The earliest English prose

This article examines the production of prose texts in pre-Alfredian England. After reviewing conventional ideas regarding the foundational role assigned to Alfred, king of Wessex, in the creation of the Old English prose genre, the discussion turns to a quite considerable number of non-poetic texts which were demonstrably produced at an earlier time, asking whether these can be regarded as prose. Following an investigation of the medieval and modern understanding of what constitutes prose, an argument is made for a more inclusive definition of this literary genre, one that does justice to the flourishing early literary culture especially of the kingdoms of Mercia and Kent. It is argued that the ninth-century prose productions of Alfred’s circles did present some innovation, but were clearly also based on earlier traditions and may to some extent have reacted against preceding compositional techniques and literary genres.Publisher PDFPeer reviewe

Early Mercian text production : authors, dialects, and reputations

There are suggestions that King Alfred’s legendary literary renaissance may have been a reaction to the efforts of the neighbouring kingdom of Mercia. According to Asser, Alfred assembled a group of literary scholars from this rival Mercian tradition at his court. But it is not clear what early literary activities these scholars could have been involved in to justify their pre-Alfredian reputation. This article tries to outline the historical and literary evidence for early Mercian text production, and the importance of this ‘other’ early literary corpus. What is our current knowledge of Mercian text production and the political and literary relationship of Mercia with Canterbury? What was the relationship of Alfred’s educational movement with its Mercian forerunner? Why is modern scholarship better informed about Alfred’s movement than any Mercian rival culture? If our current knowledge of this area is insufficient for the writing of a literary history of Mercia, a provisional list of texts and bibliography, published electronically for convenient updating, may prove useful in the meantime.PostprintPeer reviewe

Sterol regulatory element-binding proteins are regulators of the rat thyroid peroxidase gene in thyroid cells

Sterol regulatory element-binding proteins (SREBPs)-1c and -2, which were initially discovered as master transcriptional regulators of lipid biosynthesis and uptake, were recently identified as novel transcriptional regulators of the sodium-iodide symporter gene in the thyroid, which is essential for thyroid hormone synthesis. Based on this observation that SREBPs play a role for thyroid hormone synthesis, we hypothesized that another gene involved in thyroid hormone synthesis, the thyroid peroxidase (TPO) gene, is also a target of SREBP-1c and -2. Thyroid epithelial cells treated with 25-hydroxycholesterol, which is known to inhibit SREBP activation, had about 50% decreased mRNA levels of TPO. Similarly, the mRNA level of TPO was reduced by about 50% in response to siRNA mediated knockdown of both, SREBP-1 and SREBP-2. Reporter gene assays revealed that overexpression of active SREBP-1c and -2 causes a strong transcriptional activation of the rat TPO gene, which was localized to an approximately 80 bp region in the intron 1 of the rat TPO gene. In vitro- and in vivo-binding of both, SREBP-1c and SREBP-2, to this region in the rat TPO gene could be demonstrated using gel-shift assays and chromatin immunoprecipitation. Mutation analysis of the 80 bp region of rat TPO intron 1 revealed two isolated and two overlapping SREBP-binding elements from which one, the overlapping SRE+609/InvSRE+614, was shown to be functional in reporter gene assays. In connection with recent findings that the rat NIS gene is also a SREBP target gene in the thyroid, the present findings suggest that SREBPs may be possible novel targets for pharmacological modulation of thyroid hormone synthesis

What are the Marital Problems of Happy Couples? A Multimethod, Two‐Sample Investigation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162758/2/famp12483.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162758/1/famp12483_am.pd

Fasting Upregulates PPARα Target Genes in Brain and Influences Pituitary Hormone Expression in a PPARα Dependent Manner

PPARα is a lipid-activable transcription factor that mediates the adaptive response to fasting. Recent data indicate an important role of brain PPARα in physiological functions. However, it has not yet been shown whether PPARα in brain can be activated in the fasting state. Here we demonstrate that fasting of rats increased mRNA concentrations of typical PPARα target genes implicated in β-oxidation of fatty acids (acyl-CoA oxidase, carnitine palmitoyltransferase-1, medium chain acyl-CoA dehydrogenase) and ketogenesis (mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase) in pituitary gland and partially also in frontal cortex and diencephalon compared to nonfasted animals. These data strongly indicate that fasting activates PPARα in brain and pituitary gland. Furthermore, pituitary prolactin and luteinizing hormone-β mRNA concentrations were increased upon fasting in wild-type mice but not in mice lacking PPARα. For proopiomelanocortin and thyrotropin-β, genotype-specific differences in pituitary mRNA concentrations were observed. Thus, PPARα seems to be involved in transcriptional regulation of pituitary hormones

Novel machine learning approaches revolutionize protein knowledge

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Appraisal Skills Program (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community

Novel machine learning approaches revolutionize protein knowledge

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific communit

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Deep-learning (DL) methods like DeepMind's AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) which exploits novel DL methods for structural comparison and classification. Of ~370,000 confident models, 92% can be assigned to 3253 superfamilies in our CATH domain superfamily classification. The remaining cluster into 2367 putative novel superfamilies. Detailed manual analysis on 618 of these, having at least one human relative, reveal extremely remote homologies and further unusual features. Only 25 novel superfamilies could be confirmed. Although most models map to existing superfamilies, AF2 domains expand CATH by 67% and increases the number of unique 'global' folds by 36% and will provide valuable insights on structure function relationships. CATH-Assign will harness the huge expansion in structural data provided by DeepMind to rationalise evolutionary changes driving functional divergence

Mann and gender in Old English prose : a pilot study

It has long been known that OE mann was used in gender-neutral as well as gender-specific contexts. Because of the enormous volume of its attestations in Old English prose, the more precise usage patterns of mann remain, however, largely uncharted, and existing lexicographical tools provide only a basic picture. This article aims to present a preliminary study of the various uses of mann as attested in Old English prose, particularly in its surprisingly consistent use by an individual author, namely that of the ninth-century Old English Martyrology. Patterns emerging from this text are then tested against other prose material. Particular attention is paid to gender-specific usage, examples of which are shown to be exceptional for a word which largely occurs in gender-neutral contexts.Publisher PDFPeer reviewe