פרסומים

There is a long history of experiments, in which participantsare instructed to generate a long sequence of binary random numbers.The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper,we usedgeneralized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, weused logistic regression analysis in order to characterize the temporal sequence of participants’ choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seem irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect area monotonousdecreasing function of the delay, yet these individual sequential effectsare largely averaged outin a population analysis because of heterogeneity.The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation.Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in therandom sequence generation task, different participants adoptdifferent cognitive strategiesto suppress sequential dependencies when generating the “random” sequences.

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

In microemulsions, changes in droplet size and shape and possible transformations occur under various conditions. They are difficult to characterize by most analytical tools because of their nano-sized structure and dynamic nature. Several methods are usually combined to obtain reliable information, guiding the scientist in understanding their physical behavior. We felt that there is a need for a technique that complements those in use today in order to provide more information on the microemulsion behavior, mainly as a function of dilution with water. The improvement of NMR chemical shift measurements independent of bulk magnetization effects makes it possible to study the very weak intermolecular chemical shift effects. In the present study, we used NMR high resolution magic angle spinning to measure the chemical shift very accurately, free of bulk magnetization effects. The chemical shift of microemulsion components is measured as a function of the water content in order to validate the method in an interesting and promising, U-type dilutable microemulsion, which had been previously studied by a variety of techniques. Phase transition points of the microemulsion (O/W, bicontinuous, W/O) and changes in droplet shape were successfully detected using high-accuracy chemical shift measurements. We analyzed the results and found them to be compatible with the previous studies, paving the way for high-accuracy chemical shifts to be used for the study of other microemulsion systems. We detected two transition points along the water dilution line of the concentrate (reverse micelles) corresponding to the transition from swollen W/O nano-droplets to bicontinuous to the O/W droplets along with the changes in the droplets' sizes and shapes. The method seems to be in excellent agreement with other previously studied techniques and shows the advantage of this easy and valid technique. (C) 2014 Elsevier Inc. All rights reserved.

A series of new mesoporous metal–organic frameworks (MOFs) made from extended bisbenzenetriazolate linkers exhibit coordinatively unsaturated metal sites that are responsible for high and reversible uptake of ammonia. Isostructural Mn, Co, and Ni materials adsorb 15.47, 12.00, and 12.02 mmol of NH₃/g, respectively, at STP. Importantly, these near-record capacities are reversible for at least three cycles. These results demonstrate that azolate MOFs are sufficiently thermally and chemically stable to find uses in recyclable sorption, storage, and potentially separation of chemically challenging and/or corrosive gases, especially when designed to exhibit a high density of open metal sites.

Cystic fibrosis (CF; OMIM #219700) is a common autosomal recessive disease. The spectrum and frequency of CFTR mutations vary significantly in different populations and ethnic groups. A genetic epidemiological study was conducted in the indigenous ethnic group of people known as the Karachais. They live in the Republic of Karachay-Cherkessia, which lies in the northwest of Russia's North Caucasus region. Karachai's are Turkic-speaking and consist of 194 thousand people (approximately 40% of the population of the Republic). Molecular genetic analysis was performed in 10 unrelated Karachai families with CF patients from three districts in the Republic. A high frequency of W1282X mutation was found (18 of 20 mutant alleles): eight patients were homozygous for the W1282X mutation, and two were compound heterozygous (the second alleles were R1066C and R709X). Analysis for 13 common CF mutations in the sample of 142 healthy Karachais identified two 1677delTA and two W1282X mutation carriers. Thus, the most common CFTR mutation, F508del, was not detected among the CF patients or in healthy Karachais. The most frequent mutation among Karachai patients is W1282X (90%). Its frequency in healthy Karachais is approximately 0.007. Haplotype analysis using the CFTR intragene DNA markers IVS1CA, IVS6aGATT, IVS8CA and IVS17bCA showed that the origins of the W1282X mutation in Karachay-Cherkessia and the Eastern European part of Russia are different.

Plant cells are surrounded by a cell wall, the composition of which determines their final size and shape. The cell wall is composed of a complex matrix containing polysaccharides that include cellulose microfibrils that form both crystalline structures and cellulose chains of amorphous organization. The orientation of the cellulose fibers and their concentrations dictate the mechanical properties of the cell. Several methods are used to determine the levels of crystalline cellulose, each bringing both advantages and limitations. Some can distinguish the proportion of crystalline regions within the total cellulose. However, they are limited to whole-organ analyses that are deficient in spatiotemporal information. Others relying on live imaging, are limited by the use of imprecise dyes. Here, we report a sensitive polarized light-based system for specific quantification of relative light retardance, representing crystalline cellulose accumulation in cross sections of Arabidopsis thaliana roots. In this method, the cellular resolution and anatomical data are maintained, enabling direct comparisons between the different tissues composing the growing root. This approach opens a new analytical dimension, shedding light on the link between cell wall composition, cellular behavior and whole-organ growth.

The properties of many functional materials critically depend on the spatial distribution of surface active sites. In the case of solid catalysts, the geometric and electronic properties of different surface sites will directly impact their catalytic properties. However, the detection of catalytic sites at the single nanoparticle level cannot be easily achieved and most spectroscopic measurements are performed with ensemble-based measurements in which the reactivity is averaged over millions of nanoparticles. It is hereby demonstrated that chemically-functionalized N-heterocyclic carbene molecules can be attached to the surfaces of Pt nanoparticles and utilized as a model system for studying catalytic reactions on single metallic nanoparticles. The formation of a carbene self-assembled layer on the surface of a Pt nanoparticle and its stability under oxidizing conditions were investigated. IR nanospectroscopy measurements detected the chemical properties of surface-anchored molecules on single nanoparticles. A direct correlation was identified between IR nanospectroscopy measurements and macroscopic ATR-IR measurements. These results demonstrate that high spatial resolution mapping of the catalytic reactivity on single nanoparticles can be achieved with this approach.

Summary Screening large populations for carriers of known or de novo rare single nucleotide polymorphisms (SNPs) is required both in Targeting induced local lesions in genomes (TILLING) experiments in plants and in screening of human populations. We previously suggested an approach that combines the mathematical field of compressed sensing with next-generation sequencing to allow such large-scale screening. Based on pooled measurements, this method identifies multiple carriers of heterozygous or homozygous rare alleles while using only a small fraction of resources. Its rigorous mathematical foundations allow scalable and robust detection, and provide error correction and resilience to experimental noise. Here we present a large-scale experimental demonstration of our computational approach, in which we targeted a TILLING population of 1024 Sorghum bicolor lines to detect carriers of de novo SNPs whose frequency was less than 0.1%, using only 48 pools. Subsequent validation confirmed that all detected lines were indeed carriers of the predicted mutations. This novel approach provides a highly cost-effective and robust tool for biologists and breeders to allow identification of novel alleles and subsequent functional analysis.

Silver grids are attractive for replacing indium tin oxide as flexible transparent conductors. This work aims to improve the electrochem. stability of silver-based transparent conductors. A silver grid/PEDOT:PSS hybrid film with high cond. and excellent stability is successfully fabricated. Its functionality for flexible electrochromic applications is demonstrated by coating one layer of WO3 nanoparticles on the silver grid/PEDOT:PSS hybrid film. This hybrid structure presents a large optical modulation of 81.9% at 633 nm, fast switching, and high coloration efficiency (124.5 cm2 C-1). More importantly, an excellent electrochem. cycling stability (sustaining 79.1% of their initial transmittance modulation after 1000 cycles) and remarkable mech. flexibility (optical modulation decay of only 7.5% after 1200 compressive bending cycles) is achieved. A novel smart supercapacitor is presented that functions as a regular energy-storage device and simultaneously monitors the level of stored energy by a rapid and reversible color variation even at high current charge/discharge conditions. The film sustains an optical modulation of 87.7% and a specific capacitance of 67.2% at 10 A g-1 compared to their initial value at a c.d. of 1 A g-1. The high-performance silver grid/PEDOT:PSS hybrid transparent films exhibit promising features for various emerging flexible electronics and optoelectronic devices. [on SciFinder(R)]

In the absence of water-soluble photoinitiators with high absorbance in the ultraviolet (UV)-visible range, rapid three-dimensional (3D) printing of hydrogels for tissue engineering is challenging. A new approach enabling rapid 3D printing of hydrogels in aqueous solutions is presented on the basis of UV-curable inks containing nanoparticles of highly efficient but water-insoluble photoinitiators. The extinction coefficient of the new water-dispersible nanoparticles of 2,4,6-trimethylbenzoyl-diphenylphosphine oxide (TPO) is more than 300 times larger than the best and most used commercially available water-soluble photoinitiator. The TPO nanoparticles absorb significantly in the range from 385 to 420 nm, making them suitable for use in commercially available, low-cost, light-emitting diode-based 3D printers using digital light processing. The polymerization rate at this range is very fast and enables 3D printing that otherwise is impossible to perform without adding solvents. The TPO nanoparticles were prepared by rapid conversion of volatile microemulsions into water-dispersible powder, a process that can be used for a variety of photoinitiators. Such water-dispersible photoinitiator nanoparticles open many opportunities to enable rapid 3D printing of structures prepared in aqueous solutions while bringing environmental advantages by using low-energy curing systems and avoiding the need for solvents.[on SciFinder (R)]

Over the past decade, high-throughput short-read 16S rRNA gene amplicon sequencing has eclipsed clone-dependent long-read Sanger sequencing for microbial community profiling. The transition to new technologies has provided more quantitative information at the expense of taxonomic resolution with implications for inferring metabolic traits in various ecosystems. We applied single-molecule real-time sequencing for microbial community profiling, generating full-length 16S rRNA gene sequences at high throughput, which we propose to name PhyloTags. We benchmarked and validated this approach using a defined microbial community. When further applied to samples from the water column of meromictic Sakinaw Lake, we show that while community structures at the phylum level are comparable between PhyloTags and Illumina V4 16S rRNA gene sequences (iTags), variance increases with community complexity at greater water depths. PhyloTags moreover allowed less ambiguous classification. Last, a platform-independent comparison of PhyloTags and in silico generated partial 16S rRNA gene sequences demonstrated significant differences in community structure and phylogenetic resolution across multiple taxonomic levels, including a severe underestimation in the abundance of specific microbial genera involved in nitrogen and methane cycling across the Lake’s water column. Thus, PhyloTags provide a reliable adjunct or alternative to cost-effective iTags, enabling more accurate phylogenetic resolution of microbial communities and predictions on their metabolic potential.