Consequently, there is nothing inherently worrisome (or indeed surprising) about M&W’s assertion that our findings could also be accounted for by a set of assumptions that are, in essence, incompatible with the theory we intended to test.

So, it seems worthwhile to consider the alternative account offered.

As an alternative theory, M&W put forward a framework that conceptualizes decisions in social dilemmas as a self-control problem [5].

Specifically, decisions are assumed to be the outcome of a two-stage process (see Figure 1 in their comment): First, people may not identify a self-control conflict and follow their impulse (in M&W’s framework referred to as “reflexive, default behavior”).

If people do identify a self-control conflict, they may next try to resist their impulse.

They may either fail to do so, thus following their impulse (i.e., choosing the default), or exercise successful self-control, thus following their deliberative goal instead.

Which behavior constitutes the default (cooperation vs. defection) is assumed to be context-dependent [5], but it remains entirely open which context would apply to our experiment.


To derive predictions for cognitive conflict, M&W further make the following assumptions: Following the default because no self-control conflict was identified leads to a “no conflict experience”.

Identifying a self-control conflict leads to a “conflict experience” in which case either self-control failure (sometimes also called unsuccessful resistance to impulse by M&W) or successful self-control may lead to a relatively higher degree of conflict (both cases are considered to be equally plausible in M&W’s theoretical framework).

M&W argue that, according to their framework, six different scenarios are possible, and that two of these are compatible with the results of our experiment.

As one of these scenarios assumes that cooperation is the majority default (which is compatible with the theory by Rand et al.) but the other presumes that defection is the default, M&W conclude that our findings do not allow for any inference whether cooperation or defection is the default behavior.


However, the scenario in which defection is the default option does require a number of additional assumptions in order to fit our results.

First, M&W assume that self-control failure (leading to defection) is associated with more cognitive conflict than successful self-control (leading to cooperation).

However, M&W explicitly state that the opposite (more conflict for successful self-control than failed resistance) is equally plausible.


Moreover, according to M&W’s framework a defection choice can be reached by two routes when assuming that defection is the default: People can either directly follow their impulse to defect (without identifying a self-control conflict) or they can identify a self-control conflict and fail to resist (and thus choose defection).

Importantly, when directly following the impulse, defection should be accompanied by a “no conflict experience” (see Figure 1 in M&W).

Yet, this would run contrary to the results in our study that M&W are trying to explain (more conflict for defection than cooperation).

To account for this, M&W additionally assume “that the difference in conflict [between self-control failure and successful self-control] more than offsets the low levels of conflict exhibited by those individuals who, without resisting (see Figure 1), act on the[ir] impulse” (Footnote 2 as referred to in M&W’s Table 1, squared brackets added).

However, we believe that it is questionable to assume that the difference in conflict between self-control failure and successful self-control is so large that it offsets a “no conflict experience”—especially since M&W themselves consider a reversed pattern (more conflict for successful self-control than self-control failure) to be equally plausible.

Importantly, even if one assumes that the amount of cognitive conflict for successful self-control and self-control failure is comparable, the additional impulsive choices for defection would lead to the prediction that cooperation is associated with more conflict than defection (i.e., the prediction would run contrary to the observed empirical pattern).


In other words, the proposed alternative account by M&W requires additional untested and, in our view, debatable assumptions.

If, in line with Occam’s razor, the “goal of model selection is to choose the simplest (i.e., least complex) model that describes the data well (i.e., descriptive adequacy)” [6], it is our impression that M&W’s alternative theoretical explanation is more complex (i.e., less parsimonious) than the theory by Rand et al.


Besides, M&W do not make any predictions as to the overall relative frequency of cases in which a conflict is identified or not—although this is clearly relevant for the predicted results pattern.

Only when talking about our finding that individuals high in Honesty-Humility (HH) experience more conflict when defecting vs. cooperating than individuals low in HH, M&W assume that the likelihood of identifying a self-control conflict differs: M&W assume that individuals low in HH should have a low likelihood while individuals high in HH should have a high likelihood.

However, based on this assumption people low in HH should show less conflict when defecting even when compared to individuals high in HH that cooperate (since no conflict identification should be accompanied by no conflict experience).

Yet, this is incompatible with the results we reported (see Figure 4; Kieslich & Hilbig, 2014).


Finally, M&W argue that by altering the assumptions within their framework one can actually explain every possible empirical pattern in our experiment (more conflict for cooperation, more conflict for defection, and no difference between the two).

However, M&W do not provide any theoretical prediction as to when which of their assumptions should hold.

As a consequence, there is no empirical pattern in our experiment that could falsify M&W’s theoretical framework.

It thus does not have sufficient empirical content [7] (see also Glöckner & Betsch, 2011) in the context of our study, at least not without further theoretical specification.

( See Postscript by Jonathan Baron who points out that more specific predictions for M&W’s framework can be derived, which are only partly supported by our data.)


In sum, we agree with M&W that other theoretical explanations for our result are possible.

Nonetheless, we test a theoretical approach that is falsifiable, excluding certain plausible results a priori—as any useful theory should [8].

Consequently, its descriptive adequacy indeed speaks for the theory.

By comparison, the alternative framework proposed by M&W requires several additional (and debatable) assumptions to account for the findings (especially when assuming that defection is the default behavior).

Moreover, it could have accounted for any finding in our experiment.

We would of course welcome any suggestion for a critical experiment that can disentangle the different theoretical explanations and conclusively test them against each other.

For the time being, we conclude that the approach of Rand et al. and the explanation assuming a spontaneous disposition to cooperate has more empirical content while requiring fewer assumptions, and, consequently, being more parsimonious than M&W’s alternative framework.


IX intensity was the only significant independent predictor of DSS.

These data implicate that intensity of CA IX expression seems to be a powerful independent prognostic factor, not confounded by the mode of treatment or other established prognostic factors (Giatromanolaki et al, 2001).


This study could not show a correlation between the expression of CA IX and other clinical or histopathological factors.

Most of the patients in this study had received RT with or without chemotherapy before operation, which may have an influence on tumour size, stage and grade, nodal status and consequently, CA IX staining in the operative sample.

To compensate this problem, CA IX expression was compared with the preoperative biopsies and respective surgical specimens.

However, some of the available diagnostic biopsy samples were scarce, precluding adequate comparison of all sample pairs.


Tumour oxygenation is known to have an effect on patient prognosis.

In advanced cancer of the uterine cervix, Hockel et al (1996) showed that low oxygen pressure was associated with larger tumours and more frequent parametrial spread as compared with tumours of the same stage and higher oxygen pressure.

Patients with hypoxic tumours had poorer disease outcome (Hockel et al, 1996).

Hypoxia induces CA IX, especially in or near necrotic regions (Wykoff et al, 2000) and in areas with high proliferation (Saarnio et al, 1998).

Preoperative treatment can shrink the tumour and even complete remissions are seen (Ruo et al, 2002).

As the tumour shrinks, its oxygenation may improve.

Hence, preoperative therapy may reduce tumour hypoxia and thereby CA IX expression may be downregulated.

In this study, there were two histologically complete responses after preoperative therapy.

One complete response was unexpectedly seen after short-course RT (Marijnen et al, 2001), which remains to be explained but may have been related to exceptional radiosensitivity.


The present data are in alignment with the results reported in other human malignancies, where CA IX expression has been shown to be a prognostic marker as well (Table 4).

This includes non-small-cell lung cancer (studied by Swinson et al (2003) and Giatromanolaki et al (2001)), bladder cancer (Hoskin et al, 2003), invasive breast cancer (Chia et al, 2001; Brennan et al, 2006) and oligodendroglioma (Järvelä et al, 2008).

Interestingly, in renal cancer, low CA IX expression and absence of VHL mutation were related to a more advanced tumour and unfavourable outcome (Patard et al, 2008).

Currently, antibodies against CA IX are being studied in phase three trials in the treatment of renal cancer (Pastorekova et al, 2007).

Also, sulphonamides have been tested for therapeutic purposes against CA IX for several years (Pastorekova et al, 2007).

It is possible that this strategy will also be evaluated in the treatment of other types of tumours.


To our knowledge, this is the first study to report the important prognostic significance of CA IX in rectal cancer.

This study shows that strong staining intensity of CA IX is an adverse prognostic factor in rectal cancer.

Further studies are needed to evaluate its potential therapeutic implications.


Tumour regression grade was analysed after long-course RT in the HE-stained sections according to the modified Dworak and Rödel scales, using three categories: poor, moderate and excellent response (Dworak et al, 1997; Rödel et al, 2005).

The response was assessed as poor, if only minimal or no tumour regression was seen and there was a dominant tumour mass left (Dworak 0 - 1).

When only a few tumour cells or tumour cell groups were left in the primary tumour, lymph nodes or perirectal fat, the response was assessed as moderate (Dworak 2).

The response was defined as excellent, if there were only very few or no tumour cells left (Dworak 3 - 4).

In case of moderate and excellent response, a total of 2 - 8 separate histological slides were studied to confirm the regression grade.


Carbonic anhydrase isozyme IX expression was analysed in all preoperative diagnostic biopsies (control group excluded) avail- able for the study, as well as in all tumour samples obtained at operation.

In each case, the most representative blocks were selected, cut to 5 mm sections and subjected to immunohisto- chemical (IHC) staining with rabbit polyclonal antibody for CA IX (ab15086, Abcam, Cambridge, UK).

The slides were pre-treated in microwave oven twice for 7 min in 10 mM sodium citrate buffer, pH 6.

The antibody was diluted to a 1 : 8000 concentration in DAKO Antibody diluent-solution.

The staining was carried out using the PowerVision þ Poly-HRP IHC-kit (Immunovision Technologies, Vision BioSystems, Norwell, MA, USA).

The HE staining was performed according to the standard laboratory protocol.


The IHC stainings of the samples were evaluated by two observers (EK and JS), blinded to clinical and radiological information.

Light microscopes with Â 4 and Â 10 objectives (EK) and Â 5 and Â 10 objectives (JS) were used for evaluation.

Carbonic anhydrase isozyme IX staining was graded using three approaches: (1) general grouping of the cases into positive or negative, (2) proportion of positive staining and (3) staining intensity.


The slides were assessed as negative, if the proportion of positive carcinoma cells in the section was less than 10%.

For positive cases, the proportion of positively stained carcinoma cells was analysed using four categories: (i) 10 - 25%, (ii) 26 - 50% and (iii) over 50%.

The positive slides were further evaluated to determine the staining intensity.


In grading the staining intensity, three categories were used: 0 for negative, 1 for weak and 2 - 3 for moderate to strong staining intensity.

Renal cell carcinoma was used as a positive control for strong staining intensity and normal rectal mucosa as a negative control.

In weak staining intensity, there was a faint positive staining in the cytoplasm and occasional staining in the cell membranes impossible to detect at a small magnification (objectives Â 4 to Â 5).

The staining was assessed as moderate or strong, if the positive reaction in the cell membranes could easily be identified at a small magnification (objectives Â 4 to Â 5).

If there were areas of a variety of staining intensities, the predominant intensity was chosen.

In preoperative biopsies, only positive/negative staining and staining intensity were assessed, but not the proportion of staining, because of the small size of the samples.


To support the IHC reaction of CA IX in this study, a small tissue material from CRC patients (n ¼ 4) was further studied for CA IX protein.

RNA from these tumours and corresponding normal colorectal mucosa had been previously isolated for cDNA microarray analysis, and CA9 was shown to be the most upregulated gene (Talvinen et al, 2006).

The protein from these samples was isolated to confirm CA IX expression in western blotting analysis.

Total protein was quantified for each sample in duplicate by Bio-Rad Protein Assay Dye (Bio-Rad Laboratories, Hercules, CA, USA) and the quality was verified with Coomassie blue staining.

For western blotting, equal amounts of denaturated protein samples were size fractioned using 10% SDS - PAGE and electroblotted onto nitrocellulose membrane (Whatman Protran, Perkin Elmer, Boston, MA, USA).

Uniform loading and blotting was checked with Ponceau S staining.

Carbonic anhydrase isozyme IX primary antibody (ab15086, Abcam) was diluted 1 : 1000 for western detection.

Horseradish peroxidase conjugated anti-rabbit immunoglobulins (Dako, Glostrup, Denmark) and Pierce ECL Western Blotting Substrate (Thermo Scientific, Rockford, IL, USA) were used according to the manufacturers' instructions for visualisation of the signal.


All statistical analyses were run using SPSS (SPSS Inc., Chicago, IL, USA) and STATA (Stata Corp., College Station, TX, USA) software packages (SPSS for Windows, version 16.0.2 and STATA/SE 10.1).

Frequency tables were analysed using the w 2 -test, with the likelihood ratio (LR) or Fisher's exact test for categorical variables.

Differences in the means of continuous variables were analysed using Mann - Whitney's test or Kruskal - Wallis's test for two and multiple independent samples, respectively.


Inter-observer reproducibility of the CA IX assessments was tested using regular (Cohen's) k and weighted k. To CA IX in rectal cancer E Korkeila et al 875 British Journal of Cancer (2009) 100(6), 874 - 880 & 2009 Cancer Research UK Clinical Studies calculate the latter, the ICC (intra-class correlation coefficient) test was used, with parallel mode and two-way random model, using consistency assumption and average measures option to interpret the ICC (95% CI).

Concordance of CA IX expres- sion between preoperative biopsies and operative samples was analysed using non-parametric paired-samples test (Wilcoxon signed ranks test or McNemar test).

The inter-observer reproducibility of all CA IX assessments was almost perfect, with regular k having values k ¼ 0.988, k ¼ 0.931 and k ¼ 0.892, for CA IX þ /À, staining proportion and intensity, respectively.

Using weighted k (ICC), even higher values were obtained: ICC ¼ 0.994, 0.985 and 0.984, respectively.

This indicates that all three classifications of CA IX staining used in this study are highly reproducible.


Univariate survival analysis for DFS and DSS was based on the Kaplan-Meier method, where stratum-specific outcomes were compared using log-rank (Mantel-Cox) statistics.

To adjust for covariates, Cox proportional hazards regression model was used, covariates being entered in stepwise backward manner.

All statistical tests were two-sided and declared significant at P-value o0.05 level.


Carbonic anhydrase isozyme IX staining was positive in 49% of the diagnostic biopsies and 44% of the operative specimens.

Staining intensity was weak in 15% and moderate or strong in 29% of the operative samples.

The proportion of CA IX positive staining and staining intensity were directly related (Spearman R ¼ 0.917, P ¼ 0.0001).

A low proportion of CA IX-positive staining (10 - 25%) was associated with weak staining intensity and vice versa.

Negative, weak, moderate and strong staining intensity of CA IX has been illustrated in Figure 1.

The results of IHC analysis were confirmed by the demonstration of CA IX protein in western blotting analysis (Figure 2).


Biopsy samples (B) stained for CA IX were compared with the respective operative samples (S).

Using CA IX positive/negative ( þ /À) grading, 48 out of 78 (61%) B - S pairs were concordant and 30 out of 78 (39%) were discordant.

Of the discordant samples, positive CA IX expression in biopsies was downregulated in 18 out of 38 (48%) cases, and negative CA IX expression was upregulated in 12 out of 40 (30%) cases.

Using CA IX intensity evaluation 43 out of 78 (55%) B - S pairs were concordant and 35 out of 78 (45%) were discordant.

In pairwise comparison (Wilcoxon), CA IX expression pattern in B - S pairs was not significantly different, P ¼ 0.268 and 1.000 for CA IX þ /À and CA IX intensity, respectively.

Results of staining intensity in the biopsy specimens are shown in Table 2.


In Table 3, the intensity of CA IX staining is compared with the traditional prognostic factors and survival.

When the long-course RT group was divided into two treatment categories (with or without concomitant chemotherapy), there was a statistically significant difference in staining intensity (P ¼ 0.006) between the groups, showing more intense staining in the group that had not received concomitant chemotherapy.

Tumour regression grade was possible to be evaluated in 36 long-course RT group patients.

The staining was more intense in the poor response group than in the moderate/excellent response group (P ¼ 0.010).

On the other hand, CA IX expression pattern was not significantly associated with the size, nodal status or grade of the tumour, number of examined lymph nodes, resection type or its radicality, circum- ferential-, proximal- or distal margins, vessel invasion, tumour necrosis or patient gender.


In univariate survival (Kaplan - Meier) analysis, patients with negative or weak CA IX staining intensity had significantly longer DFS (P ¼ 0.001), as depicted in Figure 3A.

Also, DSS was significantly (P ¼ 0.009) longer among patients who had tumours with negative or weak CA IX staining intensity (Figure 3B).

To analyse CA IX staining intensity as independent predictor of DFS and DSS, the following variables were entered in the multivariate (Cox) proportional hazards regression model: sex, age (young/old with median age as cutoff), treatment series (short-course RT, long-course RT and control group), preoperative tumour assess- ment (T), blood vessel invasion (yes/no), number of metastatic lymph nodes (LNN) (with four positive LNN as cutoff) and CA IX staining intensity.

In this multivariate model, only two variables were independent predictors of DFS: (1) number of metastatic


In this study, CA IX expression pattern was analysed from the samples of 166 patients, of whom 29 had received pre- operative chemoradiotherapy, 8 patients long-course RT without chemotherapy, 75 short-course RT and 54 patients had no therapy before surgery.

The intensity of CA IX expression was shown to be a significant prognostic factor in this study.

When analysed with univariate (Kaplan - Meier) survival model, we found that the intensity of CA IX expression was significantly related to both DFS and DSS.

Patients whose tumours were negative or weakly positive in CA IX staining had better prognosis.

The difference in DFS between patients with negative/weak staining intensity and those with moderate/strong staining intensity was evident from the early postoperative period, and the difference increased over time to about 50% at 3 years.

This is important while considering potential interventions with adjuvant therapies.

Similar data have not been previously reported in rectal cancer.


When examined in multivariate (Cox) proportional hazards regression model controlled for several covariates, CA IX intensity together with the number of metastatic lymph nodes were shown to be independent predictors of DFS.

Carbonic anhydrase isozyme


We have read with great interest the article by Korkeila et al (2009) 'Expression of carbonic anhydrase IX suggests poor outcome in rectal cancer' in a recent issue of the British Journal of Cancer.

As the authors rightly state, CA-IX has been found to be of clinical value in different tumour types.

The authors, however, state that their study was 'designed to assess the prognostic and predictive value of CA-IX in rectal cancer treated by either short or long course of radiotherapy'.

Although this might be the case, their data analysis does not support this statement.


The prognostic value of CA-IX, if present, cannot be deduced from the applied patient cohort, as all patients received radio- and/or chemotherapy.

A pure prognostic factor can only be detected in an untreated patient population.

CA-IX is also known to be strongly induced by hypoxia through HIF1a upregulation [2].

The hypoxic phenotype that is represented by the high expression levels of CA-IX is very likely to cause a predictive value of CA-IX, predicting in this case resistance to radiotherapy and possibly also chemotherapy.

Earlier, we have found CA-IX to be predictive for different adjuvant treatments in breast cancer [3].


On the other hand, the authors also describe a control group that would seem to have received no neoadjuvant chemo- or radiotherapy.

If these patients also did not receive adjuvant therapy, which is regrettably unclear from the paper, the authors should be able to determine whether the clinical value of CA-IX is similar in treated and untreated rectal cancer patients.

This could add valuable information to this subject and would warrant publication.


In our opinion, the results shown so far only allow the statement 'Expression of Carbonic Anhydrase IX suggests poor response to therapy in rectal cancer'.


Sir,


In our study, there were two index patient groups: (i) 75 patients who had received preoperative short-course radiotherapy and (ii) 37 patients who had received long-course radiotherapy with or without chemotherapy.

The third group included 54 control patients, none of whom had received any preoperative treatment.

In this group there were 34 patients who had not received any postoperative adjuvant treatment (chemotherapy or chemoradiotherapy) either.

To test the effect of adjuvant treatment, we compared these 34 patients with all the other patients (n = 132).

There was no statistically significant difference (P = 0.604) in disease-free survival, and the same was true with disease-specific survival (P = 0.111) in Kaplan – Meier analysis.


To explore this issue further, we also analysed these 54 control group patients separately, i.e., 20 patients who had received and 34 who had not received any adjuvant chemotherapy.

Among those 34 patients who had not received adjuvant therapy, 17 tumours were CA IX-positive and 17 CA IX-negative.

Among those 20 who had been treated with adjuvant therapy, 11 tumours were CA IX-positive and 9 were CA IX-negative (P = 0.722).

Among the control group patients who had not received adjuvant therapy, 35% of the tumours had moderate or strong and 15% weak intensity in CA IX staining.

Among the patients who had been treated with adjuvant therapy, seven tumours had weak and four tumours had moderate or strong staining intensity (P = 0.225).

There were no statistically significant differences in DFS or DSS between the patients who had or had not received adjuvant therapy.


However, a direct comparison between the groups might be biased by the fact that the patients who had received preoperative therapy had more advanced tumours.

According to our practice, adjuvant therapy is given whenever indicated due to a high recurrence risk (node-positive tumours, perforated tumours or tumours that have caused bowel obstruction, tumours with nerve or vessel invasion).

All these patients who had received adjuvant therapy had tumours with a higher risk of recurrence.


In conclusion, we agree that 'Expression of carbonic anhydrase IX suggests poor response to therapy in rectal cancer' [1].

We were delighted to learn that our article 'Expression of carbonic anhydrase IX suggests poor outcome in rectal cancer' (Korkeila et al, 2009) had been read with great interest.

Span et al (2009) pointed out that the results presented in our study only allow the statement that 'Expression of carbonic anhydrase IX suggests poor response to therapy in rectal cancer', which we agree upon [2].


The world's tropical forests are essential places for environmental sustainability and the future of our planet.

They combine high biodiversity and significant carbon storage with ecological services [1,2].

Since the 1980s, the world's tropical forests have undergone substantial change.

Agricultural expansion worldwide happened at the expense of tropical forest areas [3].

In particular, the Brazilian Amazon rain forest has suffered significant deforestation.

According to Brazil's National Institute for Space Research (INPE), deforestation has reached 20% of the Amazon rain forest in the country [4].


Producing qualified assessments of land-use and land cover change in Amazonia is essential for evidence-based policies that can protect the forest [5,6].

Earth observation data is the primary source of assessments of deforestation in Amazonia.

Since the late 1980s, INPE produces annual estimates of clear-cut areas of forest with the PRODES project and daily indications of deforestation alerts with the DETER initiative [7].

PRODES and DETER are the authoritative sources of information that support Brazil's actions in protecting Amazonia [8,9].

As a companion to the monitoring of clear-cut areas, INPE and its partners also produce periodic maps of land-use change in deforested areas in Amazonia with the TerraClass project [10].

Universities and research groups complement INPE's work [11,12].

Altogether, there is a substantial amount of land-use change information on the Brazilian Amazonia derived from remote sensing data.


Despite the widespread data availability, the complexity of land-use transitions in Amazonia requires continuous improvements in image classification.

Law enforcement actions by the Brazilian federal government managed to reduce deforestation from 2.7 Mha (million hectares) in 2004 to an average of 0.6 Mha between 2009 and 2018.

Despite such reduction, deforestation in Amazonia is still at a relatively high level.

To understand the complex interplay between crop production, cattle ranching, and land speculation, ever more detailed data is required.


To improve monitoring of the Amazon forest from satellites, researchers are investigating the use of big data analytics [13,14].

Such methods rely on the increase of data provided by the new generation of satellites such as Sentinel-2 [15].

However, to use these large data sets in tropical forest areas, researchers need suitable methods of automated cloud detection in optical imagery.


Traditional alternatives for dealing with cloud cover include combining information from various dates and selecting a "best pixel" for an extended period [16].

These methods lead to the loss of temporal information required to identify crop types [14,17] and pasture management [18,19].

To capture temporal information, many researchers prefer methods that identify cloud-covered pixels and replace them with interpolated estimates [20].

When different satellites are combined to produce a denser time series [21], replacing cloudy pixels by interpolated values becomes feasible.

For this reason, automated cloud detection algorithms are a necessary complement to big Earth observation data analytics.


Cloud detection algorithms are an active research field [20,22-25].

Each algorithm has specific characteristics and ad hoc techniques; thus, comparing them on a theoretical basis is hard.

In practice, performance assessment is done by selecting representative images and assessing how well each algorithm performs in each image.

As an example, Baetens et al. [ 26] compared three cloud detection methods (MAJA, Sen2Cor, and Fmask) using 32 images from 10 different locations.

As a reference for comparison, the authors use a machine learning method to identify clouds for Sentinel-2 images.

Given the different land cover, the diversity of sensors, and the advances in detection methods, such comparisons serve as general guidance only.


In this paper, we approach the problem of comparing different cloud detection algorithms from a regional viewpoint.

Given the importance of monitoring land change in Amazonia, we consider cloud detection methods for Sentinel-2 MSI images in this region.

We consider four cloud detection algorithms: Fmask 4 [25], MAJA [23], Sen2Cor 2.8 [22], and s2cloudless [27].


Cloud formation in Amazonia is distinct from most continental areas [28].

The forest produces its own rain [29].

The rainforest generates the aerosols that make up the cloud condensation nucleus in the region [30].

The probabilities of cloud coverage in satellite imagery depend not only on the month of the year but also on the location inside Amazonia [31].


Cloud types are heterogeneous in the Amazon biome; the southern region of the Amazonia has high aerosol concentration, whereas the northern and northwestern regions have low aerosol concentration and high precipitation [30,32].

These characteristics indicate different processes in cloud formation in subregions of the Amazon biome.


During the wet season, the precipitating clouds in the Amazon basin are either low-level stratus type clouds (up to 2-5 km altitude) or high-level convective systems (more than 6 km altitude) [30].

The different land cover influences the amount and type of clouds.

Deep clouds are commonly found over the forest while shallow clouds are frequent over deforested areas [33,34].

Water bodies absorb visible and near-infrared radiation diminishing the reflectivity of the thin clouds above [35].

The high reflectivity of artificial surfaces induces commission errors in cloud detection over urban areas [36].

Such differences pose a challenge for cloud detection algorithms in Amazonia; they need to consider many types of cloud formations and associated shadows.

These singular characteristics suggest that it is useful to take images over Amazonia as a study case when comparing cloud detection methods.


The rest of this article is organized as follows.

We first introduce the study area and the sample regions.

Then we present the cloud detection algorithms and how we configured them.

Later we show how the classes resulting from each cloud detection algorithm compares to the others.

Finally, we introduce our results and then we discuss some implications of this work.


The Amazon forest covers half of Brazil (49.3%) and provides four-fifths of its groundwater (81%) with an average rainfall of approximately 2300 mm per year [37].

Persistent cloud cover in Amazonia is a significant limitation for deforestation monitoring by satellite.

Using the Landsat archive from 1984 to 1997, Asner [31] shows how the probability of cloud cover on Landsat images depends not only on the month of the year but also on its location inside Amazonia.

From June to August, the chance of finding one image with less than 30% cloud cover is 60-90% in southeastern Amazonia.

In the southwestern part, cloud cover is persistent all year round.

While the recent availability of medium-resolution (10-100 m) sensors with higher temporal frequency than Landsat has improved the chances of obtaining cloud-free pixels, cloud cover in rain forests such as Amazonia will always be a challenge for optical remote sensing.


This study uses images from the Sentinel-2A satellite, launched in 2015.

The satellite is part of the Copernicus Earth Observation program of the European Union, which is operated by the European Space Agency (ESA) and managed by the European Commission.

It carries the Multispectral Instrument (MSI), which detects 13 bands of the electromagnetic spectrum spanning from the visible to the short infrared (SWIR) wavelengths at spatial resolutions of 10 m, 20 m, and 60 m, with a revisit period of 10 days [15] (see Table 1).

MSI's three bands at 60 m resolution are dedicated to atmospheric correction and cloud screening, leaving ten bands for land observation [38].

Sentinel-2A data enables researchers to explore the changes on Earth's surface due to its open data access policy and its temporal, spatial, and spectral resolutions.

To assess cloud detection algorithms over Amazonia, we chose five areas representative of its climate heterogeneity.

We identify them using the tiling system of Sentinel-2:


round and low deforestation.

Tile T20NPH overlaps forest and natural savanna, where emerging mining activities are menacing indigenous territories (Figure 1).


The paper compares four algorithms: Fmask 4 [25], MAJA [23], Sen2Cor 2.8 [22], and S2cloudless [27].

Fmask 4 and s2cloudless are specific for cloud detection.

MAJA and Sen2Cor 2.8 are image processors; they generate cloud masks as part of image conversion from radiance at the top of the atmosphere to reflectance from ground targets.


To process Landsat 8 data, USGS uses a version of the Fmask method that requires the thermal band [40].

Fmask 4 is a version of Fmask that has been adjusted to be used with sensors without thermal bands.

ESA uses Sen2Cor to process Sentinel-2 images.

MAJA is developed by CNES and is used by applications such as Sen2Agri [41].

The Sentinel Hub uses S2cloudless for the fast generation of cloud masks [27].

These methods represent the latest generation of cloud detection algorithms for optical remote sensing images.


Fmask 4 [25] is the most recent version of Fmask [20].

Earlier versions of Fmask required a thermal band and worked only on Landsat images.

The latest version also works on Sentinel-2 images [25].

To distinguish between clouds and bright surfaces in Landsat 8 images, Fmask 4 uses the thermal band.


In the case of Sentinel-2 images, it takes the view angle parallax of the NIR bands [24].

To reduce false positives resulting from snow and built-up areas, Fmask 4 uses spectral and contextual features.

To distinguish land from water, it relies on global surface water map [42].


Fmask 4 matches clouds with their shadows based on similarity.

It iterates cloud height from a minimum to a maximum level; for each possible height, it computes the similarity between cloud and cloud shadows [43].

When processing Sentinel-2 images, its cloud and cloud shadow masks have a 20 m resolution [25].


Sen2Cor processes Sentinel-2 data to estimate Bottom-Of-Atmosphere (BOA) reflectances from Top-Of-Atmosphere (TOA) data [22].

It takes Level-1C images and adjusts for atmospheric effects, generating Level-2A surface reflectance products [22,39].

It generates two types of results: (1) atmospheric correction products, such as aerosol optical thickness, surface reflectance, and water vapor maps; and (2) cloud screening and scene Classification (SCL), which assigns a class to each pixel.

Sen2Cor provides two quality indicators: A cloud confidence map and a snow confidence map with values ranging from 0 to 100%.

The distinction between cloudy, clear and water pixels in the SCL and the output of the cloud confidence map are used to produce the cloud confidence information [39].

The current version of Sen2Cor (2.8) increases the accuracy of classification on water, urban, and bare areas while reducing false positives for snow.

Other improvements include cirrus detection, false cloud detection due to permanent bright targets, classification of water pixels inside of cloud borders, and discrimination between topographic and cloud shadow pixels [44].


Sentinel Hub's S2cloudless is a machine-learning-based cloud detector [27].

Its input is Level-1C top of atmosphere data from 10 Sentinel-2 bands (bands 1-5 and 8-12) combined with pairwise band differences and band ratios.

It uses the LightGBM algorithm [45] trained over multiple clouded and non-clouded samples over the world.

As training data, is uses cloud masks provided by MAJA as a proxy for ground truth.

S2cloudless trained its classification model with 15,000 Sentinel-2 tiles from 596 geographically unique areas in 77 different countries.


MAJA (MACCS-ATCOR Joint Algorithm) combines two methods: (a) the Multi-sensor Atmospheric Correction and Cloud Screening (MACCS) and (b) the Atmospheric and Topographic Correction (ATCOR).

It builds on these methods by including time-series of images to improve detection of reflectance changes due to clouds [46].

The method assumes that surface reflectances without clouds are stable in time, while clouds or cloud shadows result in quick variations [23].

MAJA uses multi-temporal images that contain the most recent cloud-free observation for each pixel.

At each new image, the algorithm updates this composite with the newly-available cloud-free pixels.

Thus, it processes the data for a given location in chronological order [26].

The algorithm needs to be initialized to fix cases where a given pixel has no cloud-free observations.

To cover these specific cases, MAJA also uses a mono-temporal criterion based only on spectral information [23].


To run Fmask 4, MAJA 3.2.2, and Sen2Cor 2.8, we used Linux Docker containers.

The Fmask 4 implementation uses the MATLAB code available at GitHub (https://github.com/GERSL/Fmask).

For Sen2Cor 2.8, we installed the version provided by ESA (https://step.esa.int/main/third-party- plugins-2/sen2cor/).

The MAJA implementation was obtained from CNES (https://logiciels.cnes.fr/ fr/content/maja).

We downloaded S2cloudless 1.4.0 from the Sentinel Hub (https://www.sentinel- hub.com/).

Run-time parameters are described below.


Fmask 4: Dilation parameters for cloud, cloud shadows, and snow were set to 3, 3, and 0 pixels, respectively.

The cloud probability threshold was 20%, following Qiu et al. [ 47].


Cloud probability threshold was set to 70%, using a four-pixel convolution for averaging cloud probabilities and dilation of two pixels, following the parameters set by Zupanc et al. [ 27].


Sen2Cor 2.8: The tests used the same configuration as that of the Land Cover maps of ESA's Climate Change Initiative (http://maps.elie.ucl.ac.be/CCI/viewer/download.php).


MAJA: The evaluation used the same configuration as that of the Sen2Agri application (http://www.esa-sen2agri.org).


To validate the resulting cloud masks, we used sample points tagged by remote sensing experts through visual interpretation, following the work of Foga et al. and Zhu et al. [ 36,40].

We selected a set of random locations inside each Sentinel-2 tile.

Since Sentinel-2 images at 10 m resolution have over 120 million pixels, standard statistical techniques indicate that using about 400 samples per image is enough to achieve a 95% confidence level with a 5% margin of error [48].


Five experts labeled those points in all 20 images.

The labels were "cloud", "cloud shadow", "clear", and "other".

The "other" label is a placeholder for samples that the experts could not tag.

Since the areas of cloud shadow are small compared to other labels, we tried to ensure there were at least 50 samples of cloud shadows.


Two different experts classified each point; only those where both experts agreed were selected.

Because of the need for agreement between experts, the final number of selected samples changes from image to image (see Table 2).


Since the algorithms tested use different labels, we recoded their results to match the labels in the validation sample set.

In particular, MAJA produces an 8-bit mask, so that many labels can be applied to a pixel, allowing combinations that are not available in the results of other algorithms.

For example, MAJA's mask allows tagging a pixel as a shadow projected on top of a cloud from another cloud in a neighboring image.

To make MAJA's more detailed results compatible with the output of the other methods, we prioritize clouds over cloud shadows and cloud shadows over clear pixels.

Table 3 shows how the original codes for each method were relabelled for compatibility.


To compare the results of the algorithms, we use the F1 score [49] and the user's, producer's, and overall accuracies [50].

The F1 score (Equation (1)) is the harmonic mean of the precision (Equation (2)) and recall rates (Equation (3)).

The producer's accuracy measures how well a certain label has been classified.

It is computed by dividing the correctly classified pixels in each class by the total number of pixels of the corresponding class.

The user's accuracy indicates the probability that prediction represents reality.

It is computed by dividing the correctly classified pixels in each label by the total number of pixels classified in that label.

The overall accuracy indicates the quality of the map classification.

It is calculated by dividing the total number of correctly classified pixels by the total number of reference pixels.


In our experiments, Fmask 4 has the best overall accuracy, followed by Sen2Cor, MAJA, and S2cloudless (see Table 4).

Fmask 4 consistently outperforms the other algorithms in overall, user's and producer's accuracies for all classes.

It also has the best results considering individual tiles.

For cloud shadow detection, Fmask 4 has a better performance than Sen2Cor.

Although MAJA has the ability to detect cloud shadows, in practice the methods extend its cloud mask to include shadows.

MAJA is a conservative method that uses dilation operators to improve the user's accuracy of the clear sky.

Therefore, no shadows are reported by MAJA.


This is observable in either the images themselves or Figure 2, in which MAJA consistently detects more cloud pixels than the other methods.

Furthermore, Fmask 4 and Sen2Cor also distinguish cirrus clouds from clouds, as shown in Figure 2.

For accuracy assessment, we merged both types of clouds for computing the information in Tables 4 and 5 to be able to compare Fmask 4 and Sen2Cor with the other methods.


When comparing the overall accuracy of Sen2Cor with that of MAJA, their design choices stand out.

MAJA has a better user's accuracy for clear sky pixels than Sen2Cor; for producer's accuracy of this class, Sen2Cor is superior.

Conversely, MAJA has a better producer's accuracy than Sen2Cor for cloud pixels; for the user's accuracy, the situation is inverted.

This situation also holds in individual tiles (see Table 5).

The designers of MAJA have chosen to maximize the probability that pixels labeled as a clear sky are correct.


The behavior of S2cloudless is erratic; sometimes it produces results visually similar to those of Fmask 4 or Sen2Cor (see Figure 3), while in some other occasions it misclassifies clear pixels as clouds (see Figure 2).

For example, for tile T21LXH on 28 March 2017 and tile T22MCA on 28 June 2018, S2cloudless has a particularly poor performance.

Figure 4 shows tile T21LXH on 28 March 2017, a case where S2cloudless performs differently from the other methods.


These results showed that the four algorithms produce their best results on images with few well-defined (crisp) clouds.

Except for s2cloudless, the algorithms agree on the shape and the number of areas classified as either cloud or clear.

However, they cannot adequately approximate the shape of clouds and their shadows on thin semi-transparent cirrus or tightly packed clouds (see Figure 5).

The accurate detection of cloud shadows is challenging because dark surfaces, such as wetlands, burned areas, and terrain shadows can be easily confused with cloud shadows [25].


The pictures shown in Figure 4 confirm the results discussed above.

While Fmask4 has the best performance, it is interesting to compare MAJA with Sen2Cor.

MAJA uses squircles (i.e., a shape between a circle and a rectangle) to fill in the cloud shape, ensuring the total coverage of each cloud in detriment of cloud shadows.

Thus, MAJA sometimes incorporates clear pixels in its cloud mask, as also reported by [26].

By contrast, Sen2Cor approximates the shape of the cloud from the inside, filling in the clouds' boundaries with saturated labels-particularly with thin cirrus clouds-which produces rough borders (see Figure 5).

Furthermore, Sen2Cor cannot detect small clouds which are correctly identified by Fmask 4 and MAJA (e.g., see the small clouds at the center of Figure 3).


The shadow masks produced by Fmask 4 are displaced regarding to the shadows visible in the images.

This is a consequence of the coarse spatial resolution of the digital elevation model used by the method.

As for their shapes, Fmask 4 matches well the cloud shadows respect to the clouds producing them, while in Sen2Cor the cloud shadows have smoother and different boundaries than their clouds (see Figure 5).

Our results confirm the work of Qiu et al. [ 25] and Baetens et al. [ 26], who report that Fmask 4 works better in detecting clouds and cloud shadows than Sen2Cor for Sentinel-2A images.


We could not compute the accuracies for cloud shadow detection for MAJA and S2cloudless.

This is expected from s2cloudless but it comes as a surprise in the case of MAJA.

An explanation is MAJA's greedy behavior regarding clouds; it tends to tag pixels as clouds in disregard of their shadows (see Figure 2).

An alternative explanation is due to our interpretation of the MAJA's bit mask where we prioritized clouds over cloud shadows (see Section 2.6).


Sen2Cor tags many pixels as saturated, defective, or unclassified which we labelled as other (see Table 3).

A visual inspection reveals most of the saturated pixels are the external border of clouds (see Figure 3).

On the other hand, Sen2Cor in tile T21LXH for 08 March 2017 (see Figure 4) mistakenly displays cloud and cloud shadow pixels along the riverbank, almost perfectly profiling the whole river; this could be caused by suspended matter in the water.

Sen2Cor problems to detect cloud cover over water were also reported by [25,51].

As discussed above, the shapes of clouds in the Sen2Cor mask are rougher compared to the smoother results using the other algorithms.

Despite these issues, Sen2Cor is a reliable method for cloud detection.

Its producer's accuracies for clear sky and clouds are respectively 89% and 88%.

If its errors in detecting cloud shadows can be tolerated by the user, its efficiency and ease of use may justify its choice for bulk processing.


S2cloudless erratically mixes land features and clouds, particularly on images with few clouds.

It does not spot cloud shadows.

We could not confirm the claims made by the authors of this method [27] about the good performance of this algorithm.

One explanation is that the clouds in tropical forests such as Amazonia are not adequately included in the S2cloudless training set.


The results of this study show that the Fmask 4 algorithm consistently performs better than the alternatives for Sentinel-2 images of the Amazon rain forest.

Fmask 4 had an overall accuracy of 90%, followed by Sen2Cor (79%), MAJA (69%) and s2cloudless (52%).

Our results are different from those of Baetens et al. [ 26] who concluded that MAJA and Fmask 4 perform similarly with an overall accuracy around 90%, while Sen2Cor had an overall accuracy of 84%.

We now consider some hypothesis that could account for such significant differences.


As noted by Baetens et al. [ 26], for satellites without thermal bands cloud detection methods use thresholds.

Different thresholds are set for the visible bands, the 1.38 µm band, and the Normalized Difference Snow Index.

These approximations address important challenges for cloud detection methods: distinguishing clouds from snow, mountain tops, bright deserts, and large built-up objects.

Since each cloud detection method relies on different ad hoc hypotheses, its usefulness varies from scene to scene.

For this reason, no single study can provide definitive guidance.

Studies that target specific regions, such as the current paper, provide valuable advice even though its results cannot be generalized to non-forest areas.


The comparison done by Baetens et al. [ 26] uses 10 different sites, including equatorial forests, deserts and semi-deserts, agricultural areas, mountains, and snowy areas.

Their results provide a balance between different targets that could be confused with clouds.

By contrast, our study deals only with forest and agriculture areas; the images tested have no deserts, mountains or snow.

By focusing on the Amazon biome, our results are intended as guidance for experts interested in measuring land change in the region.

Given its focus, these results cannot be generalized to non-forest regions.


A further consideration that could explain part of the differences between our work and that of Baetens et al. [ 26] is the choice of training data sets.


While we use random sampling, those authors preferred to rely on active learning.


An active learning model uses a few good quality samples instead of a large ensemble of random points.


These good quality samples are used to train a machine learning model (random forest) whose output provides labels to a large set point for classification.


In theory, this method has the advantage of being able to provide a larger number of points to test the algorithm.


Machine learning models have a tendency to overfit their training data, which could cause wrong predictions [52].


The alternative is to use random samples, which rely on standard statistical assumptions.


However, random samples can miss some cloud properties.


Clouds come in different shapes, sizes, and transparencies; it is often hard to distinguish overlapping clouds at different heights from images.


Random samples can also misrepresent minority labels such as cloud shadows.


Therefore, both random sampling and active learning have their advantages and shortcomings for evaluating cloud detection algorithms.


Further testing and comparison are required to evaluate these approaches.


Another source of divergence between our result and that of Baetens et al. [ 26] is due to class relabeling.

Cloud detection algorithms codify their results using different levels of detail.

To enable comparisons, we had to recode them to the same set of labels.

This process implies a loss of information, in particular for MAJA, which provides the most detailed data about its detection process.

Thus, our recoding process could have had a negative impact on our evaluation of MAJA.


Despite the differences discussed above, there are points of convergence between our work and earlier papers such as Baetens et al. [ 26] and Qiu et al. [ 25] related to Fmask 4 performance.

The overall user's and producer's accuracy values for Fmask 4 are broadly consistent in the three studies.

Qiu et al. [ 25] report producer's accuracies for clouds, shadows and clear pixels to be 93%, 70%, and 97%, while our results are 96%, 75%, and 90%.

Thus, we consider that Fmask 4 to be a reliable method that we recommend to be used for cloud detection in Sentinel-2 images of the Amazon rain forest.


In this work, we compared four cloud detection algorithms on Sentinel-2A images of the Amazon tropical forest, and we found that Fmask 4 performs the best.

We tested four cloud detection algorithms-FMask 4, Sen2Cor, MAJA, and S2cloudless-on 20 images with a different amount of cloud coverage, spread over five regions of Amazonia.

We validated the results of the cloud detection algorithms using the criteria of experts on remote sensing who classified approximately 400 random points on each image.

To determine the best algorithm, we computed the F1 score and the overall, user, and producer accuracies.

We found that FMask 4 has an overall accuracy of 90% to detect clouds, while Sen2Cor's OA is 79%, MAJA's OA is 69%, and S2cloudless's OA is 52%.

Based on these results, we recommend the use of Fmask 4 for cloud detection of Sentinel-2 images of the Amazon region.


The choice of method depends on the intended use.

Therefore, users should consider the benefits of each method before making their choices.

Since MAJA reduces the number of false positives by design, users that aim to improve the producer's accuracy should consider its use.

These characteristics could make MAJA suitable, for example, to build cloud-free monthly mosaics.

Despite the poor performance of S2cloudless in our study, we consider that the use of machine learning methods for cloud detection is a promising way forward.

As more good quality samples become available, its performance will improve.

Finally, Sen2Cor is an efficient method to detect clouds in Sentinel-2 images.

Despite not having the best performance, its ease of use may appeal to those that need fast processing of large data sets.


We expect our work to impact on the building of data cubes of analysis-ready data from satellite imagery, like those currently under construction by the Brazil Data Cube project. ( Brazil Data Cube project http://brazildatacube.org/) Another application is for improving the time series analysis of Land Use and Land Cover change of deforested areas, which is particularly hard because of cloud coverage.

Given the performance of FMask 4, space agencies and committees such as CEOS should consider the value of working together to develop a standardized best quality cloud detection methods that could be shared and used for remote sensing optical imagery.


The R and Python scripts used to compare the performance of cloud detection algorithms are available on GitHub: https://github.com/brazil-data-cube/compare-cloud-masks.


In their recent study, Sanchez et al. compared various cloud detection methods applied to Sentinel-2, specifically on images acquired over the Amazonian region, known for its frequent cloud cover.

Comparison of cloud screening methods for optical satellite images is a complex task, which must take several parameters into account, such as the definition of a cloud, which can differ according to the methods, the different coding of the cloud and shadow masks, the possible dilation of masks, and also the way the method must be used to perform in nominal conditions.

We found that the otherwise serious and useful comparison of cloud masks by Sanchez et al. is not fair to the real performances of MAJA cloud detection, for two reasons: (i) two thirds of the images used in the comparison were acquired before the launch of Sentinel-2B satellite, when the revisit of the Sentinel-2 mission was 20 days instead of five days for the nominal conditions of the mission, and (ii) there is an error in the understanding of how MAJA cloud masks are coded which also probably artificially degraded the results of MAJA as compared to the other methods.


In their study over Amazonia, Sanchez et al. [ 1] found that the performances of MAJA cloud screening software are not as good as those obtained in a similar study by Baetens et al [2].

A large part of the discussion in the article was dedicated to explaining the differences.

It concluded that they are probably related to the specificities of cloud cover and surface reflectances in Amazonia.

The authors also noticed that MAJA (MACCS-ATCOR Joint Algorithm) was surprisingly not able to detect cloud shadows in Amazonia.

While most cloud detection methods process successive images independently, our method for cloud detection and atmospheric correction, MAJA, uses the generally slow variations of surface reflectances as a function of time to better detect the clouds and their shadows [3].

Our cloud detection method uses time series of images as input, processes the images in chronological order, and the quality of its results improves when the interval between successive observations of the land surface is reduced.


In their study, Sanchez et al. used twenty manually classified images taken over five tiles scattered in the Amazonia (Table 2 of their paper).

Thirteen of these images were acquired by Sentinel-2A, before Sentinel-2B was put in operation in July 2017.

Moreover, until November 2017, the revisit of each Sentinel-2 satellites was 10 days in Europe and Africa, and 20 days on the other continents.

As an example, Table 1 shows the number of images acquired by Sentinel-2A or Sentinel-2B over an Amazonian tile used in the Sanchez et al. study, for the same three-month period from 2016 to 2020.

MAJA has been designed to perform optimally in the nominal conditions of Sentinel-2, with a revisit of five days from the same path.

For most images in the time series used by Sanchez et al., the revisit was 20 days.

Given the frequent cloud cover in Amazonia, MAJA does not always have sufficient cloud free observations of the surface, and works with a fallback mono-temporal method to detect the clouds and shadows.

Sanchez et al. do not provide validation statistics per image, but Table 5 of Sanchez et al. provides results per tiles, accumulating the four available dates for each tile.

The two tiles which have only dates in 2016 and 2017 (T20NPH and T22NCG) exhibit the worst performances for MAJA, which shows that MAJA’s performance should be much better for the nominal configuration of Sentinel-2 with a revisit of five days.


Moreover, we found that the authors wrongly interpreted MAJA’s cloud mask.

For each pixel, MAJA not only provides a binary valid/invalid mask, but the mask is expressed as bit mask which tells which of MAJA’s tests declared a pixel invalid.

Such a representation is a little complex but has the advantage to provide a complete information on a small volume (one byte per pixel).

Table 3 of the original paper provides a conversion table for MAJA cloud mask which is partly false, for instance, only the value 0 in MAJA cloud masks correspond to a cloud free/shadow free pixel while Table 3 lists 0 and 1.

Two figures of the Sanchez et al. paper (the graphical abstract and Figure 3) show that the shadows detected by FMASK are also classified as invalid in MAJA, but the authors wrongly indicated that MAJA classifies them as clouds instead of shadows.

For the sake of comparison, we provide in Figure 1 the MAJA cloud mask for the same tile and date as the Figure 3 in Sanchez et al. with the correct representation of the cloud bit-mask.

We understood that, in Sanchez et al., the classification performances have been computed for three classes (clear, cloud, shadow), and, in that case, the wrong interpretation of MAJA mask values must also have a large impact on the performances.

In addition, we are not sure it is optimal to separate cloud and cloud shadows in distinct classes for validation purposes, as shadows on other clouds and mask dilation process lead to mixed classes.

End-users usually expect a mask to discriminate between valid and invalid pixels with the highest possible accuracy, whether invalid ones are clouds or cloud shadows should remain a second order performance criterion.


The study of Sanchez et al. is a very interesting initiative, since the Amazonian regions pose specific challenges to cloud masking, as the cloud cover is usually high and the surface reflectances are low.

In such regions, the multi-temporal method, which is a corner stone of MAJA, partly loses its advantages as the surface is seldom visible and reflectances can significantly evolve from one cloud free observation to the next.

Even if Amazonia is not a use case where MAJA will obtain its best results, we think that the two shortcomings detailed above have resulted in performances for MAJA that are not the nominal ones for Sentinel-2, even over Amazonia.


In their comments about our paper, the authors remark on two issues regarding our results relating to the MACCS-ATCOR Joint Algorithm (MAJA).

The first relates to the sub-optimal performance of this algorithm under the conditions of our tests, while the second corresponds to an error in our interpretation of MAJA’s bit mask.

To answer the first issue, we acknowledge MAJA’s capacity to improve its performance as the number of images increases with time.

However, in our paper, we used the images we had available at the time we wrote our paper.

Regarding the second issue, we misread the MAJA’s bit mask and mistakenly labelled shadows as clouds.

We regret our error and here we present the updated tables and images.

We corrected our estimation and, consequently, there is an increment in MAJA’s accuracy in the detection of clouds and cloud shadows.

However, these increments are not enough to change the conclusion of our original paper.


In our paper [1], we compare four cloud detection algorithms for the Amazon forest using Sentinel images from October 2016 to November 2018.

Three of the algorithms (Fmask4, s2cloudless, and Sen2Cor) identify clouds using only spacial features.

On the other hand, MACCS-ATCOR Joint Algorithm (MAJA) uses time series of images for this purpose.

We compared the results of the four algorithms using points collected and classified by experts on remote sensing.


In their comments, Hagolle and Colin [2] pointed to two issues in the method reported in our paper [1], both related to MAJA.

The first issue is related to the sub-optimal performance due to the interval between successive observations.

The second issue indicates a misinterpretation of MAJA’s bit mask.


Regarding the second issue, we misread the MAJA’s bit mask.

We mistakenly took shadows for clouds.

We apologise for our error.

To fix this error, the MAJA column in Table 3 of our paper must be ignored and instead one should use Table 1.

To correctly interpret MAJA’s cloud mask, please use Table 2.

This is because of the bit structure used by MAJA.


Because of the misinterpretation of MAJA’s bit mask, MAJA’s accuracy in identifying clouds and cloud shadows needs to be updated, while the accuracy of clear observation was not affected.

We updated the accuracy reported on Tables 4 and 5 of our paper and now they are presented here in Tables 3 and 4, respectively.


However, these updates do not change the conclusion in our original paper.


How- ever, by supplementing interphase extracts with baculo- virus-purified hCdh1 protein, we were able to reconsti- tute Cdh1-dependent degradation (Fig. 1).


hCdc20 and the amino-terminal fragment of Xenopus cyclin B are both stable in interphase extracts in which APC, presumably, is inactive because of the absence of a bound activator.

Both proteins become unstable upon the addition of Cdh1 to the interphase extracts (Fig. 1A).

When ⌬90 cyclin B is added to an interphase extract, the extract enters mitosis, in which the active form of APC is the form associated with Cdc20.

hCdc20 remains stable, but amino-terminal Xenopus cyclin B degrades rapidly.

This demonstrates a clear difference in specific- ity between the Cdc20- and Cdh1-activated forms of APC in the complete extract system.


The direct recognition of hCdc20 by Cdh1-APC can be studied by generating ubiquitin conjugates in a purified system.

In this system, APC is immunopurifed from in- terphase extracts with anti-Cdc27 antibody coupled to beads and activated with baculovirus-expressed hCdh1 or hCdc20 protein.

The subsequent addition of purified E1, Ubcx, ubiquitin, and an energy-regenerating system results in ubiquitination of [ 35 S]-methionine-labeled in vitro translation products of APC substrates (Fang et al. 1998).

As substrates become conjugated to ubiquitin, the signal corresponding to the unmodified substrate disap- pears and higher molecular weight species are produced.


In the purified system there is always some very low level of ubiquitin conjugation of APC substrates.

Hence, it is the quantitative loss of the unmodified protein and its conversion of the input peptide into high molecular weight conjugates that we consider evidence for signifi- cant activity on a given substrate.


Few conjugates of ubiquitin to hCdc20 or the amino terminus of xcyclin B occur with control beads (beads incubated with extract, but lacking Cdc27 antibodies), or with unactivated interphase APC on beads (Fig. 1B).

In the presence of Cdc20-APC, the pattern of conjugation to hCdc20 and to xcyclin B is strikingly different.

Except for a few monoubiquitinated species, there is no evi- dence of conjugation of Cdc20 to ubiquitin.

In contrast, the cyclin B signal is converted completely to conjugated species.

With Cdh1-activated APC, there is conjugation of ubiquitin to both substrates.

The difference in speci- ficity between Cdc20-APC and Cdh1-APC for degrada- tion, therefore, is preserved in the purified system for ubiquitination and does not require additional elements from the extract.

In the presence of unactivated inter- phase APC, there is a small background of monoubiqui- tination, but little quantitative conversion of the un- modified substrate.


The budding yeast homolog of Cdc20 contains two de- struction boxes (shown schematically in Fig. 2A), but the vertebrate homologs lack any motif similar to the R-L-N of the D box.

Furthermore, previous work (Fang et al. 1998; Gieffers et al. 1999) reports the ability of Cdh1- APC in the purified system to recognize the amino ter- minus of Xenopus cyclin B with the D box deleted.


To determine how Cdh1-APC recognizes substrates lacking a D box, whereas apparently both WD-repeat-containing proteins confer on APC the capacity to recognize D box- containing substrates, we first tested the less interesting possibility that Cdh1-APC simply shows little substrate selectivity.

Previous work (Fang et al. 1998) had indi- cated that Cdh1 was not completely permissive in the purified system.

In the Cdh1-APC ubiquitination assay on a random sampling of in vitro-translated individual proteins as well as in vitro-translation products from cDNA pools, they showed that Cdh1did not recognize any of the proteins chosen.


We performed a similar ran- dom sampling of 10 cDNA pools from a Xenopus-dorsal- ized gastrula library containing ∼30 clones each.

We tested the in-vitro translation products of each pool in the purified system Cdh1-APC ubiquitination assay as well as the degradation assay of Cdh1-supplemented in- terphase extracts.

Of the 300 clones assayed in both sys- tems, we found only 2 positives.

The positives were iso- lated from the pools, sequenced, and identified to be xcy- clin B1 and xcyclin B3.

Because the only two clones to respond positively in these assays are well-documented


Given the absence of an obvious D box in vertebrate Cdc20, we systematically deleted sequences from the amino and carboxyl termini of human Cdc20 (repre- sented schematically in Fig. 2A) and assayed ubiquitina- tion using Cdh1-activated APC (results summarized to the right of the schematic).

Deletion of the amino-ter- minal 96 amino acids did not alter the ubiquitination profile in terms of amount of full-length protein con- verted to polyubiquitinated species, or the average num- ber of ubiquitins added per molecule, but deletion of the amino-terminal 100 amino acids blocked ubiquitination almost completely.

A few conjugates formed, but most of the original protein remained unconjugated.

Those conjugates that did form were probably monoubiquiti- nated on, at most three sites.

The amino-terminal 120 amino acids showed a wild-type ubiquitination profile, and the amino-terminal 100 and 105 amino acids were each sufficient for a ubiquitin laddering of up to nine ubiquitins added per molecule, even though we did not observe the smearing associated with branched polyu- biquitinated chains.

The amino-terminal 90 amino acids remained largely unconjugated or formed only mono- ubiquitinated species.


Despite the lower stoichiometry of ubiquitination, the 100 and 105 amino acid amino-terminal fragments formed conjugates with almost complete modification of the initial input peptide.

The reduced level of conjuga- tion as compared with that of the amino-terminal 120 amino acid fragment might have been due to the pres- ence of additional lysines and not specific sequence ele- ments involved in recognition.

To test this idea, we tagged the amino-terminal 105 mutant with four addi- tional lysines at its carboxyl terminus.

This restored the branched polyubiquitin smearing seen with the 120 amino acid mutant, confirming that the smearing profile resulted from availability of lysines, not from specific sequence recognition elements.


Given the identification of the region between amino acids 97-105 as required for ubiquitination, we created a series of alanine substitutions in this region within the ⌬96 construct.

Substitution of four amino acids pro- duced a marked drop in ubiquitination (Fig. 2B) com- pared with the ⌬96 construct.

The K97, E98, N99, and N103 alanine substitutions showed a collapse from the polyubiquitin smearing to a monoubiquitin laddering.


Because we had seen significant ubiquitination of an amino-terminal 100 amino acid fragment, we were puzzled by the effect of alanine substitution at position N103, outside of the amino-terminal domain.

To study the role of the N103 position, we created a ⌬96 mutant containing an internal deletion of amino acids 101-105.

This mutant formed polyubiquitinated chains of signifi- cantly greater average length than the ⌬96 mutant (Fig. 2B).

N103, in the context of the amino-terminal frag- ment of Cdc20, therefore, is not required for recognition, but it might contribute to recognition, partially in the context of surrounding residues.


PEST sequences are sequences enriched for the amino acids proline, glutamic acid, serine, and threonine and have been shown previously to contribute to protein in- stability (Rogers et al. 1986).

Amino acids 97-108 of Cdc20 qualify as a PEST sequence (http://www.at.emb- net.org/embnet/tools/bio/PESTfind/).

Changing S101, E102, S104, T106, or P107 to alanine should reduce ubiq- uitination of Cdc20 if recognition depends on this se- quence serving as a PEST sequence.

However, these point mutants within the ⌬96 context did not reduce recognition.

The average length of the polyubiquitin chains formed actually increased following substitution of alanine for the PEST residues S101, E102, and S104.


The human, rat, and Xenopus homologs of Cdc20 in this region (Fig. 2C) are conserved for the required K-E-N residues.

The fourth position, shown to contribute to recognition, although not being required, is an aspara- gine in the human sequence, and an aspartic acid in the other vertebrate homologs shown.

Replacement of N103 in the ⌬96 context with aspartic acid has no effect on the ubiquitination profile (data not shown), confirming that a similar structure that carries a negative charge is ac- ceptable at this position.


To confirm the importance of the K-E-N-X-X-X-N se- quence for ubiquitination and subsequent degradation of Cdc20, we constructed a mutant in which all four resi- dues shown to be important by alanine scanning (K97, E98, N99, and N103) were changed to alanine within the full-length protein.

This mutant (hCdc20KENN-A) showed decreased ubiquitination compared with wild- type Cdc20, both in terms of the extent and rate of con- jugation (Fig. 3A).

Furthermore, hCdc20KENN-A was much more stable than wild-type protein in Cdh1- supplemented extracts (Fig. 3B).

To rule out gross mis- folding of the mutant protein, we compared the ability of hCdc20KENN-A with wild-type hCdc20 to activate APC.

Mutant protein showed similar activity to wild- type protein in stimulating cyclin ubiquitination in the purified system (Fig. 3C).


Previous work showed that Cdh1-APC can promote the ubiquitination of an amino-terminal fragment of Xeno- pus cyclin B lacking the D box at position 36-44 (Fang et al. 1998; Gieffers et al. 1999).

Although there were no KEN-box sequences in this fragment, we did find another R-L-N motif at the extreme amino terminus, positions 7-15 that had been overlooked previously (Fig. 4A).

This Pfleger and Kirschner 658 GENES & DEVELOPMENT Cold Spring Harbor Laboratory Press on September 23, 2025 - Published by Downloaded from potential D box is present only in Xenopus cyclin B, and is not found in other species.

Because deletion of the conserved 36-44 D box blocks degradation in mitotic extracts (King et al. 1996), the 7-15 D box of cyclin B cannot in itself promote mitotic degradation.

To test whether this additional R-L-N motif was responsible for the recognition of cyclin ⌬D box by Cdh1 in the purified system, we constructed mutants of the amino-terminal fragment that lacked this second D box or double mu- tants lacking both D boxes.


Ubiquitination assays re- vealed that deletion of the 7-15 D box alone resulted in a slight drop in ubiquitination by Cdh1 as compared with wild type.

However, deletion of both D boxes elimi- nates ubiquitination completely (summarized quantita- Both in degradation assays and in ubiquitination as- says, we have found quantitative differences in target sequence recognition.

For example, in Figure 4b, Cdh1- APC recognizes the standard D-box deletion (⌬36-44), by promoting nearly 30% of the original protein into poly- ubiquitinated species; wild type show nearly 100% con- version.


Even though there is some ubiquitination of this deletion in the purified system, that particular deletion is completely inactive for degradation in the Cdh1- supplemented extracts (Fig. 4C).

In contrast, both the wild-type and ⌬7-15 constructs were rapidly degraded under similar conditions.

The difference between ubiq- uitination with purified components and degradation in extracts may be due to the optimized conditions for ubiquitination in the purified system, in which there is no isopeptidase activity.

Alternatively, certain crucial components may be limiting or be subject to competi- tion in the cell and in extracts, so that recognition occurs only for optimal substrates.


To restore artificially some of the selectivity to the purified system, we reduced the concentration of ubiq- uitin.

At a ubiquitin concentration of 20% of the original amount, selectivity at the level of ubiquitination was much more apparent (Fig. 4D).

Conjugation of the ⌬36- 44 D-box mutant was reduced to monoubiquitination with no higher molecular weight conjugates, whereas the ⌬7-15 mutant was ultimately fully conjugated to ubiquitin, although there was a significant decrease in the rate and extent of conjugation compared with wild type.

Thus, under less optimal conditions, substrate se- lectivity was enhanced and different D boxes were dif- ferentially active.


The amino-terminal peptides of Xenopus cyclin B (Mc- Garry and Kirschner 1998; Zou et al. 1999) and sea ur- chin cyclin B (Holloway et al. 1993) containing the con- served D box, have been well characterized as competi- tive substrates for Cdc20-APC degradation in mitotic extracts.

Because Cdh1-APC recognizes D-box sub- strates, we asked whether the amino-terminal peptide of cyclin B can compete for Cdh1-dependent degradation.

We find that the amino-terminal cyclin B peptide com- peted for hCdc20 degradation in Cdh1-supplemented, whereas the corresponding deletion mutant of the D box did not compete (Fig. 5).

Because the amino-terminal 120 amino acids of hCdc20 is an excellent Cdh1 substrate, we tested the ability of this peptide also to inhibit Cdh1- dependent degradation.

As shown in Figure 5, wild-type amino-terminal Cdc20 blocked degradation of full- length hCdc20 5 µM. The KEN-box mutant amino-ter- minal peptide shows partial inhibition at 10 µM and none at 5 µM. At 5 µM, the mutant caused no increase in in- hibition over a buffer control, whereas 5 µM of wild-type KEN-box peptide stabilized initial substrate protein to >75%.


A key property of the cyclin B D box is that the degra- dation signal is transposable.

Fusion of a small region containing the cyclin B D box with the immunoglobulin binding region of protein A (a protein stable in mitotic extracts, therefore not recognized by Cdc20-APC) re- sulted in rapid degradation of the fusion protein in mi- totic extracts (Glotzer et al. 1991; King et al. 1996).

The immunoglobulin-binding region of protein A is not rec- ognized by Cdh1-APC in ubiquitination or degradation assays.


We constructed a series of fusions of the region containing 97-103 to protein A and assayed these fusion proteins for recognition by Cdh1-APC using both the ubiquitination and degradation assays.

Fusion of the amino-terminal 103 amino acids, or even the amino-ter- minal 100 amino acids (containing KENQ, but lacking the second asparagine residue shown to contribute to recognition in the point mutant analysis) to protein A resulted in a ubiquitination profile similar to full-length wild-type Cdc20 protein (not shown).

Smaller fusions re- duced the ubiquitination profile but still yielded visible ubiquitin conjugates, whereas protein A alone was not recognized at all.


These fusion proteins were stable in interphase extracts supplemented with buffer, but de- graded in the presence of Cdh1 (Fig. 6).

Fusion of the KEN box alone did not result in ubiquitination or degra- dation (not shown), but fusion of the 28 amino acid re- gion from 75-103 was sufficient to allow ubiquitination and degradation, although the degradation occurred somewhat more slowly than full-length hCdc20 or the larger fusion proteins.


Because the KEN box directs the ubiquitination of Cdc20 by Cdh1 and can be transferred to another protein, we investigated to see whether this sequence serves a gen- eral targeting signal or is specific to Cdc20.

Database searching for other KEN-box-containing proteins re- vealed that the serine-threonine kinase Nek2, a NimA related kinase, contains a region extremely similar to the recognition signal in Cdc20 (Fig. 7A).

NimA has been identified recently as an APC substrate in Aspergillis nidulans (for review, see Zachariae and Nasmyth 1999) making Nek2 a likely Cdh1-APC candidate.

Further- more, Nek2 has been reported to vary with the cell cycle, peaking in protein expression level and activity in S and G 2 and disappearing during mitosis and G 1 (Schultz et al. 1994).

The sequence of B99, a p53-inducible gene that peaks in expression during G 2 (Utera et al. 1998), also contains a putative KEN box (Fig. 7A).


In the purified system, both Nek2 and B99 show sig- nificant ubiquitin conjugation by Cdh1-APC (not shown) but not with antibody control beads.

Nek2, but not B99, shows some degree of recognition by unacti- vated interphase APC, but conjugation is still not as great as that achieved by Cdh1-APC.

Nek2, in particu- lar, forms significant branched chains of varying lengths that extend over a much broader range than for other substrates.

Because the conjugates are more spread out as they run on a gel, we do not visualize the dark smear commonly seen for hCdc20.


Nek2 is degraded in interphase extracts with a half-life of 50-60 min (not shown).

Addition of Cdh1 to these extracts results in complete degradation of Nek2 by 10 min (Fig. 7B), a 12-fold increase in rate.

Alanine substi- tution of the KEN box in Nek2 significantly stabilizes the protein against both ubiquitination in the purified system (not shown) as well as degradation in extracts (Fig. 7B).

B99 is completely stable in interphase extracts but is degraded with a half-life of 25-30 min when the extracts are supplemented with Cdh1.

Alanine substitu- tion of the B99 KEN box completely stabilizes the pro- tein to degradation (Fig. 7C).


Genetic and biochemical studies suggest that the WD repeat proteins, Cdc20 and Cdh1, sequentially activate the ubiquitin ligase APC during mitosis to achieve a temporal order of substrate degradation.

Cdc20 itself is a substrate for APC when APC is activated by Cdh1, and therefore, Cdc20 is no longer present at the end of mito- sis; Cdh1 is a stable protein present throughout the cell cycle, whose interaction with APC is most likely regu- lated by phosphorylation (Kotani et al. 1999).

Although it has been suggested that Cdc20 and Cdh1 might confer different substrate specificities on APC, the nature of substrate recognition is still unclear.


In the SCF com- plex, the corresponding WD proteins such as HOS or ␤TrCP (Margottin et al. 1998; Yaron et al. 1998; Fuchs et al. 1999) bind directly to the substrate, whereas the af- finity of Cdc20 and Cdh1 with potential substrates has not been shown, possibly because it is too low to mea- sure.

Mutagenesis experiments in many species defines a target sequence recognized by APC that has been acti- vated by Cdc20.

This sequence, which includes the de- struction box plus some nonconserved lysine containing sequences, has been identified on all species of cyclin B plus several other proteins degraded at metaphase or soon after, such as Geminin, Pds1 (securin), Ase1, and others.


The targeting sequence to APC activated by Cdh1 has, up to now, been undetermined.

Its character- ization, the subject of this work, would allow a better understanding of specificity and temporal order of pro- tein degradation during mitosis and in G 1 .


We have used Cdc20 itself as a substrate for Cdh1- APC to define recognition sequences within Cdh1-APC substrates.

Cdc20 has been shown to be degraded late in mitosis (Weinstein 1997) in an APC-dependent manner (Prinz et al. 1998; Shirayama et al. 1998) and to be a


GENES & DEVELOPMENT 661 Cold Spring Harbor Laboratory Press on September 23, 2025 - Published by Downloaded from substrate of Cdh1-APC in vitro.

Using ubiquitination assays in a completely defined system and degradation assays in complete extracts, we have examined mutants of Cdc20 to learn the requirements for ubiquitination and degradation by Cdh1-APC.

Deletion mutagenesis narrowed down the recognition region to amino acids 97-105, and point mutants in this region identified four residues that decreased recognition when changed to ala- nine, K-E-N-X-X-X-N, (in which aspartic acid in the final position supported similar polyubiquitination as the as- paragine).

However, within a truncated fragment of Cdc20, deletion of residues 101-105, containing the sec- ond crucial asparagine, showed a greater than wild-type response.

This indicates to us that the second asparagine is not required for recognition overall, but that it may be required within the context of the surrounding residues, perhaps modulating a negative regulatory region.


Sequence alignment between the vertebrate Cdc20 se- quences and the Cdc20 sequence of S. cerevisiae show no KEN-similar motif in the same region of the protein.

However, there is a potential KEN box at position 586 near the carboxyl terminus of S. cerevisiae Cdc20 (pro- tein YGL116w), K-E-N-X-X-X-N (pointed out to us by E. Hildebrant and A. Hoyt, pers.

comm.).

The carboxy-ter- minal KEN boxes of hNek2 and mB99 support ubiquiti- nation and degradation, suggesting the possibility that the KEN box in any part of the protein could be func- tional.

We do not know if recognition of the KEN box extends to the yeast system or is limited to vertebrate systems, or whether there are undiscovered Cdh1-APC recognition signals in other proteins.


Previous reports already suggested that Cdh1-APC dif- fered from Cdc20-APC in that it could mediate the ubiq- uitination of substrates like hCdc20 that lack a D box and substrates like amino-terminal xcyclin B from which the conserved D box had been deleted (deletion of residues 36-44).

A possible interpretation of these results is that Cdh1-APC is promiscuous in substrate recogni- tion, particularly since there were no known conserved sequence elements in the amino-terminus of cyclin B other than the D box that could serve as a targeting se- quence.

Yet, countering this conclusion is the lack of activity of Cdh1-APC against 300 random substrates.

By mutagenesis of Cdc20 and Xenopus cyclin B, we could demonstrate that Cdh1-APC shows specificity for two classes of sequences, those that contain the R-L-N motif of the D box and those that show a new targeting se- quence, the KEN box.

The paradoxical observation that Cdh1-APC recognizes a cyclin fragment lacking both a D box and a KEN box was explained when an additional D box sequence in Xenopus cyclin B was found upstream of the conserved D box.

Deletion of this second, noncon- served motif blocks polyubiquitination completely.


There are notable quantitative differences between ubiquitination in the purified system and degradation in extracts.

For example, there is significant polyubiquiti- nation of cyclin B from which the conserved D box has been deleted (⌬36-44 N), whereas the protein is very stable in Cdh1-supplemented extracts.

The degree and rate of ubiquitination must be a very complex function of many factors in extracts and in living cells.

It may depend on competition among substrates, the activity of isopeptidases, and other factors that could modify any of the several steps in the process.

In the purified in vitro system, ubiquitination, is optimized and inhibitory fac- tors are absent.

Dilution of ubiquitin reduced the effi- ciency of ubiquitination, but most importantly, it also elicited a difference between the behavior of the two D boxes in Xenopus cyclin B in the purified system, a dif- ference reflected in the aggregate rates of degradation in extracts.


Like the D box, the KEN-box domain was transposable to other proteins.

A fusion of only 28 amino acids around the KEN-box sequence (residues 75-103 of hCdc20) to protein A resulted in ubiquitination in the purified sys- tem and degradation in the extract system.

Degradation was slower than larger fusion constructs and wild-type protein.

Fusion of the KEN box alone did not confer deg- radation, suggesting that there may be additional struc- tural elements required for recognition.

Alternatively, there could be separate signals mediating recognition and degradation, as suggested by work with cyclin pro- tein A fusions (Klotzbü cher et al. 1996).

There also may be a minimum size for proper degradation.


In the initial mutagenesis studies of KEN box, we found unexpectedly that alanine substitution of the two serines and one of the glutamic acids in a region carboxy- terminal to the KEN sequence noticeably increased polyubiquitination.

A similar increase in the average chain length of the polyubiquitin chain was seen upon deletion of five amino acids including the previously mentioned serines and glutamate.

This increased activ- ity in the mutants suggested that negative charge in that region could inhibit ubiquitination and that phosphory- lation might be used physiologically to regulate the pro- cess.

It may be significant that there is a conserved Cdc2 site just carboxy-terminal to the KEN box, making it a likely target for cell cycle regulated phosphorylation.

Al- though phosphorylation has been postulated to control the ubiquitination machinery in APC-dependent pro- cesses, it has not yet been implicated in controlling sub- strate selectivity.

For the SCF complex the situation is reversed, phosphorylation regulates substrate selectivity positively, although there is no known regulation of the ubiquitination machinery.

These mutagenesis results suggest that it would be useful to examine APC sub- strates for phosphorylation regulation, which might fur- ther specify the timing of protein degradation at the end of mitosis.


The explanation for the partially overlapping specifici- ties of Cdc20-APC and Cdh1-APC for substrates will ultimately require studies at an atomic level resolution.

The amino-terminal peptide of cyclin B containing the D box shows no regular structure in solution by NMR (H. Yu and M. Kirschner, unpubl.), so there is little hope that the structure of the peptide independent of APC would be informative.

One way of thinking of the structural basis for the overlap in specificity mediated by Cdc20 and Cdh1 is a bump and hole model (assuming binding of both sequences to the same site).

In this model, the KEN Pfleger and Kirschner 662 GENES & DEVELOPMENT Cold Spring Harbor Laboratory Press on September 23, 2025 - Published by Downloaded from box peptide contains a bump that is accommodated by a hole in Cdh1 when bound to APC.

It would not be ac- commodated by Cdc20; hence, KEN peptides would not be able to bind to Cdc20-APC.

The D-box sequence would not have this feature and would bind to both Cdc20-APC and Cdh1-APC.


Knowledge of the respective roles of the D box and KEN box has provided us with a new understanding of how APC manages to achieve temporal, spatial, and sub- strate specificity throughout the cell cycle.

In addition, it has given us new predictive tools to identify Cdh1 sub- strates.

Given the low stringency of the KEN sequence, each candidate substrate needs to be validated by other means.

However, using this information, we were able to identify two new substrates, Nek2 and B99, which show a pattern of abundance in vivo expected for a Cdh1 sub- strate.

Both have functional KEN boxes.

In addition, the Aurora kinase family all have the conserved KEN resi- dues (for review, see Bischoff and Plowman 1999), mak- ing them likely candidates to test for KEN-box-depen- dent degradation.


Recently, we carried out an unbiased screen by in vitro expression cloning for Cdh1-APC sub- strates and identified two novel proteins, which on se- quencing, have KEN sequences which, by mutagenesis, are essential for degradation (S. Rankin, N. Ayad, and C. Pfleger, unpubl.).

Because Cdh1-APC activity is found to be high in nondividing cell populations, such as brain (Gieffers et al. 1999), we anticipate finding substrates that are involved in cellular processes beyond cell cycle events.


Extracts were prepared as described previously (Murray 1991; Fang et al. 1998).

In brief, eggs from HCG-injected PMSG- primed frogs were collected and washed in 1XMMR.

Eggs were dejellied with 2% cysteine and activated by calcium ionophore, and then washed into XB and XB containing protease inhibitors.

A 1-min packing spin at 1000 rpm was performed, followed by a crushing spin at 10,000 rpm.

The cytoplasmic layer was col- lected and subjected to a clarifying spin also at 10,000 rpm.

The clarified cytoplasmic layer was collected.

After addition of an energy-regenerating system, protease inhibitors, cytochalasin B, and glycerol to 4%, extracts were frozen in liquid nitrogen, and stored at −80°C.


Mutants were generated by PCR with primers designed for the desired mutations.

Construction of deletion mutants was achieved by designing primers with a restriction site, a start ATG (for amino-terminal deletions) or stop codon (for carboxy- terminal deletions) and sequence for at least five amino acids at the position of the desired deletion.

Point mutations within the 96 amino acid deletion mutant were achieved using an amino- terminal primer encoding the altered codon for the desired amino acid change.

Insertion of an alanine cassette in place of the KEN box in the full-length Cdc20 was achieved by designing both up and down primers introducing a NotI restriction site and sequence for amino acids on either side of the deletion.

Two separate fragments were PCRed, digested with NotI, and ligated together and into a pCS2 + based vector, in position for in vitro translation with the Sp6 primer.

For more information on primer sequences used for construction of mutants of Cdc20 and other proteins used in this study, please contact C. Pfleger or M. Kirschner.


Insertion of a StuI site at the beginning of the IgG-binding region of protein A allowed ligation to small regions of Cdc20 PCRed with a carboxy-terminal primer lacking a stop site and contain- ing a StuI site in the same frame.

Coding sequence for three additional lysines was added just amino-terminal to the StuI site to ensure the close proximity of lysines to the KEN box in the fusion protein.

Inserts were cloned into a pCS2 + vector for in vitro translation with the Sp6 primer.


His-tagged hCdc20 and hCdh1 were expressed in insect cells by the baculovirus system (virus obtained from G. Fang, Stanford University, Palo Alto, CA).

Sf9 cells were infected with the appropriate virus, and harvested 2 days later.

Protein was puri- fied over a nickel column and eluted with high imidazole.

The resulting eluate was run over a sizing column with PBS and containing 1 mM DTT.

The appropriate fractions were collected and reconcentrated.

Glycerol was added to 10%, then protein was frozen in liquid nitrogen and stored at −80°C.


Amino-terminal Cdc20 peptides corresponding to the first 120 amino acids of either human wild-type sequence or KENN-A mutant were cloned into a pET 28 bacterial expression vector tagging them with an amino-terminal His tag.

Protein was ex- pressed in bacteria and purified on nickel beads.

Previously de- scribed Xenopus cyclin B amino-terminal peptides (amino acids 1-102) containing or lacking the ⌬36-44 D box (Fang et al. 1998) were expressed in bacteria, purified on nickel columns, and eluted with high imidazole.

Peptides were dialyzed into XB- sucrose containing 1 mM DTT, reconcentrated, and glycerol was added to 10%.

Peptides were frozen in liquid nitrogen and stored at −80°C.


Ubiquitination assays were described previously (Fang et al. 1998).

In brief, interphase APC (iAPC) was immunopurified from interphase extract with anti-Cdc27 antibodies covalently coupled to protein A beads and washed 5 times with XB (minus sucrose) containing 500 mM NaCl and 0.5% NP-40 detergent and two times with XB (minus sucrose).

iAPC beads were incu- bated for 1 hr with baculovirus-expressed and purified Cdc20 or Cdh1, unless otherwise stated.

Activator-bound APC on beads was then washed three times with XB (minus sucrose) followed by addition of 50 µg/ml Ubcx, 1.25 mg/ml ubiquitin, 200 µg/ml recombinant E1, 0.1 mg/ml cycloheximide, 1-5 µM ubiquitin aldehyde, and one-fifth the volume of 35 S-labeled in vitro-trans- lated substrate (typically, 2 µl of beads would be added to a 5-µl reaction).

Ubiquitination assays were scored either visually, by observing the decrease in original signal with the appearance of polyubiquitinated (greater than three ubiquitins added per mol- ecule) conjugates, or quantitated with NIH Image 1.61.


In vitro degradation assays were performed in the presence of increasing amounts of bacterially expressed his-tagged peptides of amino-terminal Cdc20 wild-type or KEN-box mutant or amino-terminal cyclin B wild type or ⌬D box.


Nek2 was cloned by PCR using the marathon cDNA library and the advantage polymerase from Clontech.

B99 was obtained from L. Collavin in Claudio Schneider's laboratory (Trieste) and cloned into a pCS2 + vector.

Mutants of Nek2 were made by PCR by introducing an alanine cassette containing a NotI site in the manner described for the Cdc20 KENN-A mutant.

The B99 mu- tant was also made by PCR, using a carboxy-terminal primer encoding the alanine substitution.


The precise order of molecular events during cell cycle progression depends upon ubiquitin-mediated proteolysis of cell cycle regulators.

We demonstrated previously that Hsl1p, a protein kinase that inhibits the Swe1p protein kinase in a bud morphogenesis checkpoint, is targeted for ubiquitin-mediated turnover by the anaphase-promoting complex (APC) [1].

Here, we investigate regions of Hsl1p that are critical both for binding to the APC machinery and for APC-mediated degradation.

We demonstrate that Hsl1p contains both a destruction box (D box) and a KEN box motif that are necessary for Hsl1p turnover with either APCCdc20 or APCCdh1.

In coimmunoprecipitation studies, the D box of full-length Hsl1p was critical for association with Cdc20p, whereas the KEN box was important for association with Cdh1p.

Fusion of a 206-amino-acid fragment of Hsl1p containing these motifs to a heterologous protein resulted in APC-dependent degradation of the fusion protein that required intact D box and KEN box motifs.

Finally, this bacterially expressed Hsl1p fusion protein interacted with Cdc20p and Cdh1p either translated in vitro or expressed in and purified from insect cells.

Binding to Cdc20p and Cdh1p was disrupted completely by a D box/KEN box double mutant.

These results indicate that D box and KEN box motifs are important for direct binding to the APC machinery, leading to ubiquitination and subsequent protein degradation.


Ubiquitin-mediated protein degradation is necessary for the linear progression of molecular events during the cell cycle.

Protein ubiquitination and subsequent degradation by the 26S proteasome is required for the initiation of DNA replication, the onset of anaphase, and for mitotic exit [2][3][4].

The ubiquitin pathway is a multi-step process in which the 76-amino-acid ubiquitin molecule is activated by an E1 enzyme, transferred to an E2 enzyme, and then attached covalently to the protein substrate either directly or in conjunction with an E3 enzyme (ubiquitin ligase) [5][6].

Proteins destined to be degraded are subject to multiple rounds of ubiquitin attachment (polyubiquitination) and are then proteolyzed by the 26S proteasome.

The E3 or ubiquitin ligase is involved in protein substrate recognition and thereby confers specificity to the ubiquitination reaction [5][6].