[ccp4bb] Faculty position all levels at Stony Brook University, New York

[Markus Seeliger]

Dear all,
the Department of Pharmacological Sciences and the Stony Brook Cancer
Center have a job opening for a tenure track faculty position at all levels
(assistant, associate, full). The search is very wide and details can be
found here .
Stony Brook is near wonderful beaches and beamlines at NSLS2.

Best

Markus
***
Markus Seeliger

Associate Professor
Department of Pharmacological Sciences
Stony Brook University Medical School, BST 7-170
Stony Brook, NY 11794-8651
office: (631) 444-3558
lab: (631) 638-1299
fax: (631) 444-9749

https://www.pharm.stonybrook.edu/markus-seeliger-lab-welcome

markus.seeli...@stonybrook.edu
***



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Re: [ccp4bb] am I doing this right?

[Filipe Maia]

I forgot to add probably the most important. James is correct, the expected
value of u, the true mean, given a single observation k is indeed k+1 and
k+1 is also the mean square error of using k+1 as the estimator of the true
mean.

Cheers,
Filipe

On Wed, 13 Oct 2021 at 23:17, Filipe Maia  wrote:

> Hi,
>
> The maximum likelihood estimator for a Poisson distributed variable is
> equal to the mean of the observations. In the case of a single observation,
> it will be equal to that observation. As Graeme suggested, you can
> calculate the probability mass function for a given observation with
> different Poisson parameters (i.e. true means) and see that function peaks
> when the parameter matches the observation.
>
> The root mean squared error of the estimation of the true mean from a
> single observation k seems to be sqrt(k+2). Or to put it in another way,
> mean squared error, that is the expected value of (k-u)**2, for an
> observation k and a true mean u, is equal to k+2.
>
> You can see some example calculations at
> https://colab.research.google.com/drive/1eoaNrDqaPnP-4FTGiNZxMllP7SFHkQuS?usp=sharing
>
> Cheers,
> Filipe
>
> On Wed, 13 Oct 2021 at 17:14, Winter, Graeme (DLSLtd,RAL,LSCI) <
> 6a19cead4548-dmarc-requ...@jiscmail.ac.uk> wrote:
>
>> This rang a bell to me last night, and I think you can derive this from
>> first principles
>>
>> If you assume an observation of N counts, you can calculate the
>> probability of such an observation for a given Poisson rate constant X. If
>> you then integrate over all possible value of X to work out the central
>> value of the rate constant which is most likely to result in an observation
>> of N I think you get X = N+1
>>
>> I think it is the kind of calculation you can perform on a napkin, if
>> memory serves
>>
>> All the best Graeme
>>
>> On 13 Oct 2021, at 16:10, Andrew Leslie - MRC LMB <
>> and...@mrc-lmb.cam.ac.uk> wrote:
>>
>> Hi Ian, James,
>>
>>   I have a strong feeling that I have seen this
>> result before, and it was due to Andy Hammersley at ESRF. I’ve done a
>> literature search and there is a paper relating to errors in analysis of
>> counting statistics (se below), but I had a quick look at this and could
>> not find the (N+1) correction, so it must have been somewhere else. I Have
>> cc’d Andy on this Email (hoping that this Email address from 2016 still
>> works) and maybe he can throw more light on this. What I remember at the
>> time I saw this was the simplicity of the correction.
>>
>> Cheers,
>>
>> Andrew
>>
>> Reducing bias in the analysis of counting statistics data
>> Hammersley, AP  
>> (Hammersley,
>> AP) Antoniadis, A
>>  (Antoniadis, A)
>> NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS
>> SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT Volume 394
>> Issue 1-2
>> Page 219-224
>> DOI 10.1016/S0168-9002(97)00668-2
>> Published JUL 11 1997
>>
>> On 12 Oct 2021, at 18:55, Ian Tickle  wrote:
>>
>>
>> Hi James
>>
>> What the Poisson distribution tells you is that if the true count is N
>> then the expectation and variance are also N.  That's not the same thing as
>> saying that for an observed count N the expectation and variance are N.
>> Consider all those cases where the observed count is exactly zero.  That
>> can arise from any number of true counts, though as you noted larger values
>> become increasingly unlikely.  However those true counts are all >= 0 which
>> means that the mean and variance of those true counts must be positive and
>> non-zero.  From your results they are both 1 though I haven't been through
>> the algebra to prove it.
>>
>> So what you are saying seems correct: for N observed counts we should be
>> taking the best estimate of the true value and variance as N+1.  For
>> reasonably large N the difference is small but if you are concerned with
>> weak images it might start to become significant.
>>
>> Cheers
>>
>> -- Ian
>>
>>
>> On Tue, 12 Oct 2021 at 17:56, James Holton  wrote:
>>
>>> All my life I have believed that if you're counting photons then the
>>> error of observing N counts is sqrt(N).  However, a calculation I just
>>> performed suggests its actually sqrt(N+1).
>>>
>>> My purpose here is to understand the weak-image limit of data
>>> processing. Question is: for a given pixel, if one photon is all you
>>> got, what do you "know"?
>>>
>>> I simulated millions of 1-second experiments. For each I used a "true"
>>> beam intensity (Itrue) between 0.001 and 20 photons/s. That is, for
>>> Itrue= 0.001 the average over a very long exposure would be 1 photon
>>> every 1000 seconds or so. For a 1-second exposure the observed count (N)
>>> is almost always zero. About 1 in 1000 of them will see one photon, and
>>> roughly 1 in a million will get N=2. I do 10,000 such experiments and
>>> put the results into a pile.  I then repeat with 

Re: [ccp4bb] am I doing this right?

[Filipe Maia]

Hi,

The maximum likelihood estimator for a Poisson distributed variable is
equal to the mean of the observations. In the case of a single observation,
it will be equal to that observation. As Graeme suggested, you can
calculate the probability mass function for a given observation with
different Poisson parameters (i.e. true means) and see that function peaks
when the parameter matches the observation.

The root mean squared error of the estimation of the true mean from a
single observation k seems to be sqrt(k+2). Or to put it in another way,
mean squared error, that is the expected value of (k-u)**2, for an
observation k and a true mean u, is equal to k+2.

You can see some example calculations at
https://colab.research.google.com/drive/1eoaNrDqaPnP-4FTGiNZxMllP7SFHkQuS?usp=sharing

Cheers,
Filipe

On Wed, 13 Oct 2021 at 17:14, Winter, Graeme (DLSLtd,RAL,LSCI) <
6a19cead4548-dmarc-requ...@jiscmail.ac.uk> wrote:

> This rang a bell to me last night, and I think you can derive this from
> first principles
>
> If you assume an observation of N counts, you can calculate the
> probability of such an observation for a given Poisson rate constant X. If
> you then integrate over all possible value of X to work out the central
> value of the rate constant which is most likely to result in an observation
> of N I think you get X = N+1
>
> I think it is the kind of calculation you can perform on a napkin, if
> memory serves
>
> All the best Graeme
>
> On 13 Oct 2021, at 16:10, Andrew Leslie - MRC LMB <
> and...@mrc-lmb.cam.ac.uk> wrote:
>
> Hi Ian, James,
>
>   I have a strong feeling that I have seen this result
> before, and it was due to Andy Hammersley at ESRF. I’ve done a literature
> search and there is a paper relating to errors in analysis of counting
> statistics (se below), but I had a quick look at this and could not find
> the (N+1) correction, so it must have been somewhere else. I Have cc’d Andy
> on this Email (hoping that this Email address from 2016 still works) and
> maybe he can throw more light on this. What I remember at the time I saw
> this was the simplicity of the correction.
>
> Cheers,
>
> Andrew
>
> Reducing bias in the analysis of counting statistics data
> Hammersley, AP  
> (Hammersley,
> AP) Antoniadis, A
>  (Antoniadis, A)
> NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS
> SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT Volume 394
> Issue 1-2
> Page 219-224
> DOI 10.1016/S0168-9002(97)00668-2
> Published JUL 11 1997
>
> On 12 Oct 2021, at 18:55, Ian Tickle  wrote:
>
>
> Hi James
>
> What the Poisson distribution tells you is that if the true count is N
> then the expectation and variance are also N.  That's not the same thing as
> saying that for an observed count N the expectation and variance are N.
> Consider all those cases where the observed count is exactly zero.  That
> can arise from any number of true counts, though as you noted larger values
> become increasingly unlikely.  However those true counts are all >= 0 which
> means that the mean and variance of those true counts must be positive and
> non-zero.  From your results they are both 1 though I haven't been through
> the algebra to prove it.
>
> So what you are saying seems correct: for N observed counts we should be
> taking the best estimate of the true value and variance as N+1.  For
> reasonably large N the difference is small but if you are concerned with
> weak images it might start to become significant.
>
> Cheers
>
> -- Ian
>
>
> On Tue, 12 Oct 2021 at 17:56, James Holton  wrote:
>
>> All my life I have believed that if you're counting photons then the
>> error of observing N counts is sqrt(N).  However, a calculation I just
>> performed suggests its actually sqrt(N+1).
>>
>> My purpose here is to understand the weak-image limit of data
>> processing. Question is: for a given pixel, if one photon is all you
>> got, what do you "know"?
>>
>> I simulated millions of 1-second experiments. For each I used a "true"
>> beam intensity (Itrue) between 0.001 and 20 photons/s. That is, for
>> Itrue= 0.001 the average over a very long exposure would be 1 photon
>> every 1000 seconds or so. For a 1-second exposure the observed count (N)
>> is almost always zero. About 1 in 1000 of them will see one photon, and
>> roughly 1 in a million will get N=2. I do 10,000 such experiments and
>> put the results into a pile.  I then repeat with Itrue=0.002,
>> Itrue=0.003, etc. All the way up to Itrue = 20. At Itrue > 20 I never
>> see N=1, not even in 1e7 experiments. With Itrue=0, I also see no N=1
>> events.
>> Now I go through my pile of results and extract those with N=1, and
>> count up the number of times a given Itrue produced such an event. The
>> histogram of Itrue values in this subset is itself Poisson, but with
>> mean = 2 ! If I similarly count up events 

[ccp4bb] Postdoctoral Fellowship at Pfizer

[Mashalidis, Ellene]

Dear all,

We are seeking an enthusiastic and driven postdoctoral fellow to join the 
Structural and Molecular Sciences (SMS) Department at the Pfizer campus in 
Groton, Connecticut. This fellowship offers the unique opportunity to learn 
about the drug and vaccine development process in the laboratories of an 
innovative industry leader. The successful candidate will receive 
state-of-the-art training in protein structural biology and biochemistry with 
access to unparalleled resources supporting prokaryotic and eukaryotic protein 
expression, purification, and structure determination by X-ray crystallography 
or cryo-EM. At SMS, we have regular access to all the major synchrotron beam 
lines in the world and dedicated in-house cryo-EM facilities.  Ideal candidates 
will have experience in molecular biology, protein expression, protein 
purification, and structure determination.

To apply, please follow the link below:
Postdoctoral Fellow, Structural Biology and Biophysics 
(myworkdayjobs.com)


Kind regards,
Ellene

Ellene H. Mashalidis, PhD
Senior Scientist
Structural and Molecular Sciences
Pfizer Global Research and Development
445 Eastern Point Road
Groton, CT 06340
Building 220/322J
Phone: 860-441-6172
ellene.mashali...@pfizer.com




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[ccp4bb] Postdoctoral positions in the MRC Laboratory of Molecular Biology

[Roger Williams - MRC LMB]

Dear CCP4-ers,

In the Roger Williams group in the Laboratory of Molecular Biology in 
Cambridge, UK, we have several openings for postdoctoral researchers to focus 
on structural biology of cell proliferation & stress responses in cancer cells. 
It is essential that candidates have a strong interest in structural biology. 
Ideally, candidates would have some background in some structural approach, 
such as single particle cryo-EM, cryo-ET, X-ray crystallography, molecular 
dynamics or structural mass spectrometry, but training in any of these methods 
is also possible.

Details of the position can be seen here:
https://www.nature.com/naturecareers/job/postdoctoral-scientist-pnac-dr-roger-williams-lmb-1684-mrc-laboratory-of-molecular-biology-747391
 


Please feel free to contact me, if you would like to discuss any aspect of the 
positions.

Roger Williams


Dr. Roger Williams, FMedSci, FRS
r...@mrc-lmb.cam.ac.uk
MRC Laboratory of Molecular Biology
Francis Crick Avenue
Cambridge CB2 0QH
UK
Phone: +44 1223 267094






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[ccp4bb] Tenure-track Assistant or Associate Professor in Molecular Biophysics at UConn Health

[Page,Rebecca]

Tenure-track Assistant or Associate Professor in Molecular Biophysics at UConn 
Health

The Department of Molecular Biology and Biophysics (MBB) at the University of 
Connecticut Health Center (UConn Health) seeks exceptionally creative 
candidates for an early-to-mid career position (tenure-track Assistant or 
Associate Professor), who use molecular and translational approaches to 
elucidate the molecular basis of disease.

Research in MBB integrates multiple approaches to molecularly define the 
proteins and pathways affected in human disease. We are especially interested 
in creative and innovative candidates whose research interests focus on the use 
of cryo-EM and/or other structural biology methods (X-ray Crystallography, NMR 
spectroscopy, single molecule techniques) to examine key biological questions 
in microbiology, host-pathogen interactions, molecular signaling (including 
areas such as posttranslational modifications, protein ubiquitination, 
kinase/phosphatase signaling/regulation, bacterial signaling, among others) or 
other critical biological processes. The candidate will augment existing 
strengths in the department and the larger UConn Health research community. 
Programs that focus on the identification and development of translational 
outcomes, including probes that will allow for in-depth analysis of key 
biological pathways or novel drug-like molecules, are also of particular 
interest.

MBB and UConn Health provide a rich and diverse research environment and is 
deeply committed to the development and success of its incoming faculty. The 
UConn Health School of Medicine is home to 30 departments and institutes, 
including Cell Biology, Genetics and Genome Sciences, the Richard D. Berlin 
Center for Cell Analysis and Modeling, among others. It is also closely 
affiliated with The Jackson Laboratory, which is located on the same campus. 
Candidates will also have access to 22 exceptional institutional core 
facilities, including the CCAM Microscopy, NMR Structural Biology and 
Biophysics, High-Performance Computing, EM, MS, FACS and Histology​

Successful applicants will have a PhD, MD or equivalent degree; a sustained 
record of exceptional scholarly success; and the promise of future innovative 
accomplishments via a proposed (early-career) or established (mid-career, 
including evidence of successful extramural grant writing) research programs 
that utilize state-of-the-art molecular, genetic, cellular, biochemical, 
biophysical and/or structural methodologies. Candidates with an existing 
funding record are preferred. Candidates are also expected to be highly 
collaborative and integrate with the growing number of researchers and 
physician scientists in the basic and clinical departments at UConn Health.

UConn Health offers exceptional health benefits, retirement benefits and also 
tuition waivers for dependents. UConn Health is situated in the beautiful and 
affordable Farmington River Valley near Hartford, CT, only 2 hours from Boston 
and New York City.

Interested individuals should submit:

  *   a curriculum vitae
  *   cover letter
  *   statements of research (up to 3 pages) and teaching (up to 1 page)

To apply, go to https://jobs.uchc.edu/​ and search for the position by 
selecting the "Molecular Biology and Biophysics" department. The search number 
is 2022-330.

Review of applications will begin November 15, 2021. For inquiries about the 
position, please contact Dr. Wolfgang Peti, Chair of the Search Committee 
(p...@uchc.edu).

For more information regarding the Department of Molecular Biology and 
Biophysics, please visit the department website at 
https://health.uconn.edu/molecular-biology-biophysics/.


Rebecca Page, PhD
Professor
UConn Health | Cell Biology (L5080)
263 Farmington Avenue | Farmington, CT 06030



Rebecca Page, PhD
Professor
UConn Health | Cell Biology (L5080)
263 Farmington Avenue | Farmington, CT 06030
rp...@uchc.edu

Page laboratory website



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Re: [ccp4bb] am I doing this right?

[Winter, Graeme (DLSLtd,RAL,LSCI)]

This rang a bell to me last night, and I think you can derive this from first 
principles

If you assume an observation of N counts, you can calculate the probability of 
such an observation for a given Poisson rate constant X. If you then integrate 
over all possible value of X to work out the central value of the rate constant 
which is most likely to result in an observation of N I think you get X = N+1

I think it is the kind of calculation you can perform on a napkin, if memory 
serves

All the best Graeme

On 13 Oct 2021, at 16:10, Andrew Leslie - MRC LMB 
mailto:and...@mrc-lmb.cam.ac.uk>> wrote:

Hi Ian, James,

  I have a strong feeling that I have seen this result 
before, and it was due to Andy Hammersley at ESRF. I’ve done a literature 
search and there is a paper relating to errors in analysis of counting 
statistics (se below), but I had a quick look at this and could not find the 
(N+1) correction, so it must have been somewhere else. I Have cc’d Andy on this 
Email (hoping that this Email address from 2016 still works) and maybe he can 
throw more light on this. What I remember at the time I saw this was the 
simplicity of the correction.

Cheers,

Andrew

Reducing bias in the analysis of counting statistics data
Hammersley, AP 
(Hammersley, AP) Antoniadis, 
A (Antoniadis, A)
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS 
SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
Volume
394
Issue
1-2
Page
219-224
DOI
10.1016/S0168-9002(97)00668-2
Published
JUL 11 1997

On 12 Oct 2021, at 18:55, Ian Tickle 
mailto:ianj...@gmail.com>> wrote:


Hi James

What the Poisson distribution tells you is that if the true count is N then the 
expectation and variance are also N.  That's not the same thing as saying that 
for an observed count N the expectation and variance are N.  Consider all those 
cases where the observed count is exactly zero.  That can arise from any number 
of true counts, though as you noted larger values become increasingly unlikely. 
 However those true counts are all >= 0 which means that the mean and variance 
of those true counts must be positive and non-zero.  From your results they are 
both 1 though I haven't been through the algebra to prove it.

So what you are saying seems correct: for N observed counts we should be taking 
the best estimate of the true value and variance as N+1.  For reasonably large 
N the difference is small but if you are concerned with weak images it might 
start to become significant.

Cheers

-- Ian


On Tue, 12 Oct 2021 at 17:56, James Holton 
mailto:jmhol...@lbl.gov>> wrote:
All my life I have believed that if you're counting photons then the
error of observing N counts is sqrt(N).  However, a calculation I just
performed suggests its actually sqrt(N+1).

My purpose here is to understand the weak-image limit of data
processing. Question is: for a given pixel, if one photon is all you
got, what do you "know"?

I simulated millions of 1-second experiments. For each I used a "true"
beam intensity (Itrue) between 0.001 and 20 photons/s. That is, for
Itrue= 0.001 the average over a very long exposure would be 1 photon
every 1000 seconds or so. For a 1-second exposure the observed count (N)
is almost always zero. About 1 in 1000 of them will see one photon, and
roughly 1 in a million will get N=2. I do 10,000 such experiments and
put the results into a pile.  I then repeat with Itrue=0.002,
Itrue=0.003, etc. All the way up to Itrue = 20. At Itrue > 20 I never
see N=1, not even in 1e7 experiments. With Itrue=0, I also see no N=1
events.
Now I go through my pile of results and extract those with N=1, and
count up the number of times a given Itrue produced such an event. The
histogram of Itrue values in this subset is itself Poisson, but with
mean = 2 ! If I similarly count up events where 2 and only 2 photons
were seen, the mean Itrue is 3. And if I look at only zero-count events
the mean and standard deviation is unity.

Does that mean the error of observing N counts is really sqrt(N+1) ?

I admit that this little exercise assumes that the distribution of Itrue
is uniform between 0.001 and 20, but given that one photon has been
observed Itrue values outside this range are highly unlikely. The
Itrue=0.001 and N=1 events are only a tiny fraction of the whole.  So, I
wold say that even if the prior distribution is not uniform, it is
certainly bracketed. Now, Itrue=0 is possible if the shutter didn't
open, but if the rest of the detector pixels have N=~1, doesn't this
affect the prior distribution of Itrue on our pixel of interest?

Of course, two or more photons are better than one, but these days with
small crystals and big detectors N=1 is no longer a trivial situation.
I look forward to hearing your take on this.  And no, this is not a trick.

-James Holton
MAD Scientist

Re: [ccp4bb] am I doing this right?

[Andrew Leslie - MRC LMB]

Hi Ian, James,

  I have a strong feeling that I have seen this result 
before, and it was due to Andy Hammersley at ESRF. I’ve done a literature 
search and there is a paper relating to errors in analysis of counting 
statistics (se below), but I had a quick look at this and could not find the 
(N+1) correction, so it must have been somewhere else. I Have cc’d Andy on this 
Email (hoping that this Email address from 2016 still works) and maybe he can 
throw more light on this. What I remember at the time I saw this was the 
simplicity of the correction.

Cheers,

Andrew

Reducing bias in the analysis of counting statistics data

Hammersley, AP  
(Hammersley, AP) Antoniadis, A 
 (Antoniadis, A)

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS 
SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT

Volume

394

Issue

1-2
Page

219-224
DOI

10.1016/S0168-9002(97)00668-2
Published

JUL 11 1997

> On 12 Oct 2021, at 18:55, Ian Tickle  wrote:
> 
> 
> Hi James
> 
> What the Poisson distribution tells you is that if the true count is N then 
> the expectation and variance are also N.  That's not the same thing as saying 
> that for an observed count N the expectation and variance are N.  Consider 
> all those cases where the observed count is exactly zero.  That can arise 
> from any number of true counts, though as you noted larger values become 
> increasingly unlikely.  However those true counts are all >= 0 which means 
> that the mean and variance of those true counts must be positive and 
> non-zero.  From your results they are both 1 though I haven't been through 
> the algebra to prove it.
> 
> So what you are saying seems correct: for N observed counts we should be 
> taking the best estimate of the true value and variance as N+1.  For 
> reasonably large N the difference is small but if you are concerned with weak 
> images it might start to become significant.
> 
> Cheers
> 
> -- Ian
> 
> 
> On Tue, 12 Oct 2021 at 17:56, James Holton  > wrote:
> All my life I have believed that if you're counting photons then the 
> error of observing N counts is sqrt(N).  However, a calculation I just 
> performed suggests its actually sqrt(N+1).
> 
> My purpose here is to understand the weak-image limit of data 
> processing. Question is: for a given pixel, if one photon is all you 
> got, what do you "know"?
> 
> I simulated millions of 1-second experiments. For each I used a "true" 
> beam intensity (Itrue) between 0.001 and 20 photons/s. That is, for 
> Itrue= 0.001 the average over a very long exposure would be 1 photon 
> every 1000 seconds or so. For a 1-second exposure the observed count (N) 
> is almost always zero. About 1 in 1000 of them will see one photon, and 
> roughly 1 in a million will get N=2. I do 10,000 such experiments and 
> put the results into a pile.  I then repeat with Itrue=0.002, 
> Itrue=0.003, etc. All the way up to Itrue = 20. At Itrue > 20 I never 
> see N=1, not even in 1e7 experiments. With Itrue=0, I also see no N=1 
> events.
> Now I go through my pile of results and extract those with N=1, and 
> count up the number of times a given Itrue produced such an event. The 
> histogram of Itrue values in this subset is itself Poisson, but with 
> mean = 2 ! If I similarly count up events where 2 and only 2 photons 
> were seen, the mean Itrue is 3. And if I look at only zero-count events 
> the mean and standard deviation is unity.
> 
> Does that mean the error of observing N counts is really sqrt(N+1) ?
> 
> I admit that this little exercise assumes that the distribution of Itrue 
> is uniform between 0.001 and 20, but given that one photon has been 
> observed Itrue values outside this range are highly unlikely. The 
> Itrue=0.001 and N=1 events are only a tiny fraction of the whole.  So, I 
> wold say that even if the prior distribution is not uniform, it is 
> certainly bracketed. Now, Itrue=0 is possible if the shutter didn't 
> open, but if the rest of the detector pixels have N=~1, doesn't this 
> affect the prior distribution of Itrue on our pixel of interest?
> 
> Of course, two or more photons are better than one, but these days with 
> small crystals and big detectors N=1 is no longer a trivial situation.  
> I look forward to hearing your take on this.  And no, this is not a trick.
> 
> -James Holton
> MAD Scientist
> 
> 
> 
> To unsubscribe from the CCP4BB list, click the following link:
> https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB=1 
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[ccp4bb] PhD position at MRC-LMB, Cambridge UK

[Zhang, Suyang]

Dear all,

My lab at the MRC-Laboratory of Molecular Biology in Cambridge (UK) is 
recruiting PhD students to start in October 2022.

The Zhang lab works on the mechanism of transcription coupled alternative 
splicing using a multidisciplinary approach including cryo-EM, X-ray 
crystallography, biochemistry and cellular assays. A detailed description of 
the PhD project can be found here: 
https://www2.mrc-lmb.cam.ac.uk/students/international-phd-programme/projects/suyang-zhang/

Applications should be made through the University of Cambridge: 
https://www2.mrc-lmb.cam.ac.uk/students/international-phd-programme/how-to-apply/
The application deadline is 2nd December 2021.

For those who would like to find out more, I am happy to informally discuss the 
project by email 
(suyang.zh...@mpibpc.mpg.de).

Looking forward to your applications and enquires.

Best regards,
Suyang Zhang



Structural Studies Division
MRC Laboratory of Molecular Biology
Francis Crick Avenue
Cambridge CB2 0QH, UK



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[ccp4bb] Hiring! Exciting Biochemist RS opportunity at Sosei Heptares

[Darren Beckinsale]

Biochemist - Research Scientist


Company Description
Sosei Heptares (the corporate brand of Sosei Group Corporation) is a 
research-focused biotechnology group, publicly listed in Japan (TSE: 4565) and 
with sites in Tokyo, London and Cambridge (UK).

Our mission is to make a significant contribution to improving the quality of 
life and health of people around the world. To achieve this, we will discover, 
design and develop the most innovative and effective medicines for patients 
worldwide, through our world-leading patent-protected technology and platform.

Our primary focus is the discovery and early development of new medicines 
originating from its proprietary G Protein-Coupled Receptor (GPCR)-targeted 
StaR® technology and Structure-Based Drug Design (SBDD) platform capabilities. 
The company is advancing a broad and deep pipeline of novel medicines across 
multiple therapeutic areas, including CNS, immuno-oncology, gastroenterology, 
inflammation and other rare/specialty indications.

We have established partnerships with some of the world’s leading 
pharmaceutical companies, including Allergan, AstraZeneca, Daiichi-Sankyo, 
Genentech (Roche), Novartis, Pfizer and Takeda and additionally with multiple 
emerging technology companies

Sosei Heptares has approximately 161 employees based at its new R research 
facility at Granta Park. The size of the company ensures a transparent linkage 
between all activities from early discovery through to clinical development.


Position
We are now seeking a membrane protein biochemist to join the Biochemistry group 
at our research facility on Granta Park, Cambridge, UK, working on membrane 
protein biochemical characterisation to enable drug discovery efforts. 

At Sosei Heptares we are equipped with state-of-the-art protein expression, 
purification, SPR, mass-spectrometry, crystallisation and cryoEM facilities. 
Applicants should have experience in the expression and purification of 
challenging membrane proteins destined for biochemical, biophysical and 
structural studies. Applicants with a proven track record of publications 
related to the expression, purification, and biochemical characterisation of 
difficult membrane protein targets in peer-reviewed journals would be 
advantageous.

This is an exceptional opportunity to participate in pioneering science with an 
industry-leading drug discovery company.

The successful candidate is expected to:

•   Express and purify membrane proteins
•   Perform appropriate quality control analyses and biochemically and 
biophysically characterise purified membrane proteins
•   Keep up to date, propose, develop, implement and optimise new 
techniques in the fields of membrane protein expression, purification and 
biochemical characterisation
•   Work together with scientists responsible for successfully progressing 
membrane protein targets through Sosei Heptares’ structure-based drug design 
pipeline
•   Collaborate with other Sosei Heptares scientists on research projects, 
working closely with molecular biologists, biophysicists, structural 
biologists, molecular pharmacologists, computational and medicinal chemists
•   Prepare and effectively present results (written and oral) to project 
teams
•   Have strong interpersonal skills and interact effectively with 
collaborators across a wide range of disciplines


Requirements:
The preferred candidate will have the following:

•   A PhD in biochemistry, biophysics or a related discipline
•   Experience in membrane protein expression, purification and 
characterisation in either an academic or industry setting
•   Good molecular biology skills
•   Knowledge in prokaryotic and eukaryotic expression vectors
•   Knowledge in expression construct design and engineering to 
successfully express membrane proteins destined for biochemical, biophysical 
and structural studies
•   Expertise in the expression of membrane proteins from mammalian and 
insect cells
•   Expertise in the purification and extensive biochemical 
characterisation of membrane proteins
•   Experience in the reconstitution of purified membrane proteins in 
membrane-mimetic environments
•   Familiarity with biochemical and biophysical techniques used to 
characterise membrane proteins such as luminescence, fluorescence and 
FRET-based techniques
•   Understanding of the requirements of purified membrane proteins 
destined for structural (X-ray and/or CryoEM) and biophysical studies (SPR, 
mass spectrometry)
•   Excellent organisational skills


Other information
The successful candidate will be employed by Heptares Therapeutics Ltd, a UK 
wholly owned subsidiary of Sosei Group Corporation.  

We offer a competitive salary, commensurate with qualifications and experience, 
and benefits package including pension and healthcare schemes.

Applications should include your curriculum vitae and covering letter, 
providing a short 

[ccp4bb] Research Technician position at EMBL Grenoble

[Jose A. Marquez]

Dear All,

A Research Technician position is available in the High Throughput 
Crystallization Laboratory at EMBL Grenoble.


See the job description at: https://www.embl.org/jobs/position/GR00172

Applications should be submitted through the
www.embl.de/jobs/ portal.

Deadline for applications is November 7th.

For questions concerning this position please contact Jose  A.
Marquez (marq...@embl.fr)

Best regards

_

Jose A. Marquez Ph.D.
Team Leader, Head of the Crystallization Facility
EMBL Grenoble Outstation
Postal address: European Molecular Biology Laboratory
71, Avenue des Martyrs
CS 90181 38042 Grenoble Cedex 9, France
Delivery address: European Molecular Biology Laboratory
71, Avenue des Martyrs
38000 Grenoble, France
Phone +33 (0)476 20 74 25
Fax. +33 (0)476 20 71 99

https://www.embl.org/groups/marquez/
https://www.embl.org/services-facilities/grenoble/high-throughput-crystallisation/
https://htxlab.embl.fr/
_



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