A stochastic model for NFL games and point spread assessment.

Statistical modelling of sports data is indispensable to analyse the sports behaviour and apprehend significant inferences that are helpful to adopt decisive strategies before or during the sports events. This paper introduces a stochastic model as the distribution of difference derived from the Bivariate Affine-Linear Exponential distribution. The distribution of difference is first ever used to model the margin of victory that provides an adequate fitting on the observed data. A simulation study is carried out to observe the stability of the model parameters through their average estimated values, biases, standard errors, root mean square errors and confidence intervals. The performance of the proposed model is examined by applying it on the real data of the National Football League and comparing the results with those of the existing models. Finally, the quantile function of the proposed distribution is used to assess the possible range of point spreads for winning the bet in a particular game.

Survival and hormone production of isolated mouse follicles in three-dimensional artificial scaffolds after stimulation with bpV(HOpic).

PURPOSE: To preserve fertility before gonadotoxic therapy, ovarian tissue can be removed, cryopreserved, and transplanted back again after treatment. An alternative is the artificial ovary, in which the ovarian follicles are extracted from the tissue, which reduces the risk of reimplantation of potentially remaining malignant cells. The PTEN inhibitor bpV(HOpic) has been shown to activate human, bovine and alpacas ovarian follicles, and it is therefore considered a promising substance for developing the artificial ovary. The purpose of this study was to examine the impact of different scaffolds and the vanadate derivative bpV(HOpic) on mice follicle survival and hormone secretion over 10 days. METHODS: A comparative analysis was performed, studying the survival rates (SR) of isolated mice follicle in four different groups that differed either in the scaffold (polycaprolactone scaffold versus polyethylene terephthalate membrane) or in the medium-bpV(HOpic) versus control medium. The observation period of the follicles was 10 days. On days 2, 6, and 10, the viability and morphology of the follicles were checked using fluorescence or confocal microscopy. Furthermore, hormone levels of estrogen (pmol/L) and progesterone (nmol/L) were determined. RESULTS: When comparing the SR of follicles among the four groups, it was observed that on day 6, the study groups utilizing the polycaprolactone scaffold with bpV(HOpic) in the medium (SR: 0.48 ± 0.18; p = 0.004) or functionalized in the scaffold (SR: 0.50 ± 0.20; p = 0.003) exhibited significantly higher survival rates compared to the group using only the polyethylene terephthalate membrane (SR: 0). On day 10, a significantly higher survival rate was only noted when comparing the polycaprolactone scaffold with bpV(HOpic) in the medium to the polyethylene terephthalate membrane group (SR: 0.38 ± 0.20 versus 0; p = 0.007). Higher levels of progesterone were only significantly associated with better survival rates in the group with the polycaprolactone scaffold functionalized with bpV(HOpic) (p = 0.017). CONCLUSION: This study demonstrates that three-dimensional polycaprolactone scaffolds improve the survival rates of isolated mice follicles in comparison with a conventional polyethylene terephthalate membrane. The survival rates slightly improve with added bpV(HOpic). Furthermore, higher rates of progesterone were also partly associated with improved survival.

Long-term psychodynamic psychotherapy in a face-to-face versus videoconferencing setting: A single case study.

OBJECTIVE: Due to the coronavirus pandemic and crisis, psychotherapists around the world were forced to switch to video- or tele-based treatments overnight. To date, only a few studies on the effectiveness of video-based psychodynamic psychotherapy via the Internet exist. Therefore, the goal of the present study was to examine symptom improvement, therapeutic relationship, nonverbal synchrony processes, and intersession processes within a systematic single case design and compare face-to-face to video-based approaches in long-term psychodynamic-oriented psychotherapy. METHODS: We examined 85 sessions of a client with major depression whose psychodynamic psychotherapy changed from a face-to-face setting to a video-based setting. Video recordings were analyzed using motion energy analysis, and nonverbal synchrony was computed using a surrogate synchrony approach. Time series analyses were performed to analyze changes in symptom severity, therapeutic relationship, and intersession processes. RESULTS: The results showed that symptom severity improved descriptively, but not significantly, across the entire course of psychotherapy. There were significant differences, however, in the therapeutic relationship, intersession experiences, and synchronous behavior between the face-to-face and video-based settings. CONCLUSION: The results indicate that the presented methodology is well situated to investigate the question whether psychodynamic psychotherapy in video-based setting works in the sameway as in a face-to-face setting.

Geo-additive modelling of malaria in Burundi.

BACKGROUND: Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. METHODS: The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. RESULTS: The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain them. CONCLUSIONS: In this paper, semiparametric models are used to model the effects of both climatic covariates and spatial effects on malaria distribution in Burundi. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature of the previous month. From the spatial effects, important spatial patterns of malaria that are related to factors other than climatic variables are identified. Potential explanations (factors) could be related to socio-economic conditions, food shortage, limited access to health care service, precarious housing, promiscuity, poor hygienic conditions, limited access to drinking water, land use (rice paddies for example), displacement of the population (due to armed conflicts).

Bayesian modelling of the effect of climate on malaria in Burundi.

BACKGROUND: In Burundi, malaria is a major public health issue in terms of both morbidity and mortality with around 2.5 million clinical cases and more than 15,000 deaths each year. It is the single main cause of mortality in pregnant women and children below five years of age. Due to the severe health and economic cost of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies have been done on the subject yielding different results as which factors are most responsible for the increase in malaria. The purpose of this study has been to undertake a spatial/longitudinal statistical analysis to identify important climatic variables that influence malaria incidences in Burundi. METHODS: This paper investigates the effects of climate on malaria in Burundi. For the period 1996-2007, real monthly data on both malaria epidemiology and climate in the area of Burundi are described and analysed. From this analysis, a mathematical model is derived and proposed to assess which variables significantly influence malaria incidences in Burundi. The proposed modelling is based on both generalized linear models (GLM) and generalized additive mixed models (GAMM). The modelling is fully Bayesian and inference is carried out by Markov Chain Monte Carlo (MCMC) techniques. RESULTS: The results obtained from the proposed models are discussed and it is found that malaria incidence in a given month in Burundi is strongly positively associated with the minimum temperature of the previous month. In contrast, it is found that rainfall and maximum temperature in a given month have a possible negative effect on malaria incidence of the same month. CONCLUSIONS: This study has exploited available real monthly data on malaria and climate over 12 years in Burundi to derive and propose a regression modelling to assess climatic factors that are associated with monthly malaria incidence. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature (night temperature) of the previous month. An open question is, therefore, how to cope with high temperatures at night.