Controlled self-assembly of periodic and aperiodic cluster crystals

Soft particles are known to overlap and form stable clusters that self-assemble into periodic crystalline phases with density-independent lattice constants. We use molecular dynamics simulations in two dimensions to demonstrate that, through a judicious design of an isotropic pair potential, one can control the ordering of the clusters and generate a variety of phases, including decagonal and dodecagonal quasicrystals. Our results confirm analytical predictions based on a mean-field approximation, providing insight into the stabilization of quasicrystals in soft macromolecular systems, and suggesting a practical approach for their controlled self-assembly in laboratory realizations using synthesized soft-matter particles.Comment: Supplemental Material can be obtained through the author's website at: http://www.tau.ac.il/~ronlif/pubs/ClusterCrystals-Supp.pd

Phosphorylation of Cdc5 regulates its accumulation

<p>Abstract</p> <p>Background</p> <p>Cdc5 (polo kinase/Plk1) is a highly conserved key regulator of the <it>S. cerevisiae </it>cell cycle from S-phase until cytokinesis. However, much of the regulatory mechanisms that govern Cdc5 remain to be determined. Cdc5 is phosphorylated on up to 10 sites during mitosis. In this study, we investigated the function of phosphorylation site T23, the only full consensus Cdk1 (Cdc28) phosphorylation site present.</p> <p>Findings</p> <p><it>Cdc5^T23A</it>introduces a degron that reduces its cellular amount to undetectable levels, which are nevertheless sufficient for normal cell proliferation. The degron acts <it>in cis </it>and is reversed by N-terminal GFP-tagging. Cdk1 kinase activity is required to maintain Cdc5 levels during G2. This, Cdk1 inhibited, Cdc5 degradation is APC/C^Cdh1independent and requires new protein synthesis. Cdc5^T23Eis hyperactive, and reduces the levels of Cdc5 (<it>in trans</it>) and drastically reduces Clb2 levels.</p> <p>Conclusions</p> <p>Phosphorylation of Cdc5 by Cdk1 is required to maintain Cdc5 levels during G2. However, phosphorylation of T23 (probably by Cdk1) caps Cdc5 and other <it>CLB2 </it>cluster protein accumulation, preventing potential protein toxicity, which may arise from their overexpression or from APC/C^Cdh1inactivation.</p

Israel and the Palestinian Dilemma: Strengthening the Palestinian Authority or Containing Hamas

In the reality of the zero-sum game between Hamas and the Palestinian Authority (PA), a strong Hamas and a strong PA cannot coexist. The weakness of the PA alongside a strengthened Hamas, compounded by the erosion of deterrence against Hezbollah and Iran and the increased likelihood of a multi-front conflict, poses a strategic dilemma for Israel. Israel must define its strategic goal vis-à-vis the Palestinian arena, and consider whether there is any value to a formative military move against Hamas that is not part of a broader political plan. Weakened military capabilities would significantly reduce the challenge Hamas poses to the PA that accelerates its weakening, and remove an obstacle to effective moves to strengthen the PA. A weakened Hamas would also loosen the Gordian knot between the various arenas that Hamas seeks to tighten, and presumably also strengthen Israeli deterrence in the region. Under the existing political conditions, the current Israeli government is unlikely to agree on the need to strengthen the PA, or at least stop weakening it. Therefore, the government does not face a strategic dilemma on taking proactive steps to strengthen the PA, even though the PA's weakness harms Israeli interests: a move of this magnitude can only be led by a national unity government with broad public backing. At the same time, the status of the Palestinian Authority is so shaky and problematic that it is doubtful it can be restored under the existing conditions

Beyond the echo chamber:Modelling open-mindedness in citizens’ assemblies

A Citizens’ assembly (CA) is a democratic innovation tool where a randomly selected group of citizens deliberate a topic over multiple rounds to generate, and then vote upon, policy recommendations. Despite growing popularity, little work exists on understanding how CA inputs, such as the expert selection process and the mixing method used for discussion groups, affect results. In this work, we model CA deliberation and opinion change as a multi-agent systems problem. We introduce and formalise a set of criteria for evaluating successful CAs using insight from previous CA trials and theoretical results. Although real-world trials meet these criteria, we show that finding a model that does so is non-trivial; through simulations and theoretical arguments, we show that established opinion change models fail at least one of these criteria. We therefore propose an augmented opinion change model with a latent ‘open-mindedness’ variable, which sufficiently captures people’s propensity to change opinion. We show that data from the CA of Scotland indicates a latent variable both exists and resembles the concept of open-mindedness in the literature. We calibrate parameters against real CA data, demonstrating our model’s ecological validity, before running simulations across a range of realistic global parameters, with each simulation satisfying our criteria. Specifically, simulations meet criteria regardless of expert selection, expert ordering, participant extremism, and sub-optimal participant grouping, which has ramifications for optimised algorithmic approaches in the computational CA space

Discovering students’ learning strategies in a visual programming MOOC through process mining techniques

Funding: This work was supported by the Medical Research Council [grant number MR/N013166/1].Understanding students’ learning patterns is key for supporting their learning experience and improving course design. However, this is particularly challenging in courses with large cohorts, which might contain diverse students that exhibit a wide range of behaviours. In this study, we employed a previously developed method, which considers process flow, sequence, and frequency of learning actions, for detecting students’ learning tactics and strategies. With the aim of demonstrating its applicability to a new learning context, we applied the method to a large-scale online visual programming course. Four low-level learning tactics were identified, ranging from project- and video-focused to explorative. Our results also indicate that some students employed all four tactics, some used course assessments to strategize about how to study, while others selected only two or three of all learning tactics. This research demonstrates the applicability and usefulness of process mining for discovering meaningful and distinguishable learning strategies in large courses with thousands of learners.Publisher PD

Providing insights into health data science education through artificial intelligence

We would like to thank the Precision Medicine programme of the University of Edinburgh, as well as the Medical Research Council, for their support of this project aimed at enhancing health data science education. Additionally, we would like to express our appreciation to the Coursera platform and the students who participated in the course, whose contribution was invaluable to this research. This work was supported by the Medical Research Council [grant number MR/N013166/1].Background: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students’ learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. Methods: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students’ engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. Results: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. Conclusions: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.Peer reviewe

Early prediction of student performance in a health data science MOOC

Funding: This work was supported by the Medical Research Council [grant number MR/N013166/1].Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is particularly important in healthcare courses, given the acute shortages of healthcare staff and the urgent need to train data-literate experts in the healthcare field. In this paper, we analysed a health data science MOOC taken by over 3,000 students. We developed a novel three-step pipeline to predict student performance in the early stages of the course. In the first step, we inferred the transitions between students' low-level actions from their clickstream interactions. In the second step, the transitions were fed into Artificial Neural Network (ANN) that predicted student performance. In the final step, we used two explanation methods to interpret the ANN result. Using this approach, we were able to predict learners' final performance in the course with an AUC ranging from 83% to 91%. We found that students who interacted predominately with lab, project, and discussion materials outperformed students who interacted predominately with lectures and quizzes. We used the DiCE counterfactual method to automatically suggest simple changes to the learning behaviour of low- and moderate-performance students in the course that could potentially improve their performance. Our method can be used by instructors to help identify and support struggling students during the course.Publisher PD