed to estimate yields of SS, but as demonstrated by previous researchers, this study showed that discharge is a poor predictor of SS concentrations and therefore any yields estimated from this technique are likely to be highly uncertain. Nevertheless, for the purpose of providing estimates of yields that are comparable to previous studies on other land uses/sources, this technique was adopted albeit in an uncertainty-based framework. The findings suggest that contrary to the common perception, grasslands can be erosive landscapes with SS yields from this catchment estimated to be between 0.54 and 1.21 t ha(-1) y(-1). In terms of on-site erosion problems, this rate of erosion does not significantly exceed the commonly used 'tolerable' threshold in the UK ( approximately 1 t ha(-1) y(-1)). In terms of off-site erosion problems, it is argued here that the conventional expression of SS yield as a bulk annual figure has little relevance to the water quality and ecological status of surface waters and therefore an alternative technique (the concentration-frequency curve) is developed within this paper for the specific purpose of assessing the ecological threat posed by the delivery of SS into surface waters. This technique illustrates that concentrations of SS recorded at the catchment outlet frequently exceed the water quality guidelines, such as those of the EU Freshwater Fisheries Directive (78/659/EC), and pose a serious threat to aquatic organisms. It is suggested that failure to recognise improved temperate grasslands as a potential source of particulate material could result in the non-compliance of surface waters to water quality guidelines, deterioration of ecological status and failure of water quality remediation measures.

Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.

Background: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+ BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings.Results: We developed a gene classifier consisting of 181 genes belonging to 13 biological clusters. In the independent set of adjuvantly-treated samples, it was able to define two distinct prognostic groups (HR 2.01 95% CI: 1.29-3.13; p = 0.002). Six of the 13 gene clusters represented pathways involved in cell cycle and proliferation. In 112 metastatic breast cancer patients treated with tamoxifen, one of the classifier components suggesting a cellular inflammatory mechanism was significantly predictive of response.Conclusion: We have developed a gene classifier that can predict clinical outcome in tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the important role of proliferation genes in prognosis, our approach proposes other genes and pathways that may elucidate further mechanisms that influence clinical outcome and prediction of response to tamoxifen.

Local alignment refinement using structural assessment.

Homology modeling is the most commonly used technique to build a three-dimensional model for a protein sequence. It heavily relies on the quality of the sequence alignment between the protein to model and related proteins with a known three dimensional structure. Alignment quality can be assessed according to the physico-chemical properties of the three dimensional models it produces.In this work, we introduce fifteen predictors designed to evaluate the properties of the models obtained for various alignments. They consist of an energy value obtained from different force fields (CHARMM, ProsaII or ANOLEA) computed on residue selected around misaligned regions. These predictors were evaluated on ten challenging test cases. For each target, all possible ungapped alignments are generated and their corresponding models are computed and evaluated.The best predictor, retrieving the structural alignment for 9 out of 10 test cases, is based on the ANOLEA atomistic mean force potential and takes into account residues around misaligned secondary structure elements. The performance of the other predictors is significantly lower. This work shows that substantial improvement in local alignments can be obtained by careful assessment of the local structure of the resulting models.