Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis that features two stable steady states: healthy skin and psoriasis. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis induced by UVB phototherapy returned the epidermis back to the healthy state. The flexibility of our model permitted the development of a patient-specific "UVB sensitivity" parameter that enabled accurate simulation of individual patients' clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the "UVB sensitivity" parameter and the prediction of patient outcome at the end of phototherapy. Notably, our model was able to distinguish disease flares and offers the potential for clinical application in early assessment of response to UVB therapy outcome, and for individualised optimisation of phototherapy regimes to improve clinical outcome.

Drilling Optimization Applying Machine Learning Regression Algorithms

Machine Learning is an artificial intelligence subprocess applied to automatically and quickly perform mathematical calculations to data in order to build models used to make predictions. Technical papers related to machine learning algorithms applications have being increasingly published in many oil and gas disciplines over the last five years, revolutionizing the way engineers approach to their works, and sharing innovating solutions that contributes to an increase in efficiency. In this paper, Machine Learning models are built to predict inverse rate of penetration (ROPI) and surface torque for a well located at Gulf of Mexico shallow waters. Three type of analysis were performed. Pre-drill analysis, predicting the parameters without any data of the target well in the database. Drilling analysis, running the model every sixty meters, updating the database with information of the target well and predicting the parameters ahead the bit. Sensitivity parameter optimization analysis was performed iterating weight on bit and rotary speed values as model inputs in order identify the optimum combination to deliver the best drilling performance under the given conditions. The Extreme Gradient Boosting (XGBoost) library in Python programming language environment, was used to build the models. Model performance was satisfactory, overcoming the challenge of using drilling parameters input manually by drilling bit engineers. The database was built with data from different fields and wells. Two databases were created to build the models, one of the models did not consider logging while drilling (LWD) data in order to determine its importance on the predictions. Pre-drill surface torque prediction showed better performance than ROPI. Predictions ahead the bit performance was good both for torque and ROPI. Sensitivity parameter optimization showed better resolution with the database that includes LWD data.

On the Speed Sensitivity Parameter in the Lognormal Model for Response Times and Implications for High-Stakes Measurement Practice

In high-stakes testing, often multiple test forms are used and a common time limit is enforced. Test fairness requires that ability estimates must not depend on the administration of a specific test form. Such a requirement may be violated if speededness differs between test forms. The impact of not taking speed sensitivity into account on the comparability of test forms regarding speededness and ability estimation was investigated. The lognormal measurement model for response times by van der Linden was compared with its extension by Klein Entink, van der Linden, and Fox, which includes a speed sensitivity parameter. An empirical data example was used to show that the extended model can fit the data better than the model without speed sensitivity parameters. A simulation was conducted, which showed that test forms with different average speed sensitivity yielded substantial different ability estimates for slow test takers, especially for test takers with high ability. Therefore, the use of the extended lognormal model for response times is recommended for the calibration of item pools in high-stakes testing situations. Limitations to the proposed approach and further research questions are discussed.

Passage-of-Time Dysphoria: A Highly Replicable Decline in Mood During Rest and Simple Tasks that is Moderated by Depression

Does our mood change as time passes, and is this change different in people with depression? These questions are central to affective neuroscience theory and methodology, yet they remain largely unexamined. Here we demonstrate that rest periods lowered participants' mood, an effect we call "passage-of-time dysphoria." This finding was replicated in 15 cohorts totaling 27,882 adult and adolescent participants. The dysphoria was (1) relatively large (13.8% after 7.3 minutes, Cohen’s d = 0.574), (2) variable across and within individuals but consistent across cohorts, and (3) present during simple visuomotor and gambling tasks. Rest also impacted behaviour: participants were less likely to gamble at the beginning of a task if it was preceded by rest. The dysphoria was inversely related to depression risk and a computationally estimated reward sensitivity parameter. Our results have theoretical implications for the nature of mood and its aberrations, and methodological consequences for the design and interpretation of experiments.

Peeling the Layers of Caddisfly Diversity on a Longitudinal Gradient in Karst Freshwater Habitats Reveals Community Dynamics and Stability

Freshwater biodiversity is facing a severe crisis due to many human impacts, yet the diversity dynamics of freshwater communities and possibilities of assessing these are vastly unexplored. We aimed at emphasizing different aspects of portraying diversity of a species-rich, aquatic insect group (caddisflies; Trichoptera) across four different habitats in an anthropogenically unimpacted, connected karst barrage lake/riverine system. To define diversity, we used common indices with pre-set sensitivity to species abundance/dominance; i.e., sensitivity parameter (species richness, Shannon, Simpson, Berger-Parker) and diversity profiles based on continuous gradients of this sensitivity parameter: the naïve and non-naïve diversity profiles developed by Leinster and Cobbold. The non-naïve diversity profiles show diversity profiles with regard to the similarity among species in terms of ecological traits and preferences, whereas the naïve diversity profile is called mathematically “naïve” as it assumes absolute dissimilarity between species that is almost never true. The commonly used indices and the naïve diversity profile both ranked the springs as least diverse and tufa barriers as most diverse. The non-naïve diversity profiles based on similarity matrices (using feeding behavior and stream zonation preferences of species), showed even greater differences between these habitats, while ranking stream habitats close together, regardless of their longitudinal position. We constructed the Climate Score index (CSI) in order to assess how diversity and species’ vulnerability project the community’s resistance and/or resilience to climate change. The CSI ranked the springs as most vulnerable, followed by all habitats longitudinally placed below them. We highlight the importance of integrating ecological information into biodiversity and vulnerability assessment of freshwater communities.

Optimizing motor-timing decision via adaptive risk-return control

Human’s ability of optimal motor-timing decision remains debated. The optimality seems context-dependent as the sub-optimality was often observed for tasks with different gain/loss configurations: people achieved optimality with symmetric gain configuration but not with asymmetric configuration. In the current study, we designed a temporal decision-making task where participants could adjust the sensitivity parameter (i.e., risk-return trade-off) of the gain function, in order to testify whether people could optimize the responses for asymmetric gain configuration by adjusting the sensitivity parameter. Participants were asked to click a point within a certain spatial region at a specified timing, where the click timing determined the reward whilst the click position determined the sensitivity parameter. We prepared three types of gain functions (symmetric, risk-after and risk-before conditions) and tested whether or not the participants achieved Bayesian optimality irrespective of gain structure. Most participants’ performance reached optimality not only in the symmetric condition but also in the asymmetric condition, albeit some discrepancies from optimality observed in the risk-before condition. This confirmed that people could achieve Bayesian optimality even for asymmetric gain configuration. We argued that the adaptive risk-return is beneficial for the performance optimality.

Erosion Mechanism and Sensitivity Parameter Analysis of an innovative shaped Curved Pipeline

In the process of natural gas transportation, it is unavoidable for particles to collide with the wall, which will cause erosion of curved pipeline. Reasonable curved pipeline structure can effectively avoid the erosion failure. In this paper, an innovative shaped curved pipeline formed by extrusion of cylindrical indenter is presented. The erosion mechanism and sensitivity parameter analysis of the innovative shaped curved pipeline is studied by numerical simulation and compared with that of ordinary elbow. In addition, the effects of extrusion parameters and particle parameters on erosion of innovative shaped curved pipeline were also studied. The results show that the dent can effectively reduce the maximum erosion rate of elbow. With the increase of dent depth, the maximum erosion rate of elbow is decreasing. With the increase of indenter diameter, the ability to reduce the maximum erosion rate decreases. Under the harsh conditions of large particle diameter and high particle velocity, the dent has a better ability to reduce the maximum erosion wear rate, and the maximum erosion rate can be reduced by 26.8%.