The SMFA program for quantum chemistry calculations on large molecules

SMFA is a general program package for performing quantum chemistry calculations on large molecules, using an energy-based fragmentation approach. The program can calculate electronic energies, energy gradients and second derivatives; perform geometry optimization; find first order saddle points (transition states); perform energy optimized scans along a user-defined path; and evaluate various molecular properties. The program can use any of the following quantum chemistry packages: GAMESS(US), GAUSSIAN, NWChem and Q-Chem. In addition, SMFA provides a number of utility programs that, inter alia, calculate vibrational frequencies and infrared spectra with isotopic substitutions, the electrostatic potential on the solvent-accessible-surface, and isodesmic and higher order near-iso-energetic reaction schemes. Calculations of the electronic energy and related properties can be carried out using a scheme that provides a computation time that is linearly dependent on the size of the molecule or, if the user has enough processing units available, in a walltime that is independent of the size of the molecule

Minimum sample size for external validation of a clinical prediction model with a continuous outcome.

Clinical prediction models provide individualized outcome predictions to inform patient counseling and clinical decision making. External validation is the process of examining a prediction model's performance in data independent to that used for model development. Current external validation studies often suffer from small sample sizes, and subsequently imprecise estimates of a model's predictive performance. To address this, we propose how to determine the minimum sample size needed for external validation of a clinical prediction model with a continuous outcome. Four criteria are proposed, that target precise estimates of (i) R2 (the proportion of variance explained), (ii) calibration-in-the-large (agreement between predicted and observed outcome values on average), (iii) calibration slope (agreement between predicted and observed values across the range of predicted values), and (iv) the variance of observed outcome values. Closed-form sample size solutions are derived for each criterion, which require the user to specify anticipated values of the model's performance (in particular R2 ) and the outcome variance in the external validation dataset. A sensible starting point is to base values on those for the model development study, as obtained from the publication or study authors. The largest sample size required to meet all four criteria is the recommended minimum sample size needed in the external validation dataset. The calculations can also be applied to estimate expected precision when an existing dataset with a fixed sample size is available, to help gauge if it is adequate. We illustrate the proposed methods on a case-study predicting fat-free mass in children

Biological impact assessment of nanomaterial used in nanomedicine. introduction to the NanoTEST project.

Therapeutic nanoparticles (NPs) are used in nanomedicine as drug carriers or imaging agents, providing increased selectivity/specificity for diseased tissues. The first NPs in nanomedicine were developed for increasing the efficacy of known drugs displaying dose-limiting toxicity and poor bioavailability and for enhancing disease detection. Nanotechnologies have gained much interest owing to their huge potential for applications in industry and medicine. It is necessary to ensure and control the biocompatibility of the components of therapeutic NPs to guarantee that intrinsic toxicity does not overtake the benefits. In addition to monitoring their toxicity in vitro, in vivo and in silico, it is also necessary to understand their distribution in the human body, their biodegradation and excretion routes and dispersion in the environment. Therefore, a deep understanding of their interactions with living tissues and of their possible effects in the human (and animal) body is required for the safe use of nanoparticulate formulations. Obtaining this information was the main aim of the NanoTEST project, and the goals of the reports collected together in this special issue are to summarise the observations and results obtained by the participating research teams and to provide methodological tools for evaluating the biological impact of NPs

Calculating the sample size required for developing a clinical prediction model.

Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

Objective Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. Methods A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks. Results Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Conclusion Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions

The impact of predation by marine mammals on Patagonian toothfish longline fisheries

Predatory interaction of marine mammals with longline fisheries is observed globally, leading to partial or complete loss of the catch and in some parts of the world to considerable financial loss. Depredation can also create additional unrecorded fishing mortality of a stock and has the potential to introduce bias to stock assessments. Here we aim to characterise depredation in the Patagonian toothfish (Dissostichus eleginoides) fishery around South Georgia focusing on the spatio-temporal component of these interactions. Antarctic fur seals (Arctocephalus gazella), sperm whales (Physeter macrocephalus), and orcas (Orcinus orca) frequently feed on fish hooked on longlines around South Georgia. A third of longlines encounter sperm whales, but loss of catch due to sperm whales is insignificant when compared to that due to orcas, which interact with only 5% of longlines but can take more than half of the catch in some cases. Orca depredation around South Georgia is spatially limited and focused in areas of putative migration routes, and the impact is compounded as a result of the fishery also concentrating in those areas at those times. Understanding the seasonal behaviour of orcas and the spatial and temporal distribution of “depredation hot spots” can reduce marine mammal interactions, will improve assessment and management of the stock and contribute to increased operational efficiency of the fishery. Such information is valuable in the effort to resolve the human-mammal conflict for resources

Fluorescent markers rhodamine B and uranine for Anopheles gambiae adults and matings

Background Marking mosquitoes is vital for mark-release-recapture and many laboratory studies, but their small size precludes the use of methods that are available for larger animals such as unique identifier tags and radio devices. Fluorescent dust is the most commonly used method to distinguish released individuals from the wild population. Numerous colours and combinations can be used, however, dust sometimes affects longevity and behaviour so alternatives that do not have these effects would contribute substantially. Rhodamine B has previously been demonstrated to be useful for marking adult Aedes aegypti males when added to the sugar meal. Unlike dust, this also marked the seminal fluid making it possible to detect matings by marked males in the spermatheca of females. Here, marking of Anopheles gambiae sensu stricto with rhodamine B and uranine was performed to estimate their potential contribution. Methods Two fluorescent markers, rhodamine B and uranine, were dissolved in sugar water and fed to adult An. gambiae. Concentrations that are useful for marking individuals and seminal fluid were determined. The effects on adult longevity, the durability of the marking and detection of the marker in mated females was determined. Male mating competitiveness was also evaluated. Results Rhodamine B marking in adults is detectable for at least 3 weeks, however uranine marking declines with time and at low doses can be confused with auto-fluorescence. Both can be used for marking seminal fluid which can be detected in females mated by marked males, but, again, at low concentrations uranine-marking is more easily confused with the natural fluorescence of seminal fluid. Neither dye affected mating competitiveness. Conclusions Both markers tested could be useful for field and laboratory studies. Their use has substantial potential to contribute to a greater understanding of the bio-ecology of this important malaria vector. Rhodamine B has the advantage that it appears to be permanent and is less easily confused with auto-fluorescence. The primary limitation of both methods is that sugar feeding is necessary for marking and adults must be held for at least 2 nights to ensure all individuals are marked whereas dusts provide immediate and thorough marking

Comparative population structure of <i>Plasmodium malariae</i> and <i>Plasmodium falciparum</i> under different transmission settings in Malawi

&lt;b&gt;Background:&lt;/b&gt; Described here is the first population genetic study of Plasmodium malariae, the causative agent of quartan malaria. Although not as deadly as Plasmodium falciparum, P. malariae is more common than previously thought, and is frequently in sympatry and co-infection with P. falciparum, making its study increasingly important. This study compares the population parameters of the two species in two districts of Malawi with different malaria transmission patterns - one seasonal, one perennial - to explore the effects of transmission on population structures. &lt;BR/&gt; &lt;b&gt;Methods:&lt;/b&gt; Six species-specific microsatellite markers were used to analyse 257 P. malariae samples and 257 P. falciparum samples matched for age, gender and village of residence. Allele sizes were scored to within 2 bp for each locus and haplotypes were constructed from dominant alleles in multiple infections. Analysis of multiplicity of infection (MOI), population differentiation, clustering of haplotypes and linkage disequilibrium was performed for both species. Regression analyses were used to determine association of MOI measurements with clinical malaria parameters. &lt;BR/&gt; &lt;b&gt;Results:&lt;/b&gt; Multiple-genotype infections within each species were common in both districts, accounting for 86.0% of P. falciparum and 73.2% of P. malariae infections and did not differ significantly with transmission setting. Mean MOI of P. falciparum was increased under perennial transmission compared with seasonal (3.14 vs 2.59, p = 0.008) and was greater in children compared with adults. In contrast, P. malariae mean MOI was similar between transmission settings (2.12 vs 2.11) and there was no difference between children and adults. Population differentiation showed no significant differences between villages or districts for either species. There was no evidence of geographical clustering of haplotypes. Linkage disequilibrium amongst loci was found only for P. falciparum samples from the seasonal transmission setting. &lt;BR/&gt; &lt;b&gt;Conclusions:&lt;/b&gt; The extent of similarity between P. falciparum and P. malariae population structure described by the high level of multiple infection, the lack of significant population differentiation or haplotype clustering and lack of linkage disequilibrium is surprising given the differences in the biological features of these species that suggest a reduced potential for out-crossing and transmission in P. malariae. The absence of a rise in P. malariae MOI with increased transmission or a reduction in MOI with age could be explained by differences in the duration of infection or degree of immunity compared to P. falciparum

Minimum sample size for external validation of a clinical prediction model with a binary outcome

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.</p