Strategic influences on implementing instructions for future actions

Temporal and strategic factors that might influence the transformation of verbal task rules into functional stimulus–response associations were investigated in three experiments. In a dual task paradigm of the ABBA type participants were presented new S–R instructions for the A-task at the beginning of each trial. On varying proportions of trials No-go signals rendered the instructed A-task mappings irrelevant before instruction implementation was assessed during performance of an unrelated B-task. Our results indicate that participants refrain from implementing the mappings during instruction presentation when No-go signals appear frequently and late (Exp. 2), and that they can interrupt implementing instructed S–R mappings when frequent No-go signals appear early enough during implementation (Exp. 3). When No-go signals are rare and late, however (Exp. 1), the instructed stimulus features always activate their associated responses during performance of the embedded B-task in an automatic manner. Together, these findings suggest that participants strategically control whether or not they implement verbal instructions. Once implemented, however, instructed S–R associations influence behaviour even when the instructed mappings are no longer task relevant

Robust normalization protocols for multiplexed fluorescence bioimage analysis

study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner (Nat Biotechnol 24:1270–8, 2006; Proc Natl Acad Sci 110:11982–7, 2013). Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples

Action–effect anticipation in infant action control

There is increasing evidence that action effects play a crucial role in action understanding and action control not only in adults but also in infants. Most of the research in infants focused on the learning of action–effect contingencies or how action effects help infants to infer goals in other persons’ actions. In contrast, the present research aimed at demonstrating that infants control their own actions by action–effect anticipation once they know about specific action–effect relations. About 7 and 9-month olds observed an experimenter demonstrating two actions that differed regarding the action–effect assignment. Either a red-button press or a blue-button press or no button press elicited interesting acoustical and visual effects. The 9-month olds produced the effect action at first, with shorter latency and longer duration sustaining a direct impact of action–effect anticipation on action control. In 7-month olds the differences due to action–effect manipulation were less profound indicating developmental changes at this age

Repositories for Taxonomic Data: Where We Are and What is Missing

AbstractNatural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000–20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use of molecular data in species diagnosis and description. In 2018, of the 2661 papers published in specialized taxonomic journals, molecular data were widely used in mycology (94%), regularly in vertebrates (53%), but rarely in botany (15%) and entomology (10%). Images play an important role in taxonomic research on all taxa, with photographs used in &amp;gt;80% and drawings in 58% of the surveyed papers. The use of omics (high-throughput) approaches or 3D documentation is still rare. Improved archiving strategies for metabarcoding consensus reads, genome and transcriptome assemblies, and chemical and metabolomic data could help to mobilize the wealth of high-throughput data for alpha-taxonomy. Because long-term—ideally perpetual—data storage is of particular importance for taxonomy, energy footprint reduction via less storage-demanding formats is a priority if their information content suffices for the purpose of taxonomic studies. Whereas taxonomic assignments are quasifacts for most biological disciplines, they remain hypotheses pertaining to evolutionary relatedness of individuals for alpha-taxonomy. For this reason, an improved reuse of taxonomic data, including machine-learning-based species identification and delimitation pipelines, requires a cyberspecimen approach—linking data via unique specimen identifiers, and thereby making them findable, accessible, interoperable, and reusable for taxonomic research. This poses both qualitative challenges to adapt the existing infrastructure of data centers to a specimen-centered concept and quantitative challenges to host and connect an estimated $\le$2 million images produced per year by alpha-taxonomic studies, plus many millions of images from digitization campaigns. Of the 30,000–40,000 taxonomists globally, many are thought to be nonprofessionals, and capturing the data for online storage and reuse therefore requires low-complexity submission workflows and cost-free repository use. Expert taxonomists are the main stakeholders able to identify and formalize the needs of the discipline; their expertise is needed to implement the envisioned virtual collections of cyberspecimens. [Big data; cyberspecimen; new species; omics; repositories; specimen identifier; taxonomy; taxonomic data.]</jats:p

Context dependent learning in the serial RT task

This study investigated the development of contextual dependencies for sequential perceptual-motor learning on static features in the learning environment. In three experiments we assessed the effect of manipulating task irrelevant static context features in a serial reaction-time task. Experiment 1 demonstrated impaired performance after simultaneously changing display color, placeholder shape, and placeholder location. Experiment 2 showed that this effect was mainly caused by changing placeholder shape. Finally, Experiment 3 indicated that changing context affected both the application of sequence knowledge and the selection of individual responses. It is proposed either that incidental stimulus features are integrated with a global sequence representation, or that the changed context causes participants to strategically inhibit sequence skills

TACOA – Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

Diaz NN, Krause L, Goesmann A, Niehaus K, Nattkemper TW. TACOA - Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach. BMC Bioinformatics. 2009;10(1):56.Background: Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning. Results Our novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp – 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments ≥ 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained. Conclusion: An accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date. Background

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

Sensory information in perceptual-motor sequence learning: visual and/or tactile stimuli

Sequence learning in serial reaction time (SRT) tasks has been investigated mostly with unimodal stimulus presentation. This approach disregards the possibility that sequence acquisition may be guided by multiple sources of sensory information simultaneously. In the current study we trained participants in a SRT task with visual only, tactile only, or bimodal (visual and tactile) stimulus presentation. Sequence performance for the bimodal and visual only training groups was similar, while both performed better than the tactile only training group. In a subsequent transfer phase, participants from all three training groups were tested in conditions with visual, tactile, and bimodal stimulus presentation. Sequence performance between the visual only and bimodal training groups again was highly similar across these identical stimulus conditions, indicating that the addition of tactile stimuli did not benefit the bimodal training group. Additionally, comparing across identical stimulus conditions in the transfer phase showed that the lesser sequence performance from the tactile only group during training probably did not reflect a difference in sequence learning but rather just a difference in expression of the sequence knowledge

Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

Abstract. The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible. This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.