Computational algebraic methods in efficient estimation

A strong link between information geometry and algebraic statistics is made by investigating statistical manifolds which are algebraic varieties. In particular it it shown how first and second order efficient estimators can be constructed, such as bias corrected Maximum Likelihood and more general estimators, and for which the estimating equations are purely algebraic. In addition it is shown how Gr\"obner basis technology, which is at the heart of algebraic statistics, can be used to reduce the degrees of the terms in the estimating equations. This points the way to the feasible use, to find the estimators, of special methods for solving polynomial equations, such as homotopy continuation methods. Simple examples are given showing both equations and computations. *** The proof of Theorem 2 was corrected by the latest version. Some minor errors were also corrected.Comment: 21 pages, 5 figure

Automated and unsupervised detection of malarial parasites in microscopic images

<p>Abstract</p> <p>Background</p> <p>Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria.</p> <p>Method</p> <p>A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives.</p> <p>Results</p> <p>The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites.</p> <p>Conclusion</p> <p>Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite clearance curves.</p

Decoding negative affect personality trait from patterns of brain activation to threat stimuli

INTRODUCTION: Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. METHODS: fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. RESULTS: The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). CONCLUSION: These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts

Carbon and nitrogen isotopic variability in foxtail millet (Setaria italica) with watering regime.

RATIONALE: Carbonised plant remains are analysed for reconstruction of past climates and agricultural regimes. Several recent studies have used C4 plants to address related questions, and correlations between modern C4 plant δ13 C values and rainfall have been found. The millets were important food crops in prehistoric Eurasia, yet little is known about causes of isotopic variation within millet species. Previous research has shown there to be significant isotopic variation between millet accessions. Here we compare isotope ratios from plants grown under different watering regimes. This allows for a consideration of whether or not Setaria italica is a good proxy for environmental reconstruction. METHODS: We compare stable isotope ratios of Setaria italica plants grown in a controlled environment chamber with different watering regimes. We compare the carbon isotope ratios of leaves and grains, and the nitrogen isotope ratios of grains, from 12 accessions of Setaria italica. RESULTS: We find significant isotopic variability between watering regimes. Carbon isotope ratios are positively correlated with water availability, and on average vary by 1.9‰ and 1.7‰ for leaves and grains, respectively. Grain nitrogen isotope ratios also vary with watering regime; however, the highest isotope ratios are found with the 130-mL watering regime. CONCLUSIONS: The carbon isotope ratios of Setaria italica are strongly correlated with water availability. However, the correlation is the opposite to that seen in studies of C3 plants. The difference in isotopic ratio due to watering regime is comparable with that seen between different accessions; thus distinguishing between changing varieties of Setaria italica and changing climate is problematic. In terms of grain nitrogen isotope ratios, the highest δ15 N values were not associated with the lowest watering regime. Again, δ15 N variation is comparable with that which would be expected from an aridity effect or a manuring effect, and thus distinguishing between these factors is probably problematic

Klinefelters Syndrome: Change in T-Scores with Testosterone, Bisphosphonate, and Vitamin D Treatment over 6 Years

Background: Klinefelter's syndrome (KS) is characterized by extra X chromosomes and features of primary hypogonadism including osteopenia and osteoporosis. Testosterone therapy (TTh) is widely used to treat men with KS and low serum testosterone/hypogonadal symptoms, though studies on its efficacy in improving bone density show varied outcomes. Materials and Methods: We studied the effects of TTh, bisphosphonates, and vitamin D/calcium in 38 men with KS and low testosterone, hypogonadal symptoms, and T-scores consistent with osteoporosis. Our aim was to investigate at the end of follow-up (median: 87 months, range: 27-147 months), associations between age, baseline total testosterone, and T-scores, and change in T-scores after treatment. Results: At final assessment, all men had T-score values outside the osteoporotic range (-1.1 standard deviation [SD],-1.8 SD). Baseline age but not median baseline testosterone appeared associated with change in T-score and T-score at final assessment. All men had dual-energy X-ray absorptiometry every 6 months and demonstrated continued improvement in T-scores after 3 months and up to 72 months. Baseline age and T-scores (stratified by median) were associated with change in T-score at final assessment. Compared with men ≥51 years, those aged <51 years showed significantly greater improvement in T-scores between 6 and 30 months. Men with worse T-score values (<3.7 SD) showed significantly greater improvement at every time point up to 36 months. Our results indicate that TTh, bisphosphonates, and vitamin D/calcium improve osteoporosis although there is a need to better understand the effects of the individual therapies, age, and baseline T-score on treatment efficacy

A survey of psychological practitioner workplace wellbeing

The well-being of the psychological workforce is an area of concern. However, it has been sparsely studied in a holistic manner encompassing workplace well-being as well as burnout. This study reports a survey of 1,678 psychological practitioners accessed through professional networks. The short Warwick Edinburgh Mental Well-being Scale (SWEMWBS) and the Psychological Practitioner Workplace Well-being Measure (PPWWM) were administered with a demographic questionnaire. The mean for the SWEMWBS was below that of a national population survey. The intercorrelation of these tests was .61. Subgroup analyses showed significant differences: assistant psychologists, counsellors and psychological well-being practitioners demonstrated better than average workplace well-being. But for general well-being (SWEMWBS), trainee clinical psychologists and assistant psychologists showed lower than average well-being, whereas psychological well-being practitioners were higher than average. Other factors associated with well-being were contract type—both measures (higher workplace well-being in those with temporary contracts and the self-employed); employment sector—for PPWWM only (private organisation/independent workers and third sector/charitable organisation workers scored above the PPWWM mean); ethnicity—for both measures (Asian groups except Chinese had higher well-being than average for the PPWWM and SWEMWBS) and disability was strongly associated with lower well-being on both measures. Harassment, feeling depressed or a failure and wanting to leave the National Health Service (NHS) were associated with lower well-being. Greater age, pay and years of service were negatively correlated with well-being. A five-factor structure was obtained with this sample. The results confirmed psychological practitioners as an at-risk group and identified a number of factors associated with workplace well-being

Вимоги видавничого відділу ІМФЕ ім. М. Т. Рильського до оформлення авторами рукописів

Industrial parts are manufactured to tolerances as no production process is capable of delivering perfectly identical parts. It is unacceptable that a plan for a manipulation task that was determined on the basis of a CAD model of a part fails on some manufactured instance of that part, and therefore it is crucial that the admitted shape variations are systematically taken into account during the planning of the task. We study the problem of orienting a part with given admitted shape variations by means of pushing with a single frictionless jaw. We use a very general model for admitted shape variations that only requires that any valid instance must contain a given convex polygon PI while it must be contained in another convex polygon PE. The problem that we solve is to determine, for a given h, the sequence of h push actions that puts all valid instances of a part with given shape variation into the smallest possible interval of final orientations. The resulting algorithm runs in O(hn) time, where n=|PI|+|PE|

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Cross-sectional associations between sleep duration, sedentary time, physical activity, and adiposity indicators among Canadian preschool-aged children using compositional analyses

Abstract Background Sleep duration, sedentary behaviour, and physical activity are three co-dependent behaviours that fall on the movement/non-movement intensity continuum. Compositional data analyses provide an appropriate method for analyzing the association between co-dependent movement behaviour data and health indicators. The objectives of this study were to examine: (1) the combined associations of the composition of time spent in sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) with adiposity indicators; and (2) the association of the time spent in sleep, sedentary behaviour, LPA, or MVPA with adiposity indicators relative to the time spent in the other behaviours in a representative sample of Canadian preschool-aged children. Methods Participants were 552 children aged 3 to 4 years from cycles 2 and 3 of the Canadian Health Measures Survey. Sedentary time, LPA, and MVPA were measured with Actical accelerometers (Philips Respironics, Bend, OR USA), and sleep duration was parental reported. Adiposity indicators included waist circumference (WC) and body mass index (BMI) z-scores based on World Health Organization growth standards. Compositional data analyses were used to examine the cross-sectional associations. Results The composition of movement behaviours was significantly associated with BMI z-scores (p = 0.006) but not with WC (p = 0.718). Further, the time spent in sleep (BMI z-score: γ sleep  = −0.72; p = 0.138; WC: γ sleep  = −1.95; p = 0.285), sedentary behaviour (BMI z-score: γ SB  = 0.19; p = 0.624; WC: γ SB  = 0.87; p = 0.614), LPA (BMI z-score: γ LPA  = 0.62; p = 0.213, WC: γ LPA  = 0.23; p = 0.902), or MVPA (BMI z-score: γ MVPA  = −0.09; p = 0.733, WC: γ MVPA  = 0.08; p = 0.288) relative to the other behaviours was not significantly associated with the adiposity indicators. Conclusions This study is the first to use compositional analyses when examining associations of co-dependent sleep duration, sedentary time, and physical activity behaviours with adiposity indicators in preschool-aged children. The overall composition of movement behaviours appears important for healthy BMI z-scores in preschool-aged children. Future research is needed to determine the optimal movement behaviour composition that should be promoted in this age group

A [4Fe-4S]-Fe(CO)(CN)-L-cysteine intermediate is the first organometallic precursor in [FeFe] hydrogenase H-cluster bioassembly.

Biosynthesis of the [FeFe] hydrogenase active site (the 'H-cluster') requires the interplay of multiple proteins and small molecules. Among them, the radical S-adenosylmethionine enzyme HydG, a tyrosine lyase, has been proposed to generate a complex that contains an Fe(CO)2(CN) moiety that is eventually incorporated into the H-cluster. Here we describe the characterization of an intermediate in the HydG reaction: a [4Fe-4S][(Cys)Fe(CO)(CN)] species, 'Complex A', in which a CO, a CN- and a cysteine (Cys) molecule bind to the unique 'dangler' Fe site of the auxiliary [5Fe-4S] cluster of HydG. The identification of this intermediate-the first organometallic precursor to the H-cluster-validates the previously hypothesized HydG reaction cycle and provides a basis for elucidating the biosynthetic origin of other moieties of the H-cluster