Bone Load Estimation for the Proximal Femur Using Single Energy Quantitative CT Data

A density-based load estimation method was applied to determine femoral load patterns. Two-dimensional finite element models were constructed using single energy quantitative computed tomography (QCT) data from two femora. basic load cases included parabolic pressure joint loads and constant tractions on the greater trochanter. An optimization procedure adjusted magnitudes of the basic load cases, such that the applied mechanical stimulus approached the ideal stimulus throughout each model. Dominant estimated load directions were generally consistent with published experimental data for gait. Other estimated loads suggested that loads at extreme joint orientations may be important to maintenance of bone structure. Remodeling simulations with the estimated loads produced density distributions qualitatively similar to the QCT data sets. Average nodal density errors between QCT data and predictions were 0·24 g/cm³ and 0·28 g/cm³. The results indicate that density-based load estimation could improve understanding of loading patterns on bones.     

QT interval measurement using RMED curve; a novel approach based on wavelet techniques

Hypothesis/objective: Prolonged QT interval is an index of propensity for dangerous ventricular tachyarrhythmias. The aim of this article is to establish an automatic algorithm for QT interval measurement.

Method: The proposed method is based on the continuous wavelet transform. In this method, the concepts of the rescaled wavelet coefficients and dominant scales of the electrocardiogram (ECG) components are used to perform detection of ECG characteristic points. A new concept of rescaled maximum energy density is introduced so as to perform the estimation of the QT interval.

Results and conclusion: We have applied the algorithm to the PTB database of the Physiobank?Physionet in lead II. Then, the results were evaluated using pertinent reference QT. The criterion used for evaluation of the method's performance is the root mean square (RMS) error. The method approached the RMS error of 27.89 ms for 549 subjects. The proposed method is fast, simple and is applicable to a wide range of ECG cardio cycle morphologies.     

When to estimate sex ratio in natural populations of insects? A study on sex ratio variations of gall midges within a generation

Precise estimation of arthropods' sex ratio is an important issue in a wide range of ecological studies and biological control programs. Although, in many cases changes in arthropods' sex ratio may be under the control of parents or some symbiotic microorganisms, biased sex ratios in some other species are caused by some extrinsic factors, neglect of which may lead to under/overestimation of true sex ratio. In this paper, we pursued those factors that cause false estimation of sex ratio in insects' species. We studied the predatory gall midge, Aphidoletes aphidimyza Rondani (Diptera: Cecidomyiidae), an important biological control agent of aphids, that shows protandry (i.e. early male emergence), differential lifespan of sexes, and differential distribution of sexes across habitat. Ten populations of A. aphidimyza were released separately in transparent cages and their sex ratio variations were recorded every 12 hours. The primary sex ratio in this species seems to be slightly male‐biased (52.41% males), however early emergence of males biases the sex ratio up to 72% males in a few hours after emergence. Shortly after the emergence of females, the sex ratio reaches its primary situation, but as a result of male‐biased mortality after mating, the proportion of females increases gradually to 97% by the fourth and fifth days after emergence. These results explicitly suggest that direct estimation of sex ratio in natural populations may be affected by some secondary factors such as differential mortality of sexes, protandry, and differential distribution of males and females over time and/or across habitat.     

Life history parameters and diet of Risso's dolphins,Grampus griseus,from southeastern South Africa

The life history of Risso's dolphins (Grampus griseus) remains poorly known and data from strandings can help provide important information. Data from 126 Risso's dolphins stranded or bycaught along the southeastern coastline of South Africa between 1958 and 2017 were analyzed in relation to their sex, age structure, and diet. Mean estimated length at birth was 146.9 cm, while maximum length was 325 cm for males and 313 cm for females; small sample sizes precluded detailed examination of sexual dimorphism. Age estimates for 33 individuals (14 males, 17 females, 2 unknown sex) indicated a maximum age of 13 years (males) and 17 years (females), respectively; the oldest animal was 19 years (unknown sex). Mean length and age at attainment of sexual maturity were estimated at 280 cm and 7.1 years in males and at 282 cm and 7.7 years in females. Stomach contents from 27 individuals showed that diets of immature and mature males and females overlapped and consisted predominantly of cephalopods. Reported strandings decreased between 2000 and 2017, possibly due to a lack of reporting associated with a ban on driving on beaches or related to the collapse of the local “chokka” squid (Loligo reynaudii) fishery in 2014–2015.     

One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in Sage Grouse Centrocercus urophasianus

The Sage Grouse Centrocercus urophasianus is a species of conservation concern throughout its range in western North America. Since the 1950s, the high count of males at leks has been used as an index for monitoring populations. However, the relationship between this lek-count index and population size is unclear, and its reliability for assessing population trends has been questioned. We used non-invasive genetic mark-recapture analysis of faecal and feather samples to estimate pre-breeding population size for the Parachute-Piceance-Roan, a small, geographically isolated population of Sage Grouse in western Colorado, during two consecutive winters from 2012 to 2014. We estimated total pre-breeding population size as 335 (95% confidence interval (CI): 287–382) in the first winter and 745 (95% CI: 627–864) in the second, an approximate doubling in abundance between years. Although we also observed a large increase in the spring lek-count index between those years, high male count data poorly represented mark-recapture estimates of male abundance in each year. Our data suggest that lek counts are useful for detecting the direction and magnitude of large changes in Sage Grouse abundance over time but they may not reliably reflect small changes in abundance that may be relevant to small populations of conservation concern.