Artificial intelligence for porous organic cages

Porous organic cages are a novel class of molecules with many promising applications, including in separation, sensing, catalysis and gas storage. Despite great promise, discovery of these materials is hampered by a lack of computational tools for exploring their chemical space, and significant expense associated with prediction of their properties. This results in significant synthetic effort being directed toward molecules which do not have targeted properties. This thesis presents multiple computational tools which can aid the discovery and design of these materials by increasing the number of synthetic candidates which are likely to exhibit desired, targeted properties. Firstly, a broadly applicable methodology for the construction of computational models of materials is presented. This facilitates the automated modelling and screening of materials that would otherwise have to be carried out in a more labour-intensive way. Secondly, an evolutionary algorithm is implemented and applied to the design of porous organic cages. The algorithm is capable of producing cages closely matching user-defined design criteria, and its implementation is designed to allow future applications in other fields of material design. Finally, machine learning is used to accurately predict properties of porous organic cages, orders of magnitude faster than has been possible with traditional, simulation-based approaches.Open Acces

Evaluating the Emotional State of a User Using a Webcam

In online learning is more difficult for teachers identify to see how individual students behave. Student’s emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student’s performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student’s learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner’s facial expressions and verbalizations. The majority of current software does not work in real time – scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states

Treatment with Cladribine Tablets Beyond Year 4: A Position Statement by Southeast European Multiple Sclerosis Centers

Based on the results of the pivotal CLARITY study, cladribine tablets were approved for use in the European Union in 2017 as a high-efficacy therapy for highly active relapsing-remitting multiple sclerosis (MS). Cladribine tablets are used as an induction therapy: half of the total dose is given in year 1 and the other half in year 2. In the CLARITY Extension trials, repeating the dose routinely in years 3 and 4, was not associated with significantly improved disease control. However, there is very limited evidence on how to manage people with MS (pwMS) beyond year 4, which is increasingly important because more and more patients are now ≥ 4 years after cladribine treatment. Overall, postapproval data show that treatment with two cladribine cycles effectively controls disease activity in the long term. However, there is general agreement that some pwMS with suboptimal response could benefit from retreatment. This study reviews the practical aspects of using cladribine tablets, summarizes the evidence from clinical trials and real-world studies on the safety and efficacy of cladribine, and proposes a treatment algorithm developed by expert consensus for pwMS previously treated with cladribine. In brief, we propose that additional courses of cladribine tablets should be considered in patients with minimal (no relapses, 1-2 new lesions) or moderate (1 relapse, 3-4 new lesions) disease activity, while significant disease activity (> 1 relapse, > 3 new lesions) or progression should warrant a switch to another high-efficacy treatment (HET). More evidence is needed to improve the treatment guidelines for pwMS who previously received cladribine

Machine Learning for Organic Cage Property Prediction

We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4 + 6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt

Preliminary Results on Predation of Gypsy Moth Pupac during a Period of Latency in Slovakia

Proceedings : IUFRO Kanazawa 2003 "Forest Insect Population Dynamics and Host Influences"., Scedule:14－19 September 2003, Vemue: Kanazawa Citymonde Hotel, Kanazawa, Japan, Joint metting of IUFRO working groups : 7.01.02 Tree resistance to Insects | 7.03.06 Integrated management of forset defoloating insects | 7.03.07 Population dynamics of forest insects, Sponsored by: IUFRO-J | Ishikawa Prefecture | Kanazawa City | 21st-COE Program of Kanazawa University, Editors: Kamata, Naoto | Liebhold, Nadrew M. | Quiring, Dan T. | Clancy, Karen M

The Last Trees Standing: Climate modulates tree survival factors during a prolonged bark beetle outbreak in Europe

Plant traits are an expression of strategic tradeoffs in plant performance that determine variation in allocation of finite resources to alternate physiological functions. Climate factors interact with plant traits to mediate tree survival. This study investigated survival dynamics in Norway spruce (Picea abies) in relation to tree-level morphological traits during a prolonged multi-year outbreak of the bark beetle, Ips typographus, in Central Europe. We acquired datasets describing the trait attributes of individual spruce using remote sensing and field surveys. We used nonlinear regression in a hypothesis-driven framework to quantify survival probability as a function of tree size, crown morphology, intraspecific competition and a growing season water balance. Extant spruce trees that persisted through the outbreak were spatially clustered, suggesting that survival was a nonrandom process. Larger diameter trees were more susceptible to bark beetles, reflecting either life history tradeoffs or a dynamic interaction between defense capacity and insect aggregation behavior. Competition had a strong negative effect on survival, presumably through resource limitation. Trees with more extensive crowns were buffered against bark beetles, ostensibly by a more robust photosynthetic capability and greater carbon reserves. The outbreak spanned a warming trend and conditions of anomalous aridity. Sustained water limitation during this period amplified the consequences of other factors, rendering even smaller trees vulnerable to colonization by insects. Our results are in agreement with prior research indicating that climate change has the potential to intensify bark beetle activity. However, forest outcomes will depend on complex cross-scale interactions between global climate trends and tree-level trait factors, as well as feedback effects associated with landscape patterns of stand structural diversity

Thrombolysis in Stroke With Unknown Onset Based on Non-Contrast Computerized Tomography (TRUST CT).

Background Intravenous thrombolysis (IVT) in wake-up stroke (WUS) or stroke with unknown onset (SUO) has been recently proven to be safe and effective using advanced neuroimaging (magnetic resonance imaging or computerized tomography-perfusion) for patient selection. However, in most of the thrombolyzing centers advanced neuroimaging is not instantly available. We hypothesize that pragmatic non-contrast computed tomography-based IVT in WUS/SUO may be feasible and safe. Methods and Results TRUST-CT (Thrombolysis in Stroke With Unknown Onset Based on Non-Contrast Computerized Tomography) is an international multicenter registry-based study. WUS/SUO patients undergoing non-contrast computed tomography-based IVT with National Institute of Health Stroke Scale ≥4 and initial Alberta Stroke Program Early Computerized Tomography score ≥7 were included and compared with propensity score matched non-thrombolyzed WUS/SUO controls. Primary end point was the incidence of symptomatic intracranial hemorrhage; secondary end points included 24-hour National Institute of Health Stroke Scale improvement of ≥4 and modified Rankin Scale at 90 days. One hundred and seventeen WUS/SUO patients treated with non-contrast computed tomography-based IVT were included. As compared with 112 controls, the median admission National Institute of Health Stroke Scale was 10 and the median Alberta Stroke Program Early Computerized Tomography score was 10 in both groups. Four (3.4%) IVT patients and one control patient (0.9%) suffered symptomatic intracranial hemorrhage (adjusted odds ratio 7.9, 95% CI 0.65-96, P=0.1). A decrease of ≥4 National Institute of Health Stroke Scale points was observed in 67 (57.3%) of IVT patients as compared with 25 (22.3%) in controls (adjusted odds ratio 5.8, CI 3.0-11.2, P<0.001). A months, 39 (33.3%) IVT patients reached a modified Rankin Scale score of 0 or 1 versus 23 (20.5%) controls (adjusted odds ratio 1.94, CI 1.0-3.76, P=0.05). Conclusions Non-contrast computed tomography-based thrombolysis in WUS/SUO seems feasible and safe and may be effective. Randomized prospective comparisons are warranted. Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT03634748