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Tag Archives: Ace in One Day
October 12, 2019 Aviation357th Fighter Group, 363d Fighter Squadron, 44-13897, 44-14888, Ace, Ace in One Day, Aerial Combat, Bayerische Flugzeugwerke Bf 109, Browning Machine Gun Caliber .50 AN-M2, Charles Elwood Yeager, Chuck Yeager, Fighter Pilot, Glamorous Glenn II, Me 109, Messerschmitt AG, North American Aviation Inc., North American Aviation P-51D Mustang, P-51D-15-NA, Packard Motor Car Company, Packard V-1650-7 Merlin, RAF Leiston, Silver Star, USAAF Station 373, World War IIBryan Swopes
1st Lieutenant Chuck Yeager. (American Air Museum in Britain)
12 October 1944: During World War II, First Lieutenant Charles Elwood Yeager, Air Corps, Army of the United States (A.U.S.), was a P-51 Mustang fighter pilot assigned to the 363d Fighter Squadron, 357th Fighter Group, stationed at RAF Leiston (USAAF Station 373), near the village of Theberton, Suffolk, England.
Recently promoted from the warrant rank of Flight Officer, Lieutenant Yeager—as one of the most experienced pilots in the group— was leading the 357th on a bomber escort mission against Bremen, Germany. While the Group's 362nd and 364th Fighter Squadrons remained with the B-24 bombers, Yeager and the 363d patrolled 50 to 100 miles (80 to 160 kilometers) ahead.
At 25,000 feet (7,620 meters) over Steinhuder Meer, northwest of Hanover, Yeager sighted a group of Messerschmitt Bf 109 fighters (also called the Me 109). He was soon able to count 22. Yeager and his squadron of 16 Mustangs circled and attacked out of the sun.
A flight of three Luftwaffe Messerschmitt Me 109 fighters, 20 July 1944. (Bundsarchive Bild 101l-676-7975-36)
As Chuck Yeager maneuvered his P-51D Mustang, named Glamorous Glenn II, to fire at a trailing Bf 109, the German fighter suddenly turned left and collided with his wingman. Both pilots bailed out of their fighters and the two Bf 109s went down.
"It was almost comic, scoring two quick victories without firing a shot. . . By now, all the airplanes in the sky had dropped their wing tanks and were spinning and diving in a wild, wide-open dogfight. I blew up a 109 from six hundred yards—my third victory—when I turned to see another angling in behind me. Man I pulled back the throttle so damned hard I nearly stalled, rolled up and over, came in behind and under him, kicking right rudder and simultaneously firing. I was directly underneath the guy, less than fifty feet, and I opened up that 109 as if it were a can of Spam. That made four. A moment later, I waxed a guy's fanny in a steep dive; I pulled up at about 1,000 feet; he went straight into the ground."
— Yeager, An Autobiography, by Chuck Yeager and Leo Janos, Bantam Books, New York, 1985, at Page 57.
1st Lieutenant Charles E. Yeager with "Glamorous Glenn II," at USAAF Station 157, Raydon, Suffolk, England, 17 October 1944. (American Air Museum in Britain)
Lieutenant Yeager's official report of the air battle reads (in part):
"H. Five Me. 109s destroyed
"I. I was leading the Group with Cement Squadron and was roving out to the right of the first box of bombers. I was over STEINHUDER LAKE when 22 Me. 109s crossed in front of my Squadron from 11:00 O'Clock to 1:00 O'Clock. I was coming out of the sun and they were about 1½ miles away at the same level of 25,000 feet. I fell in behind the enemy formation and followed them for about 3 minutes, climbing to 30,000 feet. I still had my wing tanks and had close up to around 1,000 yards, coming within firing range and positioning the Squadron behind the entire enemy formation. Two of the Me. 109s were dodging over to the right. One slowed up and before I could start firing, rolled over and bailed out. The other Me. 109, flying his wing, bailed out immediately after as I was ready to line him in my sights. I was the closest to the tail-end of the enemy formation and no one, but myself was in shooting range and no one was firing. I dropped my tanks and then closed up to the last Jerry and opened fire from 600 yards, using the K-14 sight. I observed strikes all over the ship, particularly heavy in the cockpit. He skidded off to the left. I was closing up on another Me. 109 so I did not follow him down. Lt. STERN, flying in Blue Flight reports this E/A on fire as it passed him and went into a spin. I closed up on the next Me. 109 to 100 yards, skidded to the right and took a deflection shot of about 10°. I gave about a 2 second burst and the whole fuselage split open and blew up after we passed. Another Me. 109 to the right had cut his throttle and was trying to get behind. I broke to the right and quickly rolled to the left on his tail. He started pulling it in and I was pulling 6″G". I got a lead from around 300 yards and gave him a short burst. There were hits on wings and tail section He snapped to the right 3 times and bailed out when he quit snapping at around 18,000 feet. I did not blackout during this engagement due to the efficiency of the "G" suit. Even though I was skidding I hit the second Me. 109 by keeping the bead and range on the E/A. To my estimation the K-14 sight is the biggest improvement to combat equipment for Fighters up to this date. The Me. 109s appeared to have a type of bubble canopy and had purple noses and were a mousey brown all over. I claim five Me 109s destroyed.
"J. Ammunition Expended: 587 rounds .50 cal MG.
"Charles E. Yeager, 1st Lt, AC."
Lieutenant Yeager had destroyed five enemy fighters during a single battle. He became "an Ace in one day" and was awarded the Silver Star. Of the twenty-two Me 109s, the 363rd had destroyed eight without losing a single Mustang.
Yeager's Glamorous Glenn II had previously been assigned to Captain Charles K. Peters and named Daddy Rabbit. Flown by another pilot, Second Lieutenant Horace Roycroft, 44-13897 was destroyed six days later when it crashed in bad weather. Lieutenant Roycroft was killed.
North American Aviation P-51D-5-NA 44-13366 on a test flight near the North American plant at Inglewood, California. This fighter is from the same production block as Yeager's Glamorous Glenn II.
The P-51D was the predominant version of the North American Aviation World War II fighter. It was a single-seat, single-engine fighter, initially designed for the Royal Air Force. The P-51D was 32 feet, 3.5 inches (9.843 meters) long, with a wingspan of 37 feet (11.278 meters). It was 13 feet, 4.5 inches (4.077 meters) high. The fighter had an empty weight of 7,635 pounds (3,463 kilograms) and a maximum takeoff weight of 12,100 pounds (5,489 kilograms).
The P-51D was powered by a right-hand tractor, liquid-cooled, supercharged, 1,649-cubic-inch-displacement (27.04-liter) Packard V-1650-3 or -7 Merlin single overhead cam (SOHC) 60° V-12 engine with Military Power ratings of 1,380 horsepower at Sea Level, turning 3,000 r.p.m with 60 inches of manifold pressure (V-1650-3), or 1,490 horsepower at Sea Level, turning 3,000 r.p.m. with 61 inches of manifold pressure (V-1650-7). These engines were versions of the Rolls-Royce Merlin 63 and 66, built under license by the Packard Motor Car Company of Detroit, Michigan. The engine drove a four-bladed Hamilton Standard Hydromatic constant-speed propeller with a diameter of 11 feet, 2 inches (3.404 meters) through a 0.479:1 gear reduction.
A Packard Motor Car Company V-1650-7 Merlin V-12 aircraft engine at the Smithsonian Institution National Air and Space Museum. This engine weighs 1,715 pounds (778 kilograms) and produces 1,490 horsepower at 3,000 r.p.m. Packard built 55,873 of the V-1650 series engines. Continental built another 897. The cost per engine ranged from $12,548 to $17,185. (NASM)
The P-51D with a V-1650-7 Merlin had maximum speed at Sea Level of 323 miles per hour (520 kilometers per hour) at the Normal Power setting of 2,700 r.p.m. and 46 inches of manifold pressure, and 375 miles per hour (604 kilometers per hour) at War Emergency Power, 3,000 r.p.m with 67 inches of manifold pressure (5 minute limit). At altitude, using the Military Power setting of 3,000 r.p.m. and 61 inches of manifold pressure (15 minute limit), it had a maximum speed of 439 miles per hour (707 kilometers per hour) at 28,000 feet (8,534 meters). With War Emergency Power the P-51D could reach 442 miles per hour (711 kilometers per hour) at 26,000 feet (7,925 meters).
The P-51D could climb to 20,000 feet (6,096 meters) in 6.4 minutes, and to its service ceiling, 41,600 feet (12,680 meters), in 28 minutes. The airplane's absolute ceiling was 42,400 feet (12,924 meters).
With 180 gallons (681 liters) internal fuel, the maximum range of the P-51D was 1,108 miles (1,783 kilometers).
Armorers carry Browning AN-M2 .50-caliber machine guns and belts of linked .50-caliber ammunition to a P-51 Mustang. (U.S. Air Force)
The P-51D was armed with six electrically-heated Browning AN-M2 .50-caliber machine guns, with three mounted in each wing. 400 rounds of ammunition were provided for the inner pair of guns, and 270 rounds for each of the other four guns, for a total of 1,880 rounds of ammunition. This was armor piercing, incendiary, and tracer ammunition. The fighter could also carry a 1,000 pound (453.6 kilogram) bomb under each wing in place of drop tanks, or up to ten rockets.
North American Aviation P-51D Mustang. (U.S. Air Force)
A total of 8,156 P-51Ds were produced by North American at Inglewood, California, and Dallas, Texas, and another 200 by the Commonwealth Aircraft Corporation, Melbourne, Australia.
The North American Aviation P-51D Mustang remained in service with the United States Air Force until 27 January 1957, when the last aircraft were retired from the 167th Fighter Squadron, West Virginia National Guard.
North American Aviation P-51D-25-NT Mustang 44-84900 at NACA Langley Memorial Aeronautical Laboratory, circa 1945–1952. (NASA)
April 7, 2019 AviationAce in One Day, Aichi D3A Type 99, Curtiss Electric Propellers, Grumman Aircraft Engineering Corporation, Grumman F4F-4 Wildcat, Henderson Field, James Elms Swett, Kawasaki Ki-61 Hien Type 3, Marine Fighting Squadron 221, Medal of Honor, Pratt & Whitney R-1830-86, Russell Islands, USS Bunker Hill (CV-17), VMF-221, World War IIBryan Swopes
First Lieutenant James Elms Swett, United States Marine Corps Reserve. (U.S. Naval Institute)
The President of the United States takes pleasure in presenting the MEDAL OF HONOR to
FIRST LIEUTENANT JAMES E. SWETT
UNITED STATES MARINE CORPS RESERVE
for service as set forth in the following CITATION:
For conspicuous gallantry and intrepidity at the risk of his life above and beyond the call of duty, as a division leader in Marine Fighting Squadron TWO TWENTY-ONE in action against enemy Japanese aerial forces in the Solomon Islands Area, April 7, 1943. In a daring flight to intercept a wave of 150 Japanese planes, First Lieutenant Swett unhesitatingly hurled his four-plane division into action against a formation of fifteen enemy bombers and during his dive personally exploded three hostile planes in mid-air with accurate and deadly fire. Although separated from his division while clearing the heavy concentration of anti-aircraft fire, he boldly attacked six enemy bombers, engaged the first four in turn, and unaided, shot them down in flames. Exhausting his ammunition as he closed the fifth Japanese bomber, he relentlessly drove his attack against terrific opposition which partially disabled his engine, shattered the windscreen and slashed his face. In spite of this, he brought his battered plane down with skillful precision in the water off Tulagi without further injury. The superb airmanship and tenacious fighting spirit which enabled First Lieutenant Swett to destroy eight enemy bombers in a single flight were in keeping with the highest traditions of the United States Naval Service.
/S/ FRANKLIN D. ROOSEVELT
James Elms Swett was born at Seattle, Washington, 15 June 1920, the first of three children of George Elms Swett, an electrical engineer and U.S. Marine Corps reservist, and Nellie Emily Burns Swett. He grew up in San Mateo, California, where he attended San Mateo High School and the College of San Mateo. While in college, Swett learned to fly through the Civilian Pilot Training Program.
Swett enlisted in the United States Navy as a Seaman 2nd Class, 26 August 1941. He had brown hair and blue eyes, was 5 feet, 11 inches (1.803 meters) tall and weighed 154 pounds (69.9 kilograms). Seaman Swett was assigned to flight training as an Aviation Cadet at NAS Corpus Christi, Texas. While in training, Sweet elected to serve as a U.S. Marine Corps officer. On completion of flight training, James Swett was awarded the gold wings of a Naval Aviator and commissioned a Second Lieutenant, United States Marine Corps Reserve, 1 April 1942. He was then sent to MCAS Quantico at Quantico, Virginia, for advanced training.
In July 1942, 2nd Lieutenant Swett was placed under arrest for a period of ten days for "diving and zooming over traffic" below 500 feet (152 meters), along U.S. Route 1. He was then transferred to an air station in Florida.
Swett was next assigned to Marine Fighting Squadron 221 (VMF-221), Marine Air Group 21 (MAG-21), 1st Marine Air Wing, Fleet Marine Force. In March 1943, the squadron deployed from Hawaii to the South Pacific aboard the Bogue-class escort carrier USS Nassau (CVE-16), arriving at Espiritu Santo, New Hebrides, in late March. VMF-221 then flew on to Henderson Field, Guadalcanal, in the Solomon Islands.
Grumman F4F Wildcat fighters at Henderson Field, Guadalcanal, 1943.
On the morning of 7 April 1943, Lieutenant Swett led a four-plane flight of Wildcats on a patrol, then returned to refuel at Henderson Field. While his fighter, Grumman F4F-4 Wildcat, Bu. No. 12084, was being serviced, word came of a large group of enemy aircraft approaching from the north. Swett and his flight joined a number of other fighters to intercept the attacking enemy aircraft.
Near the Russell Islands, about 30 miles (48 kilometers) northwest of Guadalcanal, the American fighters came in contact with an estimated 150 enemy aircraft. Swett, in combat for the first time, quickly engaged three Aichi D3A Type 99 (American reporting name, "Val") dive bombers. He shot them down. Becoming separated from his flight, he continued to engage the enemy, shooting down several more. His right wing was damaged by American anti-aircraft guns, but he continued. Having shot down seven Vals, he engaged an eighth. The Val's gunner fired his two 7.7 mm machine guns in defense. By this time, Swett was running out of ammunition, but his final bullets killed the enemy gunner and set the Aichi on fire. Machine gun bullets fired from the Val damaged his windshield, punctured an engine oil cooler and set the Wildcat on fire.
Aichi D3A Type 99 dive bomber, Allied reporting name, "Val". (San Diego Air and Space Museum Photo Archives)
Unable to make it back to Henderson, Swett ditched in the ocean near Tulagi. The airplane quickly sank. It was about 25 feet (7 meters) down before Swett was able to escape from the Wildcat's cockpit. He was picked up by a U.S. Coast Guard patrol boat. Lieutenant Swett was listed as wounded in action.
During only fifteen minutes, 2nd Lieutenant Swett destroyed seven enemy aircraft and damaged an eighth.¹ He had become an "Ace in One Day."
This Grumman F4F Wildcat on the sea floor near Tulagi may be Lieutenant Swett's F4F-4, Bu. No. 12084. (Dive PlanIt)
Swett was promoted to 1st Lieutenant and transitioned to the Chance Vought F4U-1 Corsair. He shot down four Mitsubishi G4M "Betty" twin-engine medium bombers and an A6M Zero, before being shot down again near New Georgia, 10 July 1943. He returned to combat in October, shot down two more Val dive bombers and a Kawasaki Ki-61 Hien Type 3 fighter, known to Allied forces as "Tony."
During a ceremony held at Espiritu Santo, 10 October 1943, Major General Ralph Johnson Mitchell, commanding the 1st Marine Air Wing, presented First Lieutenant James Elms Swett, United States Marine Corps Reserve, the Medal of Honor.
In 1944, Captain Swett was returned to the United States, and was training with VMF-221 at MCAS Santa Barbara, California. He would meet President Franklin D. Roosevelt at the White House during the Spring.
Oakland Tribune, Vol. CXL, No. 20, Thursday, 20 January 1944, Page 14, Columns 5–7
Captain Swett married Miss Lois Aileen Anderson 22 January 1944 at St. Paul's Episcopal Church in Burlingame, California. They would later have two sons, both of whom would go on to become Marine Corps officers.
After retraining in Southern California, VMF-221 returned to the War, assigned to the Essex-class aircraft carrier USS Bunker Hill (CV-17) in the western Pacific.
Chance Vought F4U-1D Corsair aboard USS Bunker Hill (CV-17), 6 May 1945. (U.S. Navy)
On the morning of 11 May 1945, Captain Swett was flying a combat air patrol in an F4U-1D Corsair, when the fleet was attacked by kamikaze suicide aircraft. Swett shot down a Yokosuka D4Y Suisei ("Judy") dive bomber.
During this attack, Bunker Hill was hit by two successive kamikazes and suffered catastrophic damage. 346 men were killed in action, 43 missing in action and 264 wounded. The carrier would survive, but was out of action for the remainder of the war.
Unable to land aboard their carrier, Captain Swett organized the airplanes still airborne and led them to USS Enterprise (CV-6).
30 seconds after the first, a second Mitsubishi A6M Zero crashes into USS Bunker Hill (CV-17), 1005 hours, 11 May 1945. (U.S. Navy)
During World War II, Major Swett flew 103 combat missions. He is officially credited with 15.5 aerial victories.
Following World War II, Major Swett remained in the Marine Corps Reserve. In 1949, he took command of Marine Fighting Squadron 141 (VMF-141) at NAS Oakland. He was recalled to active duty during the Korean War, but was not sent to the war zone.
James Swett rose to the rank of Colonel. He retired from the Marine Corps in 1970.
In addition to the Medal of Honor, during his career in the Marine Corps, Colonel Swett was awarded the Distinguished Flying Cross with gold star (2 awards); Purple Heart with gold star (2 awards); Air Medal with 4 gold stars (5 awards); Navy Combat Action Ribbon; Presidential Unit Citation with 2 bronze stars (3 awards); Navy Unit Commendation Ribbon; Asiatic-Pacific Campaign Medal with 1 gold star and 1 silver star (6 campaigns); World War II Victory medal; and the Armed Forces Reserve Medal with silver hourglass device (20 years of service).
Colonel James Elms Swett, United States Marine Corps Reserve (Retired), died at Mercy Medical Center in Redding, California, 18 January 2009, at the age of 88 years. He was buried at the Northern California Veterans Cemetery, Igo, California.
Major James E. Swett, United States Marine Corps Reserve
The Grumman F4F-4 Wildcat was a single-engine, single place mid-wing monoplane with retractable landing gear, designed for operations from United States Navy aircraft carriers. The wings could be fold alongside the fuselage for storage.
The F4F-4 was 28 feet, 10-5/8 inches (8.804 meters) long with a wingspan of 38 feet, 0 inches (11.582 meters) and height of 12 feet, 1-3/8 inches (3.693 meters). The Wildcat's wing had 0° angle of incidence. The fixed, inner wing has 0° dihedral, while the outer wing panels have 5° dihedral. There is no sweep. The width of the airplane with its wings folded was 14 feet, 6 inches (4.420 meters). The fighter's empty weight was 5,895 pounds (2,674 kilograms), and the gross weight, 7,975 pounds (3,618 kilograms).
Grumman F4F Wildcat, circa 1942-1943. (U.S. Navy)
The F4F-4 was powered by an air-cooled, supercharged, 1,829.399-cubic-inch-displacement (29.98 liter) Pratt & Whitney Twin Wasp SSC7-G (R-1830-86) two-row, fourteen cylinder radial engine with a compression ratio of 6.7:1. The R-1830-86 was rated at 1,100 horsepower at 2,550 r.p.m. at 3,500 feet (1,067 meters), 1,000 horsepower at 2,550 r.p.m. at 19,000 feet (5,791 meters), and 1,200 horsepower at 2,700 r.p.m. for takeoff, burning 100-octane gasoline. The engine drove a three-bladed Curtiss Electric propeller with a diameter of 9 feet, 9 inches (2.972 meters) through a 3:2 gear reduction. The R-1830-86 was 5 feet, 7.44 inches (1.713 meters) long, 44 feet, 0.19 inches (1.224 meters) in diameter, and weighed 1,560 pounds (708 kilograms).
The F4F-4 Wildcat had a maximum speed 275.0 miles per hour (442.6 kilometers per hour) at Sea Level, and 318.0 miles per hour (511.8 kilometers per hour) at 19,400 feet (5,913 meters). Its service ceiling was 34,800 feet (10,607 meters), and it had a maximum range of 765 miles (1,231 kilometers).
The F4F-4 was armed with six air-cooled Browning AN-M2 .50-caliber machine guns mounted in the wings with 1,440 rounds of ammunition.
Between February 1940 and August 1945, 7,898 Wildcats were produced by the Grumman Aircraft Engineering Corporation, Bethpage, New York, and General Motors Eastern Aircraft Division at Linden, New Jersey.
Grumman F4F-4 Wildcat. (U.S. Navy)
¹ Various reliable sources give different values for the number of enemy aircraft shot down by 2nd Lieutenant Swett on 7 May 1943, with the most common being five. Swett claimed eight destroyed, and this is reflected in his Medal of Honor citation. The intelligence officer who investigated determined that his claims were valid. The USMC History Division credits seven enemy aircraft destroyed: "Colonel James Elms Swett, of San Mateo, California, earned the Medal of Honor in World War II for shooting down seven Japanese bombers within 15 minutes."


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Haldane's Sieve
Discussing preprints in population and evolutionary genetics
Author post: Probabilities of Fitness Consequences for Point Mutations Across the Human Genome
Posted on September 12, 2014 by Joe Pickrell
This guest post is by Adam Siepel on his group's paper Gulko et al. Probabilities of Fitness Consequences for Point Mutations Across the Human Genome, bioRxived here.
Four Genomicists in a Subaru
The idea for this paper emerged during a long drive across New York State, from Cold Spring Harbor to Ithaca, after the 2011 Biology of Genomes meeting. Two postdocs in my research group, Ilan Gronau and Leo Arbiza, were riding with me in my old Subaru, trying not to express too much alarm at my distracted driving. Also with us in the car was Ran Blekhman, who was at the time a postdoc with Andy Clark. (Ran is now an assistant professor at the University of Minnesota.)
Our conversation turned to important open questions in computational genomics, and in particular, to ways of making better use of the vast quantities of functional genomic data being pumped out of projects such as ENCODE. At the time, Ilan, Leo, and I were thinking a lot about how to use patterns of within-species polymorphism and between-species divergence to shed light on the influence of natural selection on regulatory sequences. Along these lines, we had spent much of the spring developing our new INSIGHT method [1,2], and we had just presented this work for the first time at Biology of Genomes. Ran, however, was pushing us to think less about abstract evolutionary questions and more about genomic function and disease association. He made a strong case that the biggest obstacle to progress in medically related human genomics was the absence of adequate functional annotations in noncoding regions of the human genome.
For a while the conversation went in circles, as we grasped for ways of measuring "functional potential" across the genome that would make use of genomic data yet be grounded in evolutionary theory. Then it suddenly dawned on us that we already had in hand a key piece of what we needed. The INSIGHT program was designed to estimate, for any collection of nucleotide positions across the genome, the fraction (denoted ρ) of those positions that were directly influenced by natural selection, in the sense that point mutations at those positions tended either to increase or to decrease the fitness of an organism. We realized that an alternative way of interpreting ρ was as a probability that the nucleotide at each position in the analyzed collection influenced fitness, assuming exchangeability of sites (as INSIGHT does).
All that we needed, therefore, was a general way of partitioning nucleotide positions from across the genome into distinct classes that were reasonably homogeneous in their functional roles. We could then estimate ρ for each class using INSIGHT, and assign to each genomic position the estimate of ρ for the partition to which that position belonged. This procedure would produce a "score" across the genome that looked roughly like widely used evolutionary conservation scores, but instead of representing local divergence patterns across the mammalian phylogeny, the score at each position would be estimated from groupwise patterns of polymorphism and divergence and would be directly interpretable as a probability of fitness consequences. Later Ilan would dub these "fitCons" scores, to emphasize this fitness-related interpretation. ("FitCons" also nicely parallels "phastCons," our first conservation-scoring method.)
Because INSIGHT measures selection on recent time scales, fitCons scores would circumvent a major shortcoming of standard evolutionary conservation scores—that they require functional roles to have remained consistent over very long evolutionary time periods (tens to hundreds of millions of years) in order to be detectable in divergence patterns at individual sites or small loci. In principle, fitCons scores should be able to detect selection (hence potential function) at sites whose functional role had emerged quite recently, perhaps even along the human lineage.
The Problem of Grouping
The piece that was still missing in our plan was a particular scheme for grouping together similar genomic sites from across the genome. We did not get to the point of working this problem out in any detail during our revelatory drive to Ithaca, and, as it happened, it took several more months to settle on a solution. By this time, a Ph.D. student from Computer Science, Brad Gulko, had joined the project and assumed the lead in implementing a prototype of the scores.
At first, Brad, Ilan, Leo, and I spent some time thinking about fancy algorithms for clustering genomic sites that would consider functional and evolutionary information jointly. However, it did not take long to realize that this was a hard problem. Eventually, we decided to move forward with a simple grouping scheme, based on functional genomic data alone. This would allow us to cluster genomic sites in a pre-processing step and avoid the need for an iterative solution. Our hunch was that the scores would not be too sensitive to the grouping scheme as long as it was reasonable. As we discuss in our article, it may be worthwhile to revisit this clustering approach eventually, but it appears to be adequate for our current purposes. (I hope to convince Brad to discuss some of the technical issues with the clustering problem in an upcoming blog post.)
Relevance to the "Share Under Selection" in the Human Genome
By the fall of 2012, we had finally settled on an initial set of fitCons tracks and were beginning to observe decent prediction performance for cis-regulatory elements, when the human genomics community was thrown into a frenzy by a deluge of publications and accompanying press releases from the ENCODE Consortium. This event led to the now-famous controversy over what fraction of the genome is truly "functional" and whether ENCODE's measures of "reproducible biochemical activity" (which apply to over 80% of the genome) were comparable in any meaningful way to the "share under selection" (SUS) estimated from comparative genomics (which generally came out to 5–10%).
I do not wish to rehash the familiar terms of this debate here, but I do want to focus on one aspect of it that was particularly relevant to our work. Many of the criticisms of ENCODE reminded readers that comparative genomic analyses pointed to a SUS of ~5–10%, suggesting that 80% might be a gross over-estimate of the functional content of the genome. However, others pointed out that these comparative-genomic estimates applied only to the fraction of the genome that had been under long-term selective constraint, because evolutionary turnover of functional elements—if it occurred at appreciable rates—could bias estimates based on long-term genomic divergence substantially downward. (For the latest chapter in this saga, see a recent paper by Gerton Lunter, Chris Ponting, and colleagues [3].)
We realized that the fitCons scores could help address aspects of this controversy, because they were based on patterns of variation over much more recent time scales and should therefore be much less sensitive to turnover than scores based on divergence patterns across the mammalian phylogeny. Moreover, the fitCons scores, by making use of INSIGHT to interpret patterns of polymorphism and divergence, might provide substantially better estimates of the quantities of interest than simple analyses of SNP densities or allele frequencies, a few of which had appeared among the ENCODE publications. Finally, the INSIGHT-based estimates are unique in that they directly predict the SUS, without the need for separate thresholding, mixture deconvolutions, or enrichment analyses.
Somewhat surprisingly, when we estimated the SUS based on fitCons scores, we obtained values (4.2–7.5%) that were quite similar to those based on conservation patterns in mammals. There are a number of tricky technical issues involved in this type of estimation—for example, concerning the corrections for local mutation rates and coalescence times—but violations of our modeling assumptions generally should tend to push our estimated upper bound (7.5%) to conservatively high values, implying that the true value is lower than 7.5%. In addition, the correction we have applied to obtain our lower bound (4.2%) is quite conservative, making it likely that the true value is higher than 4.2%. Therefore we have high confidence that the fraction of the genome under detectable selection from the available polymorphism and divergence data is indeed fairly close to 5%. As we discuss in the paper, it is important to bear in mind that the absolute values of these estimates reflect constraint on the identities of individual nucleotides only, and do not take into consideration higher order constraints, for example, on element lengths or spacing. Nevertheless, the similarity of the estimates based on mammalian divergence and human polymorphism suggest that evolutionary turnover has not produced a major downward bias in conservation-based estimates of the SUS.
Ilan and I soon realized that we could go a step further in this analysis and compare the fitCons-based estimates with parallel estimates based on the same functional categories but a measure of natural selection based on divergence only. The idea here was to perform a direct "apples to apples" comparison of the fraction of the genome under selection as measured on two different time scales: the 1–5 million-year time scale measured by fitCons and the ~30 million-year time scale measured by an analogous method based on divergence patterns in four primate genomes (human, chimpanzee, orangutan, and rhesus macaque), which we called "fitConsD" (the "D" is for "divergence"). I won't attempt to describe this analysis in detail here, but our general conclusion is that the estimates of selection are highly similar on these two different time scales, suggesting further that evolutionary turnover has not had a dramatic effect on the functional content of the human genome over the past 30 million years or so. It is worth noting that Lunter and colleagues' recent analysis is not strictly incompatible with ours (they estimate 7.1–9.2% constraint at present and focus on turnover over longer time scales) but their qualitative interpretation suggests large amounts of turnover, while ours suggests modest amounts.
Scooped by CADD… or Perhaps Not
As grant proposals, other manuscripts, and job searches led to delays in writing up our work through 2013, we began to hear rumblings on social media about a method called CADD, developed by Greg Cooper and Jay Shendure's groups, that sounded alarmingly similar to fitCons. Then, in early 2014, a paper by Kircher, Witten et al. describing CADD appeared in Nature Genetics [4]. When we saw this paper, our initial impression was that we had been scooped by sitting on a good idea for too long. CADD was described as a method that integrated functional and evolutionary data and produced a measure of "relative pathogenicity" across the entire genome, and it was motivated, in part, by its potential usefulness in noncoding as well as coding regions. The paper included several impressive-looking ROC plots in which CADD apparently outperformed conservation based methods such as phastCons, phyloP, and GERP by a significant margin. In addition, CADD made use of a support vector machine (SVM), which was potentially a highly flexible and powerful means for considering large numbers of covariates with arbitrarily complex correlations.
We decided, with a certain amount of dread, that we needed to add CADD to our empirical performance comparisons on putative regulatory elements. At the time, fitCons was showing clear advantages in predictive power for cis regulatory elements compared with conservation-based methods and a functional annotation database called RegulomeDB. FitCons had several potential advantages over CADD—for example, it made direct use of polymorphism data for prediction, it considered covariates in a cell-type-specific manner, and it avoided a need for brute-force simulations through its use of INSIGHT for inference—but we thought that the use of the SVM in CADD made it unlikely that fitCons could compete with it in a pure classification task. Nevertheless, Brad dutifully downloaded the CADD scores, added them to his experiments, and displayed curves for CADD in his ROC plots for three types of regulatory elements (ChIP-seq-supported transcription factor binding sites, eQTLs, and enhancer predictions based on chromatin marks).
To our surprise, fitCons significantly outperformed CADD in all of these tests. This was true for three different types of putative regulatory elements, and true whether or not we considered cell-type-specific test sets. In fact, CADD performed essentially no better than conventional conservation scores in these tests, in apparent contradiction to the results presented in the CADD paper.
A closer reading of the CADD paper revealed a possible explanation for these observations. While the method was motivated, in part, by its applicability to the entire genome, the authors' validation experiments heavily emphasized coding regions. In fact, it appears that even the ROC plot for "genome-wide" results (Figure 3a in the paper) is actually based almost exclusively (>92% by our interpretation of the paper) on missense variants in coding regions. The experiments that included substantial numbers of noncoding sites, in turn, were much more indirect—for example, by showing correlations with derived allele frequencies (Figure 2), known disease-causing status (Figure 4), and changes in expression in saturation mutagenesis experiments at two enhancers and one promoter. It is possible to have correlations of this kind without having substantial predictive power for regulatory variants.
When Greg Cooper saw our initial preprint on bioRxiv, he raised two major objections to our validation experiments. First, he pointed out that we were measuring the sensitivity of the fitCons scores in terms of bulk coverage of elements, when those elements actually consist of a mixture of sites at which mutations are deleterious and sites at which mutations are neutral or nearly neutral (such as degenerate positions in transcription factor binding sites). This approach to measuring sensitivity may be overly generous to the fitCons scores, which are relatively "blocky" along the genome, varying little from one site to the next, in comparison to higher-resolution prediction methods that properly distinguish between functionally important and neutral sites within elements. Second, Greg pointed out that we were using a naive genome-wide background set for our eQTL, which did not properly account for the ascertainment scheme used for eQTL identification.
We felt that these were fair and reasonable criticisms, and needed to be addressed. Therefore, we revised our validation experiments to consider only high-information-content positions in transcription factor binding sites (a proxy for functionally important nucleotides), and to use a more appropriate control for eQTL. The details of these follow-up experiments are described in our revised preprint (now on bioRxiv), but the bottom line was that they had almost no effect on our ROC plots. In other words, the apparent performance advantages of fitCons over CADD and other divergence-based methods is not an artifact of our experimental design but appears to reflect real advantages of the method. While Greg is correct that the coarse, "blocky" nature of the fitCons scores is a limitation of our current methods, the method still appears to perform significantly better than any competing method in distinguishing putatively functional regulatory nucleotides from background sequence. In other words, while scores that exhibit more variation from one nucleotide to the next—such as CADD, GERP, and phyloP—may appear on the surface to have higher predictive resolution, much of that variation is uninformative about regulatory function, and, on balance, the "blocky" fitCons scores are more useful in prediction.
We have spent some time trying to understand the differences in performance between fitCons and CADD, and believe we have some insights into why fitCons performs significantly better on regulatory elements. (What follows are our conjectures only; the authors of CADD do not agree with our analysis.) While the SVM in CADD is potentially a strength, we believe that it is substantially limited in this case by the use of a linear kernel and by pooling features across cell types, rather than focusing separately on each cell type of interest. In addition, we think there is a fundamental problem with the optimization scheme used by CADD. The SVM in CADD is trained by a global strategy, in the sense that a single set of parameter values is selected (for a given choice of training set and generalization parameter) to obtain an optimal fit, on average, across all examples in the training set. Thus, if it is true that different covariates are relevant in coding and noncoding regions, as expected, then the method will have to make tradeoffs between these types of sites. If the "signal" for training (i.e., the contribution toward the SVM's objective function) is stronger in one type of region than another, it is likely that the tradeoff will favor that type of region. Because constraint will be strongest, on average, in coding regions, leading to higher rates of difference between simulated and observed variants in these regions, it seems likely that these regions will indeed dominate in the training procedure, and this may explain CADD's superior performance in coding regions and its weaker performance in noncoding regions. FitCons avoids this problem by applying INSIGHT separately to each class of sites.
These observations raise the interesting possibility of a modified CADD that addresses some of these limitations. There is no reason why CADD couldn't be trained separately on noncoding and coding regions, perhaps with different sets of covariates for each type of sites. Moreover, regulation-associated covariates could be treated in a cell-type-specific manner. A modified CADD designed along these lines (regulatory CADD, or rCADD?) could provide an interesting alternative to fitCons.
When we first discussed the idea for the fitCons scores during our drive across New York State three years ago, I envisioned a quick spin-off project that could be completed in perhaps half a year. As so often happens in research, several unanticipated challenges arose in completing this work, but we also found unexpected opportunities to connect our analysis with important open questions in the field. In addition, we were stimulated to think about the problem of combining functional and evolutionary data in new and deeper ways by another paper that addressed a similar problem but in a fundamentally different way. The end result is a paper I am quite proud of—one that provides what I think will be a useful resource to the genomics community and that also offers new insights into longstanding evolutionary questions.
[1] Gronau, I., Arbiza, L., Mohammed, J., & Siepel, A. (2013). Inference of Natural Selection from Interspersed Genomic Elements Based on Polymorphism and Divergence. Molecular Biology and Evolution. doi:10.1093/molbev/mst019
[2] Arbiza, L., Gronau, I., Aksoy, B. A., Hubisz, M. J., Gulko, B., Keinan, A., & Siepel, A. (2013). Genome-wide inference of natural selection on human transcription factor binding sites. Nature Genetics, 45(7), 723–729. doi:10.1038/ng.2658
[3] Rands, C. M., Meader, S., Ponting, C. P., & Lunter, G. (2014). 8.2% of the Human genome is constrained: variation in rates of turnover across functional element classes in the human lineage. PLoS Genetics, 10(7), e1004525. doi:10.1371/journal.pgen.1004525
[4] Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics. doi:10.1038/ng.2892
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9 thoughts on "Author post: Probabilities of Fitness Consequences for Point Mutations Across the Human Genome"
Joshua Schraiber on September 12, 2014 at 1:37 pm said:
Thanks for this awesome and detailed write-up!
When Fernando Racimo and I saw your preprint, we definitely had a few moments of dread considering our work mapping CADD scores to fitness effects ( http://biorxiv.org/content/early/2014/06/19/002345.short ). However at this point I'm relatively okay with our usage of the C-scores, because we were able to estimate the DFE based on frequency data which was NOT used to define the C-scores. Hence, we're not caught in some kind of circular argument where we find that there are more low frequency alleles in categories where there are more low frequency alleles!
A couple of things did strike me about our mapping of C-scores to selection coefficients, and I wonder if they have any relevance to the different power of CADD vs. FitCons. First, we found that there's a "plateau" in the mapping of C-to-s, suggesting that many C-scores don't correspond to qualitatively different patterns of selection. On the other hand, however, we found that we could almost never reject the null hypothesis that a C-score mapped to a single selection coefficient in favor of C-scores mapping to a distribution of selection coefficients.
I guess one thing we could try is to use FitCons scores in our pipeline; on the other hand, I'm worried about the circularity due to allele frequency info being used to learn FitCons scores.
I'm not really sure if or how this relates to anything, but I figured this was as good a time as any to brain dump 🙂
Adam Siepel (@asiepel) on September 15, 2014 at 6:18 pm said:
Thanks, Josh. I think your paper with Fernando is very interesting. Overall, I don't think it's inappropriate to use the CADD scores in your method. They do seem to perform at least as well as conservation scores for many classes of elements, and they do particularly well in coding regions. I think the main implication of our observations for your study is that it is likely that CADD is assigning too low a score to many cis-regulatory elements relative to the scores assigned in coding regions. That should tend to broaden the distribution of selective effects you infer for each CADD-based partition, and perhaps drive the mean (or MLE) values of |s| upward for weak to moderate C scores. Thus, I would expect a bias toward a shallower downward slope of -s as a function of C. You can perhaps see a hint of this in your Figure S4 for the higher B scores, which should be depleted for genes and enriched for CREs (if I am interpreting the figure correctly).
I think you are probably right that it wouldn't make a lot of sense to use fitCons in your framework. If you were to bin sites by fitCons score, you'd essentially be pulling back out the same clusters of sites that we defined, and then you'd be estimating a selection coefficient for each such cluster using methods not all that different from ours (though I would like to think we do get a little something by using the divergence data together with the polymorphism data). In a strange sort of way, I think what you have done is a bit like reverse engineering fitCons scores from CADD scores! Of course, you are doing more by pulling out actual estimates of s but you are also perhaps limited by the partitioning by C. Admittedly a fitCons-centric view but perhaps an interesting one nonetheless. Anyway, would be very interested in any additional work you do on this topic.
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redpajama_set_name: RedPajamaCommonCrawl


is used for database operations instead of writing SQL queries.
is an object that contains zero or more records from one table. Another way to say this is that a GlideRecord is an ordered list.
A GlideRecord contains both records (rows) and fields (columns). The field names are the same as the underlying database column names.
For additional information, refer to GlideRecord.


If you believe and I believe, then Africa will be saved! These stirring words, had all delegates on their feet, holding hands and singing from their hearts at the close of a colourful and emotional opening ceremony of the 10th Ordinary General Meeting of the Africa Alliance of YMCAs (AAYMCA).
Held in Dakar, Senegal, on 2 June 2015, the ceremony was attended by 150 delegates from 18 different African countries, and partners from 8 different international YMCAs or YMCA-associated organisations.
A most colourful and energetic roll call was enacted through a flag ceremony, with youth carrying the flags of their countries and delegates joining in the dancing and festivities. Poems and songs carried the formal programme which included speeches by Adrien Coly, President of the Senegal YMCA, James Ekow Rhule, President of the AAAYMCA, Johan Vilhelm Eltvik, Secretary General of World YMCA, and Gabriel Preira, Secretary General of the Ministry of Youth, Senegal.
Majory Mambwe, S2C Ambassador from Zambia YMCA, gave a strong testimony on her journey of youth empowerment and had many in tears with her as she struggled to contain her emotions.
And so the scene was set… proudly African, we joined together to celebrate our unity through diversity. Together we are moving forward as an African YMCA movement. Together, we are striving for the African Renaissance. Together, we believe.


From the breathtakingly cool Thriller celebrating the legend that is Michael Jackson to the powerful tragedies of Shakespeare and the fun of a traditional pantomime, The Everyman Theatre always has a show for everyone and the NEW Spring season looks set to be just as fabulous as the last!
The new season kicks off on the 7th February when Samuel Beckett's incredible literary classic Waiting for Godot starring Alan Digweed aka Tweedy as Estragon comes to the Everyman Theatre for the first time ever. A classic story of friendship, perseverance, patience and the belief in hope beyond hope, the new production of Waiting for Godot is brought to you by the team behind The Quartet, Mrs. Warren's Profession and The Glass Menagerie. This iconic play is followed beautifully by the powerful courtroom sensation The Verdict starring Ian Kelsey, which opens on Tuesday 19th February. Tweedy will also be back on the stage at the end of March for three performances only in Tweedy's Illusion Confusion, a brand new show of magic and mayhem.
Light relief can be found for the younger audience in the form of the 'delightful' smash hit comedy The Tiger Who Came to Tea, which takes to the stage on the 20th February for half term week. Then it's back to business with the hilarious laugh out loud comedy Caroline's Kitchen, which opens on Tuesday 26th February before a shift to a slightly darker humour in Glengarry Glen Ross, the 'razor sharp revival' of David Mehmet's Olivier and Pulitzer Prize- winning masterpiece. If this wasn't exhilarating enough, then more thrills and spills can be found in Agatha Christie's classic murder mystery The Mousetrap, which takes to the stage mid March. With murder lurking 'around every corner,' I'll be taking mum along for this one to keep me safe. Subject to my survival, I'm then hoping to catch the Classic Screen to Stage performance of Rain Man, starring the gorgeous Paul Nicholls and Chris Fountain, which comes to The Everyman towards the end of April.
If you're in need of a few laughs and a good night out then keep your eye out for the comedy masterpiece Art, which comes to the theatre in March with an all star cast including Nigel Havers, Denis Lawson and Stephen Tompkinson. The hilarious Avenue Q musical is then back by popular demand in April. There's also a fabulous line up of special productions where you can sing along to the inspirational songs of The Greatest Showman in March and spend a night with Elvis, pay tribute to the legendary Freddie Mercury and have a beer and a laugh with Al Murray as Landlord of Hope and Glory in June.
As for me, the highlight of the new season line up will once again see me tapping my toes, singing out loud and dancing in the aisles to the amazing musicals which will take to the stage in the form of the exuberant 50th anniversary production of the legendary rock musical Hair, winner of the 2018 WhatsOnStage Award. With the feel good musical of the year Club Tropicana in April I'll be transported firmly back to the electric 80's of my youth though luckily this time without the padded shoulder pads, permed hair and shellsuit. In May, Fly Eddie Fly, a new musical based on the heartwarming story of the nation's most infamous ski jumper, Eddie the Eagle Edwards, is set to continue the musical theme in style.
If you would like more information as to what else is coming up in the NEW season at The Everyman Theatre and also in the more intimate surroundings of The Irving Studio Theatre, then everything you need is on the website. The new brochure will also give you a sneak preview of what to expect later this year so make sure you pick up a copy when you can! Don't forget to check out the lovely new menu at The Café- Bar when you're at the theatre. You can also enjoy lunch or dinner in the comfort of Matchams restaurant or add a little sparkle to your visit in the Director's Lounge!
It sounds like it's going to be another season to remember! Don't miss it!


It is mandatory that all St. Patrick's students wear our school uniform. Our uniform policy conforms to the "Code of Dress" guidelines of the Toronto Catholic District School Board. The purpose of the school uniform is to instil a sense of pride and identity with the St. Patrick's community. As well, the neat appearance of students contributes to the good order and atmosphere in the school, and a uniform contributes to the overall school safety and security. Students should be neat and well groomed at all times. Head apparel of any kind is not to be worn on the school premises. Bandanas are prohibited at St. Patrick's; regardless of where or how they are worn.
Students must wear the complete uniform while on school property at all times during the school day, including the lunch period. Alternate footwear and outer jackets may be worn to and from school in inclement weather, but they must be stored in student lockers as soon as the student enters the building. Students must remove their hats as soon as they enter the building. All students are expected to wear the uniform to and from school.


text:
Q: ListPointPlot3D filling between two lists I'm trying to connect pairs of corresponding points from two lists in a ListPointPlot3D (similar to what was done here but in 3D).  Filling-> {1->{2}} isn't working for me, so I presume the problem must lie in how I'm passing the two lists.
SeedRandom[11];
sigma = DiagonalMatrix[{0.001, 0.001, 0.000001}];
sp = 5;
means1 = Flatten /@
   Transpose[{Table[RandomReal[{0, 1}, 2], {sp}], Table[0.8, {sp}]}];
dist1 = MixtureDistribution[Table[1, {sp}],
   Table[MultinormalDistribution[means1[[i]], sigma], {i,
     Table[j, {j, sp}]}]
   ];
means2 = Flatten /@
   Transpose[{Table[RandomReal[{0, 1}, 2], {sp}], Table[0.2, {sp}]}];
dist2 = MixtureDistribution[Table[1, {sp}],
   Table[MultinormalDistribution[means2[[i]], sigma], {i,
     Table[j, {j, sp}]}]
   ];
n = 50;
data1 = RandomVariate[dist1, n];
data2 = RandomVariate[dist2, n];
ListPointPlot3D[{data1, data2}, PlotRange -> {{0, 1}, {0, 1}, {0, 1}},
  Filling -> {1 -> {2}}]

A: The following might be useful for others to see my mistake:
SeedRandom[11];
sp = 4;
maxVar = 0.02;
means = Table[RandomReal[{0, 1}, 2], {sp}];
dist = MixtureDistribution[Table[1, {sp}],
   Table[BinormalDistribution[means[[i]],
     RandomReal[{0.01, maxVar}, 2], 0.5], {i, Table[j, {j, sp}]}]
   ];
n = 50;
pos = RandomVariate[dist, n];
data1 = Flatten /@ Transpose[{pos, Table[0.8, {n}]}];
data2 = Flatten /@ Transpose[{pos, Table[0.2, {n}]}];

Show[ListPointPlot3D[{data1, data2}, PlotStyle -> {Red, Blue},
  PlotLegends -> {"data1", "data2"}],
 Graphics3D[{Gray, Line@Transpose[{data1, data2}]}]]

redpajama_set_name: RedPajamaStackExchange


A venue only becomes a leading venue because they deliver what clients want, whether that be essentials done well, or memorable embellishments. Here's a short list of some of the elements that the world's best venues have in common.
Members of a professional meetings alliance, like IACC, are obligated to make a commitment to quality standards covering a range of best practices from management of the physical environment to the services and support they provide.
It could be opportunities for networking among delegates or educational sessions that happen without a hiccup. The top venues make the success of their customer's meetings and events their priority.
Technology has become an obvious part of the recipe for successful meetings. "Good quality venues offer the same levels of technology that clients enjoy in their office environments and even their homes within a setting that brings the best out of delegates," said Craig Emmerson, Group Business Development Manager at Prestige Retreats.
Top meeting venues do what it takes to accommodate their customer's technology requirements: the latest audiovisual equipment, teleconferencing tools or hi-speed internet. Staying updated on the latest meeting technology available is an important goal for the top-rated meeting venues.
No one likes to get a nasty surprise attached to the invoice. The best meeting venues work closely with meeting planners to provide a service within the company's budget, but without hidden costs. And according to Staging Connections, clients will be looking for straightforward quotes.
Flexible delegate packages incorporate catering, technology, event planning, meeting rooms and guest rooms. However they do it, venues can create meeting packages and pricing to accommodate the needs of their clients.
Of course, the key for pricing transparency is a written agreement up front. This will help meeting planners to focus on must-haves and more successfully manage event budgets.
Networking with other venue professionals that have the highest standards provides an advantage for high-caliber event and meeting properties that aim to stay in the top 1% in the world in terms of service and offerings.
A global network will also give you someone who can offer trustworthy advice. "IACC can help with the real day-to-day challenges within a business when you need a second opinion from someone not living the business you are in every day," added Karsten.


text:
September weather forecast and climate
ClimateJFMAMJJASeptemberONDYear
Weather in September
Average sea temperature in September
Average daylight in September
In September, the average sea temperature in Moss, Norway, is 15.8°C (60.4°F).
Note: For swimming, a surface water temperature of 15.8°C (60.4°F) is not enjoyable for the majority. It is not considered safe, as controlling breathing becomes progressively more difficult as water temperature decreases below 21°C (69.8°F).
In September, the average length of the day in Moss is 12.8h.
How cold is the water in September in Moss?
In Moss, Norway, in September, the average water temperature is 15.8°C (60.4°F). For swimming, a surface water temperature of 15.8°C (60.4°F) is not enjoyable for the majority. It is not considered safe, as controlling breathing becomes progressively more difficult as water temperature decreases below 21°C (69.8°F).
What is the average length of the day in September in Moss?
Average sea temperature in September: 15.8°C
Average daylight in September: 12.8h
Weather Atlas: Moss, Norway - September weather forecast and climate information. Moss, Norway - September weather averages including high and low temperatures, humidity, rainfall, snowfall, daylight, sunshine, UV index, and sea temperature. [1575846]


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The high-tech contact lens is due to be previewed at the Consumer Electronics Show in Las Vegas next week, becoming a rival to "wearable computing" Google Glass, the futuristic glasses that can shoot video or photos literally with a wink of an eye.
The contact lenses can be worn in the usual fashion, with or without the Innovega eyewear. When the eyeglasses, fitted with micro-projectors, are worn, any media can be instantly streamed onto a transparent lens, and the wearer's view of their surroundings is not hampered in any way. "The benefit of simultaneously offering vision correction is particularly important to Asian consumer segments where the prevalence of nearsightedness is near twice that of their non-Asian counterparts," Innovega co-founder and CEO Steve Willey said.
The company has thus far been funded by the National Science Foundation (NSF), the Defense Advanced Research Projects Agency (DARPA), and by an undisclosed Asian company, according to CNET.com. Willey explained that Innovega was founded to tackle the problem that has proved to discourage consumers across the world, namely that both the quality and quantity of digital media available from mobile devices often exceeds the capability of tiny mobile displays.
"Whatever runs on your smartphone would run on your eyewear," Willey told CNET. "At full HD. Whether it's a window or immersive."
According to Willey, the iOptik contact lens-based approach "has broken this bottleneck," delivering high-performance imagery from compact eyewear by eliminating all the usual magnifying optics from digital eyewear and integrating them into a nano-tech contact lens. The novel contact lens enhances vision and enables wearers to better visualize their digital world. It allows light from the display to pass through the center of the pupil, and light from the surrounding environment to pass through the outer portion of the pupil. Willey was quoted as saying that the contact lenses could generate displays with a screen size "equivalent to a 240-inch television, viewed at a distance of 10 feet."
Right now, the iOptik system syncs with Android smartphones and, according to Willey, will be able to allow developers to design specific tools for any devices in which the iOptik system could be used.
Technology companies across the world have been fighting for a place in the wearable gadgets market, becoming a key growth area. However, when Google Glass was unveiled, many worried that should the device become widely used, privacy would be breached with the help of covert recording. A "Stop the Cyborgs" campaign has been launched in the US, designed "to stop a future in which privacy is impossible and where the iron cage of surveillance, calculation and control pervades every aspect of life."


The Iowa County is monthly publication produced for Iowa county officials. The magazine is full of information about current legislation, innovations in government, features on county programs, interviews with state leaders and reports on ISAC programs and services.
If you are looking for a specific article click here for the magazine indexes.


text:
Q: How to set the splitpanes split line style to a grey solid line? I follow the splitpanes tutorial, get two panes with the split line:

But I want to have the split line style like below, a grey solid line.

I also follow the tutorial, set the custom CSS for splitpanes, but do not work, my code is below:

<template>

    <Splitpanes class="default-theme" horizontal style="height:100%;">
      <Pane size="100%;">
          1
        </Pane>
        <Pane size="5" min-size="5" max-size="5">
          2
        </Pane>
    </Splitpanes>

</template>


text:
Q: Значение свойства target объекта Event При создании обработчика событий столкнулся с непонятным поведением Mozilla Firefox 13.
Для обработки экземпляра события использовал не хитрый код:
function funcName(event){
  var event = (event)? event : window.event,
      target = (event.target)? event.target : event.srcElement;
}

Дальше я работаю с этими переменными по своему усмотрению и скрипт работает в браузерах Chrome 20, Safari 5, Opera 12, IE7-9! Но отказывается в Mozilla 13, сообщая о том, что target underfined. Не можете подсказать по какой причине это происходит и как это поведение исправить?
A: Ошибка  во втором условии. Необходимо проверять следующим образом:
var target = (event)? event.target : event.srcElement;

Очередной раз - просто невнимательность, которую другие браузеры лояльно "пропустили".

PS: ответ был написан мной же на случай подобной возникновения на форуме подобного вопроса.


Do you often wake up feeling tired and exhausted? Do you feel sleepy and lethargic for the most part of the day? Do you feel your poor night sleep is costing your work, enjoyment and other activities? Are you having serious relationship issues with your spouse or bed partner because of your snoring in the night? If your answer to most of these above mentioned questions is a big yes, there is good news for you.
With good morning anti snore solution, you need not have to suffer in silence anymore. There are so many people in the world who suffer from various kinds of sleeping problems, the most common of all these is snoring. Snoring in simplest terms can be defined as a whistling or squeaking sound that a person makes during sleep while breathing. Snoring hence is more of a breathing problem than a sleeping problem.
Since, snoring causes noise, it is not only disturbing to the victim but also affects the sleep of the person sleeping next to the victim. There are today various kinds of anti snoring devices available in the market that are targeted to help stop snoring in the night while sleeping. You must have heard about anti snoring sprays, anti snoring pillows, anti snoring mouth pieces, and anti snoring rings that claim to stop snoring.
However, despite the tall claims made by these different anti snoring aids in the market, they hardly prevent the snoring sound in the night thus hardly help with the problem. Moreover, since these products are designed to help prevent a person from making the noisy breathing sound, they do not treat the cause of the problem thus not helping much with the proper treatment or cure of the problem.
Good morning anti snore solution, in such a scenario proves to be one of the best anti snoring aid in the market with its ability to not just control the disturbing sound while breathing during sleep but also address the root cause of snoring, which is in most cases an obstruction in the airways passage in the body. As an anti snoring mouth piece, good morning anti snore solution has been designed and developed by world's renowned researchers and scientists in the related field.
The product has been clinically tested for efficiency and efficacy. Also in terms of comfort and convenience, good morning anti snore solution has been proved to be one of the best anti snoring device in the market. The device comes with a 60 day money back trial offer, which is an added bonus. There are no side effects or harm of using good morning anti snore solution and can therefore be used easily and comfortably by anyone looking to deal with his/her snoring problem.


The Palm Treo 700w is a Windows Mobile-powered smartphone that offered an alternative for users who want or need to use Microsoft software.
The Palm Treo 700w comes in silver. It is carried by Verizon. The phone has a QWERTY keyboard and a 1.3 megapixel camera. The phone runs Windows Mobile 5. The phone also comes with Bluetooth and a SD card slot which supports both memory cards and SDIO cards.


BERNINA International AG has been one of the world's leading sewing and embroidery	a good research paper service machine manufacturers for more than 120 years. The Swiss family-owned company's products are synonymous with innovation and precision. Above all, they are known for their durability. BERNINA sewing machines are used worldwide by women with a passion for sewing. Our passion is the development of products that allow you to put your creative ideas into practice. Here, you can find out what makes BERNINA special and learn about our values and our philosophy. We are proud of the fact that we are the only sewing machine manufacturer still producing in the western world. At our headquarters in Steckborn, Switzerland, we can look back on a tradition that dates back more than 120 years.


FootyFirst: that's the subject of our newest podcast.
Australian Rules football (AKA 'Footy') is big sport Down Under: by some measures it is the country's most popular sport. Footy is played on the professional level–the Australian Football League (AFL) season is fully under way. It is also played by many on the community level, and the injury rate is apparently rather high.
I came away from this podcast having learned a lot from Dr. Donaldson: about injury prevention, about FootyFirst, and about Footy. For a guy who used to think of "Arena Football League" when he heard the term "AFL"; a guy who thought of Brad Wing when he thought of Footy…..well, let's just say, I think I've come a long way.
I know I'm already looking forward to getting to the MCG (Melbourne Cricket Ground) some day to see a game! You'll feel the same way when you listen to the podcast or read the blog post interview I conducted with Dr. Donaldson. And for a limited time, this "Editor's Pick" is freely available on our website: check it out.
Injury prevention, that's the name of the game in sports medicine: whether it is the AFL (Footy) or the AFL (Arena Football)! So click on this link to hear our conversation with Dr. Donaldson, or go to iTunes to download the podcast. Enjoy!


Aaaaaargh~! Happy Halloween to all! Stay toasty and enjoy something (naturally) orange!
I'm going to spend this week trying to update the blog, giving it's look and feel a more "finished" presence. I truly am not internet and computer savvy, so there is a great learning curve here for me. I'm doing my best, it will just likely take some extra time to get things feeling the way that I want them to here. So, I might not post much while the changes occur, but please check back in a week to see the updates, posts, and love to come!
he looks SO much like you here!!!


text:
//
//  Generated by the J2ObjC translator.  DO NOT EDIT!
//  source: ./core/src/java/org/apache/lucene/analysis/Analyzer.java
//

#include "J2ObjC_header.h"

#pragma push_macro("INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer")
#ifdef RESTRICT_OrgApacheLuceneAnalysisAnalyzer
#define INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer 0
#else
#define INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer 1
#endif
#undef RESTRICT_OrgApacheLuceneAnalysisAnalyzer

#if __has_feature(nullability)
#pragma clang diagnostic push
#pragma GCC diagnostic ignored "-Wnullability"
#pragma GCC diagnostic ignored "-Wnullability-completeness"
#endif

#if !defined (OrgApacheLuceneAnalysisAnalyzer_) && (INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer || defined(INCLUDE_OrgApacheLuceneAnalysisAnalyzer))
#define OrgApacheLuceneAnalysisAnalyzer_

#define RESTRICT_JavaIoCloseable 1
#define INCLUDE_JavaIoCloseable 1
#include "java/io/Closeable.h"

@class JavaIoReader;
@class OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy;
@class OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents;
@class OrgApacheLuceneAnalysisTokenStream;
@class OrgApacheLuceneUtilCloseableThreadLocal;
@class OrgApacheLuceneUtilVersion;

/*!
 @brief An Analyzer builds TokenStreams, which analyze text.It thus represents a
  policy for extracting index terms from text.

  In order to define what analysis is done, subclasses must define their
 TokenStreamComponents in createComponents(String).
  The components are then reused in each call to tokenStream(String, Reader).

  Simple example:

  Analyzer analyzer = new Analyzer() {
   @@Override
    protected TokenStreamComponents createComponents(String fieldName) {
      Tokenizer source = new FooTokenizer(reader);
      TokenStream filter = new FooFilter(source);
      filter = new BarFilter(filter);
      return new TokenStreamComponents(source, filter);
    }
  };

@endcode
  For more examples, see the Analysis package documentation.

  For some concrete implementations bundled with Lucene, look in the analysis modules:

Common:
        Analyzers for indexing content in different languages and domains.

ICU:
        Exposes functionality from ICU to Apache Lucene.

Kuromoji:
        Morphological analyzer for Japanese text.

Morfologik:
        Dictionary-driven lemmatization for the Polish language.

Phonetic:
        Analysis for indexing phonetic signatures (for sounds-alike search).

Smart Chinese:
        Analyzer for Simplified Chinese, which indexes words.

Stempel:
        Algorithmic Stemmer for the Polish Language.

UIMA:
        Analysis integration with Apache UIMA.

 */
@interface OrgApacheLuceneAnalysisAnalyzer : NSObject < JavaIoCloseable > {
 @public
  OrgApacheLuceneUtilCloseableThreadLocal *storedValue_;
}
@property (readonly, class, strong) OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *GLOBAL_REUSE_STRATEGY NS_SWIFT_NAME(GLOBAL_REUSE_STRATEGY);
@property (readonly, class, strong) OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *PER_FIELD_REUSE_STRATEGY NS_SWIFT_NAME(PER_FIELD_REUSE_STRATEGY);

#pragma mark Public

/*!
 @brief Create a new Analyzer, reusing the same set of components per-thread
  across calls to tokenStream(String, Reader).
 */
- (instancetype __nonnull)init;

/*!
 @brief Expert: create a new Analyzer with a custom ReuseStrategy.

  NOTE: if you just want to reuse on a per-field basis, it's easier to
  use a subclass of AnalyzerWrapper such as

  PerFieldAnalyerWrapper instead.
 */
- (instancetype __nonnull)initWithOrgApacheLuceneAnalysisAnalyzer_ReuseStrategy:(OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *)reuseStrategy;

/*!
 @brief Frees persistent resources used by this Analyzer
 */
- (void)close;

/*!
 @brief Just like getPositionIncrementGap, except for
  Token offsets instead.By default this returns 1.
 This method is only called if the field
  produced at least one token for indexing.
 @param fieldName the field just indexed
 @return offset gap, added to the next token emitted from tokenStream(String,Reader).
          This value must be >= 0.
 */
- (jint)getOffsetGapWithNSString:(NSString *)fieldName;

/*!
 @brief Invoked before indexing a IndexableField instance if
  terms have already been added to that field.This allows custom
  analyzers to place an automatic position increment gap between
  IndexbleField instances using the same field name.
 The default value
  position increment gap is 0.  With a 0 position increment gap and
  the typical default token position increment of 1, all terms in a field,
  including across IndexableField instances, are in successive positions, allowing
  exact PhraseQuery matches, for instance, across IndexableField instance boundaries.
 @param fieldName IndexableField name being indexed.
 @return position increment gap, added to the next token emitted from tokenStream(String,Reader).
          This value must be >= 0.
 */
- (jint)getPositionIncrementGapWithNSString:(NSString *)fieldName;

/*!
 @brief Returns the used ReuseStrategy.
 */
- (OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *)getReuseStrategy;

/*!
 @brief Return the version of Lucene this analyzer will mimic the behavior of for analysis.
 */
- (OrgApacheLuceneUtilVersion *)getVersion;

/*!
 @brief Set the version of Lucene this analyzer should mimic the behavior for for analysis.
 */
- (void)setVersionWithOrgApacheLuceneUtilVersion:(OrgApacheLuceneUtilVersion *)v;

/*!
 @brief Returns a TokenStream suitable for fieldName, tokenizing
  the contents of reader.

  This method uses createComponents(String) to obtain an
  instance of TokenStreamComponents. It returns the sink of the
  components and stores the components internally. Subsequent calls to this
  method will reuse the previously stored components after resetting them
  through TokenStreamComponents.setReader(Reader).

  NOTE: After calling this method, the consumer must follow the
  workflow described in TokenStream to properly consume its contents.
  See the Analysis package documentation for
  some examples demonstrating this.
 NOTE: If your data is available as a String, use
 tokenStream(String, String) which reuses a StringReader-like
  instance internally.
 @param fieldName the name of the field the created TokenStream is used for
 @param reader the reader the streams source reads from
 @return TokenStream for iterating the analyzed content of reader
 @throw AlreadyClosedExceptionif the Analyzer is closed.
 @throw IOExceptionif an i/o error occurs.
 - seealso: #tokenStream(String, String)
 */
- (OrgApacheLuceneAnalysisTokenStream *)tokenStreamWithNSString:(NSString *)fieldName
                                               withJavaIoReader:(JavaIoReader *)reader;

/*!
 @brief Returns a TokenStream suitable for fieldName, tokenizing
  the contents of text.

  This method uses createComponents(String) to obtain an
  instance of TokenStreamComponents. It returns the sink of the
  components and stores the components internally. Subsequent calls to this
  method will reuse the previously stored components after resetting them
  through TokenStreamComponents.setReader(Reader).

  NOTE: After calling this method, the consumer must follow the
  workflow described in TokenStream to properly consume its contents.
  See the Analysis package documentation for
  some examples demonstrating this.
 @param fieldName the name of the field the created TokenStream is used for
 @param text the String the streams source reads from
 @return TokenStream for iterating the analyzed content of reader
 @throw AlreadyClosedExceptionif the Analyzer is closed.
 @throw IOExceptionif an i/o error occurs (may rarely happen for strings).
 - seealso: #tokenStream(String, Reader)
 */
- (OrgApacheLuceneAnalysisTokenStream *)tokenStreamWithNSString:(NSString *)fieldName
                                                   withNSString:(NSString *)text;

#pragma mark Protected

/*!
 @brief Creates a new TokenStreamComponents instance for this analyzer.
 @param fieldName the name of the fields content passed to the
            TokenStreamComponents
   sink as a reader
 @return the TokenStreamComponents for this analyzer.
 */
- (OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *)createComponentsWithNSString:(NSString *)fieldName;

/*!
 @brief Override this if you want to add a CharFilter chain.

  The default implementation returns reader
  unchanged.
 @param fieldName IndexableField name being indexed
 @param reader original Reader
 @return reader, optionally decorated with CharFilter(s)
 */
- (JavaIoReader *)initReaderWithNSString:(NSString *)fieldName
                        withJavaIoReader:(JavaIoReader *)reader OBJC_METHOD_FAMILY_NONE;

@end

J2OBJC_STATIC_INIT(OrgApacheLuceneAnalysisAnalyzer)

J2OBJC_FIELD_SETTER(OrgApacheLuceneAnalysisAnalyzer, storedValue_, OrgApacheLuceneUtilCloseableThreadLocal *)

/*!
 @brief A predefined ReuseStrategy  that reuses the same components for
  every field.
 */
inline OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *OrgApacheLuceneAnalysisAnalyzer_get_GLOBAL_REUSE_STRATEGY(void);
/*! INTERNAL ONLY - Use accessor function from above. */
FOUNDATION_EXPORT OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *OrgApacheLuceneAnalysisAnalyzer_GLOBAL_REUSE_STRATEGY;
J2OBJC_STATIC_FIELD_OBJ_FINAL(OrgApacheLuceneAnalysisAnalyzer, GLOBAL_REUSE_STRATEGY, OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *)

/*!
 @brief A predefined ReuseStrategy that reuses components per-field by
  maintaining a Map of TokenStreamComponent per field name.
 */
inline OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *OrgApacheLuceneAnalysisAnalyzer_get_PER_FIELD_REUSE_STRATEGY(void);
/*! INTERNAL ONLY - Use accessor function from above. */
FOUNDATION_EXPORT OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *OrgApacheLuceneAnalysisAnalyzer_PER_FIELD_REUSE_STRATEGY;
J2OBJC_STATIC_FIELD_OBJ_FINAL(OrgApacheLuceneAnalysisAnalyzer, PER_FIELD_REUSE_STRATEGY, OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *)

FOUNDATION_EXPORT void OrgApacheLuceneAnalysisAnalyzer_init(OrgApacheLuceneAnalysisAnalyzer *self);

FOUNDATION_EXPORT void OrgApacheLuceneAnalysisAnalyzer_initWithOrgApacheLuceneAnalysisAnalyzer_ReuseStrategy_(OrgApacheLuceneAnalysisAnalyzer *self, OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *reuseStrategy);

J2OBJC_TYPE_LITERAL_HEADER(OrgApacheLuceneAnalysisAnalyzer)

#endif

#if !defined (OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_) && (INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer || defined(INCLUDE_OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents))
#define OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_

@class JavaIoReader;
@class OrgApacheLuceneAnalysisReusableStringReader;
@class OrgApacheLuceneAnalysisTokenStream;
@class OrgApacheLuceneAnalysisTokenizer;

/*!
 @brief This class encapsulates the outer components of a token stream.It provides
  access to the source (Tokenizer) and the outer end (sink), an
  instance of TokenFilter which also serves as the
 TokenStream returned by
 Analyzer.tokenStream(String, Reader).
 */
@interface OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents : NSObject {
 @public
  /*!
   @brief Original source of the tokens.
   */
  OrgApacheLuceneAnalysisTokenizer *source_;
  /*!
   @brief Sink tokenstream, such as the outer tokenfilter decorating
  the chain.This can be the source if there are no filters.
   */
  OrgApacheLuceneAnalysisTokenStream *sink_;
  /*!
   @brief Internal cache only used by Analyzer.tokenStream(String, String).
   */
  OrgApacheLuceneAnalysisReusableStringReader *reusableStringReader_;
}

#pragma mark Public

/*!
 @brief Creates a new TokenStreamComponents instance.
 @param source the analyzer's tokenizer
 */
- (instancetype __nonnull)initWithOrgApacheLuceneAnalysisTokenizer:(OrgApacheLuceneAnalysisTokenizer *)source;

/*!
 @brief Creates a new TokenStreamComponents instance.
 @param source the analyzer's tokenizer
 @param result the analyzer's resulting token stream
 */
- (instancetype __nonnull)initWithOrgApacheLuceneAnalysisTokenizer:(OrgApacheLuceneAnalysisTokenizer *)source
                            withOrgApacheLuceneAnalysisTokenStream:(OrgApacheLuceneAnalysisTokenStream *)result;

/*!
 @brief Returns the component's Tokenizer
 @return Component's Tokenizer
 */
- (OrgApacheLuceneAnalysisTokenizer *)getTokenizer;

/*!
 @brief Returns the sink TokenStream
 @return the sink TokenStream
 */
- (OrgApacheLuceneAnalysisTokenStream *)getTokenStream;

#pragma mark Protected

/*!
 @brief Resets the encapsulated components with the given reader.If the components
  cannot be reset, an Exception should be thrown.
 @param reader a reader to reset the source component
 @throw IOException
 if the component's reset method throws an IOException
 */
- (void)setReaderWithJavaIoReader:(JavaIoReader *)reader;

// Disallowed inherited constructors, do not use.

- (instancetype __nonnull)init NS_UNAVAILABLE;

@end

J2OBJC_EMPTY_STATIC_INIT(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents)

J2OBJC_FIELD_SETTER(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents, source_, OrgApacheLuceneAnalysisTokenizer *)
J2OBJC_FIELD_SETTER(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents, sink_, OrgApacheLuceneAnalysisTokenStream *)
J2OBJC_FIELD_SETTER(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents, reusableStringReader_, OrgApacheLuceneAnalysisReusableStringReader *)

FOUNDATION_EXPORT void OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_initWithOrgApacheLuceneAnalysisTokenizer_withOrgApacheLuceneAnalysisTokenStream_(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *self, OrgApacheLuceneAnalysisTokenizer *source, OrgApacheLuceneAnalysisTokenStream *result);

FOUNDATION_EXPORT OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *new_OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_initWithOrgApacheLuceneAnalysisTokenizer_withOrgApacheLuceneAnalysisTokenStream_(OrgApacheLuceneAnalysisTokenizer *source, OrgApacheLuceneAnalysisTokenStream *result) NS_RETURNS_RETAINED;

FOUNDATION_EXPORT OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *create_OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_initWithOrgApacheLuceneAnalysisTokenizer_withOrgApacheLuceneAnalysisTokenStream_(OrgApacheLuceneAnalysisTokenizer *source, OrgApacheLuceneAnalysisTokenStream *result);

FOUNDATION_EXPORT void OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_initWithOrgApacheLuceneAnalysisTokenizer_(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *self, OrgApacheLuceneAnalysisTokenizer *source);

FOUNDATION_EXPORT OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *new_OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_initWithOrgApacheLuceneAnalysisTokenizer_(OrgApacheLuceneAnalysisTokenizer *source) NS_RETURNS_RETAINED;

FOUNDATION_EXPORT OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *create_OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents_initWithOrgApacheLuceneAnalysisTokenizer_(OrgApacheLuceneAnalysisTokenizer *source);

J2OBJC_TYPE_LITERAL_HEADER(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents)

#endif

#if !defined (OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy_) && (INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer || defined(INCLUDE_OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy))
#define OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy_

@class OrgApacheLuceneAnalysisAnalyzer;
@class OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents;

/*!
 @brief Strategy defining how TokenStreamComponents are reused per call to
 Analyzer.tokenStream(String, java.io.Reader).
 */
@interface OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy : NSObject

#pragma mark Public

/*!
 @brief Sole constructor.
 (For invocation by subclass constructors, typically implicit.)
 */
- (instancetype __nonnull)init;

/*!
 @brief Gets the reusable TokenStreamComponents for the field with the given name.
 @param analyzer Analyzer from which to get the reused components. Use
 getStoredValue(Analyzer)  and setStoredValue(Analyzer, Object)         to access the data on the Analyzer.
 @param fieldName Name of the field whose reusable TokenStreamComponents         are to be retrieved
 @return Reusable TokenStreamComponents for the field, or null
          if there was no previous components for the field
 */
- (OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *)getReusableComponentsWithOrgApacheLuceneAnalysisAnalyzer:(OrgApacheLuceneAnalysisAnalyzer *)analyzer
                                                                                withNSString:(NSString *)fieldName;

/*!
 @brief Stores the given TokenStreamComponents as the reusable components for the
  field with the give name.
 @param fieldName Name of the field whose TokenStreamComponents are being set
 @param components TokenStreamComponents which are to be reused for the field
 */
- (void)setReusableComponentsWithOrgApacheLuceneAnalysisAnalyzer:(OrgApacheLuceneAnalysisAnalyzer *)analyzer
                                                    withNSString:(NSString *)fieldName
       withOrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents:(OrgApacheLuceneAnalysisAnalyzer_TokenStreamComponents *)components;

#pragma mark Protected

/*!
 @brief Returns the currently stored value.
 @return Currently stored value or null if no value is stored
 @throw AlreadyClosedExceptionif the Analyzer is closed.
 */
- (id)getStoredValueWithOrgApacheLuceneAnalysisAnalyzer:(OrgApacheLuceneAnalysisAnalyzer *)analyzer;

/*!
 @brief Sets the stored value.
 @param storedValue Value to store
 @throw AlreadyClosedExceptionif the Analyzer is closed.
 */
- (void)setStoredValueWithOrgApacheLuceneAnalysisAnalyzer:(OrgApacheLuceneAnalysisAnalyzer *)analyzer
                                                   withId:(id)storedValue;

@end

J2OBJC_EMPTY_STATIC_INIT(OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy)

FOUNDATION_EXPORT void OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy_init(OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy *self);

J2OBJC_TYPE_LITERAL_HEADER(OrgApacheLuceneAnalysisAnalyzer_ReuseStrategy)

#endif

#if __has_feature(nullability)
#pragma clang diagnostic pop
#endif
#pragma pop_macro("INCLUDE_ALL_OrgApacheLuceneAnalysisAnalyzer")

redpajama_set_name: RedPajamaGithub


A young woman named Fuu is working as a waitress in a tea shop when she is harassed by a band of samurai. She is saved by Mugen, a mysterious rogue, and Jin, a young ronin. Mugen attacks Jin after he proves to be a worthy opponent and they begin fighting one another and inadvertently cause the death of Shibui Tomonoshina, the magistrate's son. For this crime they are to be executed. But with some help from Fuu, they escape, and Fuu asks them to travel with her to find "the samurai who smells of sunflowers."
If you seen cowboybebop, the violence is basically the same, only with swords instead of guns most of the time.


text:
Samsung quits Blu-ray players: Are we witnessing the slow death of physical media?
While books and vinyl records seem to have won the battle as physical media of choice for their individual mediums, what is the equivalent for film? Laserdisc or VHS?
Last week, Samsung revealed in a statement to CNET it would cease production of Blu-ray players for the US market by the end of 2019. Despite 2018 seeing record spending on home entertainment content, sales of physical media such as DVD and Blu-ray have been dramatically declining over the past few years. Is this the beginning of the end for physical media?
"Samsung will no longer introduce new Blu-ray or 4K Blu-ray player models in the US market," was the short and succinct statement from a Samsung spokesperson to CNET last week.
Samsung is by no means the biggest player in the US Blu-ray player market. Panasonic and Sony still rule that territory, with both companies continuing to develop and manufacture high-end disc players. However, Samsung's decision to pull out of the Blu-ray player market certainly feels like a sign of an oncoming shift. A shift that is profoundly reflected in the significant sales decline of physical media over the past few years.
"The Christmas from hell"
Retailers in the United Kingdom were hit hard late last year with reports claiming DVD and Blu-ray sales in the week leading up to Christmas were down by over 30 percent compared to the same time in 2017. Kim Bayley, from Entertainment Retailers Association called it the "Christmas from hell," but perhaps most striking was the fact that overall entertainment sales in the UK actually grew by nine percent over 2018.
Streaming services, video on demand and general digital media sales are booming worldwide. In the United States, subscription streaming revenue exploded in 2018 with 30 percent growth over past 12 months alone. While overall home entertainment revenue seems to be steadily growing, consumers are steadfastly moving away from spending their money on physical media.
For the second year running disc sales in the United States have dropped by 15 percent. Across 2018, DVD and Blu-ray sales accounted for just US$4 billion, and before you remark at how large a number that is, do note that across the same period subscription streaming generated $12.9 billion.
It is clear physical media is on the decline but what is not clear is where the bottom in this falling market is, as consumers rapidly shift to streaming and VOD models. Both the music and book industries seemed to have consolidated a form of physical media as audiences return to different forms of collectable media.
Over the last couple of years there has been a fascinating upswing in book sales as the e-book trend seems to have peaked and people returned to the pleasures of the printed page. The music industry has also battled with this shift, and while streaming services such as Spotify and iTunes still reign supreme, vinyl has become the collectable physical media of choice for music fans, with year on year sales repeatedly growing.
So what is the collectable physical medium for film and television that will dominate the future market? 4K Blu-ray? Laserdisc? VHS?
In many ways the decline of physical media is a boon for corporate interests. It disrupts our traditional conception of ownership, taking away the idea of buying a copy of a film that we can permanently hold and watch, and replacing it with a temporary access pass to an abstract digital collection. This loss of real objective ownership has surely pushed a sizable amount of consumers back into buying books and vinyl records. But a book doesn't need a special player to let you read it, and while a vinyl record needs a turntable, this kind of nostalgic technology has turned into quite the object of affection for many.
Does a DVD or Blu-ray player fill you with the same sense of nostalgic warmth? In 20 or 30 years will we lovingly pull out an old DVD from the ancient collection and slip it into a crusty, barely functioning player the same way we flick through a record collection and drop it onto a turntable?
#Blu-ray
#DVD
#HD-DVD
#New Atlas Audio
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text:
Srebro(I) fluorid je hemijsko jedinjenje, koje ima molekulsku masu od 126,867 -{Da}-.

Osobine

Reference

Literatura

Spoljašnje veze

Једињења сребра
Флуориди

redpajama_set_name: RedPajamaWikipedia


The NoShok Series 11 Sanitary Pressure Transmitter is designed for heavy-duty sanitary applications where high accuracy and durability are required. Using diffused semiconductor or sputtered thin film sensor technology, these transducers are stable, accurate, shock resistant and extremely durable.
The NoShok Series 11 Sanitary Pressure Transmitter is available in gauge ranges from vacuum to 600 psi. Basic accuracy is ±0.25% full scale with higher accuracy (±0.125% full scale) available as an option. The housing is constructed of 316SS and welded to the process connection for greater strength and integrity. The available 1-1/2 inch or 2 inch Tri-Clamp connection is constructed of 316L stainless steel and includes an integral cooling extension allowing for higher media temperatures (up to 300°F). Wetted parts are electro-polished to Ra25 micro-inch or better.
NoShok Series 11 Sanitary Transmitters meet 3A requirements for the food & beverage, dairy, pharmaceutical and biotechnology industries in addition to ASME BPE-2002 and CE compliant. The housing meets NEMA 4X, IP65 environmental ratings. Series 11 transmitters can be cleaned-in-place (CIP) or steamed-in-place (SIP).


Published 04/20/2019 08:16:28 pm at 04/20/2019 08:16:28 pm in Topsy Turvy Tomato Trees.
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