Erratum to: Hybrid maize breeding with doubled haploids: V. Selection strategies for testcross performance with variable sizes of crosses and S1 families
ERRATUM Erratum to: Hybrid maize breeding with doubled haploids: V. Selection strategies for testcross performance with variable sizes of crosses and S 1 families Thilo Wegenast • H. Friedrich Utz • C. Friedrich H. Longin • Hans Peter Maurer • Baldev S. Dhillon • Albrecht E. Melchinger Published online: 3 September 2010 Ó Springer-Verlag 2010 Erratum to: Theor Appl Genet (2010) 120:699–708 DOI 10.1007/s00122-009-1187-y In the original version of the article, an error was detected in the simulation of breeding scheme S 1 TC-DHTC. This error led to an overestimation of the selection gain (D b G ) and the probability of selecting superior genotypes ( b PðqÞ ) in this breeding scheme. The corrected results for all selection strategies of breeding scheme S 1 TC-DHTC in Tables 2 and 3, as well as Fig. 1 are presented in the fol- lowing pages. The online version of the original article can be found under doi:10.1007/s00122-009-1187-y. T. Wegenast H. F. Utz B. S. Dhillon A. E. Melchinger (&) Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany e-mail: melchinger@uni-hohenheim.de T. Wegenast e-mail: wegenast@uni-hohenheim.de C. F. H. Longin Limagrain Verneuil Holding, BP 58 Route de Lavardac, 47600 Ne ´rac, France H. P. Maurer State Plant Breeding Institute, University of Hohenheim, 70593 Stuttgart, Germany 123 Theor Appl Genet (2010) 121:1391–1393 DOI 10.1007/s00122-010-1428-0
Table 2 Optimum allocation of test resources maximizing the optimization criteria (OC), selection gain (D b G ) and the probability of selecting superior genotypes ( b Pð0:1%Þ ), in two-stage selection with evaluation of testcross progenies of (1) DH lines at both stages (DHTC) and (2) S 1 families at first stage and DH lines of S 1 families at second stage (S 1 TC-DHTC) Breeding scheme/selection strategy Optimum allocation OC SD OC H N 1 *a N 2 *b L 1 * L 2 * Optimization criterion D b G DHTC–1 5,538 = 3 9 1,846 392 = 2 9 196 2 14 3.322 0.385 0.500 DHTC–2a 5,812 = 4 9 1,453 320 2 15 3.384 0.324 0.311 DHTC–2b 6,152 330 2 12 3.335 0.340 0.324 DHTC–2c 4,704 203 3 14 3.347 0.326 0.323 S 1 TC-DHTC–1 816 = 4 9 204 646 = 2 9 1 9 323 12 14 3.581 0.516 0.750 S 1 TC-DHTC–2a 800 = 5 9 160 660 = 3 9 1 9 220 12 14 3.610 0.485 0.663 S 1 TC-DHTC–2b 824 = 4 9 206 753 10 14 3.624 0.484 0.581 S 1 TC-DHTC–2c 725 = 5 9 145 781 11 14 3.612 0.494 0.584 Optimization criterion b Pð0:1%Þ DHTC–1 5,655 = 3 9 1,885 400 = 2 9 200 2 13 0.631 0.295 0.500 DHTC–2a 5,644 = 4 9 1,411 348 2 15 0.671 0.252 0.316 DHTC–2b 6,204 320 2 12 0.640 0.258 0.322 DHTC–2c 4,640 220 3 14 0.651 0.258 0.322 S 1 TC-DHTC–1 820 = 4 9 205 680 = 2 9 1 9 340 13 12 0.771 0.295 0.750 S 1 TC-DHTC–2a 775 = 5 9 155 735 = 3 9 1 9 245 11 14 0.773 0.265 0.566 S 1 TC-DHTC–2b 1,057 = 7 9 151 712 9 13 0.779 0.258 0.541 S 1 TC-DHTC–2c 828 = 6 9 138 804 11 12 0.775 0.262 0.576 Assumptions: a budget of 20,000 testcross plot equivalents, variance component ratios VC2, and a correlation q P = 0.71 between the mean performance of the parental lines and the mean genotypic value of the testcross performance of their progeny N j * = optimum number of test candidates in stage j, L j = optimum number of test locations in stage j, SD = the standard deviation, and H = the average coefficient of coancestry among the selected DH lines a DHTC–1 and 2a: number of crosses 9 DH lines within crosses, DHTC–2b and 2c: the number of DH lines within crosses depended on the rank of the cross; S 1 TC-DHTC: number of crosses 9 S 1 families within crosses b DHTC–1: number of crosses 9 DH lines within crosses, DHTC–2: number of DH lines; S 1 TC-DHTC–1 and 2a: number of crosses 9 S 1 families within crosses 9 DH lines within S 1 families; S 1 TC-DHTC–2b and 2c: the number of S 1 families within crosses and DH lines within S 1 families depended on the rank of the cross and the S 1 family Table 3 Optimum allocation of test resources maximizing the optimization criteria (OC), selection gain (D b G ) and the probability of selecting superior genotypes ( b Pð0:1%Þ ), in two-stage selection with evaluation of testcross progenies of (1) DH lines at both stages (DHTC–2a) and (2) S 1 families at first stage and DH lines of S 1 families at second stage (S 1 TC-DHTC–2c) and its dependence on the phenotypic correlation q P (between the mean performance of the parents and the mean genotypic value of the testcross performance of their progenies), the variance component ratios (VC), and the budget in terms of testcross plot equivalents Breeding scheme/selection strategy Assumptions Optimum allocation OC SD OC H Budget VC q P N 1 *a N 2 L 1 L 2 Optimization criterion D b G DHTC–2a 10,000 2 0.71 2,874 = 3 9 958 191 2 13 3.257 0.316 0.340 DHTC–2a 20,000 2 0.71 5,812 = 4 9 1,453 320 2 15 3.384 0.324 0.311 DHTC–2a 40,000 2 0.71 9,325 = 5 9 1,865 427 3 14 3.491 0.322 0.296 DHTC–2a 20,000 1 0.71 9,556 = 4 9 2,389 390 1 13 3.662 0.309 0.318 DHTC–2a 20,000 3 0.71 3,564 = 2 9 1,782 232 4 14 3.089 0.317 0.399 DHTC–2a 20,000 2 0.50 6,069 = 7 9 867 297 2 14 3.070 0.369 0.268 1392 Theor Appl Genet (2010) 121:1391–1393 123
Theor Appl Genet (2010) 121:1391–1393
DOI 10.1007/s00122-010-1428-0
ERRATUM
Erratum to: Hybrid maize breeding with doubled haploids:
V. Selection strategies for testcross performance
with variable sizes of crosses and S1 families
Thilo Wegenast • H. Friedrich Utz •
C. Friedrich H. Longin • Hans Peter Maurer
Baldev S. Dhillon • Albrecht E. Melchinger
•
Published online: 3 September 2010
Ó Springer-Verlag 2010
Erratum to: Theor Appl Genet (2010) 120:699–708
DOI 10.1007/s00122-009-1187-y
In the original version of the article, an error was detected
in the simulation of breeding scheme S1TC-DHTC. This
b )
error led to an overestimation of the selection gain (D G
b
and the probability of selecting superior genotypes ( PðqÞ )
in this breeding scheme. The corrected results for all
selection strategies of breeding scheme S1TC-DHTC in
Tables 2 and 3, as well as Fig. 1 are presented in the following pages.
The online version of the original article can be found under
doi:10.1007/s00122-009-1187-y.
T. Wegenast H. F. Utz B. S. Dhillon A. E. Melchinger (&)
Institute of Plant Breeding, Seed Science,
and Population Genetics, University of Hohenheim,
70593 Stuttgart, Germany
e-mail: melchinger@uni-hohenheim.de
T. Wegenast
e-mail: wegenast@uni-hohenheim.de
C. F. H. Longin
Limagrain Verneuil Holding,
BP 58 Route de Lavardac, 47600 Nérac, France
H. P. Maurer
State Plant Breeding Institute,
University of Hohenheim, 70593 Stuttgart, Germany
123
1392
Theor Appl Genet (2010) 121:1391–1393
Table 2 Optimum allocation of test resources maximizing the
b ) and the probability
optimization criteria (OC), selection gain (D G
b
of selecting superior genotypes ( Pð0:1%Þ ), in two-stage selection
Breeding scheme/selection strategy
with evaluation of testcross progenies of (1) DH lines at both stages
(DHTC) and (2) S1 families at first stage and DH lines of S1 families
at second stage (S1TC-DHTC)
Optimum allocation
N*1 a
N*2 b
L*1
L*2
OC
SDOC
H
0.500
b
Optimization criterion D G
DHTC–1
5,538 = 3 9 1,846
392 = 2 9 196
2
14
3.322
0.385
DHTC–2a
5,812 = 4 9 1,453
320
2
15
3.384
0.324
0.311
DHTC–2b
6,152
330
2
12
3.335
0.340
0.324
DHTC–2c
4,704
203
3
14
3.347
0.326
0.323
S1TC-DHTC–1
816 = 4 9 204
646 = 2 9 1 9 323
12
14
3.581
0.516
0.750
S1TC-DHTC–2a
800 = 5 9 160
660 = 3 9 1 9 220
12
14
3.610
0.485
0.663
S1TC-DHTC–2b
824 = 4 9 206
753
10
14
3.624
0.484
0.581
S1TC-DHTC–2c
725 = 5 9 145
781
11
14
3.612
0.494
0.584
0.500
b
Optimization criterion Pð0:1%Þ
DHTC–1
5,655 = 3 9 1,885
400 = 2 9 200
2
13
0.631
0.295
DHTC–2a
5,644 = 4 9 1,411
348
2
15
0.671
0.252
0.316
DHTC–2b
6,204
320
2
12
0.640
0.258
0.322
DHTC–2c
4,640
220
S1TC-DHTC–1
S1TC-DHTC–2a
820 = 4 9 205
775 = 5 9 155
680 = 2 9 1 9 340
735 = 3 9 1 9 245
S1TC-DHTC–2b
1,057 = 7 9 151
712
9
13
0.779
0.258
0.541
S1TC-DHTC–2c
828 = 6 9 138
804
11
12
0.775
0.262
0.576
3
14
0.651
0.258
0.322
13
11
12
14
0.771
0.773
0.295
0.265
0.750
0.566
Assumptions: a budget of 20,000 testcross plot equivalents, variance component ratios VC2, and a correlation qP = 0.71 between the mean
performance of the parental lines and the mean genotypic value of the testcross performance of their progeny
N*j = optimum number of test candidates in stage j, Lj = optimum number of test locations in stage j, SD = the standard deviation, and
H = the average coefficient of coancestry among the selected DH lines
a
DHTC–1 and 2a: number of crosses 9 DH lines within crosses, DHTC–2b and 2c: the number of DH lines within crosses depended on the
rank of the cross; S1TC-DHTC: number of crosses 9 S1 families within crosses
b
DHTC–1: number of crosses 9 DH lines within crosses, DHTC–2: number of DH lines; S1 TC-DHTC–1 and 2a: number of crosses 9 S1
families within crosses 9 DH lines within S1 families; S1TC-DHTC–2b and 2c: the number of S1 families within crosses and DH lines within S1
families depended on the rank of the cross and the S1 family
Table 3 Optimum allocation of test resources maximizing the
b ) and the probability
optimization criteria (OC), selection gain (D G
b
of selecting superior genotypes ( Pð0:1%Þ
), in two-stage selection
with evaluation of testcross progenies of (1) DH lines at both stages
(DHTC–2a) and (2) S1 families at first stage and DH lines of S1
Breeding scheme/selection strategy
families at second stage (S1TC-DHTC–2c) and its dependence on the
phenotypic correlation qP (between the mean performance of the
parents and the mean genotypic value of the testcross performance of
their progenies), the variance component ratios (VC), and the budget
in terms of testcross plot equivalents
Assumptions
Optimum allocation
Budget
N*1 a
VC
qP
OC
N2
L1
SDOC
H
L2
b
Optimization criterion D G
DHTC–2a
10,000
2
0.71
2,874 = 3 9 958
191
2
13
3.257
0.316
0.340
DHTC–2a
20,000
2
0.71
5,812 = 4 9 1,453
320
2
15
3.384
0.324
0.311
DHTC–2a
40,000
2
0.71
9,325 = 5 9 1,865
427
3
14
3.491
0.322
0.296
DHTC–2a
20,000
1
0.71
9,556 = 4 9 2,389
390
1
13
3.662
0.309
0.318
DHTC–2a
DHTC–2a
20,000
20,000
3
2
0.71
0.50
3,564 = 2 9 1,782
6,069 = 7 9 867
232
297
4
2
14
14
3.089
3.070
0.317
0.369
0.399
0.268
123
Theor Appl Genet (2010) 121:1391–1393
1393
Table 3 continued
Breeding scheme/selection strategy
Assumptions
Optimum allocation
Budget
VC
qP
N*1 a
OC
N2
L1
SDOC
H
L2
S1TC-DHTC–2c
10,000
2
0.71
267 = 3 9 89
686
9
10
3.434
0.505
0.592
S1TC-DHTC–2c
20,000
2
0.71
725 = 5 9 145
781
11
14
3.639
0.494
0.584
S1TC-DHTC–2c
40,000
2
0.71
848 = 4 9 212
2,391
10
12
3.751
0.484
0.561
S1TC-DHTC–2c
20,000
1
0.71
888 = 4 9 222
980
10
11
3.991
0.498
0.601
S1TC-DHTC–2c
20,000
3
0.71
522 = 3 9 174
856
13
14
3.224
0.476
0.605
S1TC-DHTC–2c
20,000
2
0.50
794 = 12 9 66
720
11
14
3.376
0.524
0.551
b
Optimization criterion (OC) Pð0:1%Þ
DHTC–2a
10,000
2
0.71
3,153 = 3 9 1,051
149
2
12
0.595
0.273
0.338
DHTC–2a
DHTC–2a
20,000
40,000
2
2
0.71
0.71
5,644 = 4 9 1,411
9,375 = 5 9 1,875
348
447
2
3
15
13
0.671
0.730
0.252
0.225
0.316
0.296
DHTC–2a
20,000
1
0.71
9,728 = 4 9 2,432
371
1
13
0.833
0.197
0.315
DHTC–2a
20,000
3
0.71
3,222 = 2 9 1,611
342
4
14
0.500
0.284
0.397
DHTC–2a
20,000
2
0.50
6,216 = 7 9 888
293
2
13
0.479
0.271
0.271
S1TC-DHTC–2c
10,000
2
0.71
267 = 3 9 89
686
9
10
0.683
0.321
0.566
S1TC-DHTC–2c
20,000
2
0.71
775 = 5 9 155
735
11
14
0.778
0.265
0.566
S1TC-DHTC–2c
40,000
2
0.71
852 = 4 9 213
2,388
10
12
0.829
0.242
0.559
S1TC-DHTC–2c
20,000
1
0.71
1,115 = 5 9 223
868
9
10
0.924
0.152
0.599
S1TC-DHTC–2c
20,000
3
0.71
477 = 3 9 159
896
13
14
0.570
0.321
0.610
S1TC-DHTC–2c
20,000
2
0.50
737 = 11 9 67
820
11
13
0.648
0.316
0.552
Nj = optimum number of test candidates in stage j, Lj = optimum number of test locations in stage j, SD = the standard deviation, and
H = the average coefficient of coancestry among the selected DH lines
0.8
0.7
0.6
0.5
^
Probability P(0.1%)
3.6
3.4
3.2
0.4
3.0
2.8
b and
Fig. 1 Selection gain (D G)
the probability of selecting
b
superior genotypes ( Pð0:1%Þ)
as a function of the number of
crosses in the first stage (N1C )
for selection strategies 1 (open
square), 2a (open circle), 2b
(open triangle), and 2c (open
diamond) in breeding scheme
DHTC (solid symbols) and
S1TC-DHTC (hollow symbols)
3.8
DHTC–2a: number of crosses 9 DH lines within crosses; S1TC-DHTC–2c: number of crosses 9 S1 families within crosses
^
Selection gain ∆G
a
2
5
10
15
20
30
Number of crosses at first stage N1C
40
2
5
10
15
20
30
40
Number of crosses at first stage N1C
123
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Jorge Urbán
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Earnings management consists of both legitimate and less legitimate decisions by manager to change real actions or accounting policies so as to make their earnings smooth or accomplish their specific objectives in accounting report. Usually, managers take advantage of different methods to manage their earnings. For example, managers apply the specific patterns according to their objectives or the temporal economic condition – that is, to avoid loss, increase annual earnings and meet analysts' earnings forecasts. According to Scott (2015), there are four main patterns of earnings management conducted by managers: taking a bath, income minimization, income maximization, and income smoothing. In this paper, the pattern of income smoothing will be extended and enhanced since it has been considered as most popular method nowadays. Taking a Bath pattern mainly being used during the period of corporate reorganization or organizational stress. In taking a bath, if a firm must report its loss, managers may feel it better to report a larger loss in current accounting report, because it will help company present the expected future cost and generally " cleaning the deck ". Henry and Schmitt (2001) mentioned that a company need to report a large non-recurring loss in current year in order to make future earnings not burdened, especially when its profits are already disappointed. This practice will also reduce the variability of firms' future earnings or make its future earnings perform better. So the meaning of taking bath is, when the company earning is already depressed (i.e., bad profit), the managers need to make their firm's earning worse by clearing out the rubbish which will be harmful to the company's reputations and their management ability/capacity. Due to accrual reversal, taking a bath can effectively make the probability of future reported profits better, moreover, the market's competition will push a company relatively the same whether it loss its earnings by a lot or by a little. The next pattern is Income Minimization which consists of rapid write-offs of capital, expensing A&P, research & development expenditures and intangible assets according to accounting policies. This pattern is very similar with taking a Bath, but compared with taking a bath, taking a path is less extreme. Managers mainly engage the pattern of Income minimization during periods of high profitability, the crisis and avoiding political costs, – that is, to avoid too many competitors entering and to safeguard company's reputation. Managers can usually take on income minimization especially when companies go through the periods of high profitability (Stolowy and Breton, 2004). If Companies have outstanding earning performance but do not want to attract public or political attention, income minimization will be a good excellent choice for their managers to avoiding their competitors. In addition, managers also want to manage their earnings continually upwards, this is another reason why their managers prefer to engage this pattern. According to Scott (2015), income maximization is conducted by managers to report net income for their bonus purpose. Besides, it can also present investors expected perspective for the firms' future and avoid the risk of debt covenant violations. Usually, managers have great incentives to make their company's earning perform better, because their compensation mainly depend on their company's expected values in the future. According to Healy and Wahlen (1999), if managers have ability to convince the firms have high value in the future, their bonus will therefore become great. Moreover, if a firm's report presents investors high earning performance, investors can place high incentive to invest this firm due to their high level expectation to this company's future earnings performance. Another incentive for companies to choose this pattern is to avoid debt covenant violation. As per Watts and Zimmerman (1990), when companies confront the threat of debt covenant violation, income minimization can protect the companies' healthy development. However, for making the over-reporting earning be credible signal of firm value, there must apply strict management of cost associated, otherwise, all firms would report the earning level as high as possible. Income smoothing is to level out net income fluctuations from one period to the next by using accounting techniques. Income smoothing techniques consist of delaying the recognition of expenses during in a difficult year due to the negative expected in the future or deferring revenue during a great year but the next year is expected to be a challenging one. Dye (1988) and Lambert (1984) noted that managers take advantage of income smoothing mainly on account of their smooth managerial
Objetivos: el objetivo de este trabajo es llevar a cabo una recapitulación sobre el alcance actual de las competencias educativas de las entidades locales en España. Metodología: la metodología utilizada es la propia de las ciencias jurídicas (análisis normativo, jurisprudencial y doctrinal). Resultados: se comienza con una exposición de la evolución histórica de las competencias educativas de las entidades locales. A continuación, se analizan las tres materias relacionadas con la educación en las que, de acuerdo con la redacción actual de la Ley Reguladora de las Bases del Régimen Local, la legislación sectorial les debería atribuir a los municipios competencias propias, así como las posibilidades de delegación competencial en este ámbito. En la siguiente parte del trabajo se abordan otras competencias educativas que ejercen las entidades locales, tanto las que les atribuye la propia legislación del sector como la prestación de servicios complementarios a los educativos. Finalmente, se trata de la cuestión de los centros docentes de titularidad de las entidades locales. Conclusiones: la principal conclusión del trabajo es que reforma de 2013 no ha atenuado la desconexión que ya se producía entre la legislación de régimen local y la legislación educativa, de tal manera que el art. 25.2.n) de la Ley Reguladora de las Bases del Régimen Local sigue sin ofrecer una idea clara de las competencias que realmente ejercen las entidades locales en materia educativa. / Objectives: The aim of this work is to carry out a recapitulation of the current scope of the educational competences of local authorities in Spain. Methodology: The methodology used is that of the legal sciences (normative, jurisprudential and doctrinal analysis). Results: We begin with an exposition of the historical evolution of the educational competences of local authorities. This is followed by an analysis of the three areas related to education in which, according to the current wording of the Act on the bases of local government, the sectoral legislation should grant the municipalities their own competences, as well as the possibilities of delegating competences in this field. The following part of the paper deals with other educational competences exercised by local bodies, both those attributed to them by the sectoral legislation itself and the provision of services complementary to educational services. Finally, it deals with the question of local authority-owned educational establishments. Conclusions: The main conclusion of the paper is that the 2013 reform has not attenuated the disconnection that was already occurring between local regime legislation and education legislation, such that art. 25.2.n) of the Act on the bases of local government still does not offer a clear idea of the competences actually exercised by local authorities in educational matters.
EnglishThe objective of the article is to demonstrate how, over almost two centuries, English law has created a set of precedents that help us understand how the courts will possibly decide cases related to the COVID-19 pandemic portuguesO objetivo do artigo e demonstrar como ao longo de quase dois seculos o direito ingles criou um conjunto de precedentes que nos auxiliam a entender como os tribunais possivelmente decidirao casos relacionados a pandemia da COVID-19.
A Pseudomonas sp. bacterium was isolated from the midguts of Anopheles gambiae mosquitoes. Here we present the annotated Pseudomonas sp. draft genome sequence as a contribution to the efforts of characterization of the mosquito gut microbiome.
