Possibilistic compositions and state functions: application to the order promising process for perishables

"This is an Author's Accepted Manuscript of an article published in Grillo, H., M.M.E. Alemany, A. Ortiz, and B. De Baets. 2019. Possibilistic Compositions and State Functions: Application to the Order Promising Process for Perishables. International Journal of Production Research 57 (22). Informa UK Limited: 7006 31. doi:10.1080/00207543.2019.1574039, available online at: https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1574039"[EN] In this paper, we propose the concepts of the composition of possibilistic variables and state functions. While in conventional compositional data analysis, the interdependent components of a deterministic vector must add up to a specific quantity, we consider such components as possibilistic variables. The concept of state function is intended to describe the state of a dynamic variable over time. If a state function is used to model decay in time, it is called the ageing function. We present a practical implementation of our concepts through the development of a model for a supply chain planning problem, specifically the order promising process for perishables. We use the composition of possibilistic variables to model the existence of different non-homogeneous products in a lot (sub-lots with lack of homogeneity in the product), and the ageing function to establish a shelf life-based pricing policy. To maintain a reasonable complexity and computational efficiency, we propose the procedure to obtain an equivalent interval representation based on alpha -cuts, allowing to include both concepts by means of linear mathematical programming. Practical experiments were conducted based on data of a Spanish supply chain dedicated to pack and distribute oranges and tangerines. The results validated the functionality of both, the compositions of possibilistic variables and ageing functions, showing also a very good performance in terms of the interpretation of a real problem with a good computational performance.We would also thank Dr. José De Jesús Arias García for useful discussions during the development of this work. This research has been supported by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the Program of Innovation and Human Capital for Competitiveness (PINN) (contract number PED-019-2015-1). We acknowledge the partial support of the project 691249, RUCAPS: Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems , funded by the European Union s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions.Grillo-Espinoza, H.; Alemany Díaz, MDM.; Ortiz Bas, Á.; De Baets, B. (2019). Possibilistic compositions and state functions: application to the order promising process for perishables. International Journal of Production Research. 57(22):7006-7031. https://doi.org/10.1080/00207543.2019.1574039S700670315722Grillo, H., M. Alemany, and A. Ortiz. 2016b. Modelling Pricing Policy Based on Shelf-Life of Non-Homogeneous Available-To-Promise in Fruit Supply Chains, 608–617. doi:10.1007/978-3-319-45390-3_52Steglich, M., and T. Schleiff. 2010. “CMPL: Coliop Mathematical Programming Language.” Technische Hochschule Wildau. doi:10.15771/978-3-00-031701-9

Biological-based models of carcinogenesis in the lung from radiation and smoking

Lung adenocarcinoma and squamous cell carcinoma are the deadliest cancers worldwide. Smoking and ionizing radiation are potent carcinogens affecting strongly both lung cancer subtypes. Several biological analyses have been performed to characterise the genetic mutations leading to adenocarcinoma and squamous cell carcinoma, and different genomic spectra have been observed. Biological markers of smoking related damage could be found, leading to a deep knowledge of cellular smoking effects. Less is known about the biological effects of radiation in human carcinogenesis. Risks have been quantified with epidemiological studies of these carcinogens. Based on the biologically substantiated assumption that the number of mutations is linearly related to the dose, in radiation epidemiology it is standard to model effects linearly. These models do however not have a biological interpretation and are disconnected from general statistical methods. Here we fill both gaps. First we apply statistical generalised additive models to examine the functional relation between risk and smoking and radiation effects. Secondly, with mechanistic multi-scale models we integrate molecular biology and epidemiology to describe the carcinogenesis of lung adenocarcinoma and squamous cell carcinoma. To investigate the incidence of lung adenocarcinoma and lung squamous cell carcinoma we analysed two cohorts: first the Life Span Study cohort of atomic bomb survivors of Hiroshima and Nagasaki, and second the Eldorado cohort of Canadian Uranium miners. Exposures differed strongly between cohorts. Residents of Hiroshima and Nagasaki were exposed to a relative high dose of gamma radiation for a short time, while the miners were exposed to a protracted and lower exposure to alpha and gamma radiation. Information about smoking habits is available only for the former cohort. Three types of models were applied to analyse the effects of radiation and smoking: state-of-the-art statistical risk models of radiation protection, statistical generalized additive models and mechanistic risk models. Although there were quantitative differences in effect size and significance, each result is presented below only for a single model. For lung adenocarcinoma the best mechanistic model was a two pathway model. Smoking and radiation effects showed markedly different patterns: both acted on the apoptosis rate of precancerous cells but on different pathways without any interaction. A linear radiation effect was found in one pathway and a linear-exponential smoking effect in the other pathway. Independently of these results we analysed genomic data of American patients. It is known that the genetic damage of people with adenocarcinoma can be grouped into three pathways: the receptor mutant (RMUT ) pathway, the transducer mutant pathway (TMUT ), and other signatures (OWT ). We could show that signatures of TMUT and the OWT pathways do differ much less from each other than both differed to the RMUT pathway. Therefore, there is also genetic evidence that adenocarcinoma fall into two main classes. The two pathways of the mechanistic model could be associated to the RMUT and RMUT+OWT pathways by their risk patterns in age and smoking. On the other hand, for squamous cell carcinoma one pathway was sufficient to describe the incidence data. Although effects of radiation appeared to be highly significant, they could be traced back to arise only from the first five years of follow up (33 cases therein). When the first five years were excluded, no significant radiation effect could be found. Interestingly, for lung squamous cell carcinoma the mechanistic models could fit the effects of cigarette smoking in initiation and promotion. This was different for lung adenocarcinoma, where the main effect of smoking was a promotion of already existing pre-cancerous clones. For both, lung adenocarcinoma and squamous cell carcinoma, no interaction between radiation and smoking could be fitted for the Life Span Study cohort. Results from analysis of the Eldorado cohort were in line with the results presented above. For lung adenocarcinoma both, the state-of-the-art statistical risk models and the generalised additive models, could find only a significant effect of radiation exposure. For lung squamous cell carcinoma, vice versa, both models could find only a significant effect of gamma radiation exposure. Concluding, we showed that lung cancer cannot be investigated as a single endpoint but the different subtypes have to be analysed separately. Different radiation qualities act differently to the different subtypes, indicating different biological processes. Analogously, although smoking is an important risk factor for all subtypes, its effects were different and with different magnitudes

General features of the retinal connectome determine the computation of motion anticipation

Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for

The Efficacy of Utility Functions for Multicriteria Hospital Case-Mix Planning

A new approach to perform hospital case-mix planning (CMP) is introduced in this article. Our multi-criteria approach utilises utility functions (UF) to articulate the preferences and standpoint of independent decision makers regarding outputs. The primary aim of this article is to test whether a utility functions method (UFM) based upon the scalarization of aforesaid UF is an appropriate quantitative technique to, i) distribute hospital resources to different operating units, and ii) provide a better capacity allocation and case mix. Our approach is motivated by the need to provide a method able to evaluate the trade-off between different stakeholders and objectives of hospitals. To the best of our knowledge, no such approach has been considered before in the literature. As we will later show, this idea addresses various technical limitations, weaknesses, and flaws in current CMP. The efficacy of the aforesaid approach is tested on a case study of a large tertiary hospital. Currently UF are not used by hospital managers, and real functions are unavailable, hence, 14 rational options are tested. Our exploratory analysis has provided important guidelines for the application of these UF. It indicates that these UF provide a valuable starting point for planners, managers, and executives of hospitals to impose their goals and aspirations. In conclusion, our approach may be better at identifying case mix that users want to treat and seems more capable of modelling the varying importance of different levels of output. Apart from finding desirable case mixes to consider, the approach can provide important insights via a sensitivity analysis of the parameters of each UF.Comment: 35 pages, 6 tables, 29 figure

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics