Peer assessment and knowledge discovering in a community of learners

Thanks to the exponential growth of the Internet, Distance Education is becoming more and more strategic in many fields of daily life. Its main advantage is that students can learn through appropriate web platforms that allow them to take advantage of multimedia and interactive teaching materials, without constraints neither of time nor of space. Today, in fact, the Internet offers many platforms suitable for this purpose, such as Moodle, ATutor and others. Coursera is another example of a platform that offers different courses to thousands of enrolled students. This approach to learning is, however, posing new problems such as that of the assessment of the learning status of the learner in the case where there were thousands of students following a course, as is in Massive On-line Courses (MOOC). The Peer Assessment can therefore be a solution to this problem: evaluation takes place between peers, creating a dynamic in the community of learners that evolves autonomously. In this article, we present a first step towards this direction through a peer assessment mechanism led by the teacher who intervenes by evaluating a very small part of the students. Through a mechanism based on machine learning, and in particular on a modified form of K-NN, given the teacher’s grades, the system should converge towards an evaluation that is as similar as possible to the one that the teacher would have given. An experiment is presented with encouraging results

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Simultaneous analysis of economic and environmental performance of horticultural crop production requires qualified assumptions on the effect of management options, and particularly of nitrogen (N) fertilisation, on the net returns of the farm. Dynamic soil-plant-environment simulation models for agro-ecosystems are frequently applied to predict crop yield, generally as dry matter per area, and the environmental impact of production. Economic analysis requires conversion of yields to fresh marketable weight, which is not easy to calculate for vegetables, since different species have different properties and special market requirements. Furthermore, the marketable part of many vegetables is dependent on N availability during growth, which may lead to complete crop failure under sub-optimal N supply in tightly calculated N fertiliser regimes or low-input systems. In this paper we present two methods for converting simulated total dry matter to marketable fresh matter yield for various vegetables and European growth conditions, taking into consideration the effect of N supply: (i) a regression based function for vegetables sold as bulk or bunching ware and (ii) a population approach for piecewise sold row crops. For both methods, to be used in the context of a dynamic simulation model, parameter values were compiled from a literature survey. Implemented in such a model, both algorithms were tested against experimental field data, yielding an Index of Agreement of 0.80 for the regression strategy and 0.90 for the population strategy. Furthermore, the population strategy was capable of reflecting rather well the effect of crop spacing on yield and the effect of N supply on product grading

Theoretical studies of Mefenamic Acid Polymorphs: Solid-state 13C carbon-NMR and vibrational (IR and Raman) Spectroscopies.

The two polymorphs of mefenamic acid (MEF) or 2-[(2,3-(dimethyphenyl)amino] benzoic acid polymorphs (known as I and II forms) were studies in the framkework of density functional theory (DFT). The DFT calculations were performed using the Gaussian03 package and these results were compared with experimental data of solid-state 13C Nuclear Magnetic Resonance (NMR), vibrational Raman and infrared spectroscopies.CAPESCNPqFAPES

Let’s not forget tautomers

A compound exhibits tautomerism if it can be represented by two structures that are related by an intramolecular movement of hydrogen from one atom to another. The different tautomers of a molecule usually have different molecular fingerprints, hydrophobicities and pKa’s as well as different 3D shape and electrostatic properties; additionally, proteins frequently preferentially bind a tautomer that is present in low abundance in water. As a result, the proper treatment of molecules that can tautomerize, ~25% of a database, is a challenge for every aspect of computer-aided molecular design. Library design that focuses on molecular similarity or diversity might inadvertently include similar molecules that happen to be encoded as different tautomers. Physical property measurements might not establish the properties of individual tautomers with the result that algorithms based on these measurements may be less accurate for molecules that can tautomerize—this problem influences the accuracy of filtering for library design and also traditional QSAR. Any 2D or 3D QSAR analysis must involve the decision of if or how to adjust the observed Ki or IC50 for the tautomerization equilibria. QSARs and recursive partitioning methods also involve the decision as to which tautomer(s) to use to calculate the molecular descriptors. Docking virtual screening must involve the decision as to which tautomers to include in the docking and how to account for tautomerization in the scoring. All of these decisions are more difficult because there is no extensive database of measured tautomeric ratios in both water and non-aqueous solvents and there is no consensus as to the best computational method to calculate tautomeric ratios in different environments

Identification of a Lacosamide Binding Protein Using an Affinity Bait and Chemical Reporter Strategy: 14-3-3 ζ

We have advanced a useful strategy to elucidate binding partners of ligands (drugs) with modest binding affinity. Key to this strategy is attaching to the ligand an affinity bait (AB) and a chemical reporter (CR) group, where the AB irreversibly attaches the ligand to the receptor upon binding and the CR group is employed for receptor detection and isolation. We have tested this AB&CR strategy using lacosamide ((R)-1), a low-molecular-weight antiepileptic drug. We demonstrate that using a (R)-lacosamide AB&CR agent ((R)-2) 14-3-3 ζ in rodent brain soluble lysates is preferentially adducted, adduction is stereospecific with respect to the AB&CR agent, and adduction depends upon the presence of endogenous levels of the small molecule metabolite xanthine. Substitution of lacosamide AB agent ((R)- 5) for (R)-2 led to the identification of the 14-3-3 ζ adduction site (K120) by mass spectrometry. Competition experiments using increasing amounts of (R)-1 in the presence of (R)-2 demonstrated that (R)-1 binds at or near the (R)-2 modification site on 14-3-3 ζ. Structure-activity studies of xanthine derivatives provided information concerning the likely binding interaction between this metabolite and recombinant 14-3-3 ζ. Documentation of the 14-3-3 ζ-xanthine interaction was obtained with isothermal calorimetry using xanthine and the xanthine analogue 1,7-dimethylxanthine

Simulating massive open on-line courses dynamics

Learning without constraints of space and time has nowadays become a reality thanks to the exponential growth of the Internet, and distance learning is one of the areas for which that growth has been the most beneficial. The Web offers several platforms for distance learning, calibrated for the public or private sector. Among these, the Massive Open Online Courses (MOOCs) are one of the most popular online learning assets. In MOOCs, a crucial feature is given by the amount of enrolled learners: thousands can attend such courses. Consequently, monitoring the learning process of individual students is a challenging task for teachers in MOOCs. Peer Assessment can be used, in these case, as a teaching strategy, to follow the dynamics of a MOOC while not checking directly on each one of the individual students. In previous work we have presented a method to support peer assessment, and grades inference, mediated by a limited grading work performed by the teacher. The method is based on a definition of student model based on a modified version of the machine learning technique called K-NN. In this paper we present a system able to simulate the dynamics of a MOOC class where peer assessment, mediated by the teacher, takes place. The system supports the creation of a (big) class, and the simulation of peer assessment sessions, where peer grading, and teacher grading take place. The system, then, allows for a statistical analysis of how the student models computed by the system, and the grades inferred for the peers' tasks, converge towards the real values for the student models and grades