#TTA category A status

[plokoonsdisapprovingeyebrows]

and finally: revenge of the sith in keldeorinyaa! vocab notes under the cut.

kssalabal di! repabliki a nisinaki sislorrdi kaunto dooku de kssaban kero manteveriyeh. anakoka ttara anakoka, duraliyaan di nazadi. isakhimen di tawidawi inyendao me. dorroy tta rrewa jjeneral grievous di, ssaossa re, repabliki tta zalmerissan de voolaiki dal hadi a, repabliki tta senati tta rrewa ttaansala palpatine a staranteral. isagai anakoka di teyaadiyeh zalmerissan de dadinhaaya re dadekyaan ki ingaliyeh nna, jjedai naitaga di na staranteri ttaansala a aysakaati khooto rrewanadiyeh…

kssalabal = war. From kssabi, to attack, with an archaic suffix that’s really only used in this one word. There’s a massive taboo against aggression, attacks and violence in kel dor society - or there sort of still is, it’s fading a bit and has sort of slipped into the status where an edgelord might get interested in it. A lot of war and combat terminology derives from entirely non-war-related things because of the taboo; people mostly speak euphemistically on the subject.

nisinaki = ruthless, practical, harsh, without lenience. There's not really an association with deliberate unkindness or cruelty here - a storm or an earthquake might be described as nisinaki re.

sislorrdi = transliteration of ‘Sith Lord’. There is no ‘th’ in keldeorinyaa, and ‘rr’ is there to denote an American-style r. The final i isn’t strongly pronounced, it’s just there because words with ‘rr’ can’t end in hard consonants.

manteveri = to be broken, weakened. This is a passive verb, meaning it’s only applied to the object of an action in absence of the actor. If the book is broken, you say “tenjji a manteveri”, but if, say, your little sibling breaks the book, you say “gepdal di tenjji mani.” There’s quite a few active/passive verb pairs of this sort; for example, ikazdi/ikapsudi 'to eat/to be eaten'.

duraliyaan = a strong protector; more or less the kel dor equivalent of ‘hero’. The requirement is that you fight only to protect others, not for personal glory or gain.

nazadi = to act, to do something. Mostly used in the context of people who are distinguished by something they've done; eg. duraliyaan di nazadi here.

isakhimen = evil. A compound word, from zakha ‘purposeful, deliberate’ and circumfixed imen ‘bad’ - someone here is causing problems on purpose.

tawidawi = a quantifier that sort of means ‘lots’, but has strong connotations of ‘too fuckin many’. Used on its own, it can mean ‘pest’ or ‘vermin’. Here it’s used with the judgemental particle me. Evil is everywhere (and we think that’s fucked up, yo).

dorroy = droid. This is an evolved loanword; it used to be dorroido, but the final syllable got lost. There’s a distinction between droids of Dorin make, which are referred to as aynamaki, and droids from offworld, which are dorroy.

(aynamaki are almost without exception automata and manufacturing tools, and they're designed not to be capable of developing the sort of sentience offworld droids do - the thought is that it would be rude af to make your shovel capable of thinking. An exception is made for certain AIs, but these occupy a distinct legal category that gives them certain protections.)

ssaossa = a bolt from the blue, lightning out of a clear sky. Colloquially used as a mild swearword, and a description for a run of unanticipated bad luck. Also works for describing an unexpected attack.

zalmerissan = capital city in a galactic context. Cities in the Dorin context (and especially the big ones) belong to many clans and thus can’t really be said to belong to one entity, so the original meaning is more or less the city everyone thinks of when you say the word ‘city’ - whatever’s closest or biggest or most interesting in your area.

voolaiki di = to trespass, to invade. Typically used for people sneaking onto ur farm and running off with your best shellfish; given the war taboo, use in this manner is very rare.

hadi a = particle linking two ideas, perhaps things that are happening at the same time. Basically a vehicle for run-on sentences in the manner of the original crawls lmao.

staranteri = to be kidnapped, to be taken secretly - a compound passive verb from stari, to be hidden away, and santeri, to take. There is a verb for smuggling things, but you never use it for people, so a number of workarounds made it into common usage.

teyaadi - to break into, to crack open, to besiege. Primarily used in the sense of cracking open a nut, or shellfish, or a jar you haven’t opened for ages, or for getting into an account you've forgotten the password for... again, use in the sense of wartime is very rare.

dadinhaaya = valuable, something you could get a lot for in exchange if you wanted to sell or trade it. (There's a different word for things that are emotionally/sentimentally valuable.)

dadekyaan = prisoner, hostage, lit. someone who is leashed or bound. From dadek, a leash or a chain, something that ties someone/something or binds them in place.

ingali = to escape, to flee, to get away from a dangerous situation.

aysakaati = to rescue, to save someone's life or save them from harm.

khooto = job, mission, the task at hand, an aim being actively worked on. Differs from zaakhi/zadakh in that you have to be currently working on this one, and it's typically a more specific, limited action - think of taking a specific class, versus generally working towards a degree.

[jaimieparish2-blog]

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[russwhicker848-blog]

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[zhiyanle]

RaTG13 Is Too Good To Be True.

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RaTG13 Is Too Good To Be True. ---- WIV1 pk RaTG13 (2). Zhiyan-Le, 2010-04-03. https://sites.google.com/site/zhiyanleback/2021-1/z20210404-wiv1xratg13 https://zhiyanleback.blogspot.com/p/ratg13-is-too-good-to-be-true.html https://sites.google.com/view/zhiyanlesite2/home/sars-2-origin-wiv1-pk-ratg13 WHO China trip and its SARS-2 origin report indicated RaTG13 as the SARS-2 origin from nature. Their conclusion was base on: 1]: RaTG13 has 96.2% identity, which was done on the single gene alignment level. 2]: There is only one Amino-Acid difference, which was done on the protein alignment level. Such conclusion may not stand true. Reason: Alignment and analysis on Codon level are totally ignored, which may lead to different conclusions. Let’s take an example, we have two genomic sequences with the same Am-Acid but different Codons:  

First: Arg Phe Glu Arg Arg Ser Leu Gly Ser Ser Arg Pro Thr Cys Cys AGG TTC GAG CGC CGG AGT CTC GGC TCA TCC CGA CCG ACT TGC TGT Second: Arg Phe Glu Arg Arg Ser Leu Gly Ser Ser Arg Pro Thr Cys Cys CGT TTT GAA AGG CGT TCC TTA GGA TCC TCA CGG CCC ACC TGT TGC

Their identities on the Am-Acid or protein level is 100%. And their identities on the single gene alignment level is 56%. See below:  

Identities   25/45(56%) Query  1   AGGTTCGAGCGCCGGAGTCTCGGCTCATCCCGACCGACTTGCTGT  45             | || ||  | ||     | || || || || || || || ||  Sbjct  1   CGTTTTGAAAGGCGTTCCTTAGGATCCTCACGGCCCACCTGTTGC  45

However, their identities on the Codon level: 0%. More over, their structures are different: Which is accurate? In my view, the one on Codon level is. The reason is simple: When the virus and ACE2 are running, they work on the Codon level. The given samples may have the same Am-Acid, however, their energy level or power can be very different due to their different Codons. This can be well explained by the binary-image Codon Table and by the Buckyball system. RaTG13 Reality on the Codon Level. Now using Codon study to see the identities among relevant nCov samples (with GenBank ID): • NC_045512: WH-01, basic sample, collected from a patient in Wuhan hospital in PRC. • MN996532: bat RaTG13, said as the most possible origin of SARS-2. • KF367457: WIV1, the first one of lab-product series of SARS-like nCov. • NC_028824, Bat-2012, natural bat, collected in PRC Yunnan, 2012. • NC_030886, Bat-2014. natural bat, collected in PRC Yunnan, 2014 If RaTG13 were a bat from nature in 2013, it should behave as the same or very closely to the two natural bat sample, Bat-2012 and Bat-2014. Aligning with the basic sample, WH-01 (NC_045512), on the global Codon level, the RaTG13 has a matching score of 184 and WIV1 has 190. And here is the alignment of their Codon base-gene occurring frequency & distribution: Table 01: 

Global Codon BP Freq.Distribution (raw data:NIH GenBank, by 2021-02)   NC_045512 WIV1 RaTG13 Bat-12 Bat-14 A 3038.33 2884.67 2975.33 2342.33 2552.33 C 1868.00 2020.67 1836.67 1462.67 2135.67 G 1983.67 2098.00 1948.67 1936.00 2419.00 T 3267.00 3099.67 3190.33 3250.00 2946.00 total: 10157 10103 9951 8991 10053 avrg: 2539.25 2525.75 2487.75 2247.75 2513.25 total gap w NC_45512 -54 -206 -1166 -104 avrg gap w NC_45512 -14 -52 -292 -26

The aligning result: WIV1 has the total gap [-54] and average gap [-14] , and RaTG13 has that of [-206] and [-53]. Both Bat-2012 and Bat-2014 have much bigger gaps. Clearly, WIV1 is the closest to the basic sample, meaning an obvious greater possibility to be the SARS-2 origin than that of RaTG13, which behaves very differently from natural bat samples. Ratg13: Too Good To Be True As The SARS-2 Origin. This indicator, Codon and its genetic frequencies and distribution, is important, according to PRC-PLA doctor Chen Wei (a top virology scientist and Covid-vaccine developer in China; also, she is a leader in charge of medical treatment during the early stage of the pandemic in PRC Wuhan City), because it directly tells the similarity or difference regarding affinity, stability and mutation status and trend, especially genes C & G and their quantity and distribution. S-Gene is a key factor re SARS-2 interacts with human body. Let’s do some studies on the Codon level by borrowing Dr. Chen Wei suggestion. Below are similarities among the taken samples. Fig. 01: Indeed, as Dr. Chen Wei suggested, gene C & G, as well as their quantity and distribution, play an important role, particularly in the Codon 3rd BP genes (where base-gene A & T are 0). It seems that RaTG13 is in a good position to be the SARS-2 origin: Regarding single gene-aligning closeness to the basic sample NC_045512, WIV1 has a ratio of 0.9866, and RaTG13 has a ratio of 0.9969. However, comparing with natural bats (Bat-2012 and Bat-2014), of which one has the ratio of 0.8892 and another has it of 1.0134, far enough to be role out as the SARS-2 origin by the said sample. That is, sample RaTG13 behaves very differently from natural bats, but it does not. Further, below is the Codon-leveled aligning result: Table 02:

Codon-Leveled Similarities to NC-045512   WIV1 RaTG13 Bat-2012 Bat-2014 Total Occurring 1260 1256 1269 1132 similarities 78 693 66 94 Ratio of similarity/total 0.0619 0.5518 0.0520 0.0830

The sample RaTG13 has a matching score of 693 occurring aligning similarities, others have it from 87 to 96. The gap between RaTG13 and natural bats is too big to believe that it comes from nature. In a lab field, however, it is pretty easy to reach or even to go beyond 693 similarities score. Besides, RaTG13 similarity ratio is too close to the basic sample but too far from the natural samples. In fact, when using NIH-BLAST to search RaTG13’s all possible similarities, the result has no natural bats but in three categories: synthetic construct, clones, and vaccines, all are lab/man-made work. In sum, the indicator on Codon level should be in the must-do-list when searching the SARS-2 origin(s). By using it, the picture is very different from what the WHO report said. That is, the sample RaTG13 is too perfect to be a truthful SARS-2 origin from natural bats, rather, it is very likely a lab-product. In contrast, and by all indicators, particularly on the Codon level, WIV1 has the closest relations with basic sample NC_044512, that is, WIV1 is the most possible SARS-2 origin. Reference: Message from PLA Vaccine Patent. 2021-03-15. https://sites.google.com/site/zhiyanleback/2021-1/z20210315-patent-message-en Data Availability Table 01-02 (supplement)

SARS-2: Global Codon Freq. & Distribution (raw data: NIH GenBank, by 2021-02-22)   WH-01 WIV1 RaTG13 Bat-12 Bat-14   WH-01 WIV1 RaTG13 Bat-12 Bat-14 AAA 284 264 312 132 180 CAA 230 207 243 129 157 AAC 219 186 200 129 210 CAC 192 151 152 98 125 AAG 252 239 116 114 213 CAG 184 165 84 129 183 AAT 188 233 264 150 179 CAT 181 163 165 125 130 ACA 276 283 235 175 219 CCA 101 141 113 79 150 ACC 127 135 145 89 162 CCC 44 43 43 31 75 ACG 62 52 46 60 89 CCG 27 21 30 28 72 ACT 165 246 247 155 239 CCT 93 115 107 69 142 AGA 201 149 266 137 106 CGA 36 27 37 28 33 AGC 109 86 141 92 143 CGC 29 44 39 23 100 AGG 131 108 113 124 112 CGG 24 29 28 18 40 AGT 161 143 201 146 162 CGT 50 79 63 65 108 ATA 180 168 106 191 166 CTA 274 215 119 243 211 ATC 127 131 104 83 81 CTC 120 147 84 79 93 ATG 298 357 122 319 311 CTG 271 248 79 211 203 ATT 295 281 210 220 182 CTT 276 256 213 200 178   WH-01 WIV1 RaTG13 Bat-12 Bat-14   WH-01 WIV1 RaTG13 Bat-12 Bat-14 GAA 100 199 182 91 141 TAA 289 100 319 248 76 GAC 76 145 133 57 143 TAC 201 183 252 169 214 GAG 97 178 78 69 161 TAG 211 106 123 142 102 GAT 64 173 185 79 159 TAT 135 163 279 230 174 GCA 99 187 118 107 191 TCA 193 215 183 120 130 GCC 47 78 69 53 131 TCC 79 61 72 66 98 GCG 29 47 19 58 107 TCG 40 63 33 50 55 GCT 89 261 179 114 260 TCT 139 187 199 133 165 GGA 74 95 124 66 98 TGA 258 87 305 251 92 GGC 49 73 89 62 137 TGC 193 150 244 183 193 GGG 51 50 50 43 70 TGG 195 110 253 208 179 GGT 62 195 155 118 226 TGT 286 204 358 288 215 GTA 180 153 129 198 192 TTA 362 248 220 425 214 GTC 70 106 99 91 115 TTC 180 157 207 141 102 GTG 279 212 97 266 315 TTG 366 291 188 425 302 GTT 159 237 215 220 283 TTT 298 277 368 349 259

Table 02-02 (supplement)

S-Gene: Global Codon Matching Score (raw data: NIH GenBank, 2021-02-22)   WIV1 RaTG13 Bat-12 Bat-14   WIV1 RaTG13 Bat-12 Bat-14 AAA  0 0 0 0 CAA  0 0 0 0 AAC  3 51 4 9 CAC  0 8 0 0 AAG  2 31 0 0 CAG  4 24 1 2 AAT  0 0 0 0 CAT  0 0 0 0 ACA  0 0 0 0 CCA  0 0 0 0 ACC  7 57 8 14 CCC  2 37 2 5 ACG  0 0 0 0 CCG  0 0 0 0 ACT  0 0 0 0 CCT  0 0 0 0 AGA  0 0 0 0 CGA  0 0 0 0 AGC  11 52 11 8 CGC  0 0 0 0 AGG  2 24 2 1 CGG  0 0 0 0 AGT  0 0 0 0 CGT  0 0 0 0 ATA  0 0 0 0 CTA  0 0 0 0 ATC  5 35 3 3 CTC  0 0 0 0 ATG  1 5 2 2 CTG  7 59 7 14 ATT  0 0 0 0 CTT  0 0 0 0   WIV1 RaTG13 Bat-12 Bat-14   WIV1 RaTG13 Bat-12 Bat-14 GAA  0 0 0 0 TAA  0 0 0 0 GAC  2 32 3 1 TAC  2 33 2 4 GAG  1 22 0 1 TAG  0 0 0 0 GAT  0 0 0 0 TAT  0 0 0 0 GCA  0 0 0 0 TCA  0 0 0 0 GCC  7 38 5 9 TCC  0 0 0 0 GCG  0 0 0 0 TCG  0 0 0 0 GCT  0 0 0 0 TCT  0 0 0 0 GGA  0 0 0 0 TGA  0 0 0 0 GGC  4 49 4 5 TGC  5 23 0 3 GGG  0 0 0 0 TGG  0 7 1 0 GGT  0 0 0 0 TGT  0 0 0 0 GTA  0 0 0 0 TTA  0 0 0 0 GTC  0 0 0 0 TTC  5 46 6 2 GTG  8 60 5 11 TTG  0 0 0 0 GTT  0 0 0 0 TTT  0 0 0 0

[latestmarketresearch]

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This report provides detailed historical analysis of global market for Industrial UAVs (Drone) from 2013-2018, and provides extensive market forecasts from 2019-2028 by region/country and subsectors. It covers the sales volume, price, revenue, gross margin, historical growth and future perspectives in the Industrial UAVs (Drone) market.

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Leading players of Industrial UAVs (Drone) including:

• HanHe

• Quanfeng Aviation

• EWATT

• TTA

• All China Times

• Aibird

• MMC

• ChinaRS

 Market split by Type, can be divided into:

• Airplanes

• Multicopter

Market split by Application, can be divided into:

• Police

• Energy

• Land and Resources

• Agriculture

• Research and Rescue

Market split by Sales Channel, can be divided into:

• Direct Channel

• Distribution Channel

Market segment by Region/Country including:

• North America (United States, Canada and Mexico)

• Europe (Germany, UK, France, Italy, Russia and Spain etc.)

• Asia-Pacific (China, Japan, Korea, India, Australia and Southeast Asia etc.)

• South America Brazil, Argentina, Colombia and Chile etc.)

• Middle East & Africa (South Africa, Egypt, Nigeria and Saudi Arabia etc.)

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Table of Contents

Chapter 1 Industrial UAVs (Drone) Market Overview

1.1 Industrial UAVs (Drone) Definition

1.2 Global Industrial UAVs (Drone) Market Size Status and Outlook (2013-2028)

1.3 Global Industrial UAVs (Drone) Market Size Comparison by Region (2013-2028)

1.4 Global Industrial UAVs (Drone) Market Size Comparison by Type (2013-2028)

1.5 Global Industrial UAVs (Drone) Market Size Comparison by Application (2013-2028)

1.6 Global Industrial UAVs (Drone) Market Size Comparison by Sales Channel (2013-2028)

1.7 Industrial UAVs (Drone) Market Dynamics

1.7.1 Market Drivers/Opportunities

1.7.2 Market Challenges/Risks

1.7.3 Market News (Mergers/Acquisitions/ Expansion)

Chapter 2 Industrial UAVs (Drone) Market Segment Analysis by Player

2.1 Global Industrial UAVs (Drone) Sales and Market Share by Player (2016-2018)

2.2 Global Industrial UAVs (Drone) Revenue and Market Share by Player (2016-2018)

2.3 Global Industrial UAVs (Drone) Average Price by Player (2016-2018)

2.4 Players Competition Situation & Trends

2.5 Conclusion of Segment by Player

Chapter 3 Industrial UAVs (Drone) Market Segment Analysis by Type

3.1 Global Industrial UAVs (Drone) Market by Type

3.1.1 Airplanes

3.1.2 Multicopter

3.2 Global Industrial UAVs (Drone) Sales and Market Share by Type (2013-2018)

3.3 Global Industrial UAVs (Drone) Revenue and Market Share by Type (2013-2018)

3.4 Global Industrial UAVs (Drone) Average Price by Type (2013-2018)

3.5 Leading Players of Industrial UAVs (Drone) by Type in 2018

3.6 Conclusion of Segment by Type

Chapter 4 Industrial UAVs (Drone) Market Segment Analysis by Application

4.1 Global Industrial UAVs (Drone) Market by Application

4.1.1 Police

4.1.2 Energy

4.1.3 Land and Resources

4.1.4 Agriculture

4.1.5 Research and Rescue

4.2 Global Industrial UAVs (Drone) Sales and Market Share by Application (2013-2018)

4.3 Leading Consumers of Industrial UAVs (Drone) by Application in 2018

4.4 Conclusion of Segment by Application

Continued………………

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