The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements, such as inertial sensors, need to be extremely precise to obtain correct system diagnostics and accurate predictions, consequently requiring a rigorous calibration procedure before being employed. Most of the research over the past years has focused on delivering methods that can explain and estimate the complex stochastic components of these errors. In this context, the Generalized Method of Wavelet Moments emerges as a computationally efficient estimator with appropriate statistical properties and with different advantages over existing methods such as those based on likelihood estimation and the Allan variance. However it has this far not accounted for a significant stochastic noise that arises for many of these devices: vibration noise. This component can originate from different sources, including the internal mechanics of the sensors as well as the movement of these devices when placed on moving objects. To remove this disturbance from signals, this work puts forward a modelling framework for this specific type of noise and adapts the Generalized Method of Wavelet Moments to estimate these models. We deliver the asymptotic properties of this method when applied to processes that include vibration noise and show the considerable practical advantages of this approach in simulation and applied case studies.

The sperm-specific channel CatSper (cation channel of sperm) controls the intracellular Ca2+ concentration ([Ca2+]i) and plays an essential role in sperm function. It is mainly activated by the steroid progesterone (P4) but is also promiscuously activated by a wide range of synthetic and physiological compounds. These compounds include diverse steroids whose action on the channel is so far still controversial. To investigate the effect of these compounds on CatSper and sperm function, we developed a high-throughput-screening (HTS) assay to measure changes in [Ca2+]i in human sperm and screened 1,280 approved and off-patent drugs including 90 steroids from the Prestwick chemical library. More than half of the steroids tested (53%) induced an increase in [Ca2+]i and reduced the P4-induced Ca2+ influx in human sperm in a dose-dependent manner. Ten of the most potent steroids (activating and inhibiting) were selected for a detailed analysis of their action on CatSper and their ability to act on sperm motility, acrosomal exocytosis (AR), and penetration in viscous media. We found that these steroids show an inhibitory effect on P4 but not on prostaglandin E1-induced CatSper activation, suggesting that they compete for the same binding site as P4. Pregnenolone, dydrogesterone, epiandrosterone, nandrolone, and dehydroepiandrosterone acetate (DHEA) were found to activate CatSper at physiological concentrations. Stanozolol, epiandrosterone, and pregnenolone induced AR similarly to P4, whereas stanozolol and estropipate induced an increase in sperm penetration into viscous medium. Furthermore, using a hybrid approach integrating pharmacophore analysis and statistical modelling, we were able to screen in silico for steroids that can activate the channel and define the physicochemical and structural properties required for a steroid to exhibit agonist activity against CatSper. Overall, our results indicate that not only physiological but also synthetic steroids can modulate the activity of CatSper with varying potency and affect human sperm functions in vitro.

The global navigation satellite system (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow to study global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work, we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time-series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogenous variables: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used maximum likelihood estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results (theoretical and obtained in simulations) show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach have important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations (some of them being recorded over several decades) quickly becomes computationally prohibitive. Compared to standard likelihood-based methods, the GMWMX has a considerably reduced algorithmic complexity of order {log(𝑛)𝑛} for a time series of length n. Thus, the GMWMX appears to provide a reduction in processing time of a factor of 10–1000 compared to likelihood-based methods depending on the considered stochastic model, the length of the time series and the amount of missing data. As a consequence, the proposed method allows the estimation of large-scale problems within minutes on a standard computer. We validate the performances of our method via Monte Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power law or Matérn processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA.

In this work, we address the problem of rigorously evaluating the performances of an inertial navigation system (INS) during its design phase in presence of multiple alternative choices. We introduce a framework based on Monte Carlo simulations in which a standard extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions. This framework allows the user to make a choice between alternative setups. To show the generality of our approach, we consider an example application to the problem of stochastic calibration. Two competing stochastic modeling techniques, namely, the widely popular Allan variance linear regression and the emerging generalized method of wavelet moments, are rigorously compared in terms of the framework’s defined metrics and in multiple scenarios. We find that the latter provides substantial advantages for certain classes of inertial sensors. Our framework allows considering a wide range of problems related to the quantification of navigation system performances, such as the robustness of integrated navigation systems [such as INS/global navigation satellite system (GNSS)] with respect to outliers or other modeling imperfections. While real-world experiments are essential to assess to performance of new methods, they tend to be costly and are typically unable to lead to a sufficient number of replicates to provide suitable estimates of, for example, the correctness of the estimated uncertainty. Therefore, our method can contribute to bridging the gap between these experiments and pure statistical consideration as usually found in the stochastic calibration literature.

This study presents a novel approach for designing personalized treatment for colorectal cancer (CRC) patients. The approach combines ex vivo organoid efficacy testing with mathematical modeling of the results. The study utilized a validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) to identify optimized drug combinations (ODC) that showed low-dose synergistic effects in 3D human CRC models. The ODCs were validated using patient-derived organoids (PDO) from both primary and metastatic CRC cases. Molecular characterization of the CRC material was performed using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases, the identified ODCs demonstrated significant inhibition of cell viability, outperforming the standard CRC chemotherapy (FOLFOXIRI) administered at clinical doses. Additionally, patient-specific ODCs based on TGMO showed superior efficacy compared to the current chemotherapy standard of care. This approach enables the optimization of synergistic multi-drug combinations tailored to individual patients within a clinically relevant timeframe.

This study examines the stability of medical students' career intentions over a four-year period and investigates the associations between unstable career intentions and students' characteristics. Two cohorts of medical students were surveyed annually from the end of their pre-clinical curriculum to graduation. The survey included measures of career intention, personality, coping strategies, empathy, and motives for becoming a physician. A score ranging from 0 to 10 was developed to quantify the instability of career intentions. The results showed that most students fell on a continuum between being firmly committed and undecided. Only a small proportion of students did not change their specialty intention over the four years, while another group changed every year. The study identified that an intention to work in private practice in year 3 and the motive to care for patients were associated with more stable career intentions. The findings suggest that external factors may play a significant role in career decision-making and highlight the need for further research and support to assist students in making informed career choices.

Background: Medical schools can contribute to the insufficient primary care physician workforce by influencing students’ career preferences. Primary care career choice evolves between matriculation and graduation and is influenced by several individual and contextual factors. This study explored the longitudinal dynamics of primary care career intentions and the association of students’ motives for becoming doctors with these intentions in a cohort of undergraduate medical students followed over a four-year period. Methods: The sample consisted of medical students from two classes recruited into a cohort study during their first academic year, and who completed a yearly survey over a four-year period from their third (end of pre-clinical curriculum) to their sixth (before graduation) academic year. Main outcome measures were students’ motives for becoming doctors (ten motives rated on a 6-point scale) and career intentions (categorized into primary care, non-primary care, and undecided). Population-level flows of career intentions were investigated descriptively. Changes in the rating of motives over time were analyzed using Wilcoxon tests. Two generalized linear mixed models were used to estimate which motives were associated with primary care career intentions. Results: The sample included 217 students (60% females). Career intentions mainly evolved during clinical training, with smaller changes at the end of pre-clinical training. The proportion of students intending to practice primary care increased over time from 12.8% (year 3) to 24% (year 6). Caring for patients was the most highly rated motive for becoming a doctor. The importance of the motives cure diseases, saving lives, and vocation decreased over time. Primary care career intentions were positively associated with the motives altruism and private practice, and negatively associated with the motives prestige, academic interest and cure diseases. Conclusion: Our study indicates that career intentions are not fixed and change mainly during clinical training, supporting the influence of clinical experiences on career-related choices. The impact of students’ motives on primary care career choice suggests strategies to increase the attractivity of this career, such as reinforcing students’ altruistic values and increasing the academic recognition of primary care.

The following topics are dealt with: control system synthesis

The exponential growth in terms of data sizes and consequent storage costs has brought considerable challenges to the data science community who have had to find solutions to run learning methods on such data. While approaches from machine learning and artificial intelligence have scaled to achieve predictive accuracy also in these big data settings, the availability of statistical inference and uncertainty quantification tools is still lagging under many aspects in the face of this challenge. Indeed, areas of priority scientific and social relevance collect vast amounts of data to understand (interpret) and test the significance of different phenomena which is a task usually associated with statistical learning methods such as regression. In this setting, the estimation of the regression parameters can benefit from efficient computational procedures but the main challenge lies in the computation of the parameters of the error process when this has complex covariance (kernel) structures. The identification and estimation of these structures are essential for inference on the regression parameters (needed for interpretation) and, aside from non-parametric modelling, they are often used for uncertainty quantification in machine learning through the use of Gaussian Processes, for example. However the estimation of these structures remain burdensome (or even impossible) to estimate as data scales, thereby requiring various levels of approximations that consequently affect the reliability of their outputs. These approximations become even more unreliable when complexities such as long-range dependencies, missing and/or contaminated observations are present (which are common to find in big data settings). In this work we define and prove the statistical properties of the Generalized Method of Wavelet Moments with Exogenous variables (GMWMX) which provides a highly scalable, numerically stable and statistically valid method to estimate and deliver inference for linear models (and non-linear adaptations thereof) using stochastic processes in the presence of data complexities such as latent dependence structures and missing data. On top of the efficiency of wavelet convolutions, all this is achieved through new efficient approaches to compute theoretical and empirical quantities for the wavelet variance (which are of interest in their own right), allowing to model complex features in big data settings with high computational efficiency. Applied examples from the field of Earth Sciences are used to highlight the important advantages of the GMWMX, whose theoretical, numerical and computational properties are supported by extensive simulation studies.