The Essentials of Protein Import in the Degenerate Mitochondrion of Entamoeba histolytica

Several essential biochemical processes are situated in mitochondria. The metabolic transformation of mitochondria in distinct lineages of eukaryotes created proteomes ranging from thousands of proteins to what appear to be a much simpler scenario. In the case of Entamoeba histolytica, tiny mitochondria known as mitosomes have undergone extreme reduction. Only recently a single complete metabolic pathway of sulfate activation has been identified in these organelles. The E. histolytica mitosomes do not produce ATP needed for the sulfate activation pathway and for three molecular chaperones, Cpn60, Cpn10 and mtHsp70. The already characterized ADP/ATP carrier would thus be essential to provide cytosolic ATP for these processes, but how the equilibrium of inorganic phosphate could be maintained was unknown. Finally, how the mitosomal proteins are translocated to the mitosomes had remained unclear. We used a hidden Markov model (HMM) based search of the E. histolytica genome sequence to discover candidate (i) mitosomal phosphate carrier complementing the activity of the ADP/ATP carrier and (ii) membrane-located components of the protein import machinery that includes the outer membrane translocation channel Tom40 and membrane assembly protein Sam50. Using in vitro and in vivo systems we show that E. histolytica contains a minimalist set up of the core import components in order to accommodate a handful of mitosomal proteins. The anaerobic and parasitic lifestyle of E. histolytica has produced one of the simplest known mitochondrial compartments of all eukaryotes. Comparisons with mitochondria of another amoeba, Dictystelium discoideum, emphasize just how dramatic the reduction of the protein import apparatus was after the loss of archetypal mitochondrial functions in the mitosomes of E. histolytica

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Altering visual perception abnormalities:A marker for body image concern

The body image concern (BIC) continuum ranges from a healthy and positive body image, to clinical diagnoses of abnormal body image, like body dysmorphic disorder (BDD). BDD and non-clinical, yet high-BIC participants have demonstrated a local visual processing bias, characterised by reduced inversion effects. To examine whether this bias is a potential marker of BDD, the visual processing of individuals across the entire BIC continuum was examined. Dysmorphic Concern Questionnaire (DCQ; quantified BIC) scores were expected to correlate with higher discrimination accuracy and faster reaction times of inverted stimuli, indicating reduced inversion effects (occurring due to increased local visual processing). Additionally, an induced global or local processing bias via Navon stimulus presentation was expected to alter these associations. Seventy-four participants completed the DCQ and upright-inverted face and body stimulus discrimination task. Moderate positive associations were revealed between DCQ scores and accuracy rates for inverted face and body stimuli, indicating a graded local bias accompanying increases in BIC. This relationship supports a local processing bias as a marker for BDD, which has significant assessment implications. Furthermore, a moderate negative relationship was found between DCQ score and inverted face accuracy after inducing global processing, indicating the processing bias can temporarily be reversed in high BIC individuals. Navon stimuli were successfully able to alter the visual processing of individuals across the BIC continuum, which has important implications for treating BDD

Relationship between DCQ score and mean accuracy rates (%) of inverted face stimuli discrimination.

<p>Relationship between DCQ score and mean accuracy rates (%) of inverted face stimuli discrimination.</p

Means, standard deviations and 95% confidence intervals for all variables in the local Navon condition.

<p>Means, standard deviations and 95% confidence intervals for all variables in the local Navon condition.</p

Relationship between DCQ score and mean accuracy rates (%) of inverted face stimuli discrimination after local processing was induced.

<p>Relationship between DCQ score and mean accuracy rates (%) of inverted face stimuli discrimination after local processing was induced.</p

Example of upright non-matching (left) and matching (right) body pairs used in the discrimination task.

<p>Please see [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151933#pone.0151933.ref057" target="_blank">57</a>] for an example of the face stimuli.</p

Relationship between DCQ Score and mean accuracy rates (%) of inverted body stimuli discrimination.

<p>Relationship between DCQ Score and mean accuracy rates (%) of inverted body stimuli discrimination.</p

Means, standard deviations and 95% confidence intervals for all variables in the global Navon condition.

<p>Means, standard deviations and 95% confidence intervals for all variables in the global Navon condition.</p

Means, standard deviations and 95% confidence intervals for all variables.

<p>Means, standard deviations and 95% confidence intervals for all variables.</p