Research on and Development of Inquiry‐Type Chemistry Laboratories in Israel

Over a period of more than 60 years, the chemistry laboratory has been extensively and comprehensively researched and hundreds of research papers, reviews, and doctoral dissertations have been published, investigating the laboratory as a unique learning environment. However, there were challenges and pedagogical questions regarding its educational effectiveness and benefits for teaching and learning chemistry. At the beginning of the 21^st century there was a call to rethink (and research) the goals for learning chemistry in the laboratory. This is especially applicable in an era in which we are trying to enhance the goal of teaching “chemistry for all students” and/or for the benefit of what is fondly called “future citizens”. Working for more than 15 years with colleagues and students, we researched the potential of establishing an inquiry‐type chemistry laboratory for developing high‐order learning skills, namely, skills for the future or skills for life, including metacognitive and argumentative skills, and the ability of students to ask relevant questions resulting from an inquiry‐type chemistry laboratory.     

A high-speed method for obtaining kinetic data for exothermic or endothermic catalytic reactions under non-isothermal conditions illustrated for the ammonia synthesis

A detailed analysis of the method for fast acquisition of kinetic data by means of a polythermal-temperature-ramping reactor (PTR) as suggested by Wojciechowski in 1995 was performed. The ammonia synthesis reaction over a commercial Fe catalyst (BASF) was taken as an example. Kinetic data evaluation was based on a kinetic model as reported by Sehested et al. in 1999. As a result of data analysis, the experimental molar rate of change of ammonia could be described adequately over a wide range of N₂ and H₂ partial pressures as well as temperature. The pre-exponential factors, activation energies and adsorption enthalpies are in reasonable agreement with data for promoted iron catalysts reported in literature. Accordingly, the PTR method was evaluated as a efficient tool for kinetic data analysis. The number of experiments including different initial conditions can be significantly diminished since PTR experiments have not to follow the strict sequence of varying one experimental parameter after the other by keeping all others constant. The collected exit concentrations, exit temperature and contact time are correlated by spline-functions which allow the extraction of functional relationships required for derivation of rate expressions. If the steady-state of the catalyst is rapidly attained, temperature ramping allows fast continuous data acquisition. Thus, the PTR method has the potential of significantly reducing the time needed for determining reliable kinetics over a wide range of operating conditions.     

Computational models reveal genotype–phenotype associations in Saccharomyces cerevisiae

Genome sequencing is essential to understand individual variation and to study the mechanisms that explain relations between genotype and phenotype. The accumulated knowledge from large‐scale genome sequencing projects of Saccharomyces cerevisiae isolates is being used to study the mechanisms that explain such relations. Our objective was to undertake genetic characterization of 172 S. cerevisiae strains from different geographical origins and technological groups, using 11 polymorphic microsatellites, and computationally relate these data with the results of 30 phenotypic tests. Genetic characterization revealed 280 alleles, with the microsatellite ScAAT1 contributing most to intrastrain variability, together with alleles 20, 9 and 16 from the microsatellites ScAAT4, ScAAT5 and ScAAT6. These microsatellite allelic profiles are characteristic for both the phenotype and origin of yeast strains. We confirm the strength of these associations by construction and cross‐validation of computational models that can predict the technological application and origin of a strain from the microsatellite allelic profile. Associations between microsatellites and specific phenotypes were scored using information gain ratios, and significant findings were confirmed by permutation tests and estimation of false discovery rates. The phenotypes associated with higher number of alleles were the capacity to resist to sulphur dioxide (tested by the capacity to grow in the presence of potassium bisulphite) and the presence of galactosidase activity. Our study demonstrates the utility of computational modelling to estimate a strain technological group and phenotype from microsatellite allelic combinations as tools for preliminary yeast strain selection. Copyright © 2014 John Wiley & Sons, Ltd.     

ACOTES Project: Advanced Compiler Technologies for Embedded Streaming

Streaming applications are built of data-driven, computational components, consuming and producing unbounded data streams. Streaming oriented systems have become dominant in a wide range of domains, including embedded applications and DSPs. However, programming efficiently for streaming architectures is a challenging task, having to carefully partition the computation and map it to processes in a way that best matches the underlying streaming architecture, taking into account the distributed resources (memory, processing, real-time requirements) and communication overheads (processing and delay). These challenges have led to a number of suggested solutions, whose goal is to improve the programmer??s productivity in developing applications that process massive streams of data on programmable, parallel embedded architectures. StreamIt is one such example. Another more recent approach is that developed by the ACOTES project (Advanced Compiler Technologies for Embedded Streaming). The ACOTES approach for streaming applications consists of compiler-assisted mapping of streaming tasks to highly parallel systems in order to maximize cost-effectiveness, both in terms of energy and in terms of design effort. The analysis and transformation techniques automate large parts of the partitioning and mapping process, based on the properties of the application domain, on the quantitative information about the target systems, and on programmer directives. This paper presents the outcomes of the ACOTES project, a 3-year collaborative work of industrial (NXP, ST, IBM, Silicon Hive, NOKIA) and academic (UPC, INRIA, MINES ParisTech) partners, and advocates the use of Advanced Compiler Technologies that we developed to support Embedded Streaming.     

Longitudinal changes in the contribution of genetic and environmental influences to symptoms of depression in older male twins.

Genetically informative longitudinal data on self-reported symptoms of depression allow for an investigation of the causes of stability and change in depression symptoms throughout adult life. In this report, the authors investigated the relative contribution of genetic and environmental influences to symptoms of depression in 83 monozygotic and 84 dizygotic male twin pairs from the National Heart, Lung, and Blood Institute (NHLBI) Twin Study. Participants first completed the Center for Epidemiologic Studies—Depression (CES—D) scale in 1985–1986 and again during 1995–1997. Mean age of twins at baseline was 63 years, range 59 to 70. From cross-sectional genetic analyses we estimated the heritability of CES—D to be 25% (95% confidence interval [CI], 11%–39%) at baseline and 55% (95% CI, 40%–71%) at follow-up. Fitting longitudinal genetic models to the two-wave data, we found that stability of symptoms over the 10-year follow-up was due primarily to continuity of genetic influences. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reinforcement learning, spike-time-dependent plasticity, and the BCM rule

Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons and derive a spike-time-dependent plasticity rule that ensures convergence to a local optimum of the expected average reward. The approach is applicable to a broad class of neuronal models, including the Hodgkin-Huxley model. We demonstrate the effectiveness of the derived rule in several toy problems. Finally, through statistical analysis, we show that the synaptic plasticity rule established is closely related to the widely used BCM rule, for which good biological evidence exists.     

A Cut-Based Algorithm for the Nonlinear Dual of the Minimum Cost Network Flow Problem

We consider a convex, or nonlinear, separable minimization problem with constraints that are dual to the minimum cost network flow problem. We show how to reduce this problem to a polynomial number of minimum s,t-cut problems. The solution of the reduced problem utilizes the technique for solving integer programs on monotone inequalities in three variables, and a so-called proximity-scaling technique that reduces a convex problem to its linear objective counterpart. The problem is solved in this case in a logarithmic number of calls, O(log U), to a minimum cut procedure, where U is the range of the variables. For a convex problem on n variables the minimum cut is solved on a graph with O(n²) nodes. Among the consequences of this result is a new cut-based scaling algorithm for the minimum cost network flow problem. When the objective function is an arbitrary nonlinear function we demonstrate that this constrained problem is solved in pseudopolynomial time by applying a minimum cut procedure to a graph on O(nU) nodes.