Wikipedia’s co-founder takes on Facebook with ad-free social network - 8bitsrule
https://thenextweb.com/insider/2019/11/14/wikipedias-co-founder-takes-on-facebook-with-ad-free-social-network/
======
gniv
This feature could be a huge differentiator:

> “Almost everything on the platform is editable,” he said. “That alone gives
> a huge incentive for good behaviour because if you say something obnoxious,
> someone will just delete it.” However, while this approach has succeeded on
> Wikipedia, it is largely untested on a social network.

(Quote is from the FT article linked from TFA)

~~~
Thorentis
That sounds terrible. The whole point of a social network is to know what
somebody is saying / has said. It isn't about democratising the spread of
factual information. If I say x, but somebody else thinks I should have said
y, who cares? The point of a social network is to let people know that I said
x. If x is wrong, the point of a public encyclopedia (like Wikipedia) is for
people to be able to read y. But for a social network, this does not seem like
a useful feature at all.

------
phaedryx
I'm already on an ad-free social network: Mastodon.

~~~
m-p-3
And even better: it's federated.

The i stance you're using doesn't like what you say? Just pick another
instance and carry on.

------
pmlnr
So, Ello, again?
[https://news.ycombinator.com/item?id=8346429](https://news.ycombinator.com/item?id=8346429)

------
K0SM0S
Well, count me in, no question about it. Information is the fourth pillar of
democracy, and there's a direct qualitative relation from information to
debate.

We need such efforts to make substantial progress in producing information, to
the best of what should be our ability as of 2019 and onwards.

------
austhrow743
Weird. When I signed up I had access and could look around. I close it and
open it again and now I'm locked out and stuck on a waiting list unless I
donate.

I wasn't planning to be locked out so I didn't have the greatest of looks but
it seemed much heavier on the news side of thing than the network side of
things. Very limited profile, content appears to all come from groups?

~~~
mkbkn
Give it some time. Hopefully, it will get better.

~~~
austhrow743
Likely it will but news also seems to be an intentional focus. As someone who
is continuously trying to consume less news, it's a bad fit.

------
CB09_
Kind of reminds me of Aether. [https://getaether.net/](https://getaether.net/)

------
thesorrow
I wonder if ActivityPub or some sort of federation is on the roadmap


/*
 * Toradex Colibri PXA270 configuration file
 *
 * Copyright (C) 2010 Marek Vasut <marek.vasut@gmail.com>
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation; either version 2 of
 * the License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.	 See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston,
 * MA 02111-1307 USA
 */

#ifndef __CONFIG_H
#define __CONFIG_H

/*
 * High Level Board Configuration Options
 */
#define	CONFIG_CPU_PXA27X		1	/* Marvell PXA270 CPU */
#define	CONFIG_SYS_TEXT_BASE		0x0

/*
 * Environment settings
 */
#define	CONFIG_ENV_OVERWRITE
#define	CONFIG_SYS_MALLOC_LEN		(128 * 1024)
#define	CONFIG_ARCH_CPU_INIT
#define	CONFIG_BOOTCOMMAND						\
	"if mmc init && fatload mmc 0 0xa0000000 uImage; then "		\
		"bootm 0xa0000000; "					\
	"fi; "								\
	"if usb reset && fatload usb 0 0xa0000000 uImage; then "	\
		"bootm 0xa0000000; "					\
	"fi; "								\
	"bootm 0x80000;"
#define	CONFIG_BOOTARGS			"console=tty0 console=ttyS0,115200"
#define	CONFIG_TIMESTAMP
#define	CONFIG_BOOTDELAY		2	/* Autoboot delay */
#define	CONFIG_CMDLINE_TAG
#define	CONFIG_SETUP_MEMORY_TAGS
#define	CONFIG_LZMA			/* LZMA compression support */
#define	CONFIG_OF_LIBFDT

/*
 * Serial Console Configuration
 */
#define	CONFIG_PXA_SERIAL
#define	CONFIG_FFUART			1
#define CONFIG_CONS_INDEX		3
#define	CONFIG_BAUDRATE			115200

/*
 * Bootloader Components Configuration
 */
#include <config_cmd_default.h>

#define	CONFIG_CMD_NET
#define	CONFIG_CMD_ENV
#undef	CONFIG_CMD_IMLS
#define	CONFIG_CMD_MMC
#define	CONFIG_CMD_USB
#define	CONFIG_CMD_FLASH

/*
 * Networking Configuration
 *  chip on the Voipac PXA270 board
 */
#ifdef	CONFIG_CMD_NET
#define	CONFIG_CMD_PING
#define	CONFIG_CMD_DHCP

#define	CONFIG_DRIVER_DM9000		1
#define CONFIG_DM9000_BASE		0x08000000
#define DM9000_IO			(CONFIG_DM9000_BASE)
#define DM9000_DATA			(CONFIG_DM9000_BASE + 4)
#define	CONFIG_NET_RETRY_COUNT		10

#define	CONFIG_BOOTP_BOOTFILESIZE
#define	CONFIG_BOOTP_BOOTPATH
#define	CONFIG_BOOTP_GATEWAY
#define	CONFIG_BOOTP_HOSTNAME
#endif

/*
 * HUSH Shell Configuration
 */
#define	CONFIG_SYS_HUSH_PARSER		1

#define	CONFIG_SYS_LONGHELP
#ifdef	CONFIG_SYS_HUSH_PARSER
#define	CONFIG_SYS_PROMPT		"$ "
#else
#define	CONFIG_SYS_PROMPT		"=> "
#endif
#define	CONFIG_SYS_CBSIZE		256
#define	CONFIG_SYS_PBSIZE		\
	(CONFIG_SYS_CBSIZE+sizeof(CONFIG_SYS_PROMPT)+16)
#define	CONFIG_SYS_MAXARGS		16
#define	CONFIG_SYS_BARGSIZE		CONFIG_SYS_CBSIZE
#define	CONFIG_SYS_DEVICE_NULLDEV	1
#define	CONFIG_CMDLINE_EDITING		1
#define	CONFIG_AUTO_COMPLETE		1

/*
 * Clock Configuration
 */
#define	CONFIG_SYS_HZ			1000		/* Timer @ 3250000 Hz */
#define	CONFIG_SYS_CPUSPEED		0x290		/* 520MHz */

/*
 * DRAM Map
 */
#define	CONFIG_NR_DRAM_BANKS		1		/* We have 1 bank of DRAM */
#define	PHYS_SDRAM_1			0xa0000000	/* SDRAM Bank #1 */
#define	PHYS_SDRAM_1_SIZE		0x04000000	/* 64 MB */

#define	CONFIG_SYS_DRAM_BASE		0xa0000000	/* CS0 */
#define	CONFIG_SYS_DRAM_SIZE		0x04000000	/* 64 MB DRAM */

#define CONFIG_SYS_MEMTEST_START	0xa0400000	/* memtest works on */
#define CONFIG_SYS_MEMTEST_END		0xa0800000	/* 4 ... 8 MB in DRAM */

#define	CONFIG_SYS_LOAD_ADDR		PHYS_SDRAM_1
#define CONFIG_SYS_SDRAM_BASE		PHYS_SDRAM_1
#define	CONFIG_SYS_INIT_SP_ADDR		0x5c010000

/*
 * NOR FLASH
 */
#ifdef	CONFIG_CMD_FLASH
#define	PHYS_FLASH_1			0x00000000	/* Flash Bank #1 */
#define	CONFIG_SYS_FLASH_BASE		PHYS_FLASH_1

#define	CONFIG_SYS_FLASH_CFI
#define	CONFIG_FLASH_CFI_DRIVER		1

#define	CONFIG_SYS_MAX_FLASH_SECT	(4 + 255)
#define	CONFIG_SYS_MAX_FLASH_BANKS	1

#define	CONFIG_SYS_FLASH_ERASE_TOUT	(25 * CONFIG_SYS_HZ)
#define	CONFIG_SYS_FLASH_WRITE_TOUT	(25 * CONFIG_SYS_HZ)

#define	CONFIG_SYS_FLASH_USE_BUFFER_WRITE	1
#define	CONFIG_SYS_FLASH_PROTECTION		1

#define CONFIG_ENV_IS_IN_FLASH		1

#else	/* No flash */
#define	CONFIG_SYS_NO_FLASH
#define	CONFIG_SYS_ENV_IS_NOWHERE
#endif

#define	CONFIG_SYS_MONITOR_BASE		0x0
#define	CONFIG_SYS_MONITOR_LEN		0x80000

#define	CONFIG_ENV_ADDR			\
			(CONFIG_SYS_MONITOR_BASE + CONFIG_SYS_MONITOR_LEN)
#define	CONFIG_ENV_SIZE			0x40000
#define	CONFIG_ENV_SECT_SIZE		0x40000
#define CONFIG_ENV_ADDR_REDUND		(CONFIG_ENV_ADDR + CONFIG_ENV_SECT_SIZE)
#define CONFIG_ENV_SIZE_REDUND		(CONFIG_ENV_SIZE)

/*
 * GPIO settings
 */
#define	CONFIG_SYS_GPSR0_VAL	0x00000000
#define	CONFIG_SYS_GPSR1_VAL	0x00020000
#define	CONFIG_SYS_GPSR2_VAL	0x0002C000
#define	CONFIG_SYS_GPSR3_VAL	0x00000000

#define	CONFIG_SYS_GPCR0_VAL	0x00000000
#define	CONFIG_SYS_GPCR1_VAL	0x00000000
#define	CONFIG_SYS_GPCR2_VAL	0x00000000
#define	CONFIG_SYS_GPCR3_VAL	0x00000000

#define	CONFIG_SYS_GPDR0_VAL	0x08000000
#define	CONFIG_SYS_GPDR1_VAL	0x0002A981
#define	CONFIG_SYS_GPDR2_VAL	0x0202FC00
#define	CONFIG_SYS_GPDR3_VAL	0x00000000

#define	CONFIG_SYS_GAFR0_L_VAL	0x00100000
#define	CONFIG_SYS_GAFR0_U_VAL	0x00C00010
#define	CONFIG_SYS_GAFR1_L_VAL	0x999A901A
#define	CONFIG_SYS_GAFR1_U_VAL	0xAAA00008
#define	CONFIG_SYS_GAFR2_L_VAL	0xAAAAAAAA
#define	CONFIG_SYS_GAFR2_U_VAL	0x0109A000
#define	CONFIG_SYS_GAFR3_L_VAL	0x54000300
#define	CONFIG_SYS_GAFR3_U_VAL	0x00024001

#define	CONFIG_SYS_PSSR_VAL	0x30

/*
 * Clock settings
 */
#define	CONFIG_SYS_CKEN		0x00500240
#define	CONFIG_SYS_CCCR		0x02000290

/*
 * Memory settings
 */
#define	CONFIG_SYS_MSC0_VAL	0x000095f2
#define	CONFIG_SYS_MSC1_VAL	0x00007ff4
#define	CONFIG_SYS_MSC2_VAL	0x00000000
#define	CONFIG_SYS_MDCNFG_VAL	0x08000ac9
#define	CONFIG_SYS_MDREFR_VAL	0x2013e01e
#define	CONFIG_SYS_MDMRS_VAL	0x00320032
#define	CONFIG_SYS_FLYCNFG_VAL	0x00000000
#define	CONFIG_SYS_SXCNFG_VAL	0x40044004

/*
 * PCMCIA and CF Interfaces
 */
#define	CONFIG_SYS_MECR_VAL	0x00000001
#define	CONFIG_SYS_MCMEM0_VAL	0x00014307
#define	CONFIG_SYS_MCMEM1_VAL	0x00014307
#define	CONFIG_SYS_MCATT0_VAL	0x0001c787
#define	CONFIG_SYS_MCATT1_VAL	0x0001c787
#define	CONFIG_SYS_MCIO0_VAL	0x0001430f
#define	CONFIG_SYS_MCIO1_VAL	0x0001430f

#include "pxa-common.h"

#endif	/* __CONFIG_H */


Berlin derby

The Berlin derby is the name given to any association football match between two clubs in Berlin, Germany, but has more recently referred to the derby between Hertha BSC and 1. FC Union Berlin.

History

Before reunification
Despite producing more Bundesliga clubs than any other German city, Berlin derbies have been a rarity during the history of the current German top division. During the 1950s, an intense rivalry between Tennis Borussia Berlin and Hertha BSC developed. A proposal for a merger between the two clubs in 1958 was resoundingly rejected, with only three of the 266 members voting in favour. On 16 November 1974, the first Bundesliga Berlin derby between Tennis Borussia Berlin and Hertha BSC took place at the Olympiastadion, with Hertha winning 3–0, who were playing at their home ground despite being the designated away team. On 10 May 1975, Hertha completed the double over Tennis Borussia Berlin, winning 2–1, again at the Olympiastadion. Following TeBe Berlin's relegation at the end of the 1974–75 Bundesliga season, the pair did not meet again until 13 November 1976, with Hertha winning 2–0, and met for a final time in the 1976–77 Bundesliga on 16 April 1977, with TeBe achieving their sole victory against Die Alte Dame 2–1. All meetings between the pair were hosted at the Olympiastadion. A decade later, three Berlin clubs were involved in the 1985–86 2. Bundesliga: Hertha BSC and Tennis Borussia, both relegated, and Blau-Weiß 1890 who finished runners-up to reach the top division for the only season in their history. A few years prior, in 1982, plans for another merger involving Hertha were drawn up with Tennis Borussia Berlin, Blau-Weiß Berlin and SC Charlottenburg. The plan, nicknamed "FC Utopia" by critics, ultimately failed.

Meanwhile in East Berlin, in the DDR-Oberliga, city derbies were more commonplace. BFC Dynamo, moved into Berlin from Dresden in 1954, were seen as the club of the East German establishment, with Stasi chief Erich Mielke backing the club to such an extent the club won ten consecutive Oberliga titles between 1979 and 1988, with allegations of sporting misconduct fueling a rivalry with Köpenick-based club 1. FC Union Berlin. Clashes between Dynamo and Union fans regularly broke out at games between the pair, with Union Berlin being seen as an anti-establishment club, with anti-GDR sentiment becoming commonplace in Union Berlin's fanbase. In 1953 another club, known as Vorwärts, originally founded in Leipzig and associated with the East German army, was transplanted to Berlin to increase the military profile in the capital; they won six GDR titles and two cups in the 1960s before being relocated again to Frankfurt an der Oder in 1971 and later disbanded (their successor today being 1. FC Frankfurt), which allowed BFC Dynamo to take their place as the dominant state-supported team in East Berlin.

During the separation of East and West Germany, Hertha BSC and Union Berlin developed a friendship between both sets of supporters, with Hertha fans visiting the Stadion An der Alten Försterei and Union Berlin fans accompanying Hertha BSC fans whenever Hertha played in East Germany or Eastern Bloc countries (e.g. when Hertha played Dukla Prague in the 1978–79 UEFA Cup quarter-finals). The slogan "Hertha und Union – eine Nation" (Hertha and Union - one nation) was a popular phrase between both sets of supporters during the divide of Germany.

During Hertha and Union's separation, Hertha also held a rivalry with fellow Berlin club SC Tasmania 1900 Berlin (who unexpectedly spent one season in the Bundesliga in 1965–66 when Hertha were demoted for rule breaches and a Berlin entrant was sought for political reasons, with a record low points total recorded by the part-time Tasmania squad) however the rivalry was more one-sided on Tasmania's part and still exists today in their successor SV Tasmania Berlin.

After reunification
On 9 November 1989, the Berlin Wall fell after 28 years of politically, and physically, dividing Berlin. On 27 January 1990, 79 days after the fall of the Berlin Wall, Hertha hosted Union Berlin at the Olympiastadion in a friendly in front of 51,270 spectators. Fans of both club's paid for admission in East and West Germany's respective currencies and sang songs of German reunification as Hertha won 2–1. New Hertha signing Axel Kruse opened the scoring at the Olympiastadion in the 13th minute, before Union Berlin midfielder  levelled the scores at 1–1 before half-time. Hertha BSC eventually won the tie 2–1, thanks to a long range strike from Dirk Greiser. After reunification, Union Berlin were placed into the third tier NOFV-Oberliga Mitte, winning the division in all three seasons it existed. Numerous lower key friendlies followed the historic January 1990 meeting at the Olympiastadion. On 8 July 2009, Union and Hertha played in a friendly at the Stadion An der Alten Försterei to celebrate the re-opening of the stadium following a season-long renovation period that saw 2,000 volunteers contribute to the building of the stadium. Hertha won the game 5–3.

In two consecutive seasons at the end of the 1990s, Tennis Borussia Berlin were drawn to face Hertha BSC in the DFB-Pokal, during a period when Hertha were among German's strongest teams but TeBe had also acquired a rich backer and made expensive signings in an effort to climb through the divisions. In their first meeting in 1998, TeBe won 4–2 to progress to the quarter-finals in a surprise result (particularly as Hertha qualified for the UEFA Champions League at the end of the season). In 1999's Round of 32, Hertha battled to a 3–2 victory but required extra time to overcome their neighbours.

Bundesliga era
In May 2009, Union Berlin won the 3. Liga, gaining promotion to the 2. Bundesliga. During the 2009–10 Bundesliga season, Hertha BSC were relegated to the 2. Bundesliga. On 17 September 2010, Union Berlin played Hertha BSC in the first ever competitive meeting between the pair. The tie at the Stadion An der Alten Försterei finished 1–1 in front of 18,432 spectators. The return game at the Olympiastadion, played in front of 74,244, finished 2–1 in favour of Union Berlin, with Union Berlin cult hero Torsten Mattuschka scoring the winning free-kick in the 71st minute. By the third competitive meeting between the two, signs that the derby was beginning to turn into a rivalry more than a friendship were beginning to show. After Hertha BSC's 2–1 win at the Stadion An der Alten Försterei, Union Berlin goalscorer Christopher Quiring labelled Hertha's fans Wessis, a semi-derogatory term for West Germans, telling Sport1 "They cheer in our stadium. That makes me puke! You have to digest that first. I don't give a shit about my goal. When the Wessis cheer in our stadium, I get sick". Union Berlin manager Uwe Neuhaus subsequently labelled Quiring a "great Unioner".

In May 2019, Union Berlin gained promotion to the Bundesliga for the first time in their history. Ahead of the first top flight Berlin derby in over 40 years, Hertha BSC expressed a desire to play the game on the 30th anniversary of the fall of the Berlin Wall on 9 November 2019. Union Berlin president Dirk Zingler refused, calling the game a "football class struggle", leading to the game being played a week earlier. An 87th minute Sebastian Polter penalty secured a 1–0 win for Union; the game was temporarily suspended by referee Deniz Aytekin, following fireworks fired by Hertha fans landing amongst Union Berlin fans, as well as on the playing surface. 1,100 police officers were on duty for the game, with Hertha fans burning Union Berlin shirts, flags and scarves during the game. Following full time, Union Berlin goalkeeper Rafał Gikiewicz won praise from fans and media alike after ushering Union Berlin ultras from the field of play, following a minor pitch invasion devised to attack Hertha supporters.

Full list of results
Includes all matches between Hertha BSC, 1.FC Union and BFC Dynamo, and other matches between all other Berlin clubs played in the 1. Bundesliga (from 1963), the 2. Bundesliga (from 1974) and the DDR-Oberliga (1949 to 1991); results listed alphabetically by main name of team, then by date. Scores list home team first in all cases.

Blau-Weiß 90 Berlin v Hertha BSC

Blau-Weiß 90 Berlin v Tennis Borussia Berlin

SC Charlottenburg v Hertha BSC

BFC Dynamo v Union Berlin

Lower divisions / cups / friendlies

DDR-Oberliga

BFC Dynamo v Vorwärts Berlin

Hertha BSC v Tennis Borussia Berlin

Lower divisions / cups / friendlies

Bundesliga

Hertha BSC v Union Berlin

Friendly

Competitive

Spandauer SV v Tennis Borussia Berlin

Spandauer SV v Wacker 04 Berlin

Tennis Borussia Berlin v Wacker 04 Berlin

Union Berlin v Vorwärts Berlin

References

Category:Hertha BSC
Category:1. FC Union Berlin
Category:Tennis Borussia Berlin
Category:Berliner FC Dynamo
Category:Association football rivalries in Germany
Category:Football in Berlin
Category:Sport in Berlin


Competitive induction of erythroid progenitors by relative concentration of BPA to erythropoietin: possible existence of intermediate compartment between BFU-E and CFU-E.
Crude preparations of erythropoietin (Epo) have been found to contain burst-promoting activity (BPA) as a contaminant. When anti-Epo antiserum was added to the culture of erythroid progenitor cells stimulated in vitro with crude Epo, the number of colonies derived from late erythroid progenitors (CFUe) was reduced, while that from early erythroid progenitors (BFUe) was increased. On the other hand, when the culture was stimulated with BPA-free Epo, which was purified from human urine, addition of anti-Epo antiserum enhanced neither BFUe nor CFUe. These results suggest that the ratio of BPA and Epo may be crucial in the assay of erythroid progenitors. Addition of anemic mouse spleen-conditioned medium (AMSCM) to the culture as a source of BPA resulted in the increase of BFUe and the reduction of CFUe under various concentrations of Epo. These findings might be explained by the existence of intermediate compartment of erythroid progenitors depending on both BPA and Epo.


3-Benzyl-2H-1,3-benzoxazine-2,4(3H)-diones, a new group of antimycobacterial compounds against potentially pathogenic strains.
A series of derivatives of 3-benzyl-2H-benzoxazine-2,4(3H)-dione substituted in positions 6, 7 or 8 on the benzoxazine, and in positions 3 or 4 on the benzyl moiety was synthesized. The compounds were evaluated for in vitro antimycobacterial activity against Mycobacterium avium and two strains of Mycobacterium kansasii. The disadvantage of the compounds is in their low solubility in water. The antimycobacterial activity of N-benzylsalicylamides correlates with that of 3-benzyl-2H-1,3-benzoxazin-2,4(3H)-diones and depends on the partition coefficients and electronic indexes.


1. Field of the Invention
The present invention relates to a differential current driver and a method of using the differential current driver to transmit data.
2. Description of the Related Art
It is known art to use low voltage differential signaling to transmit data between communication devices or large-scale integrated circuit (LSI) devices. This type of transmitting and receiving of data will be described with reference to FIG. 12. Data transmission is carried out by a differential driver 1 that switches output current between two output terminals (referred to as positive and negative terminals) of the transmitting device 40. The current is conducted by a twisted pair cable 45 to the receiving device 50, where it generates voltages at corresponding (positive and negative) Input terminals linked by a terminating resistor (not shown). A receiver 51 in the receiving device 50 compares the voltages at these input terminals in order to determine the value of the received data (Rcv_DATA).
FIG. 13 shows the internal structure of the differential driver 1. The differential driver 1 includes a p-channel metal-oxide-semiconductor (PMOS) transistor 2 that operates as a current source (and will be referred to below as a current source 2), PMOS transistors 3 and 4 (referred to below as switches 3 and 4), NAND gates 8 and 9, and inverters 6 and 7. The switches 3 and 4 switch the current output from the current source 2 between the positive (POS) and negative (NEG) output terminals according to a binary data signal received at a data input terminal. The inverters 6 and 7 invert the data signal. The NAND gates 8 and 9 control switches 3 and 4 according the data signal, the inverted data signal, and an output enable (OE) signal received at an output enable input terminal for output enable/disable control of the differential driver 1. When the OE signal is high, one of the two switches 3 and 4 is turned on according to the data signal, and current is output from the corresponding output terminal. When the OE signal goes low, both switches 3 and 4 are turned off, and no current is output.
When the differential driver 1 is switched from the output disabled state to the output enabled state, it takes time for the operation of the current source 2 to stabilize. For that reason, in a device that transmits and receives data at high speed, the internal structure of the differential driver 1 may be altered as shown in FIG. 14. In FIG. 14, when the differential driver 1 is in the output disabled state, a PMOS transistor 14 (referred to below as a switch 14) is turned on, allowing the current from the current source 2 to escape to ground. Current therefore flows from the current source 2 at all times, irrespective of the output state of the differential driver 1. Differing from the structure in FIG. 13, the structure in FIG. 14 eliminates the need to wait for stabilization of the current source 2 when the differential driver is switched from the output disabled state to the output enabled state, and high-speed data transfer becomes possible. These structures are disclosed in Japanese Unexamined Patent Application Publication Nos. 8-204557 and 2000-332610.
In the structure shown in FIG. 13, when the differential driver 1 is in the output disabled state, since the switches 3 and 4 are both turned off, the voltage at their common node N is substantially equal to the power supply voltage (VDD). When the differential driver 1 transitions from this state to the output enabled state, since one of the switches 3 and 4 is turned on, the voltage at the common node N decreases. This node is the also drain node of the current source 2, however. Since the gate and drain of a PMOS transistor are capacitively coupled, when the voltage at the common node N decreases (i.e., the drain voltage of the current source 2 decreases), the bias voltage at the gate of the current source 2 also decreases. As a result, the current flow from the current source 2 increases. For this reason, until the bias voltage returns to its normal level, more current than is required flows from the positive output terminal or the negative output terminal. In the structure shown in FIG. 14, the increase in output current accompanying a transition of the differential driver from the output disabled state to the output enabled state is prevented, but the differential driver consumes much current, since current flows from the current source 2 at all times.


Danny Villanueva 1963 Stats

In 1963, Danny Villanueva had 9 field goals and 25 extra points. He played in 110 games for a fantasy point game average of 3.71 which is 0.76 points lower than his career average. Browse all of the stats and fantasy points from the 1963 Danny Villanueva season and relive all of his memorable performances.

Danny Villanueva 1963 Stats

Jersey:Position: KickerCareer: 8 YearsHeight: 5-11Weight: 200

Other Years

In 1963, Danny Villanueva had 9 field goals and 25 extra points. He played in 110 games for a fantasy point game average of 3.71 which is 0.76 points lower than his career average. Browse all of the stats and fantasy points from the 1963 Danny Villanueva season and relive all of his memorable performances.


Studies comparing the post-treatment behavior of fibroblasts from patients with ataxia telangiectasia with that of fibroblasts from control donors showed then similar behavior after either methyl methane sulfonate (MMS) or ultraviolet (UV) treatment, but different behavior (decreased colony forming ability and usually decreased DNA-repair synthesis) after N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) treatment. Similar studies using a number of human tumor cell strains showed many of these to be extremely sensitive to MNNG-produced killing, somewhat sensitive to MMS and BCNU (bis-chloroethyl-nitrosourea) killing, but not to UV-produced killing.


1. Introduction {#sec1}
===============

Standard definitions of overweight and obesity, based on body mass index (BMI), do not differ by age \[[@B1]\]. However, many studies of older adults have found a U-shaped relationship between BMI and mortality, with the lowest mortality in the group labeled as "overweight" (BMI from 25 to 29.9) \[[@B2]\]. This surprising finding is often called the "Obesity Paradox." The work in \[[@B2]\] identifies several related research issues, including the two that are addressed here. First, BMI may not measure adiposity well in older adults, and analyses based on waist circumference (WC) may result in less paradoxical results \[[@B2]\]. Second, even if Overweight older adults live as long as persons with Normal BMI, they may spend more of those years being sicker, more disabled, or with worse physical function. This paper will attempt to provide insight into both of those issues.

We conducted a longitudinal study to measure the relation of adiposity to 16 domains of health in older adults, using both BMI and WC to classify adiposity. We hypothesized that Overweight older adults, whether classified by BMI or WC, would have as many years of healthy life (YHL) and years of life (YOL) as those classified as Normal weight. In other words, we expected the Obesity Paradox (perhaps, more aptly, the "Overweight Paradox") to hold for health status as well as for mortality. Obese older adults were expected to have fewer (worse) YHL than persons with Normal WC. We hypothesized that results for WC would be similar to those for BMI. We also explored whether results differed for men and women, blacks and whites, and for different measures of health status.

2. Methods {#sec2}
==========

2.1. Data {#sec2.1}
---------

### 2.1.1. Study Sample {#sec2.1.1}

Data came from the Cardiovascular Health Study (CHS), a population-based longitudinal study of risk factors for heart disease and stroke in 5888 adults aged 65 and older at baseline \[[@B3]\]. Participants were recruited from a random sample of Medicare eligible persons in four U.S. communities, and extensive data were collected during annual clinic visits and telephone calls. The original cohort of 5201 participants, recruited in 1989-1990, had up to ten annual clinic examinations. A second cohort of 687 African Americans, from 3 of the original study communities, were enrolled in about 1992-1993 and had up to seven annual examinations. Followup is ongoing for mortality.

ExclusionsWe excluded 19 persons who were missing baseline BMI, 44 who identified themselves as neither black nor white, and 22 more who were missing one or more key baseline variables. We also removed the 97 persons with BMI \< 18.5 (Underweight), because of their small numbers. The current study involves the remaining 4830 whites (followed for 10 years) and 904 blacks (the second cohort, plus 217 from the original cohort, all followed for 7 years).

Missing DataMortality was verified using CMS records, and is believed to be complete. Followup for other longitudinal variables was also satisfactory \[[@B4]\]. For example, in the current research, in the final study year, 95% of the subjects either had an observed value for activities of daily living or had died. (Missingness differed somewhat by variable). Missing longitudinal data were imputed by interpolation between two known values where possible. Otherwise the missing value was imputed from the person\'s last available value, self-rated health, and eventual date of death, as detailed in the appendix.

### 2.1.2. Measures of Adiposity (Independent Variables) {#sec2.1.2}

Height, weight, and waist circumference (WC) were measured in the clinic. Persons wore a hospital gown and no shoes, and a calibrated scale was used. Waist circumference (in cm) was measured over bare skin over the widest circumference above the iliac crest using a metal tape measure. Body mass index was calculated as weight in kilograms divided by the square of height in meters. Adiposity was first categorized by BMI, using standard thresholds: Normal (18.5--24.9), Overweight (25--29.9), or Obese (30+) \[[@B1], [@B5]\]. (Persons with BMI below 18.5 had already been excluded). Only 317 (17%) of the 1854 Obese persons had BMI \> 35, meaning that most persons classified as Obese had class 1 obesity.

Waist circumference (WC) thresholds of 88 cm for women and 102 cm for men have been proposed, but there is no evidence that these are appropriate for older adults \[[@B1], [@B2], [@B6]\]. To categorize WC in a manner comparable to BMI, we chose thresholds to create three groups of equal size (tertiles), referred to for convenience as Normal WC, Overweight WC, and Obese WC. To ensure adequate numbers in each category, tertiles were defined separately for white women, black women, and men. For white women, Normal WC was \<84.5 cm, Overweight WC was 84.5 to 96.4, and Obese WC was \>96.4 cm. The comparable thresholds were 94.0 and 107.5 for black women and 93.0 and 101.5 for men. About 65% of persons were in the same adiposity category for both BMI and WC. There were a few major discrepancies: 21 persons had Obese BMI with Normal WC, and 56 had Normal BMI with Obese WC. The two measures of adiposity were thus similar but not identical. The average WC for persons with BMI below 18.5 was about 13 cm lower than the mean WC for Normals. Thus, the exclusion of the underweight BMI subjects also removed persons with low WC.

### 2.1.3. Outcome Measures (Dependent Variables) {#sec2.1.3}

Sixteen definitions of YHL were used in this study as the study outcomes, calculated from the common descriptors of health status listed in [Table 1](#tab1){ref-type="table"}. The variables, measured annually, address the domains of physical function, mental and emotional health, social health, health behaviors and quality of life. Cognition, timed walk, and hospitalization were determined objectively; the others came from patient report. Each value was dichotomized into Healthy (1) or Sick (0), using the thresholds shown in [Table 1](#tab1){ref-type="table"}. If standard thresholds were not available, we chose intuitive thresholds that ensured sufficient data at each level. Persons dead at the time of the measure were coded as 0. For example, to dichotomize ADL, a person with no ADL difficulties was coded as 1 for that observation, while a person with difficulties or who had died was coded as 0.

The outcome measures for each person, calculated separately for each health variable, were the sum over time of their values, which may be interpreted as the number of years in which the person was healthy (by each definition), during the period starting 6 months before baseline to 6 months after study end. For convenience, we usually refer simply to YHL, without specifying which measure of health being used. The possible range of YHL was 0 to 10 years for whites and 0 to 7 years for blacks. Survival, or years of life (YOL), is a special case of YHL. As an example, a person who was alive at 8 of the 10 measurement times and was healthy with respect to ADL (had no ADL difficulties) at six times (not necessarily consecutive), would have YOL = 8 and YHL (from ADL) = 6. YHL does not account specifically for trends; for example, 3 healthy years followed by 3 sick yields the same YHL as 3 sick years followed by 3 healthy.

### 2.1.4. Covariates {#sec2.1.4}

Older age, smoking, and recent weight loss are usually related both to worse health and to lower weight, and are thus potential confounders. All regression analyses were adjusted for baseline age, smoking history, and whether the person had lost 10 or more pounds in the year prior to baseline. Smoking was coded 1 for never smoker, 2 for former smoker, and 3 for current smoker.

2.2. Analysis {#sec2.2}
-------------

The primary analysis was a regression of each measure of YHL (the dependent variables) on dummy variables representing Overweight and Obese BMI (or WC), controlling for the covariates of baseline age, log age, smoking, and weight loss. (Two age terms were used to permit nonlinear relationships). For example, when YHL is defined as years without ADL difficulties, the regression equation was

$$\begin{aligned}
{\text{YHL}\,\left( \text{from}{\,\,}\text{ADL} \right)} & {= b_{0} + {\,\,}b_{1}\,\text{Overweight} + {\,\,}b_{2}\,\text{Obese} + {\,\,}b_{3}\,\text{Age}} \\
 & {  + b_{4}\,\left( {\log{\,\,}\text{age}} \right) + b_{5}\,\text{Smoking} + b_{6}\,\text{Wt}{\,\,}\text{Loss}.} \\
\end{aligned}$$ Overweight and Obese were thus compared to the reference category (Normal). Because we used linear regression, the coefficient for Overweight (*b*~1~) is the adjusted difference in YHL between Overweight and Normal, measured in years. Preliminary analyses found strong and significant interactions between sex, race, and adiposity. For clarity, all regressions were performed separately by sex and race. Separate regressions were performed for each variable, within each sex and race subgroup, using both BMI and WC as the measure of adiposity. There were thus 128 separate regressions comparing Overweight to Normal weight (16 YHL variables × 4 sex/race groups × 2 measures of adiposity). The regressions may be thought of in some sense as replicate analyses. Although YHL is probably not normally distributed (YHL cannot be greater than 10 for the white group or than 7 for the black group), the sample size was large enough for the central limit theorem to guarantee that the regression coefficients would be normally distributed, making linear regression appropriate \[[@B7]\]. The regression coefficients for Overweight were graphed and tabulated. The coefficients for Obese are mentioned only briefly.

3. Findings {#sec3}
===========

3.1. Descriptive Statistics {#sec3.1}
---------------------------

[Table 2](#tab2){ref-type="table"} shows the means and standard deviations (s.d.) of all the variables, by sex and race. For example, there were 2717 white women, whose mean age was 72.4 (s.d. = 5.4), mean BMI was 26.5, and mean WC was 91.1 cm. Further, 12.1% were smokers and 11.4% had lost 10 or more pounds in the previous year. The table lines labeled YOL through TWLK present the mean YHL for each definition of "healthy." For example, the mean number of years the women survived in the 10-year period (YOL) was 9.1, and 7.2 of those years were spent with no ADL difficulties, on average. The 16 measures of YHL have different means, due primarily to differences in how the 16 thresholds for "healthy" were defined (see [Table 1](#tab1){ref-type="table"}). There are also apparent differences among the sex and race subgroups.

[Table 3](#tab3){ref-type="table"} shows the variable means by BMI category for the largest subgroup, white women. ([Table 5](#tab5){ref-type="table"} in the appendix has similar information for the other subgroups, and [Table 6](#tab6){ref-type="table"} in the appendix has the same information categorized by WC instead of BMI). For example, of the 1037 white women with Normal BMI at baseline, the mean age was 73.0 years, 16% were current smokers, and 13% had lost 10 or more pounds in the previous year. Normal weight (versus Overweight) was significantly associated with higher age and smoking (*P* \< .001 by Anova, not shown), but weight loss was not (*P* = .308). For WC categories ([Table 6](#tab6){ref-type="table"} in the appendix), Normal weight was also significantly associated with higher age and smoking (*P* \< .001), but not with weight loss (*P* = .679).

The lines in [Table 3](#tab3){ref-type="table"} labeled YOL through TWLK show the (unadjusted) mean YHL for each definition of "healthy," ordered approximately by the increasing difference in YHL between Normal and Overweight BMI. For example, in the ten years after baseline, Normal weight women averaged 7.5 years with no ADL disability. Overweight women averaged 7.4 YHL (from ADL) indicating that they had 0.1 fewer YHL (from ADL) than persons with Normal weight (unadjusted results). As seen in in the appendix [Table 5](#tab5){ref-type="table"}, in the 7 years after baseline, black women with Normal BMI (shown in the lower half of the table) averaged 4.6 years without ADL disability. [Table 6](#tab6){ref-type="table"} in the appendix shows similar information for persons classified by WC instead of BMI.

3.2. Regression Results {#sec3.2}
-----------------------

The coefficient for "Overweight" in the regressions is the adjusted difference in YHL between Overweight and Normal, with positive values favoring Overweight. We are interested in the coefficient signs (positive or negative), their sizes, the patterns across variables and sex/race groups, and the statistical significance of the coefficients. It is easier to see the signs, sizes and patterns in a graph. [Figure 1](#fig1){ref-type="fig"} shows the regression coefficients for all YHL measures, by race and sex. To permit easier assessment of the patterns, the variables that turned out to be most favorable to the Overweight are shown at the left and the least favorable at the right. For example, in the topmost panel, which displays results for white women, the coefficient for "YOL" (at the far left) is near zero, meaning that adjusted mortality was similar for the Overweight and the Normal BMI groups. At the far right, the coefficient for TWLK was −.36, meaning that Overweight persons averaged 0.36 fewer YHL (from TWLK) than persons of Normal weight; that is, 0.36 fewer years in which they were walking 15 feet in 10 seconds or less. The coefficients for white women tended to be small but negative. The trends were quite different for white men, black women, and black men, where the coefficients were also small but were usually positive, indicating that persons with Overweight BMI had somewhat higher YHL than persons with Normal BMI.

[Figure 2](#fig2){ref-type="fig"} shows the difference in YHL between Overweight and Normal weight when adiposity was classified by WC. The trends are similar to those in [Figure 1](#fig1){ref-type="fig"}. (Note that the *y* axis is slightly different in the two figures.) The preponderance of coefficients is again negative for white women, but positive for the other groups.

Columns 1 and 3 of [Table 4](#tab4){ref-type="table"} contain the regression coefficients shown in [Figure 1](#fig1){ref-type="fig"}. The coefficients that are statistically significant are marked with asterisks (\**P* \< .10 for a 2-sided test, which is equivalent to *P* \< .05 for a 1-sided alternative; \*\**P* \< .05; \*\*\**P* \< .01). For example, white men who had Overweight BMI averaged 0.26 more years of life (YOL) and 0.32 more years of being satisfied with the purpose of life (SPL) than the Normal weight, both significant at the *P* \< .05 level. Most coefficients were not significantly different from zero. Columns 2 and 4, for WC, show the coefficients corresponding to [Figure 2](#fig2){ref-type="fig"}. More than half of the coefficients were significantly negative for white women, but coefficients were rarely statistically significant for black women or for men.

\*

Although this paper is primarily about Overweight, [Table 7](#tab7){ref-type="table"} in the appendix also presents the regression coefficients for Obese BMI and WC. About half of those regression coefficients were significantly different from zero, and all but one were negative, indicating that Obese persons tended to have fewer YHL than persons of Normal weight.

4. Summary and Discussion {#sec4}
=========================

4.1. Overall {#sec4.1}
------------

This paper examined the relation between baseline adiposity and future years of healthy life in older adults. Differences in YHL between adiposity categories were examined for 16 measures of YHL, in 4 race by sex groups, using two measures of adiposity (BMI and WC). Regression coefficients, representing the adjusted difference in YHL between Overweight and Normal, were significantly positive for 16 of 128 comparisons, significantly negative for 16, and not significantly different from zero for the remaining 98 coefficients. The "Overweight Paradox," the finding of little difference between Normal and Overweight, thus seemed to hold for various measures of health status as well as for mortality. Obesity was significantly associated with worse outcomes than Normal weight in about half of the comparisons. We next discuss the relevant literature and then consider how the results vary by features of the study design.

4.2. Comparisons with the Literature {#sec4.2}
------------------------------------

As reviewed in \[[@B2]\], many studies have found that the Overweight do not have higher mortality than the Normal weight, consistent with our findings. With respect to outcomes other than mortality, cross-sectional studies in the elderly have found associations between higher BMI and worse morbidity, functional status, and quality of life \[[@B8]\]. Fewer longitudinal studies of older adults are available for outcomes other than mortality. Most of these have focused on activities of daily living (ADL), with mixed results, as was also found here \[[@B9]--[@B17]\]. Other important dimensions of health have been studied in less detail. Previous longitudinal analyses have studied the association of adiposity with self-rated health, \[[@B16], [@B18]\] years without work disability, hospitalization for coronary heart disease, long-term medication, \[[@B19]\] MI, arthritis, diabetes \[[@B15]\], dementia \[[@B20]\], and a new ADL disability \[[@B21]\]. These studies usually found higher risks for obese individuals, but mixed results for the overweight, which is consistent with the results of this paper. None of the studies used years of healthy life, as defined here, and direct comparisons are not possible.

4.3. BMI and WC {#sec4.3}
---------------

The literature has suggested that WC, rather than BMI, should be used to measure adiposity for older adults \[[@B2], [@B6]\]. BMI may not perform well for several reasons. An increase in body fat can be masked by an age-associated decrease in lean body mass. A person could thus have a stable BMI despite increasing body fat and decreasing muscle mass. Body fat also tends to have a different distribution for older adults, with visceral fat increasing with age. In addition, the usual BMI categories of "underweight," "normal," "overweight," and "obese" were derived using mortality data on younger persons, and the thresholds may not be relevant for older adults.

Here, BMI and WC were fairly similar as measures of adiposity, with two thirds of the persons categorized the same way by either measure, and few large discrepancies. In [Table 4](#tab4){ref-type="table"}, results based on BMI were more favorable to Overweight than results based on WC. (BMI had 14 significantly positive and 5 significantly negative coefficients, compared with 2 positive and 11 negative for WC). This may support recent findings that measures of central obesity are better predictors of survival than BMI \[[@B6]\]. These differences may also be in part because the BMI thresholds were the same for all persons, while the WC thresholds used here were sex and race specific. In addition, traditional BMI thresholds were based on mortality data, while the WC thresholds were defined by tertiles. In an unreported preliminary analysis we created tertiles of BMI, and found that their association with YHL was similar to that of the traditional categories.

4.4. Different Measures of Health {#sec4.4}
---------------------------------

This analysis used 16 different definitions of YHL, some of which were previously known to be associated with adiposity and others which had not been studied in this way. SPL, FLW, SOC, YOL, and DEP had the least negative (or most positive) associations with Overweight, while EXSTR, IADL, BLOCK, ADL, and TWLK had the most negative associations. If we combine the results for [Table 4](#tab4){ref-type="table"} (Overweight) and [Table 7](#tab7){ref-type="table"} in the appendix (Obese), SPL had the largest number of significant positive associations (5 of 16 coefficients), followed by DEPR and BED (2 each). The highest numbers of significant negative coefficients were for BLOCK (11), ADL and TWLK (9 each), and IADL (8). The outcomes that favored the Overweight or Obese may be thought of as psychological or socially based, while the most negative outcomes represent primarily physical function. Results thus differed somewhat by the aspect of health that was measured.

4.5. Interpretation of Individual Coefficients {#sec4.5}
----------------------------------------------

The statistical significance of the coefficients in [Table 4](#tab4){ref-type="table"} should not be over-interpreted because of the issue of multiple comparisons. Of the 128 coefficients, 10% or about 13 would have been expected to be significant by chance alone. After a conservative Bonferroni correction, only 4 coefficients remained significant; all were for white women and all were negative (BLOCKS (based on BMI and WC) and IADL, ADL, and TWLK (based on WC)).

Of the 16 variables, some of course had larger coefficients than others. Under the theory of order statistics, \[[@B22]\] however, the largest coefficient was not significantly larger than expected under the null hypothesis that all measures of YHL had a similar relation to adiposity (analysis not shown). Thus, unless the reader had a prior hypothesis about a particular variable and subgroup, the coefficients should be only used to describe patterns rather than to identify the variables most sensitive to adiposity.

The positive regression coefficients indicate cases where being Overweight seemed protective. The review paper discusses possible mechanisms for a protective effect, that are not repeated here \[[@B2]\]. The fact that none of the positive coefficients was significantly different from zero after the Bonferroni correction suggests that some of the positive results might be due to chance. It is important not to over-interpret these results without further confirmation.

4.6. Power {#sec4.6}
----------

The nonsignificant differences do not, of course, imply that results for Overweight and Normal are identical. Rather, they may be due in part to insufficient sample size, especially for the black subpopulation. With the large number of comparisons, it is prohibitive to discuss power in detail, but one example may be instructive. Assume that a difference of 0.5 additional years of healthy life (6 months) in the following 10 years is clinically important. Based on the standard deviations for ADL in [Table 2](#tab2){ref-type="table"}, the power to detect a difference of 6 months was 0.95 for an analysis with 1000 persons per group (similar to numbers for white women) and 0.44 for an analysis with 150 per group (similar to black men). Thus, the study had power to detect meaningful differences between Normal and Overweight, especially for white men and women (calculations not shown). Note that [Table 4](#tab4){ref-type="table"} has only a handful of coefficients greater than 0.5, suggesting that even with larger samples, any significant differences might not be clinically important.

4.7. Sex and Race {#sec4.7}
-----------------

As expected, women had higher YOL (survival) than men. The unadjusted data found that women often (but not always) had higher YHL as well. (see [Table 2](#tab2){ref-type="table"} and Tables [5](#tab5){ref-type="table"} and [6](#tab6){ref-type="table"} in the appendix). Overweight was negatively associated with the YHL measures for white women but was usually nonsignificant or positive for white men and for black men and women. As expected from their larger sample size, white women had more statistically significant results than the other groups. But this does not explain why results for white women were more negative. Being Overweight may have more biological consequences for white women than for other groups, perhaps related to differences in the distribution of visceral adipose fat by sex and race \[[@B23]\]. Alternatively, most of the health measures were self-assessed. If, for some reason, white women were more likely than the others to consider being overweight as a negative health characteristic, then Overweight white women might have downrated their health for that reason. Arguing against this response bias explanation, however, is the finding that YHL based on the timed walk, which was not self-reported, was also negatively associated with Overweight. The WC thresholds were lower for white women than for the other groups, but that would not seem to explain why white women had more negative results. It is interesting that even though white women had the smallest WCs and the best outcomes, the results suggest that Overweight white women might benefit the most from losing weight. (Weight loss was not, however, studied in this analysis).

These results should be considered as exploratory rather than definitive, for several reasons. The sex and race differences were not tested formally because the regressions were performed separately by sex and race. (That choice was made because preliminary analyses did find significant interactions between race, sex, adiposity, and outcomes.) In addition, the results are not directly comparable for blacks and whites because of the greater sample size and longer followup for whites. Finally, only three of the four study communities recruited a supplemental cohort of blacks, so the black and white groups are not geographically comparable.

4.8. Implications {#sec4.8}
-----------------

The consistent finding that Overweight older adults had similar outcomes to those of Normal weight, based either on mortality or on years of healthy life, suggests strongly that the usual adiposity classifications are inappropriate for older adults, both in the thresholds used and in the labels given to the categories. For older adults, "Normal" BMI is far from normal since the plurality of older adults fall in the "Overweight" category. The pejorative label "Overweight" also seems inappropriate because Overweight and Normal had very similar YHL. Better classifications and labels are needed for older adults. The new standards might be based on BMI, WC, or perhaps on a combination of BMI and WC. (Combined measures of adiposity were not considered here.) The finding that the outcomes differed by sex and race strongly suggests that any clinical guidelines should be specific to age, sex, and race. Also in need of a better label is the so-called "obesity paradox," which might better be referred to as the "overweight paradox." Since the Obese had significantly fewer YHL than the Normal weight about half the time, there may be no paradox at all; that is, higher adiposity is deleterious, but the usual thresholds are inappropriate for older adults.

Although we did not specifically study benefits of weight loss, our findings do not support any over-all recommendations for Overweight older adults to lose weight. However, results did vary somewhat by sex and race, and also by the definition of years of healthy life. Increasingly, clinicians have recognized the importance of engaging patients in defining what outcomes matter to them, using a "person-centered medicine" approach \[[@B24]\]. Our results encourage clinicians to consider not only objective health measures like mortality, cholesterol, and blood pressure in making decisions about weight loss, but also to reflect on health and quality of life as defined by the patient. The domains of health that matter to the patient, some of which we examined here, can become the basis for anticipating benefits and agreeing on any plans for weight loss. Rather than assuming that weight loss confers general benefits to all overweight or obese individuals, providers can engage patients in defining ways of maximizing each patient\'s own benefits.

4.9. Study Strengths {#sec4.9}
--------------------

The main strength of this study is its high quality longitudinal data (10 years) on 16 different health outcomes for older adults, as a function of measured BMI and WC. Tables [5](#tab5){ref-type="table"} and [6](#tab6){ref-type="table"} in the appendix should be useful to other investigators in this area, since they present information not generally available.

4.10. Limitations {#sec4.10}
-----------------

The regression analyses used here might not be considered ideal for some of the outcome variables. We chose to perform the same analysis for all of the variables, to allow comparisons. Issues of causality (e.g., whether adiposity affects physical activity or physical activity affects adiposity) were not addressed. The large number of regression coefficients presented makes it unwise to emphasize any particular coefficient, but consistency across the race and sex groups may support future confirmatory research. Standard errors for the regression coefficients are available from the authors. Only 3 communities had an enriched sample of blacks, limiting comparison of blacks to whites. The analysis does not identify the optimal BMI or WC, and classifications that used BMI and WC jointly were not considered. Different choices for the thresholds used to dichotomize the outcome variables would have changed mean YHL but probably have little effect on the difference between Normal and Overweight. We did not create a composite summary of all 16 variables because our purpose was to emphasize the different dimensions of health.

4.11. Conclusions {#sec4.11}
-----------------

Overweight older Americans lived as long as Normal weight persons and usually experienced as many years of healthy life, as defined by 16 measures of health. Thus, the Overweight Paradox was seen to hold generally, especially for men and black women, and for domains of health other than physical function. Weight loss recommendations for older adults should be tailored to the appropriate sex and race group and may not be necessary for the Overweight. If one accepts that only Obese older adults are at risk for negative health consequences from their weight, then only about a fourth of older adults may require attention or treatment. The so-called obesity epidemic for older adults may be less severe than is usually supposed. Further research should develop optimal levels of BMI and WC for older adults, which may differ substantially by sex and race, and by the criterion measure of health status used.

The research reported in this article was supported by contracts N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, and Grant HL080295 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at <http://www.chs-nhlbi.org/pi.htm>. Dr. Thielke was supported by an NIMH Beeson Career Development Award (K23 MH093591-01).

Missing Data {#sec5}
============

In CHS, most measures were taken annually from 1990 to 1999. However, self-rated health was measured semi-annually from 1990 to 2005, and mortality was known (for the current analysis) through 2007. We imputed missing self-rated health data and used that, where necessary, to help impute data for the other variables. For self-rated health, we coded the original response categories (excellent, very good, good, fair, poor) as 95, 90, 80, 30, and 15 \[[@B31]\]. These values represent the approximate percent probability that a person in that state would be in excellent, very good, or good health in the following year. Under the assumption that a dead person is not healthy and will not be healthy next year either, we assigned a value of 0 to observation that were not made because the person had died. After this recoding, we imputed missing data by linear interpolation over time, whenever there was a valid value before and a valid value or death after the missing data. The remaining unimputed data, for persons alive but missing at the end of the sequence, used the last observation carried forward. Because so much information was collected after 1999, we rarely had to impute missing data from 1990 to 1999 by extrapolation (less than 0.3% of the time). We are thus comfortable with the imputation for self-rated health data during the study period.

To impute missing data for the other variables, each variable was transformed to a new scale representing the probability of being in excellent, very good, or good health; deaths were set to zero; and data were interpolated. Data missing at the end of the sequence for persons still alive was imputed as the average of the last available observation and the estimate from self-rated health at that time (both on the same scale because of the transformation). After imputation, the variables were transformed back to the original scales.

Missing data for three variables were imputed differently. Receiving a flu shot was not associated with self-rated health. Social support and life events were not measured as often as the other variables. For these variables, we used a regression of the person\'s known values on the logarithm of time from the last measure to impute missing data, as illustrated elsewhere \[[@B32]\].

![Adjusted difference in YHL (Overweight BMI − Normal BMI).](JOBES2012-894894.001){#fig1}

![Adjusted difference in YHL (Overweight WC − Normal WC).](JOBES2012-894894.002){#fig2}

######

Definitions of "healthy" based on 16 health-related variables.

  Label   Measure                                     Definition of "Healthy"
  ------- ------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  YOL     Years of life (survival)                    Alive
  SPL     Satisfaction with the Purpose of Life       A score of 1 to 4 on a 10-point scale rating satisfaction with the purpose of life
  FLW     Feeling about life as awhole \[[@B25]\]     A score of 1 to 3 on a 6-point scale
  SOC     Social support \[[@B26]\]                   Fewer than 8 points on the 24-point social support score
  DEP     Depression                                  Center for Epidemiologic Studies Short Depression score \< 10 \[[@B27]\]
  BED     Bed days                                    No days spent in bed in the previous two weeks
  FLUSH   Flu shot                                    Had a flu shot in the previous year
  COG     Cognition                                   A modified mini mental state examination score above 89 \[[@B28]\]
  HOSP    Hospitalization                             Not hospitalized in the previous year
  EVGG    Self-rated Health                           In excellent, very good, or good self-rated health
  LEV     Stressful life events (10 point modified)   Event stress score \< 2 \[[@B29]\]. Life events, occurring either to the person or significant other, include moving, death (of significant other), illness, worsening relationship, financial changes, robbery, caregiving, job change, new grandchild, or other stressful event.
  EXSTR   Extremity strength                          No problems with lifting, reaching, or gripping
  IADL    Instrumental activities of dailyliving      No difficulties with instrumental activities of daily living---heavy or light housework, shopping, meal preparation, money management, or telephoning
  BLOCK   Blocks walked                               Walking more than 4 blocks per day, on average \[[@B30]\]
  ADL     activities of dailyliving                   No difficulties with activities of daily living*‒*walking, transferring, eating, dressing, bathing, or toileting
  TWLK    Timed Walk                                  Able to walk 15 feet in less than 10 seconds (measured)

######

Means and standard deviations.

  Name                White   Black
  ------------------- ------- ------- ------ ------ ------- ------ ------ ------
  *N*                 2717            2113          570            334
  Age (yrs.)          72.4    5.4     73.4   5.7    70.8    5.7    70.4   5.9
  BMI (Kg/M sq)       26.5    4.8     26.4   3.6    29.8    5.9    26.9   4.0
  WC (cm)             91.1    13.4    97.8   10.1   100.5   15.6   97.6   11.3
  \% Current Smoker   12.1    32.6    9.4    29.2   13.0    33.7   19.8   39.9
  \% Lost 10 + lbs    11.4    31.8    12.4   33.0   15.1    35.8   12.9   33.5
  YOL\*               9.1     2.0     8.3    2.6    6.5     1.3    6.2    1.7
  SPL                 6.4     3.2     6.4    3.2    4.8     2.2    4.5    2.3
  FLW                 8.3     2.6     7.7    3.0    6.0     1.8    5.6    2.0
  SOC                 3.5     3.2     3.0    3.0    2.3     2.4    2.2    2.3
  DEP                 7.0     3.2     6.9    3.2    4.5     2.4    4.6    2.4
  BED                 8.5     2.4     7.9    2.8    5.9     1.7    5.7    1.9
  FLUSH               5.5     3.6     5.3    3.5    3.3     2.7    3.1    2.7
  COG                 5.8     3.8     4.9    3.8    2.2     2.7    2.1    2.5
  HOSP                8.1     2.4     7.2    2.9    5.7     1.7    5.4    1.9
  EVGG                6.9     3.4     6.4    3.5    3.8     2.6    4.0    2.7
  LEV                 5.7     3.1     5.5    3.1    3.4     2.5    3.7    2.6
  EXSTR               5.7     3.4     6.6    3.3    3.3     2.6    4.8    2.4
  IADL                5.8     3.5     6.0    3.5    3.6     2.6    4.4    2.5
  BLOCK               2.4     2.8     3.4    3.1    1.0     1.7    1.7    2.1
  ADL                 7.2     3.1     7.0    3.2    4.5     2.4    5.1    2.3
  TWLK                4.8     3.7     5.2    3.6    2.3     2.5    3.1    2.6

\*Rows YOL through TWLK present average YHL, calculated from the column variable.

######

Means by BMI category (white women only).

                      BMI Category\*
  ------------------- ---------------- ------ -------
  Sample Size         1037             1069   611
  Age (mean)          73.0             72.4   71.3
  BMI (mean)          22.2             26.8   33.6
  WC (cm)             81.0             92.2   106.5
  \% Current Smoker   15.7             10.0   9.5
  \% Lost 10 + lbs    12.6             11.3   9.7
  YOL                 9.1              9.1    9.1
  SPL                 6.3              6.6    6.3
  FLW                 8.2              8.4    8.3
  SOC                 3.4              3.5    3.7
  DEP                 7.0              7.0    6.8
  BED                 8.5              8.5    8.4
  FLUSH               5.6              5.8    5.0
  COG                 5.6              5.8    5.9
  HOSP                8.1              8.2    8.0
  EVGG                7.0              7.1    6.4
  LEV                 5.8              5.7    5.5
  EXSTR               5.7              5.8    5.3
  IADL                6.1              5.9    5.0
  BLOCK               2.8              2.4    1.7
  ADL                 7.5              7.4    6.6
  TWLK                5.1              5.0    3.9

\*Normal = 18.5--24.9, Overweight = 25--29.9, Obese = 30+.

######

Adjusted difference between Overweight and Normal (years) (negative values favor Normal weight).

                      Female        Male
  ------- ---------- ------------- ---------- ---------- ------
  White   YOL        −0.08         −0.09      0.26\*\*   0.05
  SPL     0.09       −0.21         0.32\*\*   0.19
  FLW     0.02       −0.17         0.30\*\*   0.09
  SOC     −0.01      −0.03         0.28\*     0.11
  DEP     −0.10      −0.4\*\*\*    0.26\*     0.15
  BED     −0.08      −0.2\*\*      0.29\*\*   0.11
  FLUSH   0.08       0.22          0.21       0.08
  COG     −0.02      −0.12         0.20       −0.08
  HOSP    −0.08      −0.24\*\*     0.06       −0.11
  EVGG    −0.11      −0.35\*\*     0.26       0.10
  LEV     −0.19      −0.32\*\*     −0.06      0.21
  EXSTR   −0.11      −0.37\*\*     −0.02      −0.09
  IADL    −0.39      −0.51\*\*\*   0.03       −0.02
  BLOCK   −0.53      −0.5\*\*\*    −0.26\*    −0.29\*
  ADL     −0.30      −0.55\*\*\*   −0.05      −0.16
  TWLK    −0.36      −0.67\*\*\*   −0.05      −0.08

  Black   YOL        0.04          −0.07      0.42\*\*   0.11
  SPL     0.60\*\*   0.42\*        0.50\*     0.71\*\*
  FLW     0.33       0.12          0.36       0.35
  SOC     0.28       0.40          0.06       −0.19
  DEP     0.63\*\*   0.21          0.23       0.41
  BED     0.26       −0.08         0.46\*     0.13
  FLUSH   0.35       0.05          0.12       −0.13
  COG     0.38       −0.08         0.51       0.45
  HOSP    0.12       0.00          0.43\*     0.15
  EVGG    0.70\*\*   0.15          0.25       0.27
  LEV     0.17       0.34          0.04       0.02
  EXSTR   0.59\*     0.35          0.40       0.28
  IADL    0.16       0.07          0.47       0.36
  BLOCK   0.24       −0.22         0.20       0.12
  ADL     0.16       −0.17         0.09       0.10
  TWLK    0.26       −0.31         0.13       −0.19

\**P* \< .10; \*\**P* \< .05; \*\*\**P* \< .01.

######

Means by BMI\*, gender, and race.

                                            Sex
  ------------------- ------------- ------- ------- ------- ------- ------- ------- -----
  White (10 yrs)      Sample size   Sum     1037    1069    611     649     1092    372
  Age                 Mean          73.0    72.4    71.3    74.8    73.0    72.0
  \% Current smoker   Mean          15.72   10.02   9.49    12.19   9.53    4.30
  \%  Lost 10 + 1bs   Mean          12.63   11.32   9.66    12.48   11.81   13.98
  YOL                 Mean          9.09    9.12    9.11    7.96    8.49    8.49
  SPL                 Mean          6.29    6.55    6.32    5.96    6.58    6.47
  FLW                 Mean          8.23    8.41    8.34    7.32    7.92    7.65
  SOC                 Mean          3.44    3.54    3.73    2.69    3.12    3.01
  DEP                 Mean          6.98    7.04    6.79    6.59    7.18    6.88
  BED                 Mean          8.48    8.52    8.37    7.54    8.11    7.90
  FLUSH               Mean          5.59    5.75    5.05    5.05    5.39    5.42
  COG                 Mean          5.64    5.83    5.94    4.45    5.18    4.94
  HOSP                Mean          8.11    8.16    8.02    6.96    7.31    7.07
  EVGG                Mean          7.01    7.07    6.37    6.04    6.64    6.08
  LEV                 Mean          5.81    5.74    5.50    5.38    5.62    5.22
  EXSTR               Mean          5.73    5.78    5.31    6.49    6.81    6.42
  IADL                Mean          6.07    5.88    5.03    5.82    6.28    5.54
  BLOCK               Mean          2.79    2.40    1.69    3.47    3.54    2.81
  ADL                 Mean          7.47    7.36    6.57    6.89    7.21    6.39
  TWLK                Mean          5.15    5.04    3.87    5.08    5.56    4.56

  Black (7 yrs)       Sample size   Sum     102     212     256     95      165     74
  Age                 Mean          72.1    70.7    70.4    71.5    70.6    68.3
  \% Current smoker   Mean          24.51   13.74   7.84    31.58   14.02   17.57
  \% Lost 10 + 1bs    Mean          19.61   13.68   14.45   16.84   10.30   13.51
  YOL                 Mean          6.45    6.57    6.56    5.68    6.33    6.47
  SPL                 Mean          4.30    5.00    4.81    3.97    4.73    4.59
  FLW                 Mean          5.67    6.07    5.99    5.13    5.81    5.93
  SOC                 Mean          2.03    2.37    2.39    2.03    2.23    2.23
  DEP                 Mean          4.07    4.85    4.46    4.26    4.83    4.73
  BED                 Mean          5.71    6.00    5.80    5.20    5.95    5.88
  FLUSH               Mean          3.10    3.50    3.14    2.83    3.27    3.24
  COG                 Mean          1.95    2.60    2.07    1.69    2.39    2.04
  HOSP                Mean          5.68    5.88    5.57    4.97    5.66    5.43
  EVGG                Mean          3.58    4.43    3.47    3.62    4.18    4.19
  LEV                 Mean          3.25    3.49    3.47    3.53    3.79    3.53
  EXSTR               Mean          3.04    3.80    2.89    4.37    5.06    4.82
  IADL                Mean          3.73    4.06    3.20    3.88    4.73    4.54
  BLOCK               Mean          .93     1.31    .71     1.48    1.94    1.51
  ADL                 Mean          4.61    4.95    4.11    4.84    5.28    4.82
  TWLK                Mean          2.39    2.91    1.77    2.82    3.30    2.91

\*Normal = 18.5--24.9 (normal), Overweight = 25--29.9 (overweight), Obese = 30+.

######

Means by Waist Circumference\*, Gender and Race.

                                            Sex
  ------------------- ------------- ------- ------- ------- ------- ------- ------- -----
  White (10 yrs)      Sample size   Sum     928     910     870     651     757     697
  Age                 Mean          72.5    72.3    72.2    74.3    73.2    72.6
  \% Current smoker   Mean          13.79   11.43   10.93   10.62   10.04   7.47
  \% Lost 10 + lbs    Mean          11.64   12.64   9.89    11.06   12.29   13.63
  YOL                 Mean          9.13    9.10    9.08    8.20    8.36    8.39
  SPL                 Mean          6.52    6.38    6.28    6.24    6.55    6.27
  FLW                 Mean          8.40    8.29    8.27    7.61    7.85    7.59
  SOC                 Mean          3.53    3.56    3.54    2.87    3.05    2.97
  DEP                 Mean          7.19    6.86    6.81    6.86    7.17    6.77
  BED                 Mean          8.60    8.44    8.36    7.79    8.03    7.84
  FLUSH               Mean          5.59    5.84    5.15    5.22    5.34    5.31
  COG                 Mean          5.87    5.82    5.65    4.83    5.02    4.87
  HOSP                Mean          8.24    8.06    8.01    7.19    7.21    7.06
  EVGG                Mean          7.22    6.93    6.50    6.42    6.66    5.96
  LEV                 Mean          5.95    5.68    5.52    5.39    5.75    5.25
  EXSTR               Mean          5.98    5.66    5.31    6.72    6.78    6.44
  IADL                Mean          6.28    5.84    5.12    6.12    6.30    5.59
  BLOCK               Mean          2.91    2.38    1.85    3.72    3.58    2.89
  ADL                 Mean          7.73    7.24    6.69    7.16    7.17    6.57
  TWLK                Mean          5.51    4.94    3.95    5.48    5.67    4.54

  Black (7 yrs)       Sample size   Sum     186     194     188     116     119     99
  Age                 Mean          71.0    70.7    70.6    71.3    70.4    69.2
  \% Current smoker   Mean          17.74   13.92   6.99    23.48   20.17   15.15
  \% Lost 10 + lbs    Mean          13.44   15.46   16.49   13.79   10.92   14.14
  YOL                 Mean          6.60    6.56    6.47    6.03    6.28    6.23
  SPL                 Mean          4.65    5.08    4.62    4.04    4.88    4.52
  FLW                 Mean          5.98    6.11    5.78    5.35    5.88    5.70
  SOC                 Mean          2.15    2.54    2.27    2.26    2.15    2.10
  DEP                 Mean          4.58    4.79    4.25    4.37    4.93    4.63
  BED                 Mean          6.03    5.93    5.62    5.59    5.87    5.70
  FLUSH               Mean          3.25    3.31    3.23    3.09    3.16    3.16
  COG                 Mean          2.39    2.35    1.99    1.81    2.42    2.10
  HOSP                Mean          5.85    5.85    5.44    5.34    5.63    5.24
  EVGG                Mean          4.16    4.30    3.09    3.81    4.23    4.02
  LEV                 Mean          3.36    3.69    3.26    3.68    3.82    3.42
  EXSTR               Mean          3.40    3.75    2.63    4.60    5.13    4.68
  IADL                Mean          3.92    3.98    2.97    4.28    4.86    4.14
  BLOCK               Mean          1.23    1.03    .68     1.71    1.97    1.42
  ADL                 Mean          4.96    4.79    3.83    5.11    5.41    4.56
  TWLK                Mean          2.89    2.60    1.45    3.22    3.27    2.69

\*Overweight = 84.5--96.5 for White Female, 94.0--107.5 for Black Female, 93.0--101.5 for Male.

######

Adjusted difference between Obese and Normal YHL (in years; negative values favor Normal).

                          Female        Male
  ------- ------------- ------------- ------------- ------------- -------
  White   YOL           −0.21\*\*     −0.12         0.10          −0.02
  SPL     −0.32\*\*     −0.34         0.02          −0.20
  FLW     −0.22\*       −0.21\*       −0.17         −0.27\*
  SOC     0.07          −0.05         0.05          −0.03
  DEP     −0.53\*\*\*   −0.48\*\*\*   −0.25         −0.35\*\*
  BED     −0.38\*\*\*   −0.32\*\*\*   −0.10         −0.17
  FLUSH   −0.70\*\*\*   −0.48\*\*\*   0.13          −0.01
  COG     −0.26         −0.33\*\*     −0.42\*       −0.42\*\*
  HOSP    −0.39\*\*\*   −0.32\*\*\*   −0.36\*\*     −0.36\*\*
  EVGG    −1.02\*\*\*   −0.83\*\*\*   −0.51\*\*     −0.71\*\*\*
  LEV     −0.58\*\*\*   −0.51\*\*\*   −0.63\*\*\*   −0.38\*\*
  EXSTR   −0.80\*\*\*   −0.77\*\*\*   −0.63\*\*\*   −0.55\*\*\*
  IADL    −1.52\*\*\*   −1.28\*\*\*   −0.98\*\*\*   −0.88\*\*\*
  BLOCK   −1.44\*\*\*   −1.14\*\*\*   −1.21\*\*\*   −1.10\*\*\*
  ADL     −1.34\*\*\*   −1.15\*\*\*   −1.10\*\*\*   −0.88\*\*\*
  TWLK    −1.93\*\*\*   −1.71\*\*\*   −1.39\*\*\*   −1.39\*\*\*

  Black   YOL           0.05          −0.15         0.47\*        0.03
  SPL     0.41          −0.05         0.27          0.29
  FLW     0.27          −0.21         0.33          0.09
  SOC     0.28          0.12          0.02          −0.27
  DEP     0.24          −0.34         0.02          0.05
  BED     0.08          −0.37\*\*     0.28          −0.10
  FLUSH   −0.02         −0.06         −0.03         −0.19
  COG     −0.09         −0.40         −0.18         0.00
  HOSP    −0.18         −0.42\*\*     0.11          −0.28
  EVGG    −0.26         −1.03\*\*\*   0.13          0.00
  LEV     0.17          −0.11         −0.37         −0.43
  EXSTR   −0.31         −0.78\*\*\*   −0.03         −0.22
  IADL    −0.70\*\*     −0.89\*\*\*   0.08          −0.46
  BLOCK   −0.37\*       −0.57\*\*\*   −0.38         −0.52\*
  ADL     −0.69\*\*     −1.10\*\*\*   −0.53         −0.82\*\*\*
  TWLK    −0.89\*\*\*   −1.43\*\*\*   −0.59         −0.89\*\*\*

\**P* \< .10; \*\**P* \< .05; \*\*\**P* \< .01.

[^1]: Academic Editor: Francisco Lopez-Jimenez


<!DOCTYPE html>

  <meta charset="UTF-8" data-th-remove="all" />
  <meta http-equiv="X-UA-Compatible" content="IE=edge" data-th-remove="all" />
  <meta name="viewport" content="width=device-width, initial-scale=1" data-th-remove="all" />
  <meta name="description" content="petclinic" data-th-remove="all" />
  <meta name="author" content="Spring Roo" data-th-remove="all" />
  <link data-th-href="@{/public/img/favicon.ico}" data-th-remove="all" rel="icon" href="../../static/public/img/favicon.ico" />
  <link rel="shortcut icon" href="../../static/public/img/favicon.ico" data-th-remove="all" />
  <link rel="apple-touch-icon" href="../../static/public/img/apple-touch-icon.png" data-th-remove="all" />
  <title data-th-text="#{label_show_entity(#{label_owner})}">petclinic - Show Owner</title>

  <link rel="stylesheet" type="text/css" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.css" data-th-remove="all" />

  <link rel="stylesheet" type="text/css" href="https://maxcdn.bootstrapcdn.com/css/ie10-viewport-bug-workaround.css" data-th-remove="all" />

  <link data-th-href="@{/public/css/standard.css}" data-th-remove="all" href="../../static/public/css/standard.css" rel="stylesheet" />

   <link data-th-href="@{/public/css/nojs-standard.css}" data-th-remove="all" rel="stylesheet" href="../../static/public/css/nojs-standard.css" />

      petclinic

       <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1" aria-expanded="false"> Dropdown    </button>
       Main Menu

Active Menu 1

Menu 2

Dropdown Menu 3

Submenu 1

Submenu 2

Submenu 3

 User

 Last Access: 00-00-0000

<button type="submit" class="exit"> Exit</button>

 Contact

 Help

Show Owner

       firstName

       firstNameValue

       lastName

       lastNameValue

       address

       addressValue

       city

       cityValue

       telephone

       telephoneValue

       homePage

       homePageValue

       email

       emailValue

       birthDay

       birthDayValue

Pet

            List Pet

             sendReminders
             name
             weight
             owner
             type
             Tools

             sendReminders
             name
             weight
             owner
             type
             Tools

       <button onclick="location.href='list'" data-th-onclick="'location.href=\'' + @{/owners} + '\''" type="button" class="btn btn-default"> Back </button>

       <button data-th-onclick="'location.href=\'' + @{/owners/{id}/edit-form(id=*{id})} + '\''" data-sec-authorize="hasAnyRole('ROLE_ADMIN','ROLE_EMPLEADO')" type="button" class="btn btn-primary" onclick="location.href='edit'" data-th-text="#{label_edit}">Edit</button>

© Powered By Spring Roo


The Timberwolves Have Too Many Wing Players

So sayeth GM David Kahn, who then goes on to compare players to cars in a garage. You figure it out. From the Star Tribune: “Kahn said it’s ‘likely’ the Wolves will make a deal by the NBA’s Feb. 24 trading deadline, but said it’d be a ‘tactical’ and ‘surgical’ move. ‘I don’t expect it to be big,’ he said. He also said he isn’t looking to trade [Corey] Brewer, who will become a restricted free agent this summer after the team did not offer him a contract extension last fall. He also said he hasn’t determined that Brewer isn’t a part of the team’s future. He could still be re-signed next summer once a new labor agreement is reached. ‘Because Corey is in the last year of his deal and because many people like Corey’s ability and upside, we receive a lot of calls on him,’ Kahn said. ‘We also have a team that’s very deep at his position. It’s important we listen and weigh what’s the appropriate decision for the ballclub. Players are not like cars in a garage. You can’t keep accumulating cars, you can only have so many of them. There’s a balance act there and at the wing position; we’re probably too heavy there.’”


Q:

InappBilling Error- authentication is required. you need to sign into your google account

I am trying to make a demo of inappbilling. I followed this tutorial for studying this.
Lemme tell the steps I followed for doing this task

I used the below mentioned code
package com.ohn.inappbilling;

import android.content.Intent;
import android.os.Bundle;
import android.support.v7.app.ActionBarActivity;
import android.util.Log;
import android.view.View;
import android.widget.Button;

import com.hello.inappbilling.util.IabHelper;
import com.hello.inappbilling.util.IabResult;
import com.hello.inappbilling.util.Inventory;
import com.hello.inappbilling.util.Purchase;
import com.ohn.inappbilling.R;

public class MainActivity extends ActionBarActivity {

private static final String TAG = "com.hello.inappbilling";
static final String ITEM_SKU = "com.buttonclick";
IabHelper mHelper;
private Button clickButton;
private Button buyButton;

@Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.activity_main);
    buyButton = (Button) findViewById(R.id.buyButton);
    clickButton = (Button) findViewById(R.id.clickButton);
    clickButton.setEnabled(false);
    String base64EncodedPublicKey = "******";

    mHelper = new IabHelper(this, base64EncodedPublicKey);

    mHelper.startSetup(new IabHelper.OnIabSetupFinishedListener() {
        public void onIabSetupFinished(IabResult result) {
            if (!result.isSuccess()) {
                Log.d(TAG, "In-app Billing setup failed: " + result);
            } else {
                Log.d(TAG, "In-app Billing is set up OK");
            }
        }
    });
    //throw new RuntimeException();
}

public void buttonClicked(View view) {
    clickButton.setEnabled(false);
    buyButton.setEnabled(true);
}

public void buyClick(View view) {
     mHelper.launchPurchaseFlow(this, ITEM_SKU, 10001,
           mPurchaseFinishedListener, "mypurchasetoken");
}

@Override
protected void onActivityResult(int requestCode, int resultCode,
     Intent data)
{
      if (!mHelper.handleActivityResult(requestCode,
              resultCode, data)) {
        super.onActivityResult(requestCode, resultCode, data);
      }
}

IabHelper.OnIabPurchaseFinishedListener mPurchaseFinishedListener
= new IabHelper.OnIabPurchaseFinishedListener() {
public void onIabPurchaseFinished(IabResult result,
                Purchase purchase)
{
   if (result.isFailure()) {
      // Handle error
      return;
 }
 else if (purchase.getSku().equals(ITEM_SKU)) {
     consumeItem();
    buyButton.setEnabled(false);
}

}

};

public void consumeItem() {
    mHelper.queryInventoryAsync(mReceivedInventoryListener);
}

IabHelper.QueryInventoryFinishedListener mReceivedInventoryListener
   = new IabHelper.QueryInventoryFinishedListener() {
       public void onQueryInventoryFinished(IabResult result,
          Inventory inventory) {

          if (result.isFailure()) {
          // Handle failure
          } else {
                 mHelper.consumeAsync(inventory.getPurchase(ITEM_SKU),
            mConsumeFinishedListener);
          }
    }
};

IabHelper.OnConsumeFinishedListener mConsumeFinishedListener =
new IabHelper.OnConsumeFinishedListener() {
 public void onConsumeFinished(Purchase purchase,
       IabResult result) {

if (result.isSuccess()) {
      clickButton.setEnabled(true);
} else {
       // handle error
}
}
};

@Override
public void onDestroy() {
    super.onDestroy();
    if (mHelper != null) mHelper.dispose();
    mHelper = null;
}

}

I created apk using File-Export-AndroidProject and uploaded this in alphatesting.
I added users(Google group) in Manage list of testers in alpha testing.
I added the product in In-app Product and give id to it com.buttonclick
In settings in Gmail accounts with testing access I added Gmail id's here also. None of the id is developer id.

Can anyone tell solution for this problem. I have tried all the solutions available on StackoverFlow.

A:

I found solution for this problem.
I published this apk on playstore and then I downloaded apk from playstore and ran that and It worked fine
I think the reason behind this problem is
The google account that I was using for testing may be it was not the main account.


A COLLEGE degree which allowed students to study at both Mary Immaculate College and the University of Limerick has been scrapped after just two years.

The two colleges have this week confirmed the axing of the joint BA liberal arts degree, blaming cost, a lack of funding and “complex logistics in terms of mobility and scheduling”.

The move has led to fears in some quarters that Mary Immaculate College could merge with the University of Limerick (UL) – or go back to being solely a teacher training college. Job cuts are also feared among teaching staff.

“Future students are ultimately the big losers here,” said one staff member, who did not wish to be identified.

The joint BA course was established in 2016 as a way to stop the duplication of programmes at UL and Mary Immaculate College, and allow students to study different elements of their degree on campus both in Castletroy and the South Circular Road.

However, the staff member said one of the reasons the project did not work out was because of poor transport links between the two colleges – something the institutions referenced in their joint statement to the Limerick Leader.

UL and Mary Immaculate College are linked by the delay-ridden Bus Eireann 304 service, something highlighted on these pages in recent weeks.

Teachers at Mary Immaculate College have said they felt the joint BA was launched as a way to “avoid amalgamating the two colleges”, something the Higher Education Authority (HEA) was understood to be keen to do.

One Mary Immaculate Colllege lecturer, speaking on condition of anonymity,said: “Some staff are concerned about their job, especially if there are moves to a merger of the two colleges. Could staff be asked to transfer across to UL, or will their positions disappear entirely? People don’t know what will happen.”

Mary Immaculate College staff were told the news at a meeting on Friday, and it’s understood there was real concern among staff following the news.

“A lot of work has been put into this, and it feels like it has been cut dead,” one staff member, who was instrumental in setting up the course, told the Limerick Leader.

This September’s cohort of students on the joint BA course will be the final ones taking the course, the colleges have confirmed.

One of the reasons for this is the fact the course has already been advertised through CAO.

“But,” the lecturer added, “the 2020 arts intake will not offer the cross-campus subjects.”

It means popular subjects like geography may not be available at UL going forward.

“It was supposed to increase the offering to students on both campuses. Previously, if you did a BA in UL, you couldn’t do drama or philosophy. Likewise on our side, we couldn’t do geology, politics or economics. It’s a big loss,” the lecturer added.

In a joint statement, Mary Immaculate College and UL confirmed the news.

”Regrettably, the cost of resourcing the joint programme, a lack of adequate funding and complex logistics in terms of mobility and scheduling have culminated in a decision to discontinue the joint element of the programme, with both institutions continuing to offer their respective arts programmes without the joint subjects,” they said.

UL and Mary Immaculate College will, they add, “continue to build on the stimulating and fruitful engagement” developed in this partnership, “and look forward to working on new collaborative initiatives.”


Lenticular subluxation in a patient with homocystinuria undetected by neonatal screening.
A case of homocystinuria with lenticular subluxation was misdiagnosed as Marfan syndrome since the patient had no apparent mental impairment and had had a negative neonatal screen for homocystinuria. The delayed diagnosis of homocystinuria was due to a negative prior neonatal screen which was checked when he was a breastfed healthy newborn. In the absence of an autosomal dominant family history, and because of prior poor school performance, amino acid analysis and mutational analysis of the cystathionine beta-synthase gene were performed, which revealed the presence of homocystinuria. Low methionine diet with vitamin B6, folic acid, betaine, dipyridamole and aspirin was prescribed for emergency ophthalmologic surgery to prevent thromboembolic events. Fortunately, the operation was completed uneventfully. The patient has been followed-up for 4 years without any significant complaints under diet and medical control. Since homocystinuria is easily missed in neonatal screening programs, it should be suspected in patients who present with lenticular subluxation, even after a negative neonatal screen.


Q:

T-SQL stored procedures

I have a table Users with the following columns:
  Userid   Username    Password
     1     Username1   Password1
     2     Username2   Password2
     3     Username3   Password3

There is another table, MappingRoles:
 Userid   RoleId
     1       1 (FinanceUser)
     1       2 (Agent)
     1       3 (Client)

I want to write a stored procedure which should return two columns
IsValidUser
Roles -- of the user

This is my attempt:
CREATE PROCEDURE  getRoles
    @Username varchar(50),
    @Password varchar(50)
AS
BEGIN
    Select * from Users as x inner join MappingRoles as y
    on x.Userid=y.Userid
    where x.Userid=(Select Userid from Users
      where UserName='Username1' and Password='Password1');
END
GO

I am expecting optimization in this query to provide 2 return values:

IsUserAuthenticated - (the inner Select from Users)
If it's present then all the values.

Any suggestion would be highly appreciated.

A:

Try a simpler join, drop the sub query:
CREATE PROCEDURE  getRoles
    @Username varchar(50),
    @Password varchar(50)
AS
BEGIN
    Select
        *
    from Users as us
    inner join MappingRoles mr
    on us.Userid=mr.Userid
    where us.UserName=@username and us.Password=@password);
END
GO


During the summer we store the sail wrapped on the mast in the sail/mast bag and hang it horizontally from 5 hooks under the north side of our house eve. In the winter we take it off the mast roll it up, put it in the sail bag and store it inside, the mast stays on the 5 hooks. As for the hull during the winter, I remove the rudder and hiking straps. The first year we covered it with a blue tarp. This year we are not covering it. So far it looks fine.

Well, aside from some dirt the Bravo hull appears to have wintered well uncovered. Last weekend we took the boat out for the first outing of the year. It took only a couple of hours to de-winterize the boat (compared to most of a day to do the same to my H17Sport).

When I put this question, my major concern was with the sun in the summer. I live in Portugal, near Lisbon and in the winter is not very cold (minimal temperature is 5ÂºC â€“ 40ÂºF), with some rain. But in the summer the temperature is high (35-40ÂºC â€“ 100ÂºF).

Get a inside storage like me and bite the bullet. Put some heater in in and fans in summer. I pay $250 month for 15 x 50 for both cats. Hobie 17 and Mystere 4.3, but WORTH every penny to keep boats looking like new all the time.

leclerc, I live in Washington State, so the sun is not too intense in the winter. In the summer we get several days above 100oF and do not cover the boat and appears not to suffer. The UV from the sun can degrade things rather quickly, though I do not know how well the rotomolded hulls do (you might ask in the frequently asked questions). The rotomolded boats I've rented in Maui (several years old) appear to be in relatively good shape (hull wise), but I would still take off all the running gear for any longer term storage. Inside storage is nice, but might not be too cost effective if very expensive (e.g., $250.00/month; the bravo is < $3000.00 new).


So, Rihanna. She's a slag, isn't she? Such, at least, is the verdict of the tabloid press, who have once again queued up to pile opprobrium on the singer, following the example of one Alan Graham, a Northern Irish farmer who shot to fame after asking Rihanna to put her breasts away and leave his field, where she had been shooting the video for her new hit, We Found Love.

Writing for the Daily Mail, Yasmin Alibhai-Brown -- a columnist I normally admire -- praises Farmer Graham for making "a brave stand against two of the worst excesses of modern life: the sexualisation of society and our celebrity culture." She says that Graham is almost alone in taking this stand, and that she "hugely admire(s)" him.

I can't agree. I could be wrong, but I'll bet against the likelihood of this elderly fundamentalist Christian having feminist concerns at the forefront of his mind when he chose to reprimand a young woman for showing her naked body in his fields. Mistaking religious prudery for feminism gets you ten whole points in "liberals missing the issue" bingo, but there is something additionally abhorrent about the way in which this older man is being commended for stepping in, as if he were saving Rihanna from her wicked ways.

The debate about whether popular culture has become too "sexualised" is hardly restricted to Bangor, NI -- it's a debate that has run and run in nearly every major paper for over two years, partly because it's simply gagging to be illustrated with full-colour examples of such "sexualisation" for readers to cut out and keep.

It is interesting that Rihanna -- not only one of the most prominent women of colour working in pop, but a woman who is well-known for speaking up about her own experience of domestic violence -- should have become the chief scapegoat in this new culture war.

Disapproving, lip twisting pseudo-feminist articles about whether or not music videos and trainer adverts are going to turn all girls under 12 into knicker-tossing teen harlots who can hardly turn on MTV without becoming pregnant or syphilitic are usually accompanied by pictures of Rihanna in her underwear.

Sometimes it's Lady Gaga, but Gaga is weird and confusing and you never quite know when she's going to turn up dressed as a man, a lobster or all three volumes of Marx's Das Kapital at once, as opposed to the standard alien vinyl barbie look of which certain sections of the curtain-twitching middle classes love to disapprove.

No, for real, quality disapproval, it has to be Rihanna. We love to disapprove of her. We love to disapprove of her cute, pert bottom; we love to disapprove of her luscious breasts and smooth skin, barely covered by those disgustingly small leather thongs she likes to wear, the hussy. Look at her sexualising our children. Look at her, sexualising away in those horrifyingly sexualised sexy pants. We disapprove of those, too.

The hypocrisy is obvious, and it's not just the Daily Mail, which rather topped the pile by linking, in the middle of their piece on the Farmer Graham story, to another article about how "Smoking Hot!" Rihanna looked in the exact same video shoot, which they illustrated with the exact same photos, this time naming her the "Queen of Seduction".

This two-faced neo-puritanism makes mock concern for women's dignity just another reason to print enormous close-ups of their soft bits in not too much. There are po-faced men in garages across middle England who will pay a lot for that sort of disapproval, disapproval that stops extremely short of actually asking for change, because change doesn't sell papers.

I'm not saying that there are no problems at all with Rihanna's brand of arse-out sexual commodification becoming a standard feature of female celebrity -- although give the girl credit, at least she isn't claiming, as others do, that it's a non-stop shuttle to planet empowerment.

I'm not saying that there aren't big, big problems with the kind of raunch culture that has made Rihanna rich. What I am saying is that perhaps, just perhaps, the best way to address those problems might not be to applaud a religious fundamentalist for telling a young woman to cover herself up in his presence.

Some people can't seem to understand the difference between taking a stand against sexism and taking a stand against sex, but it's a distinction that we must make if we want a women's movement that's smart and brave and useful.


Barton Marine 21001 - Aluminum Winch Handle - 8” SKU# 46608Aluminum Winch Handle - 8”. All designed to fit international star sockets and standard deck filler caps. Light duty, inexpensive handles w/easy-grip roller handle and 'Spectro' grey anodised finish. Important note: These handles should not be used on large, two-speed winches as they are not designed for high loads in an anti-clockwise direction. SWL 40kg (88.18lbs).

$67.79

Barton Marine 21002 - Aluminum Winch Handle - 10” SKU# 46610CLOSE OUT SPECIAL!Aluminum Winch Handle - 10”. All designed to fit international star sockets and standard deck filler caps.Light duty, inexpensive handles w/easy-grip roller handle and 'Spectro' grey anodised finish. Important note: These handles should not be used on large, two-speed winches as they are not designed for high loads in an anti-clockwise direction. SWL 40kg (88.18lbs).Status: Close Out Special, Limited Supply