This writing sample is presented as part of the application for the Earth and Environmental Science Ph.D. Program at the University of Pennsylvania. It is an adaptation of the final assignment presented for Isotope Geology (March 2019)
We are stardust, literally. ¶
A brief introduction to nucleosynthesis ¶
María Micaela Ninni ⚬ Buenos Aires, Argentina ⚬ December 2019
As Joni Mitchel famously sang at Woodstock, ‘We are stardust’. Our current understanding of how the elements that compose all the matter surrounding us is inexorably linked to the birth and death of stars. Nucleosynthesis is the process that describes the production, on a cosmic scale, of all the species of chemical elements from perhaps one or two simple types of atomic nuclei, hydrogen, and helium, created at the Big Bang. Almost all other elements have been created by stars, making Mitchell’s statement true.
Since the very beginning of our human species’ endeavor, we have wondered about the components and origin of our reality. The first ideas on nucleosynthesis were simply that chemical elements were created at the beginning of the universe, but it was not until after World War II that nuclear fission was understood and a physical scenario started developing.
Nucleosynthesis models come from three separate lines of evidence. Firstly - and most important - the composition of our solar system. Secondly, experiments on nuclear reactions under set conditions in the laboratory. And lastly, theoretical constraints on possible sites or environments for these nucleosynthetic reactions to take place.
The knowledge of relative abundance of elements in the Solar System, shown in Figure 1, comes from several sources. Through the study of spectroscopic data of the Sun and other stars, as well as studying rock samples - not only from Earth but also from other extraterrestrial bodies like meteorites and lunar samples - the modern notion for element formation started taking shape.
Figure 1. The abundance of elements in the Solar System, logarithmic scale. Source: Wikimedia commons
A simple analysis of Figure 1 shows fundamental truths about the composition on the Universe:
- Hydrogen and helium are the most abundant elements, composing over 99% of the Universe.
- Li, Be and B have anomaly low abundances, while Fe and Pb are relatively high
- There is an alternation of abundance according to whether elements have an even or odd atomic number.
- There is a general decrease in abundance, as elements become heavier, especially after atomic number 26.
- The largest atomic number is that of Uranium, 92.
During the late 1950s, the B2FH nucleosynthesis theory was presented. Its name is an acronym of its authors Burbidge, Burbidge, Fowler, and Hoyle, whom were awarded the Physics Nobel Prize in 1983 for the theory and other contributions to experimental physics. Within this model, which has been greatly expanded since its publication, there are three categories of nuclide production: cosmological, stellar, and explosive. The first one is associated with the Big Bang and the first events in the formation of the Universe, while the remaining two are associated with stars, their births, and deaths. A fourth category of nuclide formation was added later and is associated with cosmic rays and their interaction with matter - galactic nucleosynthesis. All of these will be discussed in the following sections (the chemical reactions involved are presented in Appendix 1).
Figure 2. Photograph of Margaret and Geoffrey Burbidge, Willy Fowler, and Fred Hoyle in 1971. Source: St. John’s University.
Figure 3. A version of the periodic table indicating the origins of the elements.Source: David Darling Encyclopedia.
Cosmological Nucleosynthesis ¶
Formation of H, He and minor quantities of Li
Also known as primordial nucleosynthesis. These are the processes associated with the formation of matter at the very first stages of the Universe. A schematic sequence of the events following the Big Bang theory are presented in Figure 4.
Figure 4. A simplified version of the events occurring right after the Big Bang. Source: Encyclopædia Britannica.
After the first nanoseconds after the Big Bang (Figure 4.1), a dense “soup” of subatomic particles, quarks and leptons, and their antiparticle equivalents, condensed from energy. The collision between particles and antiparticles, and their annihilation, led to the primacy of matter over antimatter (Figure 4.2). As the Universe expanded and temperatures drop quarks united to form neutrons and protons. During this time the number of protons and neutrons were equivalent, but the decay experienced by neutrons outside the nucleus created a disequilibrium and with time protons became more abundant, leading to the ratio of 7:1 ratio observed at present (4.3).
It was not until 3.5 minutes (4.4) had passed that temperatures dropped down to 1010 K (due to the Universe expansion) that hydrogen nuclei formed. This hydrogen started to combine forming 3He and then 4He. Approximately 24% of the mass present was converted to 4He and less than 0.01% to 2H, 3He, 7Li. The formation of heavier elements was inhibited by the instability of atoms with masses of 5 and 8, due to a barrier existing at 7Li. Once you try to add another proton to 7Li, radioactive decay ends up producing helium again, coming back down to lighter isotopes, Figure 5.
Figure 5. Scheme of nuclear reaction chains for Big Bang nucleosynthesis. Source: Wikimedia commons References - n neutron, p proton, D deuterium, T tritium, ℽ gamma emission.
Around 300.000 years later electrons could merge with the protons, favoring neutral hydrogen, while recent studies have placed the formation of the first galaxies around 1.5 billion years after the Big Bang (4.5) (Tanaka et. al, 2019).
Stellar nucleosynthesis ¶
Formation of elements up until 56Fe Due to the barrier presented in the previous section, elements up to 7 atomic masses can be explained through cosmological nucleosynthesis. All heavier elements need stars to form, and this was the big breakthrough of the B2FH hypothesis. Before proceeding to the explanation of the processes of stellar and explosive nucleosynthesis there some important concepts to keep in mind. The first concept is associated with the binding energy of the nucleus for the different elements, as shown in Figure 6. The binding energy presented by 56Fe is the tipping point in this diagram. Elements to the left of 56Fe - with a smaller atomic mass - prefer to combine through fusion reactions in which energy is emitted, by coming together and forming more stable nuclei. This process is the main fuel of stars, and the formation of elements through this mechanism is known as stellar nucleosynthesis. As binding energies become smaller when moving to the right of the diagram, fusion cannot proceed above 56Fe and other mechanisms begin to take place. The right-hand side region is called the neutron- capture and the process of formation of all these elements happen as part of explosive nucleosynthesis processes.
Figure 6. Nuclear binding energies, shown as a function of atomic mass number. Source: Encyclopædia Britannica
The second concept is associated with our element factories - stars. Stars shine due to exothermic reactions that take place within their cores, which create elements up until the 56Fe threshold.
Stars are classified according to their color and spectral lines, which themselves are correlated with their temperatures. The Hertzsprung–Russell diagram shown in Figure 7, plots the relationship between luminosity and surface temperature. About 90 percent of the stars in the Universe, including the Sun, are main-sequence stars, meaning they fuse hydrogen atoms to form helium atoms at their cores. These stars can range from about a tenth of the mass of the Sun to up to 200 times as massive. The mass is in direct relationship with the star’s life span and their fate once all the hydrogen fuel is consumed. The remaining 10 percent that fall outside the main sequence are mostly giants and supergiants whose life will end in one of the most spectacular natural events, a supernova explosion.
Figure 7. The Hertzsprung–Russell diagram of the relationship between luminosity and surface temperature. Source: Encyclopædia Britannica.
Back to element formation. Approximately 0.5 - 1.0 billion years after the Big Bang inhomogeneities in the gas cloud formed during the first stages of the Universe, gave rise to the first protogalaxies and that in turn eventually gave rise to the first stars. The deuterium, 2H, burning process starts once temperatures reach 106 K, taking place while the stars are still accreting mass and temporarily stabilizing it against further collapse, a process that can continue for several million years.
As deuterium is exhausted, the core reaches a high enough temperature to ignite the PP process, also known as the hydrogen-burning stage, which continues through the main sequence life of the star. Although there are several reactions by which this can take place, described in Appendix 1, the net result is the production of 4He and the consumption of H and to a lesser extent Li.
The second or older generation of stars can also produced He from another set of processes named CNO (carbon-nitrogen-oxygen) cycle and theoretical models show that this cycle is the dominant source of energy generation for stars with 12% more mass than the Sun. The CNO Cycle, as its name implies, needs C to be ignited. Carbon works as a catalyst being neither produced nor consumed. What results from the CNO cycle is the production of a neutrino, some energy, and a 4He nucleus (similar to the PP process), by consuming four hydrogen nuclei (protons) and two positrons.
Both processes, PP and CNO, compete in main-sequence stars and their dominance is associated with the star’s temperature. In a sun-size star, the PP reactions account for about 98–99% of the energy production, with the CNO cycle producing the remainder (White, 2013).
Figure 8. Illustration of the CNO cycle, which operates in the larger second-generation stars. Source: Nobel Prize Website.
Once hydrogen is consumed at the stellar core, the star’s temperature to rise. The increase in temperature and density results in the expansion of the star’s exterior and the helium recently produced starts burning as the new source of fuel. New chemical reactions are involved, called triple-alpha (Figure 9), marking the beginning of the star’s red giant phase, Figure 10. The time spent by the star in this chapter is in direct relationship with its mass, it can last for as long as a hundred million years or as little as a few hundred thousand years. The main products are 12C and 16O (through the fusion of 4He with 12C), but minor quantities of 20Ne, 24Mg, and 22Ne can be created too.
Our Sun will become a red giant in about 5.4 billion years. Models have shown that the expanding Sun will grow large enough to encompass the orbit of Mercury, Venus, and maybe even Earth.
Figure 9. Triple Alpha process, as three He nuclei must collide nearly simultaneously. Source: Wikimedia Commons
The next stage is critical for the star’s future. Low-mass stars such as the Sun are unable to create the conditions necessary for heavier fusions to occur ending their days as O-Ne-Mg white dwarf, Figure 10, as their outer envelopes are ejected after the carbon-burning phase.
Figure 10. Type of stars and Star Cycle
High-mass stars, continue their fusion reactions by burning heavier and heavier elements entering the blue supergiant phase, Figure 10. The cycle continues by burning Carbon, Neon, Oxygen and eventually Silicon, Figure 11a. During these stages, heavier elements are synthesized such as Si, Fe and minor quantities of Mg, Na, Al, P, S, K, among others (see reactions in Appendix 1). All these fusion reactions release huge amounts of energy, as they are all exothermic.
Depending on the size of the star, and therefore its pressure and temperature built at its center, simultaneous reactions can occur creating an onion-like structure as shown in Figure 11b. As explained above, the end of the line is the production of 56Fe. Once the iron group starts building at the star’s core, the star is running out of gas and the whole system is on the verge of collapse. (a)(b)
Figure 11. (a) Evolutionary path of the core of star of 25 solar masses (after Bethe and Brown, 1985). Note that the period spent in each phase depends on the mass of the star: massive stars evolve more rapidly. (b) Schematic diagram of the stellar structure at the onset of the supernova stage. Nuclear burning processes are illustrated for each stage. Source: White 2013.
Explosive nucleosynthesis ¶
Or how to build elements heavier than iron
To create elements above 56Fe we need a different set of processes that do not involve exothermic or fusion reactions. All these elements are less stable and have lower binding energy than 56Fe as explained above.
Stars have free neutrons flying around with very high amounts of energy, and especially high neutron fluxes occur within supernovas. A supernova is a powerful and luminous stellar explosion and is the final stage in the massive-star cycle. The original body either collapses to a neutron star or black hole, or it is destroyed. Supernovas have extreme luminosities that can be comparable to that of an entire galaxy, before fading over several weeks or months. One of the first written records of a supernova comes from Chinese astronomers in 185 AD. Scriptures suggest it took about eight months to fade from the sky.
Presented below is a small section of the chart of the nuclides, from silver (Ag) all the way to antimony (Sb), Figure 12. When adding a neutron to 109Ag, its mass increases by one creating 110Ag, but as the diagram shows 110Ag is unstable and it radioactively decays (by beta decay, β-) to 110Cd. 110Cd continues being bombarded by neutrons increasing its atomic mass until it reaches 115Cd, which once again is unstable and therefore radioactively decays to 115In. This process, called s (slow), simply continues by adding more and more neutrons to existing nuclei until unstable isotopes are reached and decay rapidly to a new heavier element.
Figure 12. The s-process acting in the range from Ag to Sb. Notice gaps between isotopes created by the s- process. Source: Wikimedia Commons
The slow delivery of neutrons to existing nuclei can continue until uranium, but it is unable to account for the existence of proton-rich (i.e. 112Sn) or neutron-rich (i.e. 122Sn) isotopes of elements. To get past the barrier where unstable nuclei naturally decay to another element, these nuclei have to be rapidly bombarded with neutrons or hit with protons in stellar environments. Both processes, name R (rapid) and P (proton) respectively, are only possible in supernova scenarios, accounting for the lower abundance of isotopes created by these processes alone, Figure 13. Taking Indium as an example, the ratio between 113In and 113In is 1:19, this is due to the fact that while the former is only generated by the P-process, the latter is created by both S and R-processes.
Figure 13 presents a summary of explosive nucleosynthesis and the creation of elements associated with S, R and P processes.
Figure 13. Diagram of the R-process path on a Z versus N diagram. The diagonal lines represent the R-process; while solid, horizontal lines show the S-process path of isotope formation.
Galactic nucleosynthesis ¶
Lithium, Beryllium, and Boron are not produced by stars or any of their associated processes, but minor quantities of 7Li have been produced through primordial nucleosynthesis processes. The formation of these elements is associated with cosmic ray spallation. Cosmic rays consist mainly of protons that move through space at nearly the speed of light and that interact with interstellar gas, producing gamma rays and these low-abundance elements. The energy produced can be detected by telescopes, as shown in Figure 14, and models suggest cosmic rays are mainly produced in supernova explosions, and to a lesser extent in pulsars and black holes. The interactions involved between the cosmic rays and their surroundings are not fully understood and are currently being studied.
Figure 14. Fermi’s Large Area Telescope resolved GeV gamma rays from supernova remnants of different ages and in different environments. W51C, W44 and IC 443 are middle-aged remnants between 4,000 and 30,000 years old. Cassiopeia A, which is only 330 years old, appears as a point source. Source: NASA
Final Remarks ¶
The B2FH model explains through three main processes (Big Bang, stellar fusion and supernova explosion) the formation of all elements in the Universe. No two elements are created equal and each process plays an important contribution in the composition of our Universe. While writing this paper the news that Betelgeuse, once one of the top 20 brightest stars in the Northern Hemisphere, has been dimming since October came out. This behavior is leading astronomers believe its explosion as a supernova is approaching. Betelgeuse is a star in the Orion constellation about 700 light-years away and is approximately 12 times more massive than the Sun. If Betelgeuse goes supernova, it will be the brightest star in the night-sky for many months and it is possible it will be visible during the day. It presents an excellent time to test hypothesis and gather more information about this element-forming spectacle.
The human body is composed of around 37.2 trillion cells. Each cell is made up of different kinds of molecules, that themselves contain several different types of elements. These elements built into DNA, RNA, enzymes, proteins, hemoglobin and other molecules that are constantly executing an array of functions within your body. Without stars to provide the building blocks, none of our existence would be possible.
We are stardust, literary. ¶
Figure 15. Betelgeuse on the shoulder of Orion. Source: Telegraph Newspaper UK
Thank you! ¶
Figures ¶
- Figure 1. Abundance of elements in the Solar System, logarithmic scale. Source: Wikimedia commons.
- Figure 2. Photograph of Margaret and Geoffrey Burbidge, Willy Fowler, and Fred Hoyle in 1971. Source: St. John’s University Website.
- Figure 3. A version of the periodic table indicating the origins of the elements.Source: David Darling Encyclopedia
- Figure 4. A simplified version of the events occurring right after the Big Bang. Source: Encyclopædia Britannica.
- Figure 5. Scheme of nuclear reaction chains for Big Bang nucleosynthesis. Source: Wikimedia commons
- Figure 6. Nuclear binding energies, shown as a function of atomic mass number. Source: Encyclopædia Britannica.
- Figure 7. The Hertzsprung–Russell diagram of the relationship between luminosity and surface temperature. Source: Encyclopædia Britannica.
- Figure 8. Illustration of the CNO cycle, which operates in larger second and later generation stars. Source: Nobel Prize Website.
- Figure 9. Triple Alpha process, as three He nuclei must collide nearly simultaneously. Source: Wikimedia Commons
- Figure 10. Type of stars and Star Cycle. Sources: NASA
- Figure 11. (a) Evolutionary path of the core of star of 25 solar masses (after Bethe and Brown, 1985). Note that the period spent in each phase depends on the mass of the star: massive stars evolve more rapidly. (b) Schematic diagram of stellar structure at the onset of the supernova stage. Nuclear burning processes are illustrated for each stage. Source: White, 2013.
- Figure 12. The s-process acting in the range from Ag to Sb. Notice gaps between isotopes created by the s- process. Source: Wikimedia Commons
- Figure 13. Diagram of the r process path on a Z versus N diagram. Dashed region is r-process path; solid line through stable isotopes shows the s-process path. Source: White, 2013.
- Figure 14. Fermi’s Large Area Telescope resolved GeV gamma rays from supernova remnants of different ages and in different environments. W51C, W44 and IC 443 are middle-aged remnants between 4,000 and 30,000 years old. Cassiopeia A, which is only 330 years old, appears as a point source. Source: NASA
- Figure 15. Betelgeuse. Source: Telegraph
Bibliography ¶
-
Allègre, Claude J. Isotope geology. Cambridge University Press, 2008.
-
Clayton, Donald. Handbook of isotopes in the cosmos: Hydrogen to gallium. Vol. 1. Cambridge University Press, 2003.
-
Coursera. Origins - Formation of the Universe, Solar System, Earth and Life. Coursera MOOC University of Copenhagen, 2019
-
Dickin, Alan P. Radiogenic isotope geology. Cambridge university press, 2018.
-
Faure, Gunter, and Teresa Mensing. Introduction to planetary science. Springer, 2007.
-
Ostera, Hector. Isotope Geology Lectures. Summer Course UBA Exactas, 2019
-
Pagel, Bernard EJ. Nucleosynthesis and chemical evolution of galaxies. Cambridge University Press, 2009.
-
Sharp, Zachary. Principles of stable isotope geochemistry. Person, 2017.
-
Tanaka, Masayuki, et al. “Stellar Velocity Dispersion of a Massive Quenching Galaxy at z= 4.01.” The Astrophysical Journal Letters 885.2 (2019): L34.
-
White, William M. Isotope geochemistry. John Wiley & Sons, 2015.
-
NASA website - Multiple Entries (https://www.nasa.gov)
-
Encyclopedia Britannica Website - Cosmology (https://www.britannica.com/science/cosmology-astronomy)


| Location |
| Located in the village of Sipán, about 20 miles (31 km) from Chiclayo. The drive takes approximately 45 minutes. |
| Phone Number |
| (51)(074)-80-0048 |
| Address |
| Sipán Countryside s / n, Zaña-Lambayeque – Peru |
|<EMAIL_ADDRESS>|
| Hours |
| Monday-Friday, 9 AM- 5 PM |
| Price |
Adults and Foreigners: 8 Soles Students and Retirees: 3 Soles Children: 1 Sol |
| English/Spanish Tours |
| Tours are free, though they are only offered in Spanish. Be sure to tip your guide at the end for a job well done. |
| Time It Takes |
| Approximately 3 hours (plus travel time) |
| Bathrooms |
| Yes |
| Gift Shop |
| There are a few artisan shops right outside of the museum that sell replicas, jewelry, purses, and other artisan crafts. There is also a small restaurant that sells traditional food. |
| Activities |
| At the end of the museum, there is an area where you can take pictures with some props. They also have ceramic replicas that you can touch and hold. |
Interesting Fact: Archaeologists are currently preparing to begin excavation on another pyramid close to the museum. Who knows what kind of artifacts and history will be unearthed here!


Protocols
Polymerase Chain Reaction (PCR)
Components and amounts for PCR:
| Component |
50 μL Reaction |
| Nuclease-free water |
add until total volume is 50 μL |
| 5x Physion HF Buffer |
10 μL |
| 10 mM dNTPS |
1 μL |
| 10 μM Forward Primer |
2.5 μL |
| 10 μM Reverse Primer |
2.5 μL |
| Template DNA |
Variable |
| DMSO (optional) |
(1.5 μL) |
| Phusion DNA Polymerase |
0.5 μL |
| Step |
Temperature |
Time |
| Initial Denaturation |
98 °C |
30 seconds |
| 25-35 Cycles |
98 °C |
5-10 seconds |
|
45-72 °C |
30 seconds |
|
72 °C |
15-30 seconds |
| Final Extension |
72 °C |
5-10 minutes |
| Hold |
4 °C |
∞ |
Overlap Extension PCR
Components and amounts for Overlap Extension PCR:
| Component |
Master Mix A |
Master Mix B |
| Sterile milli-Q |
add to 25 μL |
add to 13.5 μL |
| 5X Phusion Buffer |
5.0 μL |
5.0 μL |
| 10 mM dNTPS |
0.5 μL |
0.5 μL |
| 10 μM External Forward Primer |
0 μL |
2.5 μL |
| 10 μM External Reverse Primer |
0 μL |
2.5 μL |
| DMSO |
0.75 μL |
0.75 μL |
| Template DNA |
~1 μL each |
0 μL |
| Phusion DNA Polymerase |
0.25 μL |
0.25 μL |
| Total |
25.0 μL |
25.0 μL |
| Step |
Temperature |
Time |
| 1 |
98 °C |
30 seconds |
| 2 |
98 °C |
10 seconds |
| 3 |
60 °C |
30 seconds |
| 4 |
72 °C |
20 seconds |
| 5 |
Repeat Steps 2-4 |
4 Cycles |
| 6 |
10 °C |
10 minutes* |
| 7 |
98 °C |
10 seconds |
| 8 |
60 °C |
10 seconds |
| 9 |
72 °C |
10 seconds |
| 10 |
Repeat Steps 7-9 |
31 Cycles |
| 11 |
72 °C |
5-10 minutes |
| 12 |
4 °C |
∞ |
*Add 25 μL Master Mix B to the appropriate tubes
Gibson Assembly
- Prepare 5x ISO buffer
Components and amounts for ISO Buffer in Gibson Assembly
| Component |
6mL Reaction |
| 1 M Tris-HCl pH 7.5 |
3 mL |
| 2 M MgCl₂ |
150 μL |
| 100 mM dGTP |
60 μL |
| 100 mM dATP |
60 μL |
| 100 mM dTTP |
60 μL |
| 100 mM dCTP |
60 μL |
| 1 M DTT |
300 μL |
| PEG-8000 |
1.5 g |
| 100 mM NAD |
300 μL |
| Water |
Add to 6 mL |
- Aliquot 100 μL and store at -20 °C
- Prepare the Gibson assembly master mixture.
Components and amounts for master mixture in Gibson assembly
| Component |
1.2 mL Reaction |
| 5x ISO Buffer |
320 μL |
| 10 U/μL T5 Exo |
0.64 μL |
| 2 U/μL Phusion Pol |
20 μL |
| 40 U/μL Taq Ligase |
160 μL |
| Water |
add to 1.2 mL |
- Aliquot 15 μL and store at -80 °C
- Thaw a 15 µL Gibson assembly mixture aliquot and keep on ice until it is ready to be used.
- Use 7.5 µL of the Gibson assembly mixture. This is enough for one reaction.
- Add 2.5 µL of DNA to be assembled to the master mixture. The DNA should be in equimolar amounts. Use ~50 ng of each ~6 kb DNA fragment. For larger DNA segments, increasingly proportionate amounts of DNA should be added (e.g. 125 ng of each 15 kb DNA segment).
- Incubate at 50 °C for 1 hour. Hold at 4 °C.
- Either immediately freeze at -20 °C or transform 1-2 µL of the assembly reaction into 30-50 µL electro-competent E.coli.
Gel Electrophoresis
Agarose gel formulations and DNA separations
| Agarose Concentration |
Resolution Range |
Agarose for 50 mL Gel |
| 0.8% |
500 bp - 12 kb |
0.40 g |
| 1.0% |
400 bp - 10 kb |
0.50 g |
| 1.5% |
200 bp - 4 kb |
0.75 g |
| 2.0% |
100 bp - 2 kb |
1.00 g |
| 4.0% |
10 bp - 400 bp |
2.00 g |
Pouring a Standard 1% Gel:
- Measure 1 g of agarose.
- Pour agarose powder into microwavable flask along with 1x TAE. Use ~65 mL for purification and ~35 mL for diagnosis.
- Microwave for 1-3 minutes until the agarose is completely dissolved with sufficient bubbling.
- Let agarose solution cool down for 5 minutes.
- Add SYBR Safe stain to the molten agar and swirl to mix. The stain binds to the DNA and allows you to view the DNA bands under an ultraviolet blue light.
- Pour the agarose into the gel tray with the well comb in place. Use the thick side of the comb if you are purifying from the gel.
- Place newly poured gel at 4 °C for 10-15 minutes or let it sit at room temperature for 20-30 minutes until it has completely solidified.
Loading Samples and Running an Agarose Gel:
- Add loading buffer (dyed) to each of the digest samples.
- Once solidified, place the agarose gel into the gel box (electrophoresis unit).
- Fill gel box with 1x TAE (or TBE) until the gel is covered.
- Carefully load a molecular weight ladder into the first lane of the gel and your samples in the additional wells of the gel.
- Run the gel at 80-150 V until the dye line is approximately 75-80% of the way down the gel.
- Turn off power, disconnect the electrodes from the power source, and then carefully remove the gel from the gel box.
- Use any device that has blue light to visualize your DNA fragments.
Chemical Transformation
- Take chemically competent cells (CC) out of -80 °C and thaw on ice.
- Take agar plates (containing the appropriate antibiotic) out of 4 °C to warm up to room temperature or place in 37 °C incubator.
- Mix 1 to 5 µL of DNA (usually 10 pg to 100 ng) into 20-50 µL of competent cells in a microcentrifuge or falcon tube. Gently mix by flicking the bottom of the tube with your finger a few times.
- Place the competent cell/ DNA mixture on ice for 20-30 minutes.
- Heat shock each transformation tube by placing the bottom 1/2 to 2/3 of the tube into a 42 °C water bath for 30-60 seconds (45 seconds is usually ideal, but this varies depending on the competent cells you are using).
- Put the tubes back on ice for 2 minutes.
- Add 200-500 µL LB or SOC media (without antibiotic) and grow in 37 °C shaking incubator for 45 minutes.
- Plate some or all of the transformation onto a 10 cm LB agar plate containing the appropriate antibiotic.
- Incubate plates at 37 °C overnight.
Miniprep
- Grow 1-5 mL plasmid-containing bacterial cells in LB medium with appropriate antibiotics overnight.
- Pellet the cells by centrifuging for 1-2 minutes. Decant the supernatant and remove all medium residue by pipet.
- Completely resuspend the cell pellet in 200 µL of MX1 Buffer by vortexing or scratching the bottom of the tube against a tube rack.
- Add 250 µL of MX1 Buffer by vortexing or scratching the bottom of the tube against the tube rack.
- Add 350 µL MX3 Buffer to neutralize the lysate and gently mix the solution immediately.
- Centrifuge for 5-10 minutes.
- Transfer the supernatant to a GenCatch plus Column/Collection Tube.
- Centrifuge for 30-60 seconds at 5000 rpm. Discard flow through.
- Wash the column once with 500 µL WN buffer by centrifuging for 30-60 seconds at 7000x g (9000 rpm) Discard flow through.
- Wash the column once with 700 µL WS Buffer by centrifuging for 30 seconds at 7000x g (9000 rpm). Discard flow through.
- Centrifuge the column at 10,000x g (13000 rpm) for another 2 minutes to remove residual ethanol.
- Place column into a new 1.5 mL centrifuge tube. Add 50 µL of elution buffer onto the center of the membrane.
- Stand the column for 2 minutes at room temperature and centrifuge for 30 seconds at 13000 rpm and elute DNA.
- Store plasmid DNA at 4 °C or -20 °C.
Smash and Grab (Yeast Genomic Prep)
Components and amounts for Smash and Grab buffer
| Component |
100 mL Reaction |
| 1% S DS |
10 mL 10% SDS |
| 2% Triton X-100 |
2 mL 100% Triton X-100 |
| 100 mM NaCl |
2 mL 5 M NaCl |
| 10 mM Tris-HCl pH 8.0 |
1 mL |
| 1 mM ED TA |
200 μL 0.5 M ED TA |
- Grow 3 mL yeast cultures in selective media overnight in the shaker at 30 °C.
- Cool 70% EtOH in a -20 °C freezer.
- Collect cells by centrifugation in a 1.5 mL tube for 1 minute at 2500 rpm, then remove supernatant and repeat until the entire culture is pelleted.
- Add 1 mL sterile H2O to each pellet. Vortex and collect cells by centrifugation (same as above).
- Remove supernatant with a vacuum aspirator and add 200 µL of Smash and Grab Buffer, 0.8 mm glass beads (enough so that the surface of glass beads reaches ~0.2 mL mark on the tube, NOT the liquid) and 200 µL of phenol/chloroform/iso-amyl alcohol (or isopropanol) (kept at 4 °C to each cell pellet (The top layer in the bottle is alcohol, make sure to take phenol/chloroform from deep under the surface). Add the phenol/chloroform inside the hood and be sure the caps are closed tightly.
- Vortex for 3 minutes (using the multi-tube adaptor on the vortex genie in the hood, at maximum speed)
- Centrifuge at room temperature for 7 minutes at 14,000 rpm.
- Remove the upper aqueous layer (about 180 µL) and transfer to a fresh microfuge tube. Discard tube with phenol/Chloroform in the phenol/chloroform waste collection jug.
- Add 126 µL of room temperature isopropanol (0.7 volume; or 70% of total volume of aqueous layer collected) and invert 4-6 times to mix.
- Leave at room temperature for 5 minutes.
- Centrifuge at room temperature for 10 minutes at 14,000 rpm.
- Remove supernatant with the vacuum and wash pellets of DNA with 200 µL of COLD 70% ethanol. Do not try to resuspend the pellet. Vortex briefly to wash sides of tube with the ethanol.
- Remove as much ethanol as you can with the vacuum aspirator.
- Allow pellets to air dry 10-20 minutes at room temperature (the pellets will turn transparent with a greenish tint).
- Add 50 to 100 µL TE pH 8 (depending on pellet size).
- Add 0.25 µL RNase A (20mg/ml stock found at 4 °C)
- Pipet gently to mix (pellet may need to sit in TE for a few minutes to easily resuspend).
Yeast Transformation
Components:
- 50% PEG 3350 (240 µL per transformation)
- 1.0 M LiOAc ( 36 µL per transformation)
- Salmon Sperm DNA (boiled) ( 25 µL per transformation)
- Yeast Competent Cell aliquot
- Appropriate LB agar plates
Procedure:
- Spin down all the Yeast Competent Aliquots on table top centrifuge for 20 seconds
- Add 240 µL of 50% PEG into each competent aliquot tube.
- Add 36 µL of 1M LiOAc to each tube
- Add 25 µL of Salmon Sperm DNA (boiled) to each tube
- Pipette a volume equivalent to 1ug of plasmid into aliquot tube
- Vortex each tube on highest settings until the cells are resuspended.
- Place all aliquots on 42ºC heat block for 15 minutes.
- Retrieve all tubes from 42ºC heat block.
- Spin the tube down for 20 seconds on a small tabletop centrifuge.
- Remove all the supernatant carefully with a 1000 µL pipettor (~400 µL total)
- Add 200 µL of MG water to the transformed cells
- Flip the plate(s) and add 4-5 glass beads to it, add 200 µL of mixtures on each plate.
- Shake the plates in all directions to evenly spread the culture over its surface till dry. Discard the beads in a used beads container. Throw away the 1.5 mL tubes.
- Incubate all the plates with agar side up at 37ºC
Plate Reader Fluorescence Array
Day 1:
- Select a colony and inoculate 5mL LB culture with transgenic yeast. Incubate overnight at 220 rpm and 30C.
Day 2:
- Measure OD of liquid culture and dilute to 10 mL LB final volume with OD 0.05.
- Grow for 2 hours, until OD 0.4.
- Place 8 replicates of 100 μL of yeast culture in four rows (32 wells total).
- Distribute log scale concentrations of our zinc inducer across the 8 wells, ranging from 0 to 100 μM.
- Take time based assays to measure fluorescence and absorbance over 10 mins, 20 mins, and 30 mins after inducing. These are the settings we will use on the plate reader:


You can reach this dialog via the menu entry Encrypt/Decrypt \ Symmetric (classic) \ Permutation/Transposition.
This dialog is used to enter the permutations for Encryption by permutation.
In each of the two input fields a keyword or a numerical permutation can be entered. The method for working out the relevant permutation is described under Encryption by permutation.
An entry in the first field is mandatory. An entry in the second input field is optional. If a second entry is made not one but two permutations are performed.
For each permutation it is possible to specify whether the data should be read in/out by row or column. Additionally it is possible to set the permutation to be a permutation of columns or a permutation of rows.
The maximum permutation length is 100. It can be entered either as 100 capital letters or as a number sequence that is a permutation of 1, 2, ..., 100.
By activating the checkbox within the group Options you can determine that before encryption or decryption the input permutations are inverted. Furthermore you can specify how the input data is processed: While all characters are processed in binary mode, text mode works differently: Depending on the current settings in the text options dialog (see button Text Options), characters which are not part of the current alphabet are either retained without change (and their positions are respected) or removed before the actual transposition.
You can get information on the Used permutation and its inverse by clicking the Encrypt or Decrypt button.
Remark 1: Historically the permutation exchanging columns twice was called double column transposition.
Remark 2: The first historically know variant of this cipher was the Scytale of Sparta (500 B.C.).
Remark 3: For single step permutation a known-plaintext analysis is available.


sklearn.neighbors
.NeighborhoodComponentsAnalysis¶
-
class
sklearn.neighbors.
NeighborhoodComponentsAnalysis
(n_components=None, init=’auto’, warm_start=False, max_iter=50, tol=1e-05, callback=None, verbose=0, random_state=None)[source]¶ Neighborhood Components Analysis
Neighborhood Component Analysis (NCA) is a machine learning algorithm for metric learning. It learns a linear transformation in a supervised fashion to improve the classification accuracy of a stochastic nearest neighbors rule in the transformed space.
Read more in the User Guide.
Parameters: - n_components : int, optional (default=None)
Preferred dimensionality of the projected space. If None it will be set to
n_features
.- init : string or numpy array, optional (default=’auto’)
Initialization of the linear transformation. Possible options are ‘auto’, ‘pca’, ‘lda’, ‘identity’, ‘random’, and a numpy array of shape (n_features_a, n_features_b).
- ‘auto’
Depending on
n_components
, the most reasonable initialization will be chosen. Ifn_components <= n_classes
we use ‘lda’, as it uses labels information. If not, butn_components < min(n_features, n_samples)
, we use ‘pca’, as it projects data in meaningful directions (those of higher variance). Otherwise, we just use ‘identity’.- ‘pca’
n_components
principal components of the inputs passed tofit
will be used to initialize the transformation. (Seedecomposition.PCA
)- ‘lda’
min(n_components, n_classes)
most discriminative components of the inputs passed tofit
will be used to initialize the transformation. (Ifn_components > n_classes
, the rest of the components will be zero.) (Seediscriminant_analysis.LinearDiscriminantAnalysis
)- ‘identity’
If
n_components
is strictly smaller than the dimensionality of the inputs passed tofit
, the identity matrix will be truncated to the firstn_components
rows.- ‘random’
The initial transformation will be a random array of shape
(n_components, n_features)
. Each value is sampled from the standard normal distribution.- numpy array
n_features_b must match the dimensionality of the inputs passed to
fit
and n_features_a must be less than or equal to that. Ifn_components
is not None, n_features_a must match it.
- warm_start : bool, optional, (default=False)
If True and
fit
has been called before, the solution of the previous call tofit
is used as the initial linear transformation (n_components
andinit
will be ignored).- max_iter : int, optional (default=50)
Maximum number of iterations in the optimization.
- tol : float, optional (default=1e-5)
Convergence tolerance for the optimization.
- callback : callable, optional (default=None)
If not None, this function is called after every iteration of the optimizer, taking as arguments the current solution (flattened transformation matrix) and the number of iterations. This might be useful in case one wants to examine or store the transformation found after each iteration.
- verbose : int, optional (default=0)
If 0, no progress messages will be printed. If 1, progress messages will be printed to stdout. If > 1, progress messages will be printed and the
disp
parameter ofscipy.optimize.minimize
will be set toverbose - 2
.- random_state : int or numpy.RandomState or None, optional (default=None)
A pseudo random number generator object or a seed for it if int. If
init='random'
,random_state
is used to initialize the random transformation. Ifinit='pca'
,random_state
is passed as an argument to PCA when initializing the transformation.
Attributes: - components_ : array, shape (n_components, n_features)
The linear transformation learned during fitting.
- n_iter_ : int
Counts the number of iterations performed by the optimizer.
References
[Rf9b6baee8229-1] J. Goldberger, G. Hinton, S. Roweis, R. Salakhutdinov. “Neighbourhood Components Analysis”. Advances in Neural Information Processing Systems. 17, 513-520, 2005. http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf [Rf9b6baee8229-2] Wikipedia entry on Neighborhood Components Analysis https://en.wikipedia.org/wiki/Neighbourhood_components_analysis Examples
>>> from sklearn.neighbors.nca import NeighborhoodComponentsAnalysis >>> from sklearn.neighbors import KNeighborsClassifier >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import train_test_split >>> X, y = load_iris(return_X_y=True) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... stratify=y, test_size=0.7, random_state=42) >>> nca = NeighborhoodComponentsAnalysis(random_state=42) >>> nca.fit(X_train, y_train) NeighborhoodComponentsAnalysis(...) >>> knn = KNeighborsClassifier(n_neighbors=3) >>> knn.fit(X_train, y_train) KNeighborsClassifier(...) >>> print(knn.score(X_test, y_test)) 0.933333... >>> knn.fit(nca.transform(X_train), y_train) KNeighborsClassifier(...) >>> print(knn.score(nca.transform(X_test), y_test)) 0.961904...
Methods
fit
(self, X, y)Fit the model according to the given training data. fit_transform
(self, X[, y])Fit to data, then transform it. get_params
(self[, deep])Get parameters for this estimator. set_params
(self, \*\*params)Set the parameters of this estimator. transform
(self, X)Applies the learned transformation to the given data. -
__init__
(self, n_components=None, init=’auto’, warm_start=False, max_iter=50, tol=1e-05, callback=None, verbose=0, random_state=None)[source]¶
-
fit
(self, X, y)[source]¶ Fit the model according to the given training data.
Parameters: - X : array-like, shape (n_samples, n_features)
The training samples.
- y : array-like, shape (n_samples,)
The corresponding training labels.
Returns: - self : object
returns a trained NeighborhoodComponentsAnalysis model.
-
fit_transform
(self, X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: - X : numpy array of shape [n_samples, n_features]
Training set.
- y : numpy array of shape [n_samples]
Target values.
Returns: - X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
-
get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
Parameters: - deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: - params : mapping of string to any
Parameter names mapped to their values.
-
set_params
(self, **params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: - self
-
transform
(self, X)[source]¶ Applies the learned transformation to the given data.
Parameters: - X : array-like, shape (n_samples, n_features)
Data samples.
Returns: - X_embedded: array, shape (n_samples, n_components)
The data samples transformed.
Raises: - NotFittedError
If
fit
has not been called before.


To count the number of times an activity of a specific type has been run over a period of time, a work action set needs to be created. In this example, because we are interested in the activities that are run on the entire system, the work action set will be created at the database level and is associated with the all_class_types work class set created by the first SQL statement (for more detail about work class sets, please refer to the 'Differentiation Activities by Activity Type' tutorial of this workshop). If you've already run other tutorials in this workshop, this work class set may already have been created.
This work class set contains work classes for all types of recognized activities. If we were only interested in the activities being run in a specific service class, we would create a work action set at the service class level. For this example, we are also interested in the information for all types of activities so that the work action set contains a COUNT ACTIVITY work action for each work class in the all_class_types work class set.
Additional information: Each time an activity corresponding to a work class has one or more work actions applied to it, a counter for the work class is incremented by one. The COUNT ACTIVITY work action provides an efficient way to ensure that the counter is updated. If no work action is applied to an activity corresponding to a work class, the work class is treated as though it does not exist, no activity is associated with it, and the counter will not be incremented. If you do not want to perform any other action on an activity other than counting the number of activities of that type that have been run, the COUNT ACTIVITY work action is the best approach.


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We examine metrics!
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- enable synergistic technologies
- target out-of-the-box experiences
- implement world-class schemas
- integrate cutting-edge action-items


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We are Open for Everyone
Udyam's was opened with the initiative of teaching interested candidates everything that they needed to learn how to play carrom. We take players of all levels, from beginners to advanced. Special training is given to those who need it. We teach especially for playing in tournaments and such.
What we teach for
We teach mainly for playing in tournaments. Many of our students have won various championships in various districts. Care is given for each and every individual. We also provide the necessary tournament environment for the overall development of our students.
What We Teach
Technical shots, rotation of coins and taking good judgement under pressure.


Mourinho names Alderweireld and Winks in first Tottenham team, Eriksen on the bench
by OmnisportJose Mourinho recalled Toby Alderweireld and Harry Winks for his first Tottenham game against West Ham at the London Stadium, but there was no place in the starting XI for Christian Eriksen.
The former Manchester United manager made three changes to the Spurs side that drew against Sheffield United before the international break, the final match of Mauricio Pochettino's five-year reign.
Alderweireld, who is out of contract at the end of the season, returned to the heart of defence, meaning Eric Dier moved into midfield.
Mourinho's shock appointment at Tottenham led to questions over how the lightly built, ball-playing Winks would be used but the England international got an early chance to impress, as did Lucas Moura.
Pochettino used Lucas sparingly before being dismissed on Tuesday, the Brazilian having started only four Premier League fixtures this season prior to Saturday's clash.
Moussa Sissoko and Giovani Lo Celso headed back to the bench, joining Eriksen among the substitutes, while Tanguy Ndombele was not involved in the squad due to a groin issue.
West Ham, meanwhile, recalled Angelo Ogbonna and Andriy Yarmolenko as they sought to snap a seven-match winless streak in all competitions.


if standardize:
signals = _standardize(signals, normalize=True, detrend=False)
signals *= np.sqrt(signals.shape[0]) // for unit variance
return signals
if t_r is None:
raise ValueError("Repetition time (t_r) must be specified for "
"filtering. You specified None.")
if detrend:
mean_signals = signals.mean(axis=0)
signals = _standardize(signals, standardize=False, detrend=detrend)
if low_pass is not None or high_pass is not None:
if t_r is None:


Response to a prompt to express a personal narrative about becoming creatively influenced by the grotesque. Body gum bichromate printed, cover screen-printed with yogurt and fruit.


{{currenttime}}
The Hide Removed From Admin View just hides the removed sessions from the admin view. This is to present a cleaner list for better visibility into afternoon session rankings
Clicking the removed button will hide the session from the voting page. It is for those sessions that are presented in the morning but aslo available in the afternoon. Clicking remove will keep them from showing up in the vote page during the afternoon session
| Rank | Title | Speaker | Votes | Remove |
|---|---|---|---|---|
| {{$index + 1}} | {{s.title}} | {{s.speaker.firstName}} {{s.speaker.lastName}} | {{s.inSync.total_votes}} |


POS tags
| Open class words | Closed class words | Other |
|---|---|---|
| ADJ | ADP | PUNCT |
| ADV | AUX | SYM |
| INTJ | CCONJ | X |
| NOUN | DET | |
| PROPN | NUM | |
| VERB | PART | |
| PRON | ||
| SCONJ |
ADJ
: adjective
Definition
Adjectives are words that typically modify nouns and specify their properties or attributes. They may also function as predicates, as in
To auto je zelené. “The car is green.”
The ADJ
tag is intended for ordinary adjectives only. See DET
for determiners and NUM for cardinal numerals.
In accord with the UD approach, adjectival ordinal numerals (první, sedmý, stopadesátý) are tagged as adjectives, although the traditional grammar classifies them as numerals. They behave like adjectives both morphologically and syntactically, with the exception that they cannot be compared and negated.
Most Czech adjectives inflect for cs-feat/Gender (velký – velká – velké) “big”, cs-feat/Number (velký – velcí), cs-feat/Case (velký – velkého – velkému – velkém – velkým), cs-feat/Degree (velký – větší – největší), and cs-feat/Polarity (velký – nevelký).
Examples
- velký “big”
- starý “old”
- zelený “green”
- nejneobhospodařovávatelnějšímu “to the most uncultivatable” (the longest Czech word)
- otcův, matčin “father’s, mother’s” (possessive adjectives)
- první, druhý, třetí “first, second, third”
- udělaný “done” (passive participial adjective, see below)
- scvrklý “shrivelled” (past participial adjective)
- dělající “doing” (present participial adjective, derived from present transgressive)
- udělavší “having done” (past participial adjective, derived from past transgressive)
Border cases
Passive participles lie on the border between verbs and adjectives.
Since release 2.0, both short and long forms are tagged ADJ
, although
they may have verbal features in addition to the adjectival ones.
For example:
- Short: nesen, nesena, neseno, neseni, neseny “carried”
- Long: nesený, nesená, nesené, nesení, nesené “carried”
Their meaning is almost identical but the usage slightly varies. Both groups can be used in nominal predication with copula. Only the short forms can be used to form the passive voice (but it may be sometimes difficult to distinguish from copula constructions, see AUX). On the other hand, the long forms inflect for case and thus can modify nouns. (Occasionally even the short form may inflect for case but it is extremely rare in the modern language. Example: nesenu is the short form of feminine singular accusative. The corresponding long form is nesenou.)
There is an analogy with some adjectives that preserved so called nominal (short) forms. And these adjectives are not derived from verbs. Example:
- Short (nominal) forms: stár, stára, stáro “old”
- Default long (pronominal) forms: starý, stará, staré “old”
The nominal forms are used in predication, the standard forms both in predication and to modify nouns.
References
ADP
: adposition
Definition
Czech has only prepositions but no postpositions or circumpositions. They occur before a complement noun phrase (noun, pronoun) and they form a single structure with the complement to express its grammatical and semantic relation to another unit within a clause.
Some prepositions take the form of fixed multiword expressions, e.g.
na rozdíl od “in contrast to”, v souvislosti s
“in connection with”. The
component words are then still tagged according to their basic use
(na is ADP
, rozdíl is NOUN, etc.) and their status as
multiword expressions are accounted for in the syntactic annotation.
Examples
- v “in, at”
- k “to”
- během “during”
References
ADV
: adverb
Definition
Adverbs are words that typically modify verbs for such categories as time, place, direction or manner. They may also modify adjectives and other adverbs, as in velmi významně “very significantly” or prokazatelně chybný “provably wrong”.
There is a closed subclass of pronominal adverbs that refer to
circumstances in context, rather than naming them directly; similarly
to pronouns, these can be categorized as interrogative, relative,
demonstrative etc. Pronominal adverbs also get the ADV
part-of-speech tag but they are differentiated by additional features.
In accord with the UD approach,
adverbial ordinal numerals (poprvé, posedmé, postopadesáté)
are tagged ADV
, although the traditional grammar classifies
them as numerals.
The same holds for multiplicative numerals
(jednou, sedmkrát, stopadesátkrát).
Note that Czech transgressives (also called adverbial participles or converbs)
are tagged VERB, not ADV
.
Examples
- velmi “very”
- dobře “well”
- přesně “exactly”
- zítra “tomorrow”
- nahoru, dolů “up, down”
- ordinal numeral adverbs: poprvé, podruhé, potřetí “for the first time, for the second time, for the third time”
- multiplicative numeral adverbs: jednou, dvakrát, třikrát “once, twice, three times”
- interrogative adverbs: kde, kam, kdy, jak, proč “where, where to, when, how, why”
- demonstrative adverbs: tady, tam, teď, tehdy, tak “here, there, now, then, so”
- indefinite adverbs: někde, někam, někdy, nějak “somewhere, to somewhere, sometime, somehow”
- total adverbs: všude, vždy “everywhere, always”
- negative adverbs: nikde, nikdy “nowhere, never”
References
AUX
: auxiliary verb
Definition
The only truly auxiliary verb in Czech is být “to be”, and its variant (with separate lemma) bývat “to usually be”. It accompanies the lexical verb of a verb phrase and expresses grammatical distinctions not carried by the lexical verb.
Examples
-
Future tense. Finite future form of být is combined with infinitive of the lexical verb. The auxiliary expresses person, number and tense: budu dělat “I will do”, budeš dělat “you will do”, budou dělat “they will do”. Note that a limited set of verbs can form future morphologically, without the auxiliary.
-
Past tense in the first and second person. Finite present form of být is combined with past participle of the lexical verb. The auxiliary expresses person and number, the participle expresses gender and number: dělal jsem “I did.
Masc
”, dělala jsem “I did.Fem
”, dělal jsi “you did.Masc
”, dělali jsme “we did.Masc
”. -
Conditional mood. Conditional form of být is combined with past participle of the lexical verb. The auxiliary expresses person and number, the participle expresses gender and number: dělal bych “I would do.
Masc
”, dělala bych “I would do.Fem
”, dělal bys “you would do.Masc
”, dělali bychom “we would do.Masc
”. -
Passive voice. A form of být (in various tenses and moods or in the infinitive) is combined with passive participle of the lexical verb. The auxiliary expresses person, number, tense and mood, the participle expresses gender, number and voice: je udělán “he is done”, bude udělán “he will be done”, byl udělán “he was done”, byl by udělán “he would be done”, buď udělán “be done”, být udělán “to be done”.
In UD v1, where copulas were not tagged AUX
, many instances of být were tagged VERB
.
In UD v2, copulas are tagged AUX
and the definition of copula sentences has been extended
to location-existentials (V Praze je nové divadlo. “There is a new theatre in Prague.”)
Hence almost all occurrences of být should be now tagged AUX
.
Only pure existentials without location (Vůle je a vždycky byla. “There is and always was the will.”)
have a non-auxiliary být, which should be tagged VERB
.
Note that the passive participle may be also used as nominal predicate with copula.
Hence it may be difficult to distinguish a passive construction from a copula construction.
The former focuses on the process while the latter emphasizes the result.
However, since copulas are now tagged AUX
, the distinction no longer affects the part-of-speech tag:
- Passive: Smlouva byla.
AUX
podepsána v Bílém domě. “The contract was signed in the White House.” - Copula: Smlouva byla.
AUX
podepsána červeným inkoustem. “The contract was signed in red ink.”
Modal verbs are not auxiliaries
Czech modal verbs are not considered auxiliary and they are tagged VERB
,
in accord with the annotation in the Prague Dependency Treebank.
Their behavior is only slightly different from other content verbs.
Constructions with mít and passive participle
There is a construction parallel to the perfect tenses of Germanic and Romance languages: mít “to have” + neuter singular passive participle, e.g. mít (něco) uděláno “to have (something) done”. They can also apply to intransitive verbs: mít vyhráno “to have won”. Sometimes the verb mít shares the subject (actor) with the participle, but in other contexts such relation is not guaranteed: mít (někde něco) napsáno “to have (something) written (somewhere)”. None of these constructions is considered a separate tense in the Czech grammar and the verb mít is not analyzed as auxiliary.
References
- Loos, Eugene E., et al. 2003. Glossary of linguistic terms: What is an auxiliary verb?
- Wikipedia
- Jarmila Panevová, Eva Benešová, Petr Sgall. 1971. Čas a modalita v češtině (Tense and modality in Czech). Acta Universitatis Carolinae, Philologica Monographia XXXIV, Universita Karlova Praha
CCONJ
: coordinating conjunction
Definition
A coordinating conjunction is a word that links words or larger constituents without syntactically subordinating one to the other and expresses a semantic relationship between them.
For subordinating conjunctions, see SCONJ.
Examples
- a “and”
- nebo “or”
- ale “but”
References
- Loos, Eugene E., et al. 2003. Glossary of linguistic terms: What is a coordinating conjunction?
- Wikipedia
DET
: determiner
Definition
Determiners (or pro-adjectives) are words that modify nouns or noun phrases and express the reference of the noun phrase in context. That is, a determiner may indicate whether the noun is referring to a definite or indefinite element of a class, to a closer or more distant element, to an element belonging to a specified person or thing, to a particular number or quantity, etc.
An important point to note is that the traditional grammar of Czech does not
define determiners as a separate word class. Czech does not have articles.
Most determiners are traditionally called pronouns; that is, an UD-conformant
annotation of Czech must distinguish between substantive pronouns (UD tag PRON)
and attributive pronouns (UD tag DET
).
Also note that the DET
tag includes (pronominal) quantifiers (words
like mnoho, málo “many, few”), which the traditional grammar classifies
as a special subclass of numerals. However,
cardinal numerals in the narrow sense (jeden, pět, sto) are not
tagged DET
even though some authors would include them in
quantifiers. Cardinal numbers have their own tag NUM.
Conversion from the Prague Dependency Treebank
Since the PDT tagset (like all other Czech tagsets) does not distinguish
substantive and attributive pronouns, morphological tags alone are not enough
to find the correct universal POS tag.
Morphological rules could help, as the inflection patterns of some pronouns
bear similarities to adjectival inflection (especially the ability to inflect for gender).
Unlike in UD v1, we no longer use the dependency tree to distinguish between determiners and pronouns.
Instead, we use a pre-defined list of lemmas that are DET
if their PDT tag indicates pronoun.
See also here for a Slavic-wide discussion of the distinction between determiners and pronouns.
Examples
- possessive determiners: můj, tvůj, jeho, její, náš, váš, jejich “my, your, his, her, our, your, their”
- reflexive possessive determiner: svůj “one’s own”
- demonstrative determiners: tohle as in Tohle auto jsem viděl včera. “I saw this car yesterday.”
- interrogative determiners: který as in Které auto se ti líbí? “Which car do you like?”
- relative determiners: který as in Zajímá mě, které auto se ti líbí. “I wonder which car you like.”
- relative possessive determiner: jehož “whose”
- indefinite determiners: nějaký, některý
- total determiners: každý, všechen
- negative determiners: žádný as in Nemáme žádná auta. “We have no cars available.”
References
INTJ
: interjection
Definition
An interjection is a word that is used most often as an exclamation or part of an exclamation. It typically expresses an emotional reaction, is not syntactically related to other accompanying expressions, and may include a combination of sounds not otherwise found in the language.
As a special case of interjections, we recognize feedback particles such as ano, jo, ne, etc. Note that these words are considered particles in the PDT tagset and have to be retagged during the conversion process.
Examples
(Note that no direct translation of interjections is possible. The approximate translations below are for orientation purposes and they cannot serve to judge the part of speech from the English perspective.)
- ach “oh”
- pink
- inu “well”
- hle “look”
- proboha “for God’s sake”
Diffs
Prague Dependency Treebank
At present the UD-conversion of PDT keeps the PDT convention on tagging the response words (“yes, no”) as particles. Automatic conversion would not be straightforward because the negative particle ne is sometimes used as the response particle/interjection (English “no”) and sometimes as a free negative morpheme (English “not”). These two usages would have to be distinguished and only the first one converted to interjection.
References
NOUN
: noun
Definition
Nouns are a part of speech typically denoting a person, place, thing, animal or idea.
The NOUN
tag is intended for common nouns only. See PROPN for
proper nouns and PRON for pronouns.
Czech nouns have the lexical feature cs-feat/Gender. Furthermore, the nouns inflect for cs-feat/Number and cs-feat/Case.
A verbal noun can be derived productively from almost every verb
(e.g. dělat “to do” → dělání “doing”).
While in other languages a corresponding form may be called gerund and tagged VERB,
in Czech it is tagged NOUN
. It has always the neuter gender and the full
number-case inflectional paradigm.
Examples
- dívka “girl”
- kočka “cat”
- strom “tree”
- vzduch “air”
- krása “beauty”
- plavání “swimming”
References
NUM
: numeral
Definition
A numeral is a word, functioning most typically as a determiner, adjective or pronoun, that expresses a number and a relation to the number, such as quantity, sequence, frequency or fraction.

Czech grammar distinguishes several subclasses of pronominal numerals (quantifiers):
interrogative and relative (kolik “how many”);
demonstrative (tolik “this many”);
indefinite (několik, mnoho, málo “several, many, few”).
These words behave similarly to (most) cardinal numbers,
e.g. they require that the counted noun phrase be in genitive.
They are not similar to adjectives (unlike their English counterparts).
However, in accord with the UD standard, they should be tagged DET, not NUM
.
In addition, several types of (non-pronominal) numerals, such as ordinal numerals and multiplicative numerals, are tagged ADJ or ADV, based on their syntactic and morphological behavior.
Examples
- 0, 1, 2, 3, 4, 5, 2014, 1000000, 3.14159265359
- I, II, III, IV, V, MMXIV
- jeden, dva, tři, čtyři, pět, sedmdesát “one, two, three, four, five, seventy”
- polovina, třetina, čtvrtina, pětina “one-half, one third, quarter, one fifth”: denominators of fractions constitute a separate class of cardinal numerals.
- čtvero, patero “four, five” (These are special forms, so-called generic numerals. They are used rarely, in literary or archaic style.)
- jedny, dvoje, troje, čtvery, patery, sedmdesátery “one set of, two sets of, three sets of, four sets of, five sets of, seventy sets of”
Counterexamples
- první, druhý, třetí “first, second, third”: adjectival ordinal numerals. They are tagged ADJ, and the cs-feat/NumType feature reveals their semantic relation to numbers.
- poprvé, podruhé, potřetí “for the first time, for the second time, for the third time”: adverbial ordinal numerals. They are tagged ADV, and the cs-feat/NumType feature reveals their semantic relation to numbers.
- jednou, dvakrát, třikrát “once, twice, three times”: multiplicative numerals. They are tagged ADV, and the cs-feat/NumType feature reveals their semantic relation to numbers.
- dvojí, trojí, čtverý, paterý, sedmdesáterý “twofold, three kinds of, four kinds of, five kinds of, seventy kinds of”: generic numerals. They are tagged ADJ.
- dvojice, trojice, čtveřice “pair, triplet, foursome”: n-tuples (n-tice) are not considered numerals in the Czech grammar. They are tagged NOUN.
- jednička, dvojka, trojka, čtyřka, pětka “number one, number two, number three, number four, number five”: names of numbers, or of objects identified by the number (e.g. of a bus route). They are not considered numerals and they are tagged NOUN.
- tisíc, milión, miliarda, bilión “thousand, million, billion, trillion”: words for large quantities are ambiguous between cardinal numerals (tagged
NUM
) and nouns. If they inflect as nouns, they are tagged NOUN; but the borderline is fuzzy. For instance, in phrases like tisíce lidí demonstrovaly v ulicích (“thousands of people demonstrated in the streets”), tisíce is a noun. In numeric expressions, e.g. 110 tisíc dolarů (“110 thousand dollars”), it is a cardinal numeral.
References
PART
: particle
Definition
Particles are function words that must be associated with another word or phrase to impart meaning and that do not satisfy definitions of other universal parts of speech (e.g. adpositions, coordinating conjunctions, subordinating conjunctions or auxiliary verbs). Czech particles are not inflected.
Note that response words such as ano, jo “yes”, ne “no”, etc. are considered particles in the PDT tagset but they should be retagged as interjections under the UD standard. Also note that ne can be used in two ways, one would be translated as English “no” and the other as “not”. Only the former should become interjection, while the latter will stay a particle.
Examples
- Sentence modality: ať, kéž, nechť (“Let’s do it!” “If only I could do it over.” “May you have an enjoyable stay!”)
- jen “just, only”
- až “only, as late as, even, up to” Use case: až po stovky tisíc let “up to hundreds of thousands of years”
- asi “about, roughly, maybe”
Diffs
Prague Dependency Treebank
-
li “if”: This is an encliticized morpheme that functions as subordinating conjunction but it always immediately follows the predicate of the subordinate clause. For example: Nebude-li pršet, nezmoknem. lit. Will-not-if rain, we-will-not-get-wet. “We will not get wet if it does not rain.” PDT tags the li morpheme as particle and it is currently kept so in the UD conversion but it might be changed to
SCONJ
in the future releases. -
At present the UD-conversion of PDT keeps the PDT convention on tagging the response words (“yes, no”) as particles. Automatic conversion would not be straightforward because the negative particle ne is sometimes used as the response particle/interjection (English “no”) and sometimes as a free negative morpheme (English “not”). These two usages would have to be distinguished and only the first one converted to interjection.
References
PRON
: pronoun
Definition
Pronouns are words that substitute for nouns or noun phrases, whose meaning is recoverable from the linguistic or extralinguistic context.
Pronouns under this definition function like nouns. Note that
Czech grammar traditionally extends the term pronoun to words that
substitute for adjectives. Such words are not tagged PRON
under our universal scheme. They are tagged as determiners in
order to annotate the same thing same way across languages.
For instance, tohle “this” is traditionally called pronoun in
Czech grammar, regardless of context (the notion of determiners does
not exist in the traditional Czech grammar).
In UD v2, tohle is tagged DET
.
Unlike in UD v1, we no longer use the dependency tree to distinguish between determiners and pronouns.
Instead, we use a pre-defined list of lemmas that are DET
if their PDT tag indicates pronoun.
See also here for a Slavic-wide discussion of the distinction between determiners and pronouns.
 “What do you think?”
- relative pronouns: kdo, co “who, what” as in Zajímalo by mě, co si myslíš. “I wonder what you think.”
- indefinite pronouns: někdo, něco “somebody, something”
- negative pronouns: nikdo, nic “nobody, nothing”
References
PROPN
: proper noun
Definition
A proper noun is a noun that is the name of a specific individual, place, or object. Czech proper nouns are always written starting with an uppercase letter. Note that names of days of week (pondělí, úterý, středa, čtvrtek, pátek, sobota, neděle) and names of months (leden, únor, březen, duben, květen, červen, červenec, srpen, září, říjen, listopad, prosinec) are not written capitalized (unlike in English) and are not considered proper nouns.
Single-word named entities should be tagged PROPN
even if they originate
from a common noun (Zajíc, Huť) or an adjective (Veselý, Teplá).
Even if they were originally adjectives and inflect according to adjectival
paradigms, they behave syntactically as nouns. For instance, Teplá
(a river and city in western Bohemia) is originally feminine form of the
adjective teplý “warm” but as a geographical name, it is a noun.
It denotes a concrete location (rather than a property of somebody/something)
and its feminine gender is fixed (while adjectives have forms in all three
genders).
Note that names of languages (čeština, angličtina)
and adjectives derived from geographical names (český, anglický “Czech, English”)
are written in lowercase and are not tagged PROPN
.
Personal names are typically treated as a sequence of proper nouns
(one or more given names and one or more surnames).
If the name contains prepositions, conjunctions or articles (foreign names
and old Czech names), these are tagged as ADP
, CCONJ
and DET
,
respectively.
Czech (and other Slavic) multi-word named entities have internal syntactic
structure, which is preserved in the annotation. The headword is always noun
and there may be other nouns involved. They will be tagged either PROPN
or
NOUN
and possible ambiguities must be resolved individually.
Modifying adjectives are never tagged PROPN
. Even if an adjective is the
first word of a multi-word name, and thus it starts with an uppercase letter,
it is still tagged ADJ
.
Similarly, function words in named entities retain their normal tags.
These rules are less strict for foreign named entities where the original
part of speech is hidden for a Czech speaker.
Examples
- Bečov.
PROPN
nad.ADP
Teplou.PROPN
is a city. Bečov is the head and the nad Teplou part refers to the river flowing through the city, to distinguish it from other Bečovs. - Červený.
ADJ
Újezd.PROPN
is a village. Újezd is the head and it is taggedPROPN
although it originates in the common noun újezd “district, riding”. There are many locations named Újezd and the noun is perceived as a proper noun in current Czech. Červený is an adjective meaning “red” and it is taggedADJ
. - Červená.
ADJ
řeka.NOUN
“Red River”. Even though the two words together are a name of a particular river, řeka is a common noun and is tagged as such. - Organizace.
NOUN
spojených.ADJ
národů.NOUN
“United Nations Organization” consists of three words, none of which is proper noun. However, the acronym OSN “UNO” is a single-token name and is taggedPROPN
.
Conversion from the Prague Dependency Treebank
The PDT set of morphological (part-of-speech) tags does not distinguish
common and proper nouns. However, lemmas in PDT contain additional features
that also encode types of named entities. When converting the PDT annotation
to UD, these lemma features are removed, the PROPN
tag is used and the feature
cs-feat/NameType is added to the universal features to preserve the type.
Only nouns are treated this way.
Foreign adjectives are not converted to PROPN
despite the fact
that they entered Czech as parts of foreign names and their lemmas contain
the name type feature.
The following table lists the name types together with the most frequent examples. See http://ufal.mff.cuni.cz/techrep/tr27.pdf, page 8, section 2.1 (Lemma structure) for more details.
| _;Y | given name | Jan, Jiří, Václav, Petr, Josef | “Jan, Jiří, Václav, Petr, Josef” |
| _;S | surname | Klaus, Havel, Němec, Jelcin, Svoboda | “Klaus, Havel, Němec, Yeltsin, Svoboda” |
| _;E | member of a particular nation, inhabitant of a particular territory | Němec, Čech, Srb, Američan, Slovák | “German, Czech, Serbian, American, Slovak” |
| _;G | geographical name | Praha, ČR, Evropa, Německo, Brno | “Prague, CR, Europe, Germany, Brno” |
| _;K | company, organization, institution | ODS, OSN, Sparta, ODA, Slavia | “ODS, UN, Sparta, ODA, Slavia” |
| _;R | product | LN, Mercedes, Tatra, PC, MF | “LN, Mercedes, Tatra, PC, MF” |
| _;m | other proper name: names of mines, stadiums, guerilla bases etc. | US, PVP, Prix, Rapaport, Tour | “US, PVP, Prix, Rapaport, Tour” |
Diffs
Prague Dependency Treebank
Articles in foreign names (the, die, le) are tagged ADJ, not DET. Otherwise, the morphological analysis usually includes the original part of speech of foreign words.
References
PUNCT
: punctuation
Definition
Punctuation marks are non-alphabetical characters and character groups used to delimit linguistic units in printed text.
Punctuation is not taken to include logograms such as $, %, and §, which are instead tagged as SYM.
Examples
- Period: .
- Comma: ,
- Parentheses: ()
Diffs
Prague Dependency Treebank
The PDT texts are from the early 1990s and there are no e-mail addresses.
If they were there, the PDT tokenization rules would break them up on all dots and at signs.
The same holds for telephone numbers. For example,
tel.: (05) 4321 6014 is analyzed as eight tokens (NOUN PUNCT PUNCT PUNCT NUM PUNCT NUM NUM
).
References
SCONJ
: subordinating conjunction
Definition
A subordinating conjunction is a conjunction that links constructions by making one of them a constituent of the other. The subordinating conjunction typically marks the incorporated constituent which has the status of a (subordinate) clause.
For coordinating conjunctions, see CCONJ.
Examples
- že “that”
- aby “so that”
- zda “if”
- jako “as”
- než “than”
References
- Loos, Eugene E., et al. 2003. Glossary of linguistic terms: What is a subordinating conjunction?
- Wikipedia
SYM
: symbol
Definition
A symbol is a word-like entity that differs from ordinary words by form, function, or both.
Many symbols are or contain special non-alphanumeric characters, similarly to punctuation. What makes them different from punctuation is that they can be substituted by normal words. This involves all currency symbols, e.g. $ 75 is identical to seventy-five dollars.
Mathematical operators form another group of symbols.
Another group of symbols is emoticons and emoji.
Strings that consists entirely of alphanumeric characters are not
symbols but they may be proper nouns: 130XE, DC10; others
may be tagged PROPN
(rather than SYM
) even if they contain special
characters: DC-10.
Similarly, abbreviations for single words are not symbols but are assigned the part of speech
of the full form. For example, Mr. (mister), kg (kilogram), km (kilometr), dr (doktor)
should be tagged nouns.
Acronyms for proper names such as OSN and NATO should be tagged as proper nouns.
Characters used as bullets in itemized lists (•, ‣) are not symbols, they are punctuation.
Examples
- $, %, §, ©
- +, −, ×, ÷, =, <, >
- :), ♥‿♥, 😝
-<EMAIL_ADDRESS>http://universaldependencies.org/, 1-800-COMPANY
Diffs
Prague Dependency Treebank
The PDT texts are from the early 1990s and there are no e-mail addresses.
If they were there, the PDT tokenization rules would break them up on all dots and at signs.
The same holds for telephone numbers. For example,
tel.: (05) 4321 6014 is analyzed as eight tokens (NOUN PUNCT PUNCT PUNCT NUM PUNCT NUM NUM
).
VERB
: verb
Definition
A verb is a member of the syntactic class of words that typically signal events and actions, can constitute a minimal predicate in a clause, and govern the number and types of other constituents which may occur in the clause.
Note that the VERB
tag covers main verbs (content verbs)
and modal verbs
but it does not cover auxiliary verbs and copulas, for which there is
the AUX tag. (Czech modal verbs are not considered auxiliary.)
See the description of AUX
for more information on the borderline
between VERB
and AUX
.
Czech verbs can take the following morphological forms:
- Infinitive (this is the citation form)
- Finite verb (indicative and imperative forms; conditional is constructed periphrastically)
- Active past participle (used to construct past and conditional)
- Converb (also called transgressive, adverbial participle or gerund)
There are participial forms that are tagged as adjectives (ADJ) rather than verbs. See below for examples.
A verbal noun can be derived productively from almost every verb
(e.g. dělat “to do” → dělání “doing”).
While in other languages a corresponding form may be called gerund and tagged VERB
,
in Czech it is tagged NOUN. It has always the neuter cs-feat/Gender
and it inflects for cs-feat/Number and cs-feat/Case.
Examples
- nést “to carry”
- nesu, neseš, nese, neseme, nesete, nesou “I carry, you carry, he/she/it carries, we carry, you carry, they carry”
- nes, nesme, neste “carry” (imperative in different persons and numbers)
- nesl, nesla, neslo, nesli, nesly “carried” (past participle in different genders and numbers)
- nesa, nesouc, nesouce “carrying” (present converb in different genders and numbers)
Border cases
Passive participles lie on the border between verbs and adjectives.
Since release 2.0, both short and long forms are tagged ADJ
, although
they may have verbal features in addition to the adjectival ones.
For example:
- Short: nesen, nesena, neseno, neseni, neseny “carried”
- Long: nesený, nesená, nesené, nesení, nesené “carried”
Their meaning is almost identical but the usage slightly varies. Both groups can be used in nominal predication with copula. Only the short forms can be used to form the passive voice (but it may be sometimes difficult to distinguish from copula constructions, see AUX). On the other hand, the long forms inflect for case and thus can modify nouns. (Occasionally even the short form may inflect for case but it is extremely rare in the modern language. Example: nesenu is the short form of feminine singular accusative. The corresponding long form is nesenou.)
There is an analogy with some adjectives that preserved so called nominal (short) forms. And these adjectives are not derived from verbs. Example:
- Short (nominal) forms: stár, stára, stáro “old”
- Default long (pronominal) forms: starý, stará, staré “old”
The nominal forms are used in predication, the standard forms both in predication and to modify nouns.
References
X
: other
Definition
The tag X
is used for words that for some reason cannot be assigned
a real part-of-speech category.
A special usage of X
is for cases of code-switching where it is not
possible (or meaningful) to analyze the intervening language
grammatically (and where the dependency relation flat is
typically used in the syntactic analysis;
on the morphological layer, these words should use the feature Foreign=Yes).
This rarely applies to the PDT data where many foreign words are tagged with their original
part of speech.
Even if foreign words are tagged X
, this usage does not extend
to ordinary loan words which should be assigned a normal
part-of-speech. For example, in Skotové nosí kilt “Scots wear kilts”,
kilt is an ordinary NOUN.
Examples
- A on pak akorát xfgh pdl jklw “And then he just xfgh pdl jklw”


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Overview
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Loan
- Class in
com.kolbly.mortgagecomparer.db
Loan data
Loan(Loan)
- Constructor for class com.kolbly.mortgagecomparer.db.
Loan
Loan Ctor from another loan
Loan(double, double, int)
- Constructor for class com.kolbly.mortgagecomparer.db.
Loan
Ctor
Loan(Cursor)
- Constructor for class com.kolbly.mortgagecomparer.db.
Loan
Ctor for creating a Loan from a database cursor
LoanActivity
- Class in
com.kolbly.mortgagecomparer.activities.loanactivity
Loan activity
LoanActivity()
- Constructor for class com.kolbly.mortgagecomparer.activities.loanactivity.
LoanActivity
LoanActivityFragment
- Class in
com.kolbly.mortgagecomparer.activities.loanactivity
Fragment for a single loan from the list
LoanActivityFragment()
- Constructor for class com.kolbly.mortgagecomparer.activities.loanactivity.
LoanActivityFragment
Ctor Note: Do not override but instead use a Bundle to pass arguments.
LoanActivitySectionsPagerAdapter
- Class in
com.kolbly.mortgagecomparer.activities.loanactivity
A
FragmentPagerAdapter
that returns a fragment corresponding to one of the sections/tabs/pages.
LoanActivitySectionsPagerAdapter(LoanActivity, FragmentManager)
- Constructor for class com.kolbly.mortgagecomparer.activities.loanactivity.
LoanActivitySectionsPagerAdapter
Loggable
- Interface in
com.kolbly.java.util
Convenience interface to enforce data formatting for logging purposes
lookup(String)
- Static method in enum com.kolbly.android.util.date.
DateFormat
Lookup the DateFormat by it's String value
lookup(char[])
- Static method in enum com.kolbly.android.util.date.
DateFormat
Lookup the OS Date format by the format order
lookup(String)
- Static method in enum com.kolbly.android.util.
StorageState
Lookup the StorageState by it's String value
lookup(int)
- Static method in enum com.kolbly.android.util.time.
TimeFormat
Lookup the TimeFormat by it's integer value
Overview
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| Shared Vision December 1996 |
December MoonwatchFull Moon:Dec. 24, at 12:41pm in Cancer Certain times each month are poor times to make major decisions or purchases. These are the times when your purchases turn out to be something you never wear or use. They are also disastrous times to hold important meetings! When the Moon is Ďvoid of course,í we become like floating balloons of helium, and canít trust our decision-making skills because we are not grounded. Whatever we initiate during these times tends NOT to fulfill its purpose. Keep an eye on the times listed in this column.
DURING THESE TIMES:
December 1 / 2:24pm to 10:11pm December 4 / 2:18am to 10:23am December 6 / 12:11pm to 7:39pm December 8 / 6:09pm to midnight December 10 / 12:21pm to midnight December 11 / 0:01am to 3:15am December12 / 10:00pm to midnight December 13 / 0:01am to 4:14am December 15 / 1:56am to 5:44am December 17 / 2:32am to 8:55am December 19 / 10:51am to 2:10pm December 21 / 2:43pm to 9:17pm December 23 / 11:29pm to midnight December 24 / 0:01am to 6:14am December 26 / 12:02pm to 5:09pm December 28 / ALL DAY December 29 / 0:01am to 5:45am |


shot-it is an event that assembles artist and photographers all over the world to display their gift and talent by showcasing their
work. This event happens once in a year, this year(2020) the event is taking place in Paris, France.
Day 1: 28th, August 2020
Day 2: 29th, August 2020
Day 3 (Final): 30th, August 2020
Location 1: Mandarin Oriental, Paris
Location 2: The Ritz Paris
Location 3 (Final): Le Pré Catelan Lenôtre
2016
Berlin, Germany
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This site is made for the purpose of the Group Project, "History of Internet Website" for the class IS 601, Spring 2021 Semester taught by Professor Keith Williams. We have created this website to get handson experience on git and bootstrap. The website covers information about origin story of internet, dot com bubble, browserwars, the search feature, and story of the internet and users.


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Shopify marketing is the most important for your eCommerce store. You will get more selling orders and will be benefitted
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Upselling can be more effective than acquiring a net new costumer and new sale.
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Jane Foster


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I'm a security engineer with a focus on IoT/embedded systems/binary analysis/reverse since 2019. If you'd like to contact me, please see the email on the main page.


4 Acknowledgements We have been extremely lucky in our mentors. Jens cut his teeth in the company of the Smalltalk pioneers: Alan Kay, Dan Ingalls, and the rest of the gang who invented personal computing and object oriented programming in the great days of Xerox PARC. He worked with John Maloney, of the MIT Scratch Team, who developed the Morphic graphics framework that’s still at the heart of Snap ! . The brilliant design of Scratch, from the Lifelong Kindergarten Group at the MIT Media Lab, is crucial to Snap ! . Our earlier version, BYOB, was a direct modiﬁcation of the Scratch source code. Snap ! is a complete rewrite, but its code structure and its user interface remain deeply indebted to Scratch. And the Scratch Team, who could have seen us as rivals, have been entirely supportive and welcoming to us. Brian grew up at the MIT and Stanford Artiﬁcial Intelligence Labs, learning from Lisp inventor John McCarthy, Scheme inventors Gerald J. Sussman and Guy Steele, and the authors of the world’s best computer science book, Structure and Interpretation of Computer Programs, Hal Abelson and Gerald J. Sussman with Julie Sussman, among many other heroes of computer science. (Brian was also lucky enough, while in high school, to meet Kenneth Iverson, the inventor of APL.) In the glory days of the MIT Logo Lab, we used to say, “Logo is Lisp disguised as BASIC.” Now, with its ﬁrst class procedures, lexical scope, and ﬁrst class continuations, Snap ! is Scheme disguised as Scratch. Four people have made such massive contributions to the implementation of Snap ! that we have ofﬁcially declared them members of the team: Michael Ball and Bernat Romagosa, in addition to contributions throughout the project, have primary responsibility for the web site and cloud storage. Joan Guillén i Pelegay has contributed very careful and wise analysis of outstanding issues, including help in taming the management of translations to non-English languages. Jadga Hügle, has energetically contributed to online mini-courses about Snap ! and leading workshops for kids and for adults. Jens, Jadga, and Bernat are paid to work on Snap ! by SAP, which also supports our computing needs. We have been fortunate to get to know an amazing group of brilliant middle school(!) and high school students through the Scratch Advanced Topics forum, several of whom (since grown up) have contributed code to Snap ! : Kartik Chandra, Nathan Dinsmore, Connor Hudson, Ian Reynolds, and Dylan Servilla. Many more have contributed ideas and alpha-testing bug reports. UC Berkeley students who’ve contributed code include Achal Dave. Kyle Hotchkiss, Ivan Motyashov, and Yuan Yuan. Contributors of translations are too numerous to list here, but they’re in the “About…” box in Snap ! itself. This material is based upon work supported in part by the National Science Foundation under Grants No. 1138596, 1143566, and 1441075; and in part by MioSoft, Arduino.org, SAP, and YC Research. Any opinions, ﬁndings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reﬂect the views of the National Science Foundation or other funders.


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DAUGHTERS OF THE DUST A film review by Daniel Barrett Copyright 1992 Daniel Barrett
My wife and I saw DAUGHTERS OF THE DUST this weekend, mainly on the strength of a previous review in this newsgroup (#1377). The reviewer called this "one of the best movies I have seen in years -- maybe the best." Well, here's a contrasting point of view: DAUGHTERS OF THE DUST was the *worst* movie we have seen in a long time!
The "story," if you can call it that, focuses on a black family in 1902. The people are preparing to leave their life on an isolated island and travel north to join mainstream society. The central theme is that of "old versus new": the old traditions of the African ancestors versus the new culture of the modern (read: white) world. Various members of the family deal with this dichotomy throughout the movie.
With this much information, you essentially know the whole plot. The "old versus new" theme is systematically beaten to death over a two- hour period. The characters are flat and develop little if at all. Various subplots surface but go nowhere. For example, early in the movie, we watch a secret love note get delivered by a Native American to one of the black daughters. The Native American and the love affair are barely mentioned for the rest of the movie, until he rides in at the end. As another example, there's the mysterious pregnancy of a central character, and her husband's anger over not knowing whether or not he is the father. Virtually every scene involving this subplot is identical -- the husband mopes around, gets angry, etc. So many missed opportunities....
The movie's major flaw is that the dialogue is not understandable. Although everything is spoken in English, it is an unfamiliar dialect that was 75% unintelligible to us. This is unforgivable in a modern movie, and very naive on the director's part. You can't expect people to appreciate a movie if they can't understand the words. This was particularly pathetic during the climax, in which one of the family members delivers an impassioned five-minute speech while the others are held in rapt attention. We couldn't understand a word of it.
A second flaw is that some parts of the movie are unnecessarily obscure. For example, a central character, "Yellow Mary," is constantly accompanied by a white woman. During the movie, this companion laughs, smirks, runs on the beach, and very occasionally utters a word or two. Her presence, as far as we could tell, is completely unexplained.
There are a few aspects of the movie that were adequate. The overall "feel" of the unity of the island family was well-communicated. The hired photographer (making a record of the family's last days on the island) was an enjoyable character. And the island photography was above average. Finally, I suspect that people who are already involved in this particular area of Black history might find other enjoyment in the film.
My wife and I love "art" films and non-mainstream movies, but this one was poorly directed and quite boring. DAUGHTERS OF THE DUST is a dud.
Dan
Dan Barrett -- Dept of Computer Science, Lederle Graduate Research Center University of Massachusetts, Amherst, MA 01003 --<EMAIL_ADDRESS>.
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