PRACTICUM DIRECT Simulator for Decision Making during Pandemics

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021[Abstract] The past and current situation of the SARS-CoV-2 pandemic has put the entire society, and especially all hospital systems, worldwide to the test. It is essential that health system managers and decision makers optimize the management of resources, even being forced to improvise new units, divert resources usually destined to other functions and/or change the usual care modality by considerably enhancing aspects of telemedicine. Artificial Intelligence (AI) techniques and procedures are of great help in decision making in emergency environments due to severe pandemics because of their predictive capacity. This paper presents the PRACTICUM DIRECT project, which proposes the design and implementation of a tool to assist health system managers in making decisions on the early management of hospital resources. It makes use of AI techniques to identify the most critical variables in each case and build models capable of showing the possibilities and consequences of the decisions taken on resources at each moment of the emergency. It includes a simulator that shows how they would affect management. The current status is that of the selection of the most appropriate variables, taking into account those affected during the SARS-CoV-2 pandemic: infectious diseases, cardio-neuro-circulatory diseases, metabolic diseases and rehabilitative medicine.This research was funded by the General Directorate of Culture, Education and University Management of Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03 and the GRANT FOR THE PROGRAM FOR CONSOLIDATION AND STRUCTURING OF COMPETITIVE RESEARCH UNITS Ref. ED431C 2018/49Xunta de Galicia; IN845D-2020/03Xunta de Galicia; ED431C 2018/4

Automatic Characterization of Block-In-Matrix Rock Outcrops through Segmentation Algorithms and Its Application to an Archaeo-Mining Case Study

The mechanical behavior of block-in-matrix materials is heavily dependent on their block content. This parameter is in most cases obtained through visual analyses of the ground through digital imagery, which provides the areal block proportion (ABP) of the area analyzed. Nowadays, computer vision models have the capability to extract knowledge from the information stored in these images. In this research, we analyze and compare classical feature-detection algorithms with state-of-the-art models for the automatic calculation of the ABP parameter in images from surface and underground outcrops. The outcomes of this analysis result in the development of a framework for ABP calculation based on the Segment Anything Model (SAM), which is capable of performing this task at a human level when compared with the results of 32 experts in the field. Consequently, this model can help reduce human bias in the estimation of mechanical properties of block-in-matrix materials as well as contain underground technical problems due to mischaracterization of rock block quantities and dimensions. The methodology used to obtain the ABP at different outcrops is combined with estimates of the rock matrix properties and other characterization techniques to mechanically characterize the block-in-matrix materials. The combination of all these techniques has been applied to analyze, understand and try, for the first time, to model Roman gold-mining strategies in an archaeological site in NW Spain. This mining method is explained through a 2D finite-element method numerical model

Normothermic perfusion in the assessment and preservation of declined livers prior to transplantation: hyperoxia and vasoplegia - important lessons from the first 12 cases.

BACKGROUND: A programme of normothermic ex situ liver perfusion (NESLiP) was developed to facilitate better assessment and use of marginal livers, while minimising cold ischaemia. METHODS: Declined marginal livers and those offered for research were evaluated. NESLiP was performed using an erythrocyte-based perfusate. Viability was assessed with reference to biochemical changes in the perfusate. RESULTS: 12 livers (9 from circulatory death (DCD) and 3 from brain-dead donors), median Donor Risk Index 2.15, were subjected to NESLiP for a median 284 minutes (range 122-530) after an initial cold storage period of 427 minutes (range 222-877). The first 6 livers were perfused at high perfusate oxygen tensions, and the subsequent 6 at near-physiologic oxygen tensions. After transplantation, 5 of the first 6 recipients developed postreperfusion syndrome and 4 had sustained vasoplegia; 1 recipient experienced primary nonfunction in conjunction with a difficult explant. The subsequent 6 liver transplants, with livers perfused at lower oxygen tensions, reperfused uneventfully. Three DCD liver recipients developed cholangiopathy, and this was associated with an inability to produce an alkali bile during NESLiP. CONCLUSIONS: NESLiP enabled assessment and transplantation of 12 livers that may otherwise not have been used. Avoidance of hyperoxia during perfusion may prevent postreperfusion syndrome and vasoplegia, and monitoring biliary pH, rather than absolute bile production, may be important in determining the likelihood of posttransplant cholangiopathy. NESLiP has the potential to increase liver utilization, but more work is required to define factors predicting good outcomes.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Altered organization of the intermediate filament cytoskeleton and relocalization of proteostasis modulators in cells lacking the ataxia protein sacsin

This work was supported by BBSRC [BB/L02294X/1]; the CIHR Rare Disease Emerging Team grant, the Ataxia of Charlevoix-Saguenay Foundation; Muscular Dystrophy Canada and Barts and the London Charity [417/1699]. The LSM880 confocal used in these studies was purchased through a Barts and the London Charity grant MGU0293

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)

Mountain maple and balsam fir early response to partial and clear-cut harvesting under aspen stands of northern Quebec

This study is a component of the Sylviculture et am

Refining tree recruitment models

We used a micrometeorological dispersal model to simulate seed and seedling distributions derived from subcanopy balsam fir (Abies balsamea (L.) Mill.) source trees in a trembling aspen (Populus tremuloides Michx.) dominated forest. Our first objective was to determine the effect of substituting basal area for cone production as a proxy for seed output. The results showed that the r2 from the regression of predicted versus observed densities increased by ∼5% for seeds and ∼15% for seedling simulations. Our second objective was to determine the effects of changing the median horizontal wind speed. The median speed in this forest environment varies according to the proportion of leaves abscised. For values of the median expected wind speed between the extremes of leafless and full-canopy forests, the r2 of predicted versus observed varied between 0.35 and 0.49 for seeds and between 0.33 and 0.62 for seedling simulations. We demonstrated that the simple one-dimensional model can have added precision if the dispersal parameters are chosen so as to allow more fine-scale variation

Development of a Prognostic Model That Predicts Survival After Pancreaticoduodenectomy for Ampullary Cancer.

Objectives: The aims of this study were to i) identify independent predictors of survival following pancreaticoduodenectomy for ampullary cancer and ii) develop a prognostic model of survival. Methods: Data were analysed retrospectively on 110 consecutive patients who underwent pancreaticoduodenectomy between 2002-2013. Subjects were categorised into 3 nodal sub-groups as per the recently proposed nodal sub-classification: N0 (node negative), N1 (1-2 metastatic nodes) or N2 (≥3 metastatic nodes). Clinicopathological features and overall survival were compared by Kaplan Meier and Cox regression analyses. Results: The overall 1-, 3- and 5-year survival rates were 79.8%, 42.2% and 34.9% respectively. The overall 1-, 3- and 5-year survival rates for the N0 group were 85.2%, 71.9% and 67.4% respectively. The 1-,3-,5-year survival rates for the N1 and N2 subgroups were 84.6%, 58.4%, 56.4% and 80.2%, 38.8% and 8.0% respectively (log rank, p<0.0001). After performing a multivariate Cox regression analysis vascular invasion and lymph node ratio were the only independent predictors of survival. Hence a prediction model of survival was constructed based on those 2 variables. Conclusion: Using data from a carefully selected cohort of patients we created a pilot prognostic model of post-resectional survival. The proposed model may help clinicians to guide treatments in the adjuvant setting.Mr Siong S. Liau is funded by the Medical Research Council (MRC) and Academy of Medical Sciences Clinician Scientist Fellowship

Can forest management based on natural disturbances maintain ecological resilience?

Given the increasingly global stresses on forests, many ecologists argue that managers must maintain ecological resilience: the capacity of ecosystems to absorb disturbances without undergoing fundamental change. In this review we ask: Can the emerging paradigm of natural-disturbance-based management (NDBM) maintain ecological resilience in managed forests? Applying resilience theory requires careful articulation of the ecosystem state under consideration, the disturbances and stresses that affect the persistence of possible alternative states, and the spatial and temporal scales of management relevance. Implementing NDBM while maintaining resilience means recognizing that (i) biodiversity is important for long-term ecosystem persistence, (ii) natural disturbances play a critical role as a generator of structural and compositional heterogeneity at multiple scales, and (iii) traditional management tends to produce forests more homogeneous than those disturbed naturally and increases the likelihood of unexpected catastrophic change by constraining variation of key environmental processes. NDBM may maintain resilience if silvicultural strategies retain the structures and processes that perpetuate desired states while reducing those that enhance resilience of undesirable states. Such strategies require an understanding of harvesting impacts on slow ecosystem processes, such as seed-bank or nutrient dynamics, which in the long term can lead to ecological surprises by altering the forest's capacity to reorganize after disturbance