Non-expert Scientists Research Articles

Visualization, Quantification, and Statistical Evaluation of Dynamics for DNA-Binding Proteins in Bacteria by Single-Molecule Tracking.

All DNA-binding proteins in vivo exist as a population of freely diffusing molecules and of DNA-bound molecules. The molecules bound to DNA can be split into specifically/tightly and nonspecifically bound proteins. Single-molecule tracking (SMT) is a method allowing to visualize protein dynamics in living cells, revealing their behavior in terms of mode of motion, diffusion coefficient/speed, change of dwell times, and unveiling preferred subcellular sites of dwelling. Bleaching-type SMT or fluorescent protein-tagged SMT involves rapid laser-induced bleaching of most fluorophore-labeled molecules. The remaining single fluorescent proteins are then continuously tracked. The trajectories of several fluorescent molecules per cell for a population of cells are analyzed and combined to permit a robust analysis of average behavior of single molecules in live cells, including analyses of protein dynamics in mutant cells or cells exposed to changes in environmental conditions.In this chapter, we describe the preparation of Bacillus subtilis cells, the recording of movies of those cells expressing a monomeric variant of a yellow fluorescent protein (mNeonGreen) fused to a protein of choice, and the subsequent curation of the movie data including the statistical analysis of the protein dynamics. We present a short overview of the analysis program SMTracker 2.0, highlighting its ability to analyze SMT data by non-expert scientists.

The rise of self-driving labs in chemical and materials sciences

Accelerating the discovery of new molecules and materials, as well as developing green and sustainable ways to synthesize them, will help to address global challenges in energy, sustainability and healthcare. The recent growth of data science and automated experimentation techniques has resulted in the advent of self-driving labs (SDLs) via the integration of machine learning, lab automation and robotics. An SDL is a machine-learning-assisted modular experimental platform that iteratively operates a series of experiments selected by the machine learning algorithm to achieve a user-defined objective. These intelligent robotic assistants help researchers to accelerate the pace of fundamental and applied research through rapid exploration of the chemical space. In this Review, we introduce SDLs and provide a roadmap for their implementation by non-expert scientists. We present the status quo of successful SDL implementations in the field and discuss their current limitations and future opportunities to accelerate finding solutions for societal needs. Self-driving labs (SDLs) combine machine learning with automated experimental platforms, enabling rapid exploration of the chemical space and accelerating the pace of materials and molecular discovery. In this Review, the application of SDLs, their limitations and future opportunities are discussed, and a roadmap is provided for their implementation by non-expert scientists.

SOLart: a structure-based method to predict protein solubility and aggregation.

The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools. We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists. The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/. Supplementary data are available at Bioinformatics online.

The uncertainty principle – A simplified review of the four versions

The complexity of the historical confusions around different versions of the uncertainty principle, in addition to the increasing technicality of physics in general, has made its affairs predominantly accessible only to specialists. Consequently, the clarity that has dawned upon physicists over the decades regarding quantum uncertainty remains mostly imperceptible for general readers, students, philosophers and even non-expert scientists. In an attempt to weaken this barrier, the article presents a summary of this technical subject, focussing at the prime case of the position-momentum pair, as modestly and informatively as possible. This includes a crisp analysis of the historical as well as of the latest developments. In the process the article provides arguments to show that the usually sidelined version of uncertainty—the intrinsic ׳unsharpness׳ or ׳indeterminacy׳—forms the basis for all the other three versions, and subsequently presents its hard philosophical implications.

KnoWare: A System for Citizen-based Environmental Monitoring

Non-expert scientists are frequently involved in research requiring data acquisition over large geographic areas. Despite mutual benefits for such “citizen science”, barriers also exist, including 1) difficulty maintaining user engagement with timely feedback, and 2) the challenge of providing non-experts with the means to generate reliable data. We have developed a system that addresses these barriers. Our technologies, KnoWare and InSpector, allow users to: collect reliable scientific measurements, map geo-tagged data, and intuitively visualize the results in real-time. KnoWare comprises a web portal and an iOS app with two core functions. First, users can generate scientific ‘queries’ that entail a call for information posed to a crowd with customized options for participant responses and viewing data. Second, users can respond to queries with their GPS-enabled mobile device, which results in their geo- and time-stamped responses populating a web-accessible map in real time. KnoWare can also interface with additional applications to diversify the types of data that can be reported. We demonstrate this capability with a second iOS app called InSpector that performs quantitative water quality measurements. When used in combina-tion, these technologies create a workflow to facilitate the collection, sharing and interpretation of scientific data by non-expert scientists.

Screen3D: a novel fully flexible high-throughput shape-similarity search method.

3D shape- or volume-based virtual screening is a broadly used approach in drug discovery. In recent years a large number of publications have appeared in which these tools were compared not only to competitive methods but to docking studies as well. Studies often showed that the effectiveness of docking could be highly variable due to a large number of possible confounding factors, while ligand-based, shape-based approaches were more consistent. Here, we describe a novel, fully flexible shape-based virtual screening algorithm that does not require previous 3D conformation or conformer generation. Due to its solid consistency it can easily be used on desktop computers by non-expert scientists. The algorithm is demonstrated in a study for the investigation of β-secretase inhibitors and benchmarked on the Directory of Useful Decoys data set.

Commentary: Flattening the astronomy world

Commentary: Flattening the astronomy world

Assessment, Design and Implementation of a Private Cloud for MapReduce Applications

Scientific computation and data intensive analyses are ever more frequent. On the one hand, the MapReduce programming model has gained a lot of attention for its applicability in large parallel data analyses and Big Data applications. On the other hand, Cloud computing seems to be increasingly attractive in solving these computing problems that demand a lot of resources. This paper explores the potential symbiosis between MapReduce and Cloud Computing, in order to create a robust and scalable environment to execute MapReduce workflows regardless of the underlaying infrastructure. The main goal of this work is to provide an easy-to-install interface, so as non-expert scientists can deploy a suitable testbed for their MapReduce experiments on local resources of their institution. Testing cases were performed in order to evaluate the required time for the whole executing process on a real cluster.

Pico‐PLAZA, a genome database of microbial photosynthetic eukaryotes

With the advent of next generation genome sequencing, the number of sequenced algal genomes and transcriptomes is rapidly growing. Although a few genome portals exist to browse individual genome sequences, exploring complete genome information from multiple species for the analysis of user-defined sequences or gene lists remains a major challenge. pico-PLAZA is a web-based resource (http://bioinformatics.psb.ugent.be/pico-plaza/) for algal genomics that combines different data types with intuitive tools to explore genomic diversity, perform integrative evolutionary sequence analysis and study gene functions. Apart from homologous gene families, multiple sequence alignments, phylogenetic trees, Gene Ontology, InterPro and text-mining functional annotations, different interactive viewers are available to study genome organization using gene collinearity and synteny information. Different search functions, documentation pages, export functions and an extensive glossary are available to guide non-expert scientists. To illustrate the versatility of the platform, different case studies are presented demonstrating how pico-PLAZA can be used to functionally characterize large-scale EST/RNA-Seq data sets and to perform environmental genomics. Functional enrichments analysis of 16 Phaeodactylum tricornutum transcriptome libraries offers a molecular view on diatom adaptation to different environments of ecological relevance. Furthermore, we show how complementary genomic data sources can easily be combined to identify marker genes to study the diversity and distribution of algal species, for example in metagenomes, or to quantify intraspecific diversity from environmental strains.

The BioVel Project: Robust phylogenetic workflows running on the GRID

The BioVel Project: Robust phylogenetic workflows running on the GRID

Stereological tools in biomedical research.

Stereological studies are more and more frequent in literature, particularly in the development/evolution, pathology, and neurosciences areas. The stereology challenge is to understand the structural inner three-dimensional arrangement based on the analysis of the structure slices only showing two-dimensional information. Cavalieri and Scherle's methods to estimate volume, and Buffon's needle problem, are commented in the stereological context. A group of actions is needed to appropriately quantify morphological structures (unbiased and reproducibly), e.g. sampling, isotropic and uniform randomly sections (Delesse's principle), and updated stereological tools (disector, fractionator, nucleator, etc). Through the correct stereology use, a quantitative study with little effort could be performed: efficiency in stereology means a minimum slices sample counting (little work), low cost (slices preparation), but good accuracy. In the present text, a short review of the main stereological tools is done as a background basis to non-expert scientists.

Comments on the Workshop on techniques to study Cosmic Rays with E>1019eV

Jim Cronin asked me and other colleagues to attend this Workshop to provide a critical point of view from non-expert scientists on the future developments needed to study Cosmic Rays at the highest energy. Being from outside this field, most of my comments will therefore be quite naive. I will take advantage of my involvement in the large Hadron Collider project to make comparisons and try to extract some general comments and guidelines.