The Role of the Hsp40 Chaperone Sis1 in Yeast Prion Propagation

Yeast prions are self-templating amyloid aggregates composed of misfolded cellular proteins. In order to propagate, yeast prions must be broken into heritable seeds that are passed to subsequent generations. The replication step of the prion propagation cycle is accomplished by the actions of molecular chaperones, which bind to and serve the fibers through a process called disaggregation. Prions can be thought of as molecular diseases that have hijacked the chaperones for their continued existence. When viewed in this way, the study of yeast prions has been very informative about the interactions among of the molecular chaperones. This chapter focuses on the role of a single Hsp40 or J-protein, Sis1, in the propagation of yeast prions. While Sis1 seems to be required for the maintenance of many different prions, various prions depend on Sis1 in different ways, perhaps due to differences in underlying amyloid structure. New evidence is emerging that Sis1 is important for processes that may not involve prion replication activity, providing an intriguing alternative explanation for the observed differences in the prions’ reliance on Sis1

The admission of slaves and 'prize slaves' into the Cape Colony, 1797-1818

Includes bibliography.This study supports the thesis that slaves were admitted into the Cape colony by the Cape colonial government, even though the government was opposed to slave importation in principle and law (Slave Trade Act, 1807) from 1797-1818. The colonial demand for slaves was at its height after the Vereenigde Oost-Indische Compagnie's (VOC) capitulation to the British in 1795. This demand forced the first British occupation government to forgo their anti-slave trade principles and accede to a limited importation of slaves into the colony

Satellite-based estimation of herbage mass: comparison with destructive measurements and UAV model’s estimation

Regular estimation of herbage mass (HM) is a prerequisite for efficient pasture management. In addition to classical estimation using rising plate meters, remote-sensing methods using unmanned aerial vehicles (UAV) or satellites are available. Pasture.io has developed a model that estimates HM based on daily satellite data, herbage growth models and herbage-ingested input data recorded by farmers combined with artificial intelligence. This study compared the accuracy of Pasture.io HM estimations with UAV estimations and destructive measurements. Pastures from three Swiss farms were assessed regularly in May, June and July 2021. It was found that Pasture.io estimates HM with an error value RMSE 342 kg dry matter (DM) ha-1 while the UAV model’s estimation showed a higher RMSE of 447 kg DM ha-1. The results suggest that even in small pasture structures (mean paddock size: 1.2 ha), it is possible to estimate HM with reasonable accuracy based on satellite data and artificial intelligence

Fake news and indifference to truth: Dissecting tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they di_er, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identi_ed as being more positive in sentiment than President Obama's 2016 SOUA. This is con_rmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research

On Czerwinski's "P≠NP{\rm P} \neq {\rm NP} relative to a P{\rm P}-complete oracle"

In this paper, we take a closer look at Czerwinski's "${\rm P}\neq{\rm NP}$ relative to a ${\rm P}$-complete oracle" [Cze23]. There are (uncountably) infinitely-many relativized worlds where ${\rm P}$ and ${\rm NP}$ differ, and it is well-known that for any ${\rm P}$-complete problem $A$, ${\rm P}^A \neq {\rm NP}^A \iff {\rm P}\neq {\rm NP}$. The paper defines two sets ${\rm D}_{\rm P}$ and ${\rm D}_{\rm NP}$ and builds the purported proof of their main theorem on the claim that an oracle Turing machine with ${\rm D}_{\rm NP}$ as its oracle and that accepts ${\rm D}_{\rm P}$ must make $\Theta(2^n)$ queries to the oracle. We invalidate the latter by proving that there is an oracle Turing machine with ${\rm D}_{\rm NP}$ as its oracle that accepts ${\rm D}_{\rm P}$ and yet only makes one query to the oracle. We thus conclude that Czerwinski's paper [Cze23] fails to establish that ${\rm P} \neq {\rm NP}$

Evaluation and quantification of associations between commonly suggested milk biomarkers and the proportion of grassland-based feeds in the diets of dairy cows

This study is a first step approach towards the prediction of the proportion of grassland-based feeds (%GB) in dairy cow diets with the aid of three different groups of milk biomarkers. We aimed to evaluate and quantify the associations between biomarkers commonly suggested in the literature and %GB in individual cows as a hypothesis-generating stage for the prospective establishment of accurate %GB prediction models. Consumers and governments financially encourage sustainable, local milk production making grass-based feeding, in grassland-dominated regions, of major interest. Milk from grassland-fed cows differs from that of other feeding systems by inferential fatty acids (FA), β-carotene content and yellow color; however, these biomarkers have not been evaluated together for their association with %GB. Using approved methods of parametric regression analysis, gas chromatography (GC), mid-infrared spectra (MIR) and color spectroscopy, we aimed to develop a first step towards an easy-to-implement, cost-effective milk-based control to estimate %GB in dairy cow diets. The underlying database was generated with 24 cows each fed one of 24 different diets gradually increasing in grass silage and decreasing in corn silage. Our results indicate that GC-measured α-linolenic acid, total n-3 FA and the n-6:n-3 ratio, MIR-estimated PUFA and milk red-green color index a* are robust milk biomarkers for constructing accurate prediction models to determine %GB. Based on simplified regression analysis, diets containing 75% GB should contain ≥ 0.669 and 0.852 g α-linolenic acid and total n-3 FA per 100 g total FA, respectively, and an n-6:n-3 FA ratio of < 2.02 measured with GC; estimated with MIR, polyunsaturated FA should be ≥ 3.13 g/100 g total FA. β-carotene was not a good predictor for estimating %GB. Unexpectedly, the milk became greener with increasing %GB (negative a* values, ‒6.416 for 75% GB), suggesting the red-green color index, not yellow-blue, as a suitable biomarker

Report on reasons behind voter behaviour in the Oireachtas Inquiry referendum 2011

This report draws on both quantitative and qualitative research carried out by RED C into voting on the referendum on Oireachtas Inquiries held in October 2011. It addresses three main questions: − Why did people vote as they did in the Oireachtas Inquiry referendum? − What does the public think about the political reform agenda to which the government is committed? − What lessons can be learned for future votes on reform