Fantastical Distempers: The Psychopathology of Early Modern Scholars

Fantastical Distempers: The Psychopathology of Early Modern Scholar

of New Zealand showing distruct plots of ancestry profiles for 179 <i>S. cerevisiae</i> sampled from three regions from [19].

<p>Ancestry profiles were generated using InStruct with .</p

map showing distruct plots of ancestry profiles for humans sampled from seven continents from [18].

<p>Ancestry profiles were generated using structure with .</p

Canonical discriminant analysis on the <i>Saccharomyces cerevisiae</i> dataset.

<p>(a) Mapping the individual data according to the CDA variables. The inner ellipsoid contains 50% of all individuals, the outer ellipsoid contains 95% of all individuals. Individuals are colour- and shape-coded according to their respective sampled region. (b) The HE plot shows the relation of variation in the group means on two variables relative to the error variance. The coloured arrows indicate the position of the inferred populations relative to the axes obtained by the canonical discriminant analysis. The black points indicate predefined populations (WA  =  West Auckland; WI  =  Waiheke Island; HB  =  Hawke's Bay) while numbers at the arrows indicate inferred populations.</p

Changes from the observed value when each continent and inferred population is removed in turn for the human dataset.

<p>The colours for inferred populations correspond to those seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085196#pone-0085196-g002" target="_blank">Figure 2</a>. The error reported for the continents is the standard error of calculated for three separate chains of each dataset. No such error is reported for inferred populations because the designations for inferred populations differ between chains.</p

Change in population structure over 1,000 generations of simulated population divergence as inferred by (a) Structure and (b) InStruct.

<p>Error bars denote the standard error of three separate simulations for each sampling time point.</p

values calculated for two experimental datasets of human and <i>S. cerevisiae</i> microsatellite profiles.

<p>The error reported is the standard error of calculating for three separate chains of each dataset.</p><p>Denotes ().</p

Public perceptions of the use of artificial intelligence in Defence: a qualitative exploration

There are a wide variety of potential applications of artificial intelligence (AI) in Defence settings, ranging from the use of autonomous drones to logistical support. However, limited research exists exploring how the public view these, especially in view of the value of public attitudes for influencing policy-making. An accurate understanding of the public’s perceptions is essential for crafting informed policy, developing responsible governance, and building responsive assurance relating to the development and use of AI in military settings. This study is the first to explore public perceptions of and attitudes towards AI in Defence. A series of four focus groups were conducted with 20 members of the UK public, aged between 18 and 70, to explore their perceptions and attitudes towards AI use in general contexts and, more specifically, applications of AI in Defence settings. Thematic analysis revealed four themes and eleven sub-themes, spanning the role of humans in the system, the ethics of AI use in Defence, trust in AI versus trust in the organisation, and gathering information about AI in Defence. Participants demonstrated a variety of misconceptions about the applications of AI in Defence, with many assuming that a variety of different technologies involving AI are already being used. This highlighted a confluence between information from reputable sources combined with narratives from the mass media and conspiracy theories. The study demonstrates gaps in knowledge and misunderstandings that need to be addressed, and offers practical insights for keeping the public reliably, accurately, and adequately informed about the capabilities, limitations, benefits, and risks of AI in Defence.</p

Kinetics of [anti-E Abs] and [anti-FL Abs] in sera of dengue patients.

<p>(A) [anti-E Abs] and (B) [anti-FL Abs] in samples collected 3, 6, 12 and 18 months post-infection. Green closed symbols, patients with primary DENV infection; red open symbols, patients with secondary DENV infection.</p

Concentration of anti-E Abs and anti-FL Abs and proportion of anti-FL Abs in sequential serum samples from 10 dengue cases.

a<p>Primary or secondary DENV infection was determined as described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002451#s2" target="_blank">Methods</a>.</p>b<p>The current infecting serotype was determined as described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002451#s2" target="_blank">Methods</a>. With the exception of two DHF/DSS cases (274, 233), all others were DF cases. D = DENV.</p>c<p>Sampling time was determined relative to onset of fever.</p>d<p>[anti-E Abs], [anti-FL Abs] and % anti-FL Abs were determined as described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002451#s2" target="_blank">Methods</a>.</p>e<p>Below the limit of detection (BD). The limit of detection of % anti-FL Abs is 4% as described in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002451#s2" target="_blank">Methods</a>.</p