This study investigated the effects of selected spelling teaching methods on spelling mastery of upper-elementary, learning disabled students. It also examined the value of assessing learning disabled students' modality preferences for diagnostic/prescriptive purposes.The study's significance is that it sought to (a) determine whether students classified as learning disabled can identify their preferred learning modes; (b) determine whether matching modes of instruction to students' modality reference(s) results in greater achievement; and (c) identify a systematic way of prescribing instruction for learning disabled students.

Innovation processes require organizations to transcend current boundaries. These include not only technological as well as social limitations but "above all" the way we address the future. We are used to face the future with our existing knowledge and experiences from the past. This strategy, however, can hardly lead to knowledge off the beaten path. We therefore suggest a new learning approach for organizations, which enables to literally envision a desired future scenario and thereby, allows for the creation of radical new knowledge. We argue that the created knowledge yields a higher degree of novelty and radicalness. Along with an enhanced theory of learning including learning from the future, we present our empirical findings from comparing the outputs of Learning from an Envisioned Future and learning from the past. For this purpose, we use data from two organizational learning projects; one, which was conducted with a high school in Austria and another one, which was conducted with members of the Austrian Economic Chamber. Our findings from both case studies suggest that Learning from an Envisioned Future does produce significantly more paradigm challenging knowledge compared to the output gained from conventional learning from past experiences. We conclude that the combination of both learning sources may lead to best learning outcomes in organizations.

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers.