The nucleolus is the largest structure in the nucleus of eukaryotic cells. It is best known as the site of ribosome biogenesis. Nucleoli participate in the formation of signal recognition particles and play a role in the cell's response to stress. Nucleoli are made of proteins, DNA and RNA and form around specific chromosomal regions called nucleolar organizing regions. Malfunction of nucleoli can be the cause of several human conditions called "nucleolopathies" and the nucleolus is being investigated as a target for cancer chemotherapy; the nucleolus was identified by bright-field microscopy during the 1830s. Little was known about the function of the nucleolus until 1964, when a study of nucleoli by John Gurdon and Donald Brown in the African clawed frog Xenopus laevis generated increasing interest in the function and detailed structure of the nucleolus, they found that such eggs were not capable of life. Half of the eggs had one nucleolus and 25% had two, they concluded. In 1966 Max L. Birnstiel and collaborators showed via nucleic acid hybridization experiments that DNA within nucleoli code for ribosomal RNA.

Three major components of the nucleolus are recognized: the fibrillar center, the dense fibrillar component, the granular component. Transcription of the rDNA occurs in the FC; the DFC contains the protein fibrillarin, important in rRNA processing. The GC contains the protein nucleophosmin, involved in ribosome biogenesis. However, it has been proposed that this particular organization is only observed in higher eukaryotes and that it evolved from a bipartite organization with the transition from anamniotes to amniotes. Reflecting the substantial increase in the DNA intergenic region, an original fibrillar component would have separated into the FC and the DFC. Another structure identified within many nucleoli is a clear area in the center of the structure referred to as a nucleolar vacuole. Nucleoli of various plant species have been shown to have high concentrations of iron in contrast to human and animal cell nucleoli; the nucleolus ultrastructure can be seen through an electron microscope, while the organization and dynamics can be studied through fluorescent protein tagging and fluorescent recovery after photobleaching.

Antibodies against the PAF49 protein can be used as a marker for the nucleolus in immunofluorescence experiments. Although only one or two nucleoli can be seen, a diploid human cell has ten nucleolus organizer regions and could have more nucleoli. Most multiple NORs participate in each nucleolus. In ribosome biogenesis, two of the three eukaryotic RNA polymerases are required, these function in a coordinated manner. In an initial stage, the rRNA genes are transcribed as a single unit within the nucleolus by RNA polymerase I. In order for this transcription to occur, several pol I-associated factors and DNA-specific trans-acting factors are required. In yeast, the most important are: UAF, TBP, core binding factor ) which bind promoter elements and form the preinitiation complex, in turn recognized by RNA pol. In humans, a similar PIC is assembled with SL1, the promoter selectivity factor, transcription initiation factors, UBF. RNA polymerase I transcribes most rRNA transcripts 28S, 18S, 5.8S) but the 5S rRNA subunit is transcribed by RNA polymerase III.

Transcription of rRNA yields a long precursor molecule which still contains the ITS and ETS. Further processing is needed to generate 5.8 S and 28S RNA molecules. In eukaryotes, the RNA-modifying enzymes are brought to their respective recognition sites by interaction with guide RNAs, which bind these specific sequences; these guide RNAs belong to the class of small nucleolar RNAs which are complexed with proteins and exist as small-nucleolar-ribonucleoproteins. Once the rRNA subunits are processed, they are ready to be assembled into larger ribosomal subunits. However, an additional rRNA molecule, the 5S rRNA, is necessary. In yeast, the 5S rDNA sequence is localized in the intergenic spacer and is transcribed in the nucleolus by RNA pol. In higher eukaryotes and plants, the situation is more complex, for the 5S DNA sequence lies outside the Nucleolus Organiser Region and is transcribed by RNA pol III in the nucleoplasm, after which it finds its way into the nucleolus to participate in the ribosome assembly.

This assembly not only ribosomal proteins as well. The genes encoding these r-proteins are transcribed by pol II in the nucleoplasm by a "conventional" pathway of protein synthesis; the mature r-proteins are "imported" back into the nucleus and the nucleolus. Association and maturation of rRNA and r-proteins result in the formation of the 40S and 60S subunits of the complete ribosome; these are exported through the nuclear pore complexes to the cytoplasm, where they remain free or become associated with the endoplasmic reticulum, forming rough endoplasmic reticulum. In human endometrial cells, a network of nucleolar channels is sometimes formed; the origin and function of this network has not yet been identified. In addition to its role in ribosomal biogenesis, the nucleolus is known to capture and immobilize proteins, a process known as nucleolar detention. Proteins that are detained in the nucle

The 1928 United States presidential election in Kansas took place on November 6, 1928, as part of the 1928 United States Presidential Election, held throughout all contemporary 48 states. Voters chose ten representatives, or electors to the Electoral College, who voted for president and vice president. Kansas voted for the Republican nominee, Secretary of Commerce Herbert Hoover of California, over the Democratic nominee, Governor Alfred E. Smith of New York. Hoover’s running mate was Senate Majority Leader Charles Curtis of Kansas, while Smith ran with Senator Joseph Taylor Robinson of Arkansas. Hoover won the by a margin of 44.96%. Smith only carried Ellis County. Smith was the first Roman Catholic. With 72.02% of the popular vote, Kansas would prove to be Hoover's strongest state in the 1928 presidential election in terms of popular vote percentage

Statistics education is the practice of teaching and learning of statistics, along with the associated scholarly research. Statistics is both a formal science and a practical theory of scientific inquiry, both aspects are considered in statistics education. Education in statistics has similar concerns as does education in other mathematical sciences, like logic and computer science. At the same time, statistics is concerned with evidence-based reasoning with the analysis of data. Therefore, education in statistics has strong similarities to education in empirical disciplines like psychology and chemistry, in which education is tied to "hands-on" experimentation. Mathematicians and statisticians work in a department of mathematical sciences. Statistics courses have been sometimes taught by non-statisticians, against the recommendations of some professional organizations of statisticians and of mathematicians. Statistics education research is an emerging field that grew out of different disciplines and is establishing itself as a unique field, devoted to the improvement of teaching and learning statistics at all educational levels.

Statistics educators have noncognitive goals for students. For example, former American Statistical Association President Katherine Wallman defined statistical literacy as including the cognitive abilities of understanding and critically evaluating statistical results as well as appreciating the contributions statistical thinking can make. In the text rising from the 2008 joint conference of the International Commission on Mathematical Instruction and the International Association of Statistics Educators, editors Carmen Batanero, Gail Burrill, Chris Reading note worldwide trends in curricula which reflect data-oriented goals. In particular, educators seek to have students: "design investigations; the authors note the importance of developing statistical thinking and reasoning in addition to statistical knowledge. Despite the fact that cognitive goals for statistics education focus on statistical literacy, statistical reasoning, statistical thinking rather than on skills and procedures alone, there is no agreement about what these terms mean or how to assess these outcomes.

A first attempt to define and distinguish between these three terms appears in the ARTIST website, created by Garfield, delMas and Chance and has since been included in several publications. Brief definitions of these terms are as follows: Statistical literacy is being able to read and use basic statistical language and graphical representations to understand statistical information in the media and in daily life. Statistical reasoning is being able to reason about and connect different statistical concepts and ideas, such as knowing how and why outliers affect statistical measures of center and variability. Statistical thinking is the type of thinking used by statisticians when they encounter a statistical problem; this involves thinking about the nature and quality of the data and, where the data came from, choosing appropriate analyses and models, interpreting the results in the context of the problem and given the constraints of the data. Further cognitive goals of statistics education vary across students' educational level and the contexts in which they expect to encounter statistics.

Statisticians have proposed what they consider the most important statistical concepts for educated citizens. For example, Utts published seven areas of what every educated citizen should know, including understanding that "variability is normal" and how "coincidences… are not uncommon because there are so many possibilities." Gal suggests adults in industrialized societies are expected to exercise statistical literacy, "the ability to interpret and critically evaluate statistical information… in diverse contexts, the ability to… communicate understandings and concerns regarding the… conclusions." Non-cognitive outcomes include affective constructs such as attitudes, emotions and motivation. According to prominent researchers Gal & Ginsburg, statistics educators should make it a priority to be aware of students' ideas and feelings towards statistics and how these affect their learning. Beliefs are defined as one's individually held ideas about statistics, about oneself as a learner of statistics, about the social context of learning statistics.

Beliefs are distinct from attitudes in the sense that attitudes are stable and intense feelings that develop over time in the context of experiences learning statistics. Students' web of beliefs provides a context for their approach towards their classroom experiences in statistics. Many students enter a statistics course with apprehension towards learning the subject, which works against the learning environment that the instructor is trying to accomplish. Therefore, it is important for instructors to have access to assessment instruments that can give an initial diagnosis of student beliefs and monitor beliefs during a course. Assessment instruments have monitored beliefs and attitudes together. For examples of such instruments, see the attitudes section below. Disposition has to approach a statistical problem. Dispositions is one of the four dimensions in Wild and Pfannkuch's framework for statistical thinking, contains the following elem