Rapid Commun Mass Spectrom 117 https://doi.org/10.1002/rcm.4onlinelibrary.wiley.com/XXXXXRapid Communications in Mass SpectrometrySpectroscopy of Basidiomycete Proteases117S412 June 2007 e4Research Article

Model Journal Article ‐ Research Article RCM4: Resonance Raman spectrum of [Ru(bipyridine)₃]²⁺ in water, acetonitrile and their deuterated derivatives*

The possible role of solvent in excited‐state charge localization

Nowadays that prince that can best find money to pay his army is surest of success.
Sir William Davenant, 1605–1668

Introduction

Meiosis has a central role in the sexual reproduction of nearly all eukaryotes. Saccharomyces cerevisiae has unique advantages for detailed analysis of meiosis, especially because sporulation can easily be triggered by a simple change of nutritional conditions, and landmark events are conveniently monitored in populations of relatively homogeneous single cells. Sporulation of Saccharomyces cerevisiae is restricted to one type of cell, the a/α cell, and is initiated after starvation for nitrogen in the absence of a fermentable carbon source1. Some genes are expressed only during sporulation and are referred to as meiosis‐ or sporulation‐specific genes2. Transcription of meiosis‐specific genes, as well as the initiation of meiosis, in S. cerevisiae depends on the main meiotic transcriptional activator, IME1 (inducer of meiosis)3-5. In haploid cells, the product of the gene RME1 functions as a repressor of IME12,6. For DNA‐bound Rme1p to exert repression, the genes SIN4/SSN4/TSF3 (= negative regulator of GAL1 gene expression) and RGR1 are required1-3,7. These genes are RNA polymerase II mediator subunits (SRB proteins) which are necessary for normal nucleosome density8. SIN4 plays a role in transcriptional regulation and directly or indirectly regulates a global aspect of chromatin accessibility.

Raman scattering is a second‐order effect with respect to an operator of interaction between the electromagnetic field and the matter. This operator is considered as a small perturbation and the corresponding quantum transition is realised through the intermediate states. For the correct usage of the method of small perturbation, the fulfilment of following equations is necessary:

(1)

∣E_{0} - E_{i}∣ ≫ V_{0_{i}}

where E₀ is the energy of the initial state, E_i that of the intermediate state, E_f that of the final state, V_0i the interaction operator matrix elements between the initial and intermediate states and V_if those between the intermediate and final states.

Having fulfilled this program we obtained the equation for the intensity of Raman scattering. It has the form

(2)

I = D \frac{\partial (k_{x} k_{y} k_{z})}{\partial (ω θ ϕ)} \cos δ_{1} \cos δ_{2} R \frac{1}{υ_{0}} I_{0}

where I₀ is the intensity of the incident (primary) light beam, I the intensity of scattering light, ω the frequency of incident light, ∂( )/∂( ) the Jacobian of transformations from k_xk_yk_z space of wavevectors to coordinates in the spherical system coordinates, which is determined by direction group velocity υ₀, and the constant D includes some values which change little with change in ω. While writing Eqn. (2) we focus on the factors which determine the frequency dependence of the Raman scattering intensity in a resonance field. That is why in Eqn. (2) some factors which change little in the resonance field are included in the constant D. The value of δ₁ is the angle between the k wavevector of incident light and υ₀, group velocity, and δ₂ is the similar value with respect to scattered light. R is the coefficient of light transmission through the boundary between vacuum and the crystal and n the refractive index.

As we know, the Eqn. (2) has never been mentioned in the literature. The account of Jacobian and angles δ₁ and δ₂ can be sufficient in anisotropic crystals.

It is interesting to consider some simple cases of the application of Eqn. (2).

Let us consider the situation when it is possible not to take into account the extinction of polaritons. Group velocity depends on frequency according to the equation $υ_{0} \approx {(ω_{0} - ω_{μ})}^{2}$ , and the refractive index $n \approx {(ω_{0} - ω_{μ})}^{1 / 2}$ , where $ω_{μ} = E_{μ} / h$ and ω₀ is the primary radiation frequency. Taking into account the considerations mentioned above, we obtain

(3)

I = S {(ω_{0} - ω_{μ})}^{- 3 / 4}

This result is obtained in the situation when extinction is neglected. However, if extinction is taken into account, υ₀ and n are finite values and any difficulties in describing resonance Raman scattering are absent.

It should be noted that the refractive index n, group velocity υ₀ and resonance Raman scattering intensity can be measured independently (compare Equations (2), (3)).

For notation conventions used in this article, see the Definition List.

Materials and Methods

Clinical Specimen Collection

Specimens for RT‐PCR (group 1)

Fifteen abnormal cervical biopsies were studied1. Five were CIN 1, four were CIN 2 and six were CIN 3. Nine normal cervical biopsy specimens including the ectocervix and transformation zone were used as controls (Figure 1).

Figure 1
(A) *Bcl‐2* and (B) *p53* expression profiles in 27 gastric (G) and non‐gastric (NG) high‐grade B‐cell lymphomas as separated in the stomach into primary (○) and secondary (●) high‐grade tumours. Listen to this comparison of gastric sounds:

stomach growl-PiSh_01 by XxBirdoxX (http://freesound.org/people/XxBirdoxX/)

Specimens for TGF‐β1, β2, and β3 immunostaining (group 2)

Twenty‐six abnormal specimens positive for HPV 16 were selected from the database. Eight were HPV/CIN 1, seven were CIN 2, and 11 were CIN 3. Of the normal cervical controls, 9 were ectocervical squamous epithelium, 12 were reserve cell hyperplasia, 7 were immature metaplasia, and 15 were mature squamous metaplasia.

Specimens for Microdissection and RT‐PCR (Group 3)

Twenty‐five CIN samples positive for HPV 16 were selected. Nine were CIN 1, seven were CIN 2, and nine were CIN 3. Ectocervical tissue from 11 normal cervical specimens was used as controls2.

Specimen Selection, Processing, and Histology

All abnormal cervical specimens were obtained from patients attending colposcopy outpatient clinics for diagnostic and therapeutic procedures, after recent evidence of abnormal cervical cytology (Figure 2). The biopsies were obtained by large loop excision of the transformation zone (LLETZ). The specimens were fixed in 10% neutral buffered formalin and embedded in paraffin wax for routine histology. For all cases, CIN grading was performed using standard criteria9. All specimens used as controls were from patients undergoing abdominal hysterectomy for disease outside the cervix; all had normal cervical cytology and histology with no evidence of previous cervical disease and were negative for HPV by GP5+/6+ PCR.

Figure 2
Immunohistochemical staining of β‐catenin showing preserved strong membranous and weak cytoplasmic expression in non‐neoplastic gastric mucosa showing intestinal metaplasia (×55).Accession ID

Manipulation of Effort

After having described how depressed he or she had been and how he or she had experienced this, the interviewed individual described how she got over the depression.

The bogus interview fragment in the low‐effort condition included, among other things, the following sentences:

I did not do anything special to get over my depression. I did not force myself to take the initiative to do things, but I decided just to wait and see… I don't understand really how, but gradually I began to enjoy life again… In the morning I could get up without too much effort, and begin the day. And, more importantly, I began to feel again like a worthy person. Eventually, I could cope again with life.

The bogus interview fragment in the high‐effort condition included, among other things, the following sentences:

At a certain moment I decided to try to actively overcome my depression. I felt I had to put myself together. I forced myself to take the initiative to do things, and I did my best to become interested again in things and in people… It did take a lot of effort, but gradually I began to enjoy life again… In the morning I could get up without too much effort, and begin the day. And, more importantly, I began to feel again like a worthy person. Eventually, I could cope again with life.

The molecules involved are illustrated here:

Results

Acetal Conjugate of Plasmal

These results confirmed the involvement of both the free amino group and the acetal conjugate of plasmal in the rearrangement reaction of glyceroPLPS. As shown in Scheme 1, it would be expected that the migration of plasmal residue to the free amino group of sphingosine occurs simultaneously with the elimination of glycerol or a rearranged [M + H]⁺ ion leads to the elimination of glycerol, to form a Schiff base‐type Z_A ion ( $Z_{A}^{R}$ ) (B in Scheme 1), via a cyclic intermediate (A in Scheme 1). The presence of a less abundant ion corresponding to [M + H − Hex]⁺ (m/z 548) in the product ion spectrum of PLPS‐B would also support the proposed mechanism, in which the cyclic intermediate (A) was formed via the glycerol loss. The galactose residue is freed of any modifying groups in the rearranged fragment ( $Z_{A}^{R}$ ), so that it could be eliminated by normal glycosidic cleavage to produce [ $Z_{A}^{R}$ − Hex]⁺ ion (C in Scheme 1).

Scheme 1
Proposed fragmentation/rearrangement pathway of glycerolPLPS. In order to distinguish from *Z_A* produced by normal fragmentation, the rearranged *Z_A* ion was shown as $Z_{A}^{R}$ .

New Genes Identified

Three new genes were identified; 1. YBL071W‐a, a hypothetical conserved protein (simultaneously identified by Enomoto et al12) 2. YAL044W‐a, the homologue of Sz. pombe uvi31 3. YDL085C‐a, the homologue of the human 4F5S disease‐associated gene. The new genes and coordinates are listed in Table 1 and the remaining results are summarised in Tables 2, 3, 4. Details of results of algorithm investigations are given in Box 1.

Table 1Reasons why drug safety issues may not be identified until the post‐marketing period

The adverse reaction is rare and therefore undetectable until a large number of patients have been exposed to the drug
There is a long latency between starting the drug and development of the adverse reaction
The drug has not been studied in normal clinical practice:
- patients treated in clinical practice13 are likely to have different characteristics to trial patients (e.g. demography, other diseases, other medication)
- in clinical practice a drug is less likely to be used strictly in accordance with the recommendations by both doctors and patients, and with less monitoring

Table 2Mean (minimum–maximum) values of the six physicochemical parameters of water sampled in the control and impact channels at the two positions and the three depths

	Channel	Control		Impact
	Position	Upstream	Downstream	Upstream	Downstream
	Depth (cm)
Chlorides (mg/L)	10	6.6 (5.6;7.9)	6.1 (5.5;6.9)	6.3 (5.7;7.0)	6.4 (5.8;7.0)
	50	6.1 (5.7;6.8)	6.4 (5.6;7.4)	6.2 (5.6;7.6)	6.2 (5.5;6.5)
	90	6.5 (5.5;8.2)	6.6 (5.8;8.0)	6.3 (5.6;8.6)	6.4 (5.5;7.9)
N‐NO₃⁻ (mg/L)	10	0.63 (0.51;0.77)	0.66 (0.51;0.76)	0.61 (0.52;0.70)	0.56 (0.40;0.77)
	50	0.62 (0.50;0.79)	0.62 (0.48;0.71)	0.64 (0.40;0.86)	0.60 (0.46;0.74)
	90	0.64 (0.51;0.76)	0.65 (0.57;0.88)	0.65 (0.49;0.75)	0.66 (0.50;0.78)
Specific conductance (μS/cm)	10	359 (349;371)	352 (344;361)	356 (338;362)	358 (350;365)
	50	355 (344;367)	355 (346;364)	354 (340;362)	357 (339;365)
	90	357 (348;367)	356 (343;365)	357 (344;364)	362 (340;381)
Temperature (°C)	10	21.3 (19.1;23.1)	21.8 (19.1;23.3)	22.0 (20.2;24.8)	22.2 (20.7;24.5)
	50	21.2 (19.2;22.7)	21.0 (19.1;22.4)	21.3 (19.7;23.4)	21.4 (19.1;23.1)
	90	20.8 (19.1;21.6)	20.9 (19.5;21.8)	21.0 (19.7;22.5)	21.0 (18.9;23.0)
DO (mg/L)	10	8.8 (8.1;9.3)	8.7 (8.0;9.4)	9.5 (8.6;11.2)	8.5 (7.0;10)
	50	8.9 (7.2;9.5)	8.0 (7.0;8.6)	8.4 (7.4;9.6)	8.2 (6.8;9.5)
	90	8.4 (7.6;9.0)	7.6 (6.6;8.2)	7.8 (7.1;9.0)	7.3 (4.9;9.6)
VHG (%)	10	−9.4 (−30;5)	−4.4 (−40;10)	3.8 (0;20)	−31.3 (−100;50)
	50	−23.3 (−32;‐4)	−2.4 (−10;6)	−33.6 (−56;‐12)	5.3 (−14,36)
	90	−16.9 (−22.2;‐13.3)	−2.3 (−4.4;2.2)	−18.1 (−31.1;‐4.4)	−3.5 (−22.2; 13.3)

Note. DO = dissolved oxygen; VHG = vertical hydraulic gradient.

Table 3Sample composition and descriptive statistics

Panel A: Sample Composition
Number of restatements:
Characteristics of restatements	Analyst Coverage (H1)	Forecast Dispersion (H2)	Forecast Error (H3)
Irregularity restatements	257	165	250
Other restatements	701	419	693
Total	958	584	943

Number of firm‐year observations:
Pre‐/Post‐ restatement periods	Analyst Coverage (H1)	Forecast Dispersion (H2)	Forecast Error (H3)
Full sample:
Pre‐restatement period	4,259	2,441	4,184
Post‐restatement period	8,475	4,937	8,405
Total	12,734	7,378	12,589
Irregularity restatements:
Pre‐restatement period	1,166	725	1,133
Post‐restatement period	2,073	1,280	2,050
Total	3,239	2,005	3,183

Panel B: Descriptive Statistics for Control Variables – Analyst Coverage Sample (H1)
	(1) Full Sample						(2) Irregularity Restatement Subsample
	Pre‐Restatement Period (N = 4,259)			Post‐Restatement Period (N = 8,475)			Pre‐Restatement Period (N = 1,166)			Post‐Restatement Period (N = 2,073)
Variable	Mean	Median	STD	Mean	Median	STD	Mean	Median	STD	Mean	Median	STD
SIZE	6 .505	6 .325	1 .661	6 .669***	6 .524^^^	1 .754	6 .727	6 .467	1 .732	6 .736	6 .536	1 .845
TRADING_VOL	2 .661	2 .650	1 .648	2 .983***	2 .991^^^	1 .691	3 .087	3 .072	1 .622	3 .401***	3 .405^^^	1 .623
GROWTH	0 .043	0 .026	0 .099	0 .023***	0 .019^^^	0 .076	0 .044	0 .023	0 .110	0 .015***	0 .012^^^	0 .079
RD	0 .075	0 .000	0 .180	0 .065***	0 .000	0 .157	0 .085	0 .000	0 .174	0 .078	0 .000	0 .140
SEG	0 .751	0 .693	0 .762	0 .726*	0 .693^^^	0 .798	0 .880	0 .693	0 .740	0 .869	0 .693	0 .793
CAR	−0 .039	−0 .014	0 .116	−0 .034**	−0 .013	0 .106	−0 .082	−0 .037	0 .153	−0 .077	−0 .035	0 .144
OPLEV	2 .705	1 .685	47 .532	2 .547	1 .713	47 .604	3 .485	1 .509	54 .755	3 .343	1 .566	53 .873
VSALE	0 .152	0 .105	0 .142	0 .131***	0 .088^^^	0 .127	0 .155	0 .101	0 .151	0 .122***	0 .080^^^	0 .124
LOSS	0 .312	0 .000	0 .463	0 .302	0 .000	0 .459	0 .407	0 .000	0 .491	0 .384	0 .000	0 .486
PINST	0 .367	0 .353	0 .337	0 .427***	0 .447^^^	0 .359	0 .389	0 .403	0 .355	0 .429***	0 .448^^^	0 .369

Panel C: Descriptive Statistics for Control Variables – Forecast Dispersion Sample (H2)
	(1) Full Sample						(2) Irregularity Restatement Subsample
	Pre‐Restatement Period (N = 2,441)			Post‐Restatement Period (N = 4,937)			Pre‐Restatement Period (N = 725)			Post‐Restatement Period (N = 1,280)
Variable	Mean	Median	STD	Mean	Median	STD	Mean	Median	STD	Mean	Median	STD
SIZE	7 .338	7 .122	1 .537	7 .525***	7 .335^^^	1 .577	7 .492	7 .142	1 .615	7 .578	7 .309	1 .666
TRADING_VOL	3 .482	3 .460	1 .383	3 .814***	3 .770^^^	1 .390	3 .820	3 .866	1 .401	4 .134***	4 .136^^^	1 .349
GROWTH	0 .045	0 .028	0 .090	0 .028***	0 .023^^^	0 .066	0 .044	0 .023	0 .103	0 .017***	0 .014^^^	0 .071
RD	0 .063	0 .000	0 .138	0 .052***	0 .000	0 .112	0 .083	0 .000	0 .153	0 .071*	0 .000	0 .112
SEG	0 .821	0 .693	0 .786	0 .785*	0 .693^^^	0 .826	0 .919	0 .693	0 .755	0 .910	0 .693	0 .825
CAR	−0 .032	−0 .012	0 .105	−0 .027*	−0 .011	0 .094	−0 .063	−0 .029	0 .137	−0 .056	−0 .028	0 .133
OPLEV	3 .740	1 .719	41 .860	2 .002*	1 .783	40 .456	3 .473	1 .455	48 .806	1 .778	1 .607	45 .241
VSALE	0 .143	0 .101	0 .132	0 .120***	0 .082^^^	0 .110	0 .152	0 .098	0 .148	0 .115***	0 .074^^^	0 .118
LOSS	0 .270	0 .000	0 .444	0 .238***	0 .000^^^	0 .426	0 .381	0 .000	0 .486	0 .318***	0 .000^^^	0 .466
PINST	0 .419	0 .465	0 .358	0 .487***	0 .582^^^	0 .374	0 .430	0 .484	0 .373	0 .495***	0 .569^^^	0 .379
SIZE	6 .523	6 .346	1 .664	6 .685***	6 .535^^^	1 .749	6 .775	6 .546	1 .728	6 .757	6 .561	1 .841
TRADING_VOL	2 .671	2 .661	1 .655	2 .992***	2 .997^^^	1 .689	3 .128	3 .124	1 .615	3 .412***	3 .409^^^	1 .620
GROWTH	0 .042	0 .026	0 .098	0 .023***	0 .019^^^	0 .075	0 .043	0 .023	0 .109	0 .015***	0 .012^^^	0 .079
RD	0 .074	0 .000	0 .180	0 .065***	0 .000	0 .156	0 .085	0 .000	0 .175	0 .077	0 .000	0 .137
SEG	0 .754	0 .693	0 .764	0 .728*	0 .693^^^	0 .800	0 .884	0 .693	0 .743	0 .872	0 .693	0 .795
CAR	−0 .039	−0 .014	0 .114	−0 .034**	−0 .014	0 .104	−0 .082	−0 .037	0 .150	−0 .076	−0 .035	0 .142
OPLEV	2 .501	1 .683	47 .898	2 .455	1 .718	48 .160	3 .202	1 .502	55 .893	3 .291	1 .583	54 .619
VSALE	0 .151	0 .105	0 .140	0 .129***	0 .087^^^	0 .124	0 .154	0 .101	0 .150	0 .121***	0 .080^^^	0 .123
LOSS	0 .310	0 .000	0 .463	0 .298	0 .000	0 .458	0 .409	0 .000	0 .492	0 .379*	0 .000^	0 .485
PINST	0 .370	0 .359	0 .338	0 .428***	0 .449^^^	0 .359	0 .397	0 .416	0 .356	0 .430**	0 .452^^^	0 .370

Notes: This table presents the sample composition and descriptive statistics. Panel A presents the number of restatements and firm‐year observations having the necessary data for the respective testing samples. Panels B, C and D provide the descriptive statistics of the control variables for the analyst coverage, forecast dispersion and forecast error samples, respectively. Column (1) of Panels B, C and D compares the changes in the control variables across the pre‐ and post‐restatement periods for the testing samples experiencing either irregularity or other restatements over 1997–2006, and Column (2) examines the same relationship for the testing samples experiencing irregularity restatements over 1997–2006. SIZE is the market value of equity; TRADING_VOL is the quarterly trading volume in millions of shares; GROWTH is the sales growth rate; RD is research and development expenditures scaled by sales; SEG is the logarithm of the number of geographic segments; CAR is cumulative abnormal returns in the three‐day window around the restatement announcement; OPLEV is operating leverage; VSALE is the volatility of sales; LOSS is a loss indicator variable; PINST is the percentage of institutional ownership. ***, ** and * (^^^, ^^ and ^) denote statistical significance at the 1%, 5% and 10% levels, respectively, based on t‐statistics (Z‐statistics) for difference in means (medians). The Appendix provides variable definitions in more detail.

Table 4Spleen injection assay to compare metastatic potential. (A) Intrasplenic (IS) injection of either 10⁶, 5×10⁶, or 10⁷ SW620 cells per mouse was administered and autopsies were performed at 8 weeks. (B) IS injection of 5×10⁶ SW480 or SW620 cells was administered and autopsies were performed at 10 weeks, except for three mice in the SW620 group which were euthanized between 5 and 8 weeks. Surface metastases were counted by naked eye and internal/parenchymal metastases (not in contact with the liver surface) were sought by histological analysis

(A)
No. of cells	No. of mice	No. with surface metastases (range in No.; mean No. per mouse)	No. with internal metastases
10⁶	5	0	0
5×10⁶	5	4 (0–7; 3.0)	0
10⁷	4	3 (0–5; 1.8)	0

(B)
Cell line	No. of mice	No. with surface metastases (range in No.; mean No. per mouse)	No. with internal metastases
SW480	10	0	0
SW620	11	6 (0–6; 3.0)	0

Local Chromatic Number

Here, we will construct a graph proving the following statement.

Theorem 1

There exists a graph G such that

ψ_{d, max} (G) < ψ (G) .

To prove this theorem, we will use the following Lemma.

Lemma 1

Let us have $m > k + 1 \geq 4$ and let c be a local k‐coloring of the graph $U (m, k)$ . Then c is the natural coloring up to permutation of colors.

Proof

This is straightforward if tedious and is left as an exercise for the reader. □

Here, we present the proof of Theorem 1 assuming that Lemma 1 is true for m=5 and k=3.

Proof of Theorem 1

We construct a graph G that has the property given in the statement. Let

V (G) = V (U (5, 3)) \cup {x, y, z},

with $x, y, z \notin V (U (5, 3))$ . Assume for contradiction that f is a local 3‐coloring of G. Notice that U(5, 3) is a subgraph of G, so by Lemma 1 we may assume that f is the natural coloring on U(5, 3) and the result follows. □

Why Children and not Adults?

This is one of the most intriguing questions of all. There may be several reasons why children might be at greater risk, but little research has examined such differences. Speculation as to the cause of increased susceptibility to cerebral oedema of children as compared with adults has involved a number of theories as follows:

relatively greater brain volume
more rapid changes in plasma osmolality
lack of sex steroids3
less (or more) developed mechanisms for brain cell volume regulation
difference in taurine (or other non‐perturbing osmolyte) metabolism
differences in blood–brain barrier efficiency (e.g. aquaporin channels)
other as yet unidentified causes?

Some simple rules to remember when formulating Genbank entries are:

Put each piece of information in the appropriate qualifier.
Supply as many qualifiers for each coding sequence as can reasonably be provided.
Do not attempt to be creative by adding additional information into a given qualifier. For example, adding multiple synonyms for the gene name inside a given gene qualifier violates the specification and could produce erroneous results in software that processes that qualifier.

See http://www.ai.sri.com/pkarp/misc/gbkexample.html for more examples of conformant Genbank entries, and Appendices B and C for program fragments.

Disregarded Spurious ORFs, Overlapping with Real Genes

ORFs which have all, or the majority of their translation overlapping with other annotated features, were individually assessed for similarity to all organisms, as described in Clinical Specimen Collection above, together with experimental data if available. For ORFs to be considered as spurious, they had to meet all of the following criteria:

Small size (35–250 amino acids).
Absence of similarity to known proteins.
Absence of functional data which could not have been generated by the real overlapping gene.
Greater than 25% overlap at the N‐terminus or 50% overlap at the C terminus with another coding feature; overlap with another feature at both ends; or ORF containing a tRNA.

Transposon fragments were also removed.

Very Hypothetical ORFs

In Sz. pombe, 177 ORFs which are considered unlikely to be coding but cannot yet be dismissed as spurious have been assigned as very hypothetical according to the following criteria:

Small size (100–250 amino acids) and limited complexity (Structure 3).
Absence of similarity to other known proteins.
Overlap with other features, particularly at the N‐terminus, where they might interfere with promoters (the overlaps in these cases are smaller than those observed in disregarded ORFs).
Extreme GC content (see Plate 1).

Plate 1
Pseudocolour FL maps of a single, binucleate, live Vero cell in culture (the different columns represent a deconvolution series). The top row shows fluorescence intensity images of the cell, whereas the bottom row shows FL images (‘maps’) of the same cell. The cell contains GFP‐cyclin‐B1 present in both free (cytoplasmic interstitium and nucleoplasm) and tubulin‐bound (cytoplasmic microtubules) form. The intensity images cannot distinguish the two forms, whereas the FL images clearly distinguish the bound (yellow ‐ associated with cytoplasmic filamentous structures) from the free (orange–red) cyclin‐B1. Furthermore, the free cyclin–B1 in the nucleoplasm has a slight purple tinge distinguishing it from the cytoplasmic free form. (Reproduced with permission from the J Microsc 1999; 193:36–49, courtesy of Dr Philippe Bastiaens of the ICRF and copyright © 1999 Blackwell Science Ltd.)

The annotation of Sz. pombe adequately discriminates between very hypothetical proteins and real genes and this approach has been applied to a re‐annotation of the S. cerevisiae genome.

The interference factor, E, is given by the (author's hand‐drawn original) equation

(4)

though it needs large quantities of data to provide the accuracy required in this study.

The ADC products in IgG and SIP format, described in the previous section, were first tested in tumor‐bearing mice in a pilot experiment (Figs. 3a and 3b]. Nude mice bearing subcutaneously‐grafted U87 or A431 tumors received four injections of ADC products at a dose of 7 mg/Kg for both formats (corresponding to an almost double molar amount for the SIP product). The IgG derivative cured 100% of treated mice. The ADC product in SIP format exhibited a substantial anti‐tumor activity, which however was not curative (Figs. 3a 4b]. Encouraged by these initial results, we performed a second therapy study in the A431 model, which confirmed the induction of cancer cures with F16‐MMAE at 10 mg/Kg, while a similar ADC product based on the KSF antibody (specific to hen‐egg lysozyme and serving as negative control) did not inhibit tumor growth at the same dose (Fig. 3c).

Figure 3

Therapeutic activity of F16 based ADCs in pilot experiment against A431 epidermal carcinoma (a) or U87 glyoblastoma (b) or in main experiment against A431 cells (c). When tumors reached an average of 100 mm³ of volume, mice were randomly grouped and intravenously injected every 3 days, 4 times in total (). Lower doses of IgG(F16)*‐MMAE (*e.g*., 3 mg/Kg) did not induce cures (data not shown). Data points represent mean tumor volume ±SEM, n = 3 (a,b) or n = 5 (c) per group. The right panels show the percentage of body weight change during the course of the therapy.

Conclusions

Systems should be as simple as possible but not simpler…
Albert Einstein

A theoretical equation for the excitation spectrum intensity is proposed. It is applicable when the frequency of incident light approximates to the extinction band.

The corresponding dependence can be represented by the equation

(5)

I = B R υ_{0}^{- 1}

where B is a value which changes a little as a function of ω₀, the primary radiation frequency. R is the transmission coefficient through the border between vacuum and crystal and υ₀ is the primary radiation group velocity. The last two factors change sufficiently in the field of an extinction band. The values of I, B and υ₀ can be measured independently and Eqn. (5) can be compared with experimental data.

Acknowledgements

Special thanks go to the anonymous reviewers for their comments on improving the quality of this paper. Special thanks go to Professor Werner Eichhörst from the Department of Statistics at the University of Manitoba for helpful and stimulating discussions.

Note Added in Proof

Mating and sporulation in S. pombe are repressed by exogenously added cAMP (Calleja, P.M. 1980, Plant Cell Physiol 21: 613–625). We find that in strains overexpressing SAMS the repressing effect of cAMP is significantly less than in wild type, indicating that SAMS interferes with sensing and/or transduction of the exogenous cAMP signal.

ENDNOTES

1 The survey was commissioned by the Stichting Sociaal‐culturele Wetenschappen (SSCW), Nederlandse Organisatie voor Wetenschappelijk onderzoek (NWO). The data‐set is available under the title “Aspects of life‐event history of the Dutch population. Part 1: Changes in socio‐demographic data, social mobility, relationships history, educational career, and work mobility” at the Niwi Steinmetz archives (under number P1107). The LISA data‐sets were obtained from the RIVM for the Ruimtescanner project.
2 The other Landkreise and their average TFRs in the period 1995–97 are: Aurich (1.63), Leer (1.64), Grafschaft Bentheim (1.65), Emsland (1.68), Vechta (1.69) and Borken (1.66). While the last is part of Nordrhein‐Westfalen, all other Kreise belong to Niedersachsen. Plus the LKR Alb‐Donau‐Kreis with a TFR of 1.62, and — more to the west — the LKR Tuttlingen and LKR Rottweil, each with a TFR of 1.66.
3 Typical drugs are a nucleoside reverse transcriptase inhibitor (such as Zidovudine, Retrovir, Videx, Lamivudine, Abacavir), a non‐nucleoside reverse transcriptase inhibitor (such as Sustiva, Viramune, Nevirapine) and a protease inhibitor (such as Ritonavir, Norvir, Viracept, Indinavir).

References

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2.G. J. Suggett, ‘Comments on “A simplified theory for a matrix solar collector”’, Int. J. Energy Research, Vol. 8, 1984, pp. 305–323.
3.The Fermilab E791 Collaboration, ‘Branching fractions for D0 → K+K− and D0 → pi+pi−, and a search for CP violation in D0 decays’, Phys. Lett., Vol. B421, 1998, pp. 405–411.
4.B. Beckert and J. Posegga, ‘leanT^AP: Lean, tableau‐based deduction’, Journal of Automated Reasoning, Vol. 15, 1995, pp. 339–358. This is also available on the Web from http://i12www.ira.uka.de/posegga/LeanTaP.ps.Z.
5.L. Pauling, The Nature of the Chemical Bond, 3rd edn, Cornell University Press, Ithaca, 1960, pp. 255–260.
6.P. A. Walsh and E. G. Krebs, ‘Protein kinases’, in P. D. Boyer (ed.), The Enzymes, Vol. 8, Academic Press, New York, 1973, pp. 555–581.
7.Department of Health, Caring for People: Community Care in the Next Decade and Beyond, HMSO, London, 1989.
8.William Strunk Jr., The Elements of Style, 1st edn, Ithaca, NY. Privately printed (Press of W. P. Humphrey, Geneva, NY), 1918. On‐line at http://www.columbia.edu/acis/bartleby/strunk/
9.William S. Becker (with the assistance of Roberta F. Stauffer), Rebuilding for the Future…A Guide to Sustainable Redevelopment for Disaster‐Affected Communities, U.S. Department of Energy, September 1994.
10.E. W. Dijkstra, ‘Invariance and non‐determinacy’, in C. A. R. Hoare and J. C. Shepherdson (eds), Mathematical Logic and Programming Languages, Prentice‐Hall International Series in Computer Science, Prentice‐Hall, 1985, pp. 157–165. The papers in this volume were first published in the Philosophical Transactions of the Royal Society, Series A, Vol. 312, 1984.
11.Kirill N. Ilinski, ‘Physics of finance’, Preprint hep‐th/9710148. e‐Print archive, 1997. http://xxx.lanl.gov/abs/hep‐th/9710148.
12.Yutaka Enomoto, Masahiko Fujii, Takao Furusho and Naoki Yamamoto, ‘Condom coated with acidic polysaccharides’, European Patent No. EP‐94120270, Fuji Latex Co., Ltd. Filed: 21 Dec 1994.
13.ISO Technical committee TC 69, subcommittee: SC 1, Statistics — Vocabulary and symbols — Part 1: Probability and general statistical terms, ISO 3534‐1:1993.
14.M. A. Bianchet, J. Hullihen, P. L. Pedersen and L. M. Amzel, ‘Three dimensional structure of the active form of mitochondrial ATPase: ATP hydrolysis/synthesis involves subtle conformational changes’, submitted for publication.
15.J. K. Watterson, A. M. Savill, W. N. Dawes and K. N. C. Bray, ‘Predicting confined explosions with an unstructured adaptive mesh code’, presented at the Joint Meeting of the Portuguese, British and Spanish Sections of the Combustion Institute, Madeira, 1996.
16.P. S. Barsanti, ‘Simulations of confined turbulent explosions’, Ph.D. Dissertation, University of Cambridge, 1994.
17.Ibid.
18.H.‐D. Alber, ‘On a system of equations from the theory of nonlinear visco‐elasticity’, preprint, TH‐Darmstadt, 1989.

Biographies

Jørgen van der Ploeg gained the BSc, MSc, and PhD degrees in Electrical Engineering from the University in Ljubljana, Slovenia, in 1975, 1977 and 1988, respectively. Since 1975 he has been at the Western Michigan University, where he is currently the head of Computer Systems Department. He has also been an associate professor at the University of Ljubljana. His research interests include electronic testing and diagnosis, and fault‐tolerant computing.

Supporting Information

Filename	Description
rcm4-sup-0001-video1.movvideo/quicktime	Video of C. elegans
rcm4-sup-0002-excelSheet1.zipapplication/zip	Raw statistical data for this article.
source_data.txttext/plain	Source data for figure 2

Supporting Information

Additional Supporting Information may be found online in the supporting information tab for this article.

van der Ploeg J IV, Peng MQ, O'Hara PM Jr, El‐Sheikh Z; The RANX05 Study Group. Model Journal Article ‐ Research Article RCM4: Resonance Raman spectrum of [Ru(bipyridine)₃]²⁺ in water, acetonitrile and their deuterated derivatives: The possible role of solvent in excited‐state charge localization. Rapid Commun Mass Spectrom. 2007;117(S4):e4. 10.1002/rcm.4

Appendix A

Definition List

Endogenous variables
→_a.s	almost sure convergence
→_p	convergence in probability
=_d	distributional equivalence
B(r)	Brownian motion
BM(σ²)	Brownian motion with variance σ²
1(A)	indicator of A

Exogenous variables
MN (0, G)	mixed normal distribution with mixing variate G
⇒, →_d	weak convergence
[·]	integer part of
r∧s	min(r, s)
≡	equivalence
o_p(1)	tends to zero in probability
o_a.s.(1)	tends to zero almost surely

Appendix B

Generated trains Examples

Only a subset of the 100 generated trains examples are listed here. The files can be found at http://www.site.uottawa.ca/∼jmorin/Programs/Generator

   train(([car(1,hexa,short,not double,flat,2,l(diam,1)),
           car(2,rect,short,double,peaked,2,l(rect,1))])).
   train(([car(1,rect,short,not double,none,2,l(rect,1)),
           car(2,rect,short,not double,none,2,l(tri,2)),
5          car(3,rect,short,double,flat,2,l(diam,2)),
           car(4,ell,short,not double,arc,2,l(rect,1))])).
   train(([car(1,rect,long,not double,none,3,l(utri,0)),
           car(2,ell,short,not double,arc,2,l(cir,2)),
           car(3,ell,short,not double,arc,2,l(cir,2)),
10          car(4,rect,short,not double,none,2,l(tri,1))])).
  
   etc.

Appendix C

Algorithms

The following procedures describe the generation of examples.

Generate Argument from Arg

 for each argument A in Arg do
    if A belongs to one of the lists of constants or nominal values then
15     reproduce A in the example
    else
      case A of
        . f#(Arg):
             choose a function name from the list of functions
20            generate Argument with Arg
        . n#(Name):
             generate a nominal value from the list of
             nominal values called Name
        . otherwise:
25            generate a variable name of the form v#

Although GenEx makes all its choices randomly, the user decides on the distribution of examples generated from a (sub)set of rules. For instance, to generate positive examples pos from rules 1 to 5 with an equal distribution (say 10 examples for each rule), the user invokes GenEx as follows: gen_nb_ex_for_each_rule(pos, 1, 5, 10). On the other hand, to generate examples from these rules with a random distribution the user specifies: gen_tot_ex_from_rules(pos, 1, 5, 100).

(C1)

\vec{r} = x \hat{i} + y \hat{j} + z \hat{k},

GenEx and GenTax are independent of any particular learner and could be used by the machine learning community at large as domain‐independent tools for making complete sets of examples and taxonomies for thorough empirical testing of learners. GenEx and GenTax proved to be useful tools to evaluate two ILP learners.

(C2)

Δ \vec{r} = {\vec{r}}_{2} - {\vec{r}}_{1} .