Q:

python - use previous row's value to update the new rows values

This is the current dataframe:
> ID        Date    current
> 2001980   10/30/2017  1
> 2001980   10/29/2017  0
> 2001980   10/28/2017  0
> 2001980   10/27/2017  40
> 2001980   10/26/2017  39
> 2001980   10/25/2017  0
> 2001980   10/24/2017  0
> 2001980   10/23/2017  60
> 2001980   10/22/2017  0
> 2001980   10/21/2017  0
> 2002222   10/21/2017  0
> 2002222   10/20/2017  0
> 2002222   10/19/2017  16
> 2002222   10/18/2017  0
> 2002222   10/17/2017  0
> 2002222   10/16/2017  20
> 2002222   10/15/2017  19
> 2002222   10/14/2017  18

Below is the final data frame. Column expected is what I am trying to get.

One ID might have multiple date/record/rows. (ID+Date) is unique.
this row's expected value = last row's expected - 1
the minimum value is 0.
Based on the formula in 2, if this row's expected value < this row's current value, then use this row's current value. for example, for ID 2001980 on 10/23/2017. Based on rule 2, the value should be 36, but based on rule 4, 36<60, so we use 60.

thank you so much.
> ID        Date    current expected
> 2001980   10/30/2017  1   1
> 2001980   10/29/2017  0   0
> 2001980   10/28/2017  0   0
> 2001980   10/27/2017  40  40
> 2001980   10/26/2017  39  39
> 2001980   10/25/2017  0   38
> 2001980   10/24/2017  0   37
> 2001980   10/23/2017  60  60
> 2001980   10/22/2017  0   59
> 2001980   10/21/2017  0   58
> 2002222   10/21/2017  0   0
> 2002222   10/20/2017  0   0
> 2002222   10/19/2017  16  16
> 2002222   10/18/2017  0   15
> 2002222   10/17/2017  0   14
> 2002222   10/16/2017  20  20
> 2002222   10/15/2017  19  19
> 2002222   10/14/2017  18  18

I am using Excel with the formula below:
=   if(this row's ID = last row's ID,
    max(last row's expected value - 1, this row's current value),
    this row's current value)

A:

Revised simpler:
df['expected'] = df.groupby(['ID',df.current.ne(0).cumsum()])['current']\
  .transform(lambda x: x.eq(0).cumsum().mul(-1).add(x.iloc[0])).clip(0,np.inf)

Let's have a little fun:
df['expected'] = (df.groupby('ID')['current'].transform(lambda x: x.where(x.ne(0)).ffill()) +
df.groupby(['ID',df.current.ne(0).cumsum()])['current'].transform(lambda x: x.eq(0).cumsum()).mul(-1))\
.clip(0,np.inf).fillna(0).astype(int)
print(df)

Output:
         ID        Date  current  expected
0   2001980  10/30/2017        1         1
1   2001980  10/29/2017        0         0
2   2001980  10/28/2017        0         0
3   2001980  10/27/2017       40        40
4   2001980  10/26/2017       39        39
5   2001980  10/25/2017        0        38
6   2001980  10/24/2017        0        37
7   2001980  10/23/2017       60        60
8   2001980  10/22/2017        0        59
9   2001980  10/21/2017        0        58
10  2002222  10/21/2017        0         0
11  2002222  10/20/2017        0         0
12  2002222  10/19/2017       16        16
13  2002222  10/18/2017        0        15
14  2002222  10/17/2017        0        14
15  2002222  10/16/2017       20        20
16  2002222  10/15/2017       19        19
17  2002222  10/14/2017       18        18

Details
Basically, creating series, s1 and subtracting series s2 then clipping negative values and filling nan's with zero.
#Let's calculate two series first a series to fill the zeros in an 'ID' with the previous non-zero value
s1 = df.groupby('ID')['current'].transform(lambda x: x.where(x.ne(0)).ffill())
s1

Output:
0      1.0
1      1.0
2      1.0
3     40.0
4     39.0
5     39.0
6     39.0
7     60.0
8     60.0
9     60.0
10     NaN
11     NaN
12    16.0
13    16.0
14    16.0
15    20.0
16    19.0
17    18.0
Name: current, dtype: float64

#Second series is a cumulative count of zeroes in a group by 'ID'
s2 = df.groupby(['ID',df.current.ne(0).cumsum()])['current'].transform(lambda x: x.eq(0).cumsum()).mul(-1)
s2

Output:
0     0
1    -1
2    -2
3     0
4     0
5    -1
6    -2
7     0
8    -1
9    -2
10   -1
11   -2
12    0
13   -1
14   -2
15    0
16    0
17    0
Name: current, dtype: int32

Add series together clip and fillna.
(s1 + s2).clip(0, np.inf).fillna(0)

Output:
0      1.0
1      0.0
2      0.0
3     40.0
4     39.0
5     38.0
6     37.0
7     60.0
8     59.0
9     58.0
10     0.0
11     0.0
12    16.0
13    15.0
14    14.0
15    20.0
16    19.0
17    18.0
Name: current, dtype: float64

A:

So you can do this used apply and nested functions
import pandas as pd
ID = [2001980,2001980,2001980,2001980,2001980,2001980,2001980,2001980,2001980,2001980,2002222,2002222,2002222,2002222,2002222,2002222,2002222,2002222,]
Date = ["10/30/2017","10/29/2017","10/28/2017","10/27/2017","10/26/2017","10/25/2017","10/24/2017","10/23/2017","10/22/2017","10/21/2017","10/21/2017","10/20/2017","10/19/2017","10/18/2017","10/17/2017","10/16/2017","10/15/2017","10/14/2017",]
current = [1 ,0 ,0 ,40,39,0 ,0 ,60,0 ,0 ,0 ,0 ,16,0 ,0 ,20,19,18,]

df = pd.DataFrame({"ID": ID, "Date": Date, "current": current})

Then create the function to update the frame
Python 3.X
def update_frame(df):
    last_expected = None
    def apply_logic(row):
        nonlocal last_expected
        last_row_id = row.name - 1
        if row.name == 0:
            last_expected = row["current"]
            return last_expected
        last_row = df.iloc[[last_row_id]].iloc[0].to_dict()
        last_expected = max(last_expected-1,row['current']) if last_row['ID'] == row['ID'] else row['current']
        return last_expected
    return apply_logic

Python 2.X
def update_frame(df):
    sd = {"last_expected": None}
    def apply_logic(row):
        last_row_id = row.name - 1
        if row.name == 0:
            sd['last_expected'] = row["current"]
            return sd['last_expected']
        last_row = df.iloc[[last_row_id]].iloc[0].to_dict()
        sd['last_expected'] = max(sd['last_expected'] - 1,row['current']) if last_row['ID'] == row['ID'] else row['current']
        return sd['last_expected']
    return apply_logic

And run the function like below
df['expected'] = df.apply(update_frame(df), axis=1)

The output is as expected


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As we watch the devastation of the opioid crisis escalate in a rising tide of deaths, a lesser known substance is frequently mentioned: fentanyl. Fentanyl’s relative obscurity was shattered with the well-publicized overdose death of pop star Prince. Previously used only as a pharmaceutical painkiller for crippling pain at the end of life or for surgical procedures, fentanyl is now making headlines as the drug responsible for a growing proportion of overdose deaths.

So what is fentanyl and why is it so dangerous?

Fentanyl is a synthetic opioid, meaning it is made in a laboratory but acts on the same receptors in the brain that painkillers, like oxycodone or morphine, and heroin, do. Fentanyl, however, is far more powerful. It’s 50-100 times stronger than heroin or morphine, meaning even a small dosage can be deadly.

Its potency also means that it is profitable for dealers as well as dangerous for those who use it, intentionally or unintentionally. Increasingly heroin is being mixed with fentanyl so someone who uses what they think is heroin may in fact be getting a mixture with — or even pure — fentanyl. More recently, pills made to look like the painkiller oxycodone or the anxiety medication Xanax are actually fentanyl. This deception is proving fatal. It would be like ordering a glass of wine and instead getting a lethal dose of pure ethanol. While many people don’t know they are getting fentanyl, others might unfortunately seek it out as part of the way the brain disease of addiction manifests itself into compulsively seeking the next powerful high.

Helping people who use fentanyl

The way to help patients who are using fentanyl is the same as for other forms of opioid use disorder: to provide effective addiction treatment. However, the first and most important step is helping patients stay safe and stay alive until we can get them that treatment.

It’s worth remembering that dead people don’t recover.

To stop the deaths, we must provide immediate access to lifesaving treatment on demand. While any opioid use is risky, fentanyl has raised the stakes. Every single episode of fentanyl use carries the risk of immediate death. This highlights the need to change how we think about treatment. Many of the traditional models of addiction treatment were designed for alcohol use disorder. Misuse of alcohol can be fatal, but it usually takes many years or even decades to kill someone. In contrast, opioid addiction is imminently fatal, so waiting for treatment is and should be considered unacceptable. We must try to initiate treatment at every opportunity — in the emergency department, at the hospital bed, or even on the street. The best evidence we have shows that a combination of medication and psychosocial treatments is most effective for opioid use disorder. A study of MassHealth patients found that patients on medication treatments like methadone or buprenorphine are 50% less likely to relapse. Other studies have shown that patients treated with these medications are 50% (or more) less likely to die. And yet significant stigma and misunderstanding still exists around these medications. We have treatment programs (and doctors) that don’t offer these medications and patients who are doing wonderfully in recovery thanks to them, but who are also scared to speak out and say they are on medication because the stigma is so pervasive.

Even with our best efforts, it can take time for some people to be open to treatment. In those cases, our priority is to keep them alive and to keep working with them on their readiness to consider treatment. This requires access to naloxone, the antidote to overdoses. But it also includes other education and harm reduction services. People who have loved ones who are actively using and those who are using themselves need to know how to stay safe. There is very concrete education that can reduce the risk of overdose and we need to ensure it is getting to those at the greatest risk.

Moving forward

In Massachusetts alone, deaths due to fentanyl overdose have risen to 57% between 2015 and the first half of 2016. These deaths are yet another symptom of the broader epidemic of opioid addiction. Just as deaths from AIDS are due to untreated HIV, deaths from overdose are frequently due to untreated addiction. Prince’s death is a reminder that opioid addiction is a disease that can and does affect people from all economic classes and all walks of life.


Q:

Laravel : How to use eloquent ORM to find information of NOT authenticated user?

So I have this function to get the info of a user who is currently logged in.
$users = User::get();
$loginuser = $users->find(Auth::user()->id);

How do I use Laravel's eloquent ORM to get other users and put it in an array variable? Maybe something like
$otherusers = $users->find(not(Auth::user()->id));

Of course, it will not work but you get what I mean. Bonus points for improvement of the first 2 lines above~! Thank you!

A:

You may try this to get all the users other than the logged in user:
$otherusers = User::where('id', '!=', Auth::user()->id)->get();

If you use Auth::user() then you'll get only the currently logged in user. So for example:
// Logged In user, You can directly use
// Auth::user()->id or any property, no
// need keep it in a variable for that
$loggedInUser = Auth::user(); // Returns a single Model

// Other users (Returns a collection of user models)
// Check if a user is logged in before you use Auth::user()->id
$otherusers = User::where('id', '!=', Auth::user()->id)->get();


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There's something very strange going on. It's as if you are living through your worst nightmare and unable to wake up. Won't anyone let you in on the terrible secret that haunts your walking moments. Why has Jimmy Blandford taken to drinking - has he experienced the powerful forces of darkness? Why has your father, the dutiful local Vicar, neglected his parish without any apparent reason? Why did your mother invite you to stay for the weekend, then disappear without a word? What unspeakable horr…


Cataract surgery and intraocular lens implantation in children with juvenile rheumatoid arthritis associated uveitis.
Clinical records of 6 children (7 eyes) with juvenile rheumatoid arthritis (JRA) who underwent cataract surgery with IOL implantation between January 1998 and December 2002 were reviewed. The median age at the time of cataract surgery was 8 years (range 5-14 years). The median follow up was 48 months (range 26 to 60 months). Five of six children (6 eyes) were on systemic immunosuppressive or anti-inflammatory therapy. Glaucoma was present in three eyes before surgery, and all three eyes underwent combined cataract surgery and trabeculectomy with mitomycin C. A final best corrected visual acuity of 0.5 or better was achieved in all eyes Postoperative complications included posterior capsule opacification (n = 5), glaucoma (n = 1), and cystoid macular edema (n = 1). Intraocular lens implantation in children with control of preoperative and postoperative ocular inflammation could lead to favorable visual results.


Introduction
============

Approximately 70% of the genetic risk associated with breast cancer is still unaccounted for and it is predicted that the remainder of susceptibility loci will include common, low-effect variants that most likely have regulatory effects. Recent genome-wide association studies (GWAS) have identified variants that account for an additional 5.9% of the genetic risk \[[@B1]-[@B5]\]. These variants are mostly associated with intronic and intergenic regions, with the most significant variant regulating the level of gene expression of *FGFR2*\[[@B6]\]. However, as most of the identified risk loci have small effects, very large numbers of patients will have to be examined to identify further risk variants. An alternative approach for the identification of regulatory risk variants could be to use differences in allelic gene expression in heterozygotes as a quantitative phenotype \[[@B7]-[@B9]\].

Preferential expression from one allele is a common feature of the human genome (up to 60% of genes) and has a genetic basis \[[@B6],[@B10]-[@B24]\]. Polymorphic variants at regulatory elements can cause differential allelic expression (DAE), thus using DAE as a quantitative trait could help identify such variation. The samples of choice for association studies are usually blood and saliva, however, relatively little is known about how DAE compares in multiple human tissues and it is questionable whether studying DAE in blood would be a proper surrogate for what happens in the disease target tissue. To date most DAE studies have been performed on EBV transformed lymphoblastoid cell lines (LCLs). Studies in fresh blood, liver and kidney have been reported in a small set of individuals \[[@B14],[@B16]\], and one recent study looking at the expression of one gene reported that there were large tissue differences in allelic expression ratios within the same individual \[[@B25]\]. An analogous study has been reported in mice \[[@B26]\].

We aimed to perform a more extensive evaluation of differential allelic expression between blood and breast in order to assess the potential usefulness of LCL and fresh blood in association studies, to identify regulatory polymorphisms related to susceptibility to breast cancer. Here we present an analysis of DAE in 12 candidate genes (*BRCA1*, *BRCA2*, *C1qA*, *CCND3*, *EMSY*, *GPX1*, *GPX4*, *MLH3*, *MTHFR*, *NBS1*, *TP53*and *TRXR2*) likely to be involved in breast cancer, in a large set of individuals. We compared the distribution of allelic ratios of gene expression in fresh blood (B cells and total mononuclear cells), transformed lymphoblasts, and breast tissue from unmatched healthy individuals.

Materials and methods
=====================

Samples
-------

A total of 170 white cell-reduction filters from anonymous blood donors were collected from the Blood Centre at Addenbrooke\'s Hospital. Mononuclear cells were separated by density gradient centrifugation using Lymphoprep (Sigma, St. Louis, MO, USA), according to the manufacturer\'s instructions. B cells were further isolated from these samples by magnetic sorting using CD19 labelled magnetic check beads (Milteny Biotech, Bergisch Gladbach, Germany).

Normal breast tissue was collected at Addenbroke\'s Hospital, from 40 women undergoing aesthetic surgery, for reasons not related to cancer. All samples were analysed by a histopathologist to ensure that they were free of dysplasia. Ethical approval was obtained for the collection and research use of all blood and breast samples used in this study.

Nineteen lymphoblastoid cell lines derived from unrelated CEPH individuals were obtained from the Coriell Cell Repository. Cell lines were grown in RPMI 1640 with 10% FCS, supplemented with penicillin, streptomycin and L-glutamine, at 37°C and 5% CO~2~(Invitrogen, Carlsbad, CA, USA).

All research was carried out in compliance with ethics guidelines and regulations. Human B cells (purified from waste products of blood donations) and normal breast samples were collected with approval from the Addenbrooke\'s Hospital Local Research Ethics Committee (REC reference 04/Q0108/21 and 06/Q0108/221, respectively).

RNA, DNA and cDNA preparation
-----------------------------

DNA was extracted from total mononuclear cells, B-lymphocytes, normal breast and lymphoblastoid cell lines by a conventional SDS/proteinase K/phenol method. Total RNA was extracted from all samples using Qiazol (Invitrogen, Carlsbad, CA, USA) following manufacturer\'s instructions. The RNA was subsequently treated with DNaseI and repurified using acidic phenol-chlorophorm, and ethanol precipitation.

For normal breast tissue RNA extraction, samples were soaked overnight in RNA*later*-Ice^®^(Ambion, Austin, TX, USA), homogenised in Qiazol using the Precellys^®^24 bead mechanism (Bertin Technologies, Montigny-le-Bretonneux, France), followed by an additional centrifugation step prior to addition of chlorophorm to the lysate, to eliminate excessive fat.

cDNA was prepared from 1 μg of total RNA per 20 μl reaction using random hexamers and the Reverse Transcription kit (Applied Biosystems, Foster City, CA, USA), according to the manufacturer\'s instructions, and was diluted in a final volume of 100 μl.

Genotyping
----------

All samples were genotyped using 5\' exonuclease Taqman^®^technology (Applied Biosystems, Foster City, CA, USA). Approximately 20 ng of genomic DNA was used in a 5 μl PCR reaction constituted by Taqman^®^master mix (Applied Biosystems, Foster City, CA, USA), the two primers, and FAM- and VIC- labelled probes, each designed to anneal specifically to either of the alleles of each single nucleotide polymorphism (SNP). After completion of the PCR, plates were analysed using the Allelic Discrimination analysis method in an ABI PRISM 7900 Sequence Detector (Applied Biosystems, Foster City, CA, USA). Genotyping was carried out in 384-well plates, with random replicates included, as well as no template controls (NTC), to ensure good quality of genotyping.

Quantification of differential allelic gene expression
------------------------------------------------------

Allele specific levels of gene expression were determined in heterozygous samples using Taqman^®^technology (Applied Biosystems, Foster City, CA, USA). Each PCR reaction contains a primer pair targeting the region surrounding the transcribed SNP (tSNP), and two probes that differ by a single nucleotide and are complementary to each of the SNP alleles. The probes are labelled with different fluorochromes (VIC and FAM), generating two signals for each sample during the real-time PCR. A standard curve was generated using a dilution series of heterozygote blood DNA, serving as a reference for the 50:50 allelic ratio. In this way, once the quantity of each allele in the different samples is extrapolated from the linear regression equation, a correction for the different background fluorescence and annealing characteristics of each probe is made automatically. We avoided using cDNA as control as we would be biasing our results towards the DAE ratio of the reference sample. In contrast, there is a perfect 50:50 ratio of the two alleles in a reference DNA sample from a heterozygote with normal chromosomal copy number.

All experiments contained replicates for each sample, and were repeated at least twice. Reactions were prepared as described for genotyping and run on an ABI PRISM 7900 Sequence Detector using the Absolute Quantification method. Ct values were obtained from ABI SDS 2.3 software (Applied Biosystems, Foster City, CA, USA) and quantities of allelic expression were extrapolated from the appropriate linear regression. We defined differential allelic expression as the log~2~of the allelic-expression ratio calculated as log~2~\[(VIC- allele)/(FAM - allele)\]. A gene was considered expressed if the PCR yielded Ct values lower than 35 cycles.

Quantification of total gene expression
---------------------------------------

Total gene expression levels were determined in B cell cDNA samples using Taqman^®^Gene Expression Assay pre-designed by Applied Biosystems. Results were normalized with the total levels of expression of *Actin-β*, *GAPDH*, *18S*and *β2M*.

Statistical Analysis
--------------------

Real-time PCR quantification statistics were carried out on Microsoft^®^Excel^®^2004 software. Percentage of variation between replicates was calculated as % var = (SD/Mean). Linear regression for Taqman^®^standard curves was performed using the function *linest*.

All other statistical analysis was performed using the R statistical programming language \[[@B27]\]. For analysis of DAE in B cells a One Sample t-Test was performed to test for deviations from null hypothesis that the mean is smaller than log~2~(1.20). However, genes that presented trans-effects were analysed in absolute values with the highest expressing allele divided by the lowest one. For these genes the mean could not be used as the two sides of the distribution would cancel each other out, as explained in Results and Discussion. Furthermore, we performed variance analysis for these genes using F tests for variance. Using *MLH3*as a reference gene with a distribution identical to DNA (no DAE), we compared all genes with trans-effect DAE.

To compare DAE across the three different tissue types, both two-sample t-test and Wilcoxon rank sum test with continuity correction were carried out, for comparing mean ratios and variances respectively.

Correlation analysis of the total level of expression vs genotype at the tSNP for these genes was performed using the Jonckheere-Terpstra test, a non-parametric test for trend among classes.

Results
=======

Analysis of differential allelic expression of candidate breast cancer genes in blood cells
-------------------------------------------------------------------------------------------

We studied 12 candidate genes that are implicated in the aetiology of breast cancer (Table [1](#T1){ref-type="table"}): *BRCA1*, *BRCA2*, *C1qA*, *CCND3*, *EMSY*, *GPX1*, *GPX4*, *MLH3*, *MTHFR*, *NBS1*, *TP53*and *TRXR2*\[[@B28]-[@B31]\]. Functionally, these genes are in different pathways including: DNA-damage repair, complement and coagulation cascades, cell cycle and apoptosis. For each gene, we selected single nucleotide polymorphisms (SNPs) markers in both the coding and untranslated regions (transcribed SNP or tSNP), with high heterozygosity frequency. This increased the number of informative individuals in our sample sets. To ascertain differential allelic expression we measured allele-specific transcript levels using real-time PCR Taqman^®^technology in heterozygote samples for the selected tSNPs, and calculated the ratio of one allele versus the other (plotted as Log~2~ratios in Figure [1a](#F1){ref-type="fig"}).

![Comparison of allelic expression in blood *vs*LCL *vs*breast tissue. **(a)** Heterozygote individuals are represented as dots and are coloured blue for blood, black for LCL and green for breast tissue. The mean value for each distribution is shown as a red dot, and whiskers represent the 95% confidence interval of the mean. Dotted lines delimit the cut-off of 1.2 preferential allelic expression ratio \[log~2~(1.2) = 0.263\]. **(b)** Pairwise correlation analysis of the mean log~2~allelic expression ratios for the three sample sets. Genes are identically colour coded in all three graphs. Dotted lines represent the linear regression applied to each tissue pair, and the respective equations and R^2^values are indicated on each graph.](bcr2458-1){#F1}

######

List of coding polymorphisms investigated for differential allelic expression, with the respective possible functional effects

  Gene        SNP         Aminoacid/Position   Alleles   Pathway                    Functional Effect
  ----------- ----------- -------------------- --------- -------------------------- ----------------------------------
  **BRCA1**   rs799917    P871L                C/T       DSB repair                 Unknown
  **BRCA2**   rs144848    N372H                A/C       DSB repair                 Unknown
  **C1qA**    rs172378    G92G                 A/G       Complement Cascade         Reduced Activity
  **CCND3**   rs1051130   A259S                G/T       Cell Cycle                 Unknown
  **EMSY**    rs2282611   5\'UTR               A/C       DNA repair                 Unknown
  **GPX1**    rs1050450   L200P                T/C       Antioxidant Defense        Unknown
  **GPX4**    rs713041    3\'UTR               C/T       Antioxidant Defense        Reduced Activity
  **MLH3**    rs175080    P844L                C/T       Mismatch Repair            Unknown
  **MTHFR**   rs1801133   A222V                C/T       Folate metabolism          Reduced Activity
  **NBS1**    rs709816    D399D                C/T       Homologous Recombination   None
  **TP53**    rs1042522   R72P                 G/C       Apoptosis                  Differential apoptotic potential
  **TRXR2**   rs1139793   T370I                C/T       Antioxidant Defense        Unknown

DSB = double strand break.

Initial experiments on technical and biological replicates (different cDNA preparations) revealed a very good correlation between replicates and low noise/variation intrinsic to the technique (\[mean allelic ratio: standard deviation\] \<20% and 5% for biological and technical replicates, respectively). Based on this we defined the cut-off allelic expression ratio of 1.2 for DAE presence in a heterozygote sample (indicated on Figure [1a](#F1){ref-type="fig"} by the dotted lines).

We started by analysing allelic expression in primary B-lymphocytes (magnetically sorted CD19^+^cells) from 170 unrelated healthy individuals. The aim was to first identify the genes that displayed preferential allelic expression in a homogeneous population of cells, without the possible interference of multiple cell types. We found that heterozygotes in 11 out of 12 genes (92%) showed allelic imbalances in gene expression (Table [2](#T2){ref-type="table"} and blue data points in Figure [1a](#F1){ref-type="fig"}). As in previous reports, we identified two patterns of differential expression. In *BRCA2, C1qA, GPX1, GPX4, MTHFR*and *TP53*, the same allele was consistently expressed at a higher level in all heterozygotes with allelic imbalance, indicating that for each of these genes the regulatory variant is in strong linkage disequilibrium (LD) with the assayed tSNP. On the other hand, in *BRCA1, EMSY, CCND3, NBS1*and *TRXR2*different heterozygotes preferentially expressed different alleles. In this case, expression is likely to be controlled by cis-acting elements that are not in strong linkage disequilibrium with the tSNP.

######

Differential allelic expression ratios in fresh B cells

  Gene        SNP         Heterozygotes with DAE   Mean DAE       SD     Min-Max
  ----------- ----------- ------------------------ -------------- ------ --------- -----------
  **BRCA1**   rs799917    T\>C                     44% (12/27)    0.67   0.12      0.43-0.80
                          C\>T                     26% (7/27)     1.41   0.22      1.21-1.83

  **BRCA2**   rs144848    C\>A                     65% (24/37)    1.60   0.55      1.24-3.62

  **C1qA**    rs172378    A\>G                     69% (11/16)    1.86   0.77      1.26-3.37
                          G\>T                     29% (5/17)     0.72   0.06      0.67-0.83

  **CCND3**   rs1051130   T\>G                     24% (4/17)     1.36   0.11      1.25-1.51
                          C\>A                     5% (2/37)      0.79   0.01      0.78-0.80

  **EMSY**    rs228611    A\>C                     5% (2/37)      1.38   0.21      1.23-1.53

  **GPX1**    rs1050450   C\>T                     79% (11/14)    1.32   0.09      1.23-1.50

  **GPX4**    rs713041    C\>T                     100% (21/21)   6.46   2.97      2.5-11.31

  **MLH3**    rs175080    NA                       0% (0/35)      NA     NA        NA

  **MTHFR**   rs1801133   T\>C                     82% (27/33)    0.65   0.06      0.54-0.77

  **NBS1**    rs709816    C\>T                     14%(3/21)      0.82   0.01      0.81-0.83
                          T\>C                     33% (7/21)     1.37   0.19      1.21-1.65

  **TP53**    rs1042522   C\>G                     100% (16/16)   0.34   0.06      0.25-0.49
                          T\>C                     17% (5/29)     0.75   0.06      0.66-0.82

  **TRXR2**   rs1139793   C\>T                     31% (9/29)     1.38   0.13      1.21-1.61

DAE = differential allelic expression.

We found considerable variation in the magnitude of DAE across genes, with the largest seen in *GPX4*(approximately six-fold difference between the levels of expression of the two alleles). For the genes which show DAE in at least one heterozygote, the proportion of heterozygotes with unequal expression ranged from 10% to 100% (Table [2](#T2){ref-type="table"}). Genes with cis-acting elements in LD with the tSNP showed a direct correlation between mean allelic ratio and number of heterozygotes with variation (that is, the greater the mean allelic ratio, the higher the number of heterozygotes with DAE) \[see Additional file [1](#S1){ref-type="supplementary-material"}\]. Genes with cis- regulation not in LD with the tSNP had a distribution of ratios that was commonly centred on the 50:50 ratio (log~2~= 0 in Figure [1a](#F1){ref-type="fig"}). This reflects the fact that a proportion of the heterozygotes for the tSNP will be homozygote for either of the regulatory polymorphic alleles, consequently generating an equimolar transcription level of the tSNP alleles.

Since peripheral blood is a heterogeneous tissue, composed of mononuclear cells (including B lymphocytes), polymorphonuclear cells and red blood cells, we compared the allelic expression ratios in cDNA extracted from total mononuclear cells from 59 healthy unrelated donors, with those obtained for sorted B cells. We found no significant differences in terms of pattern (cis- regulation in LD with tSNP or not) or mean ratio of DAE (data not shown).

Concerns have also been raised about the effect that transformation of lymphoblasts by Epstein-Barr Virus (EBV) may have on their expression profile \[[@B32]-[@B34]\]. As most previous studies of differential allelic expression have been performed on lymphoblastoid cell lines (LCL) and future case-control studies using DAE could be performed on this type of sample, we next compared transformed and non-transformed lymphoblasts from 19 unrelated CEPH (Centre D\'Étude du Polymorphism Humaine) individuals, who were heterozygous for most of the genes included in this study (black data points in Figure [1a](#F1){ref-type="fig"}). Eight out of 12 genes showed DAE in both transformed and fresh lymphocytes (*BRCA2, CCND3*and *GPX1*did not show DAE in LCL samples, in contrast to that observed in untransformed blood, whilst *MLH3*showed the opposite). Of the eight genes which showed DAE, five presented mean ratios and patterns of allelic preferential expression that were comparable between the two samples sets (Figure [1a](#F1){ref-type="fig"} and Tables [3](#T3){ref-type="table"} and [4](#T4){ref-type="table"}). *BRCA1*, *EMSY*and *NBS1*showed significantly different results from those obtained for fresh blood, in terms of the mean fold difference between alleles and/or patterns of DAE (cis- regulation in tight LD or not).

######

Comparison of differential allelic expression between breast and blood (fresh and transformed)

  Gene        SNP         Informative Heterozygotes   Wilcoxon rank sum test p- value
  ----------- ----------- --------------------------- --------------------------------- ---- ---------- ---------- ----------
  **BRCA1**   rs799917    27                          13                                6    4.40E-06   0.011      0.016
  **BRCA2**   rs144848    37                          6                                 8    NA         0.744      NA
  **C1qA**    rs172378    16                          5                                 7    0.076      NA         NA
  **CCND3**   rs1051130   17                          12                                7    0.207      0.266      0.090
  **EMSY**    rs228611    37                          7                                 13   2.26E-05   0.029      0.047
  **GPX1**    rs1050450   14                          4                                 12   NA         NA         NA
  **GPX4**    rs713041    21                          8                                 5    0.366      0.057      0.091
  **MLH3**    rs175080    35                          12                                13   NA         NA         0.772
  **MTHFR**   rs1801133   33                          7                                 12   0.030      1.53E-04   0.703
  **NBS1**    rs709816    21                          9                                 13   0.003      0.001      0.947
  **TP53**    rs1042522   16                          8                                 16   0.192      1.34E-05   3.80E-04
  **TRXR2**   rs1139793   29                          9                                 14   0.069      0.826      0.007

Wilcox rank sum tests were performed to compare deviations of differential allelic expression variance (only genes showing DAE were introduced in the analysis). *Blood*corresponds to fresh B cell samples and *LCL*to EBV transformed lymphoblastoid cell lines. DAE = differential allelic expression; LCL, EBV transformed lymphoblastoid cell lines.

######

Differential allelic expression concordance between breast and blood (fresh and transformed).

                                     Blood vs LCl   Blood vs Breast   Breast vs LCL
  ---------------------------------- -------------- ----------------- ---------------
  DAE present in both                8/12           9/11              10/11
  Similar DAE distribution/pattern   5/8            8/9               9/10

Number of genes concordant in terms of presence and extent/pattern of preferential allelic expression. DAE = differential allelic expression; LCL, EBV transformed lymphoblastoid cell lines.

Comparison of DAE between breast tissue and blood cells
-------------------------------------------------------

Next, we analysed 40 normal breast tissue samples (green data points in Figure [1a](#F1){ref-type="fig"}). Like blood, breast is a complex organ, comprising breast epithelium, stroma, and adipocytes. The comparison between fresh blood and breast tissue showed that DAE distributions were similar for eight out of nine genes (89%) that showed DAE in both tissues. *BRCA1, BRCA2, CCND3, EMSY, GPX4*, and *TRXR2*had similar mean ratios (based on Wilcox rank sum test) and/or patterns. In breast samples, the same alleles of *MTHFR*and *TP53*were preferentially expressed as in the fresh blood samples, although with significantly different mean ratios (Tables [2](#T2){ref-type="table"} and [3](#T3){ref-type="table"}). *GPX1*showed no DAE in breast and *MLH3*showed no DAE in blood, whilst *NBS1*showed discordant patterns and mean allelic ratio.

Comparing the results obtained for transformed lymphoblasts with those obtained for breast we found that 10 out of 11 genes showed preferential allelic expression in both types of sample. Of these 10, five genes were comparable in terms of pattern and mean allelic ratio (*CCND3, GPX4*, *MLH3, MTHFR*and *NBS1*), and four were comparable only on pattern (*EMSY, TP53*and *TRXR2*). Only *BRCA1*was significantly different between the two sample sets for both mean allelic ratio and pattern of preferential expression.

Pairwise correlation analysis with the mean allelic ratios obtained for the genes that showed evidence of DAE in each two sample types showed high correlation across types of tissue (blood vs LCL R^2^= 0.88, blood vs breast R^2^= 0.80 and breast vs LCL R^2^= 0.87) (Figure [1b](#F1){ref-type="fig"}).

Comparison between DAE analysis and linkage mapping of expression phenotypes
----------------------------------------------------------------------------

For the genes that in blood showed evidence for regulation from within the same linkage disequilibrium block (that is, genes for which all heterozygotes with imbalances showed preferential expression of the same allele), we determined total levels of expression using Taqman technology, for individuals of all genotypes. After, we performed a correlation analysis of the total level of expression vs genotype at the tSNP for these genes. We found that only *MTHFR*showed a significant correlation (*P*\< 0.005) (Figure [2](#F2){ref-type="fig"}). For other genes, for example *TP53*, we found that total expression did not vary with genotype, even though we found evidence for differential allelic expression in our initial analysis \[see Additional file [2](#S2){ref-type="supplementary-material"}\].

![Comparison between identifying cis-regulatory elements by the total expression and the allelic expression ratio method in blood samples. For both genes, the graph on the left represents the correlation between the total level of expression and the genotype at the specified tSNP (*P*-values were calculated using the Jonckheere-Terpstra test). The graphs on the right-hand side represent the log~2~ratios of allelic expression in heterozygotes only, for the corresponding tSNPs.](bcr2458-2){#F2}

Discussion
==========

Here we report an extensive analysis of differential allelic expression in breast and blood (fresh and EBV-transformed) in a set of candidate breast cancer genes using a large set of unrelated individuals of European origin. We demonstrate the feasibility of using DAE in fresh blood or transformed lymphoblasts as a quantitative trait in future association studies for susceptibility to breast cancer, as well as an approach to select genes/loci from the lists produced by the genome-wide association studies for further functional investigation and validation. We found that the magnitude (fold difference) or pattern (direction) of differential expression was concordant in eight out of nine genes which showed DAE in breast and fresh blood. The results were similar between in fresh and transformed lymphoblasts.

As reviewed by Williams *et al*. \[[@B35]\], the percentage of genes reported to be affected by genetic variation at cis-acting regulatory elements differs greatly between approaches. The most common approach to studying variation in gene expression has been linkage analysis of total gene expression \[[@B13],[@B15],[@B17]\], which in general reports 1 to 20% cis-linkages. When using imbalances of allelic expression in heterozygotes, previous reports point to a much greater proportion of genes (30 to 60%) with cis-acting regulation \[[@B14],[@B16],[@B18],[@B36]-[@B39]\]. The discrepancy of proportion of genes showing cis-regulation reported by the different methods, that we also observe in our study (for *TP53*for example), is in our view possibly the effect of a feedback control loop that maintains the total level of expression at a constant level inside the cell, irrespective of the genotype at the regulatory element. A major advantage of studying DAE is that as allelic transcript levels are compared within the same cellular and haplotype context, environmental factors, including the level and availability of transcription factors, and genetic biases are eliminated increasing the ability to detect the cis-effects (reviewed in \[[@B40],[@B41]\]). However, the high percentage of DAE that we observe in out study is likely to be biased by our list of candidate genes, and will not necessarily correspond to the percentage of DAE genome-wide for any of the tissues we studied.

The previous studies that have looked at DAE in multiple tissues have reported significant differences for one gene examined in 12 individuals \[[@B25]\], and for 11% of 92 studied genes in six mice \[[@B26]\]. Our findings differ from these for two possible reasons: we have increased statistical power due to the larger number of samples analysed compared with the Wilkins et al study \[[@B25]\], but also because we analysed a smaller number of genes than Campbell et al \[[@B26]\].

We show that the difference between allelic expression levels can vary greatly (up to six-fold) across genes and based on previous reports \[[@B14],[@B15]\] we assume that the distribution pattern of DAE can shed light on the nature of the regulatory cis-element causing DAE \[[@B7],[@B16]\]. In addition, we note that the proportion of heterozygotes displaying DAE can differ greatly between genes (11% to 100% of heterozygotes). In only a small number of genes did all heterozygotes show preferential expression of one allele (two genes in all three sample sets, and two others in transformed lymphoblasts alone). In general, high mean allelic ratios correlated with a high proportion of samples with DAE. This suggests that regulatory variants have in fact non-binary, stochastic effects on the binding of transcription factors. If in some cases the effect is a very strong one, consequently more heterozygotes will present it. For example, in the case of a polymorphism that alters the affinity of binding of a transcription factor \[[@B6],[@B20]\], the extent of the effect we detect is probably a reflection of where on the binding site sequence the nucleotide change occurs, and how specific the binding of the transcription factor is to a certain sequence. All of these considerations become important when carrying out haplotype analysis to map which regulatory variants are mechanistically responsible for DAE.

Overall, our results suggest that although the total level of expression of a gene is under tissue-specific regulation (mainly due to the availability of the necessary transcription factors), DAE is mostly tissue-independent -exerting a similar effect in most tissues where the gene is expressed - and individual specific - regulated by the genetic variation make-up of each individual (even in the same cellular/tissue context). However, it is likely that tissue-specific levels of transcription factors might also influence the magnitude of DAE, as we noted in genes that show evidence of being regulated differently in the studied tissues (*BRCA1*in B cells and breast, for example). Ideally, for validation, this study should be followed-up with another on matched blood and breast samples.

Conclusions
===========

In conclusion, we show that differential allelic expression is common in candidate breast cancer genes and is comparable between tissues to some extent. Our findings support the further exploration of DAE in blood and breast as a quantitative phenotype to reveal regulatory genetic variation that predisposes to breast cancer (as in recent reports for breast and colorectal cancers \[[@B9],[@B42]\]), as well as a mean to prioritise the candidate susceptibility hits from the GWAS for follow-up functional studies and confirmation.

Abbreviations
=============

CEPH: Centre D\'Étude du Polymorphism Humaine; DAE: differential allelic expression; EBV: Epstein-Barr virus; GWAS: genome-wide association studies; LCL: lymphoblastoid cell line; LD: linkage disequilibrium; SNP: single nucleotide polymorphism; tSNP: transcribed/transcript single nucleotide polymorphism.

Competing interests
===================

The authors declare that they have no competing interests.

Authors\' contributions
=======================

ATM conceived and designed the study, prepared samples, carried out experiments, prepared and edited the manuscript. IS carried out sample preparation and performed experiments. AJXL performed experiments. LJ carried out sample collection and elaborated ethics applications. MOR contributed to sample preparation. CC and BAJP helped conceiving the study and editing the manuscript. All authors have read and approved the final manuscript.

Supplementary Material
======================

###### Additional file 1

Adobe Acrobat document containing a graph of the correlation between mean ratio of DAE and percentage of heterozygotes with DAE in B cells.

######

Click here for file

###### Additional file 2

Adobe Acrobat document containing the graphs for all extra genes in the total level of expression vs genotype correlation analysis. *P*values correspond to the Jonckheere-Terpstra test, like for Figure [2](#F2){ref-type="fig"}.

######

Click here for file

Acknowledgements
================

The authors would like to thank Dr Paul Pharoah, Dr Cherie Blenkiron, Dr John Marioni, Professor Simon Tavaré and staff at Strangeways Laboratories for help with technical aspects of the work, and Dr Kerstin Meyer and Dr Laura Blackburn for help with preparation of the manuscript.

The work from the authors\' laboratory was supported by grants from Cancer Research UK and (ATM, IS, AJXL, LJ, MOR, CC and BAJP). BAJP is the Li Ka Shing Professor of Oncology at the University of Cambridge. We would also like to acknowledge the support of The University of Cambridge, Cancer Research UK, Hutchison Whampoa Limited and NIHR Cambridge Biomedical Research Centre.

Acknowledged contributions were funded by grants from Cancer Research UK (PP, CB, JM, ST and KM).


Am I a role model? Yes I am

Sara Bayman says Leicester City’s unlikely Premier League title win will act as inspiration ahead of Manchester Thunder’s Superleague Grand Final against Surrey Storm at the Copperbox.

The Thunder captain admitted: “We’ve all seen the Leicester celebrations and that does get you thinking about winning it. The Leicester story reminds everyone why they love sport. You never forget, week in week out, why you do it – for your team-mates and for the bond that creates.

“You know the people around you aren’t in it for the money, fame or fortune, they are there because they want to win. You saw the celebrations when we won the semi-finals. It will be massive whoever wins, and to go through so much as a team, creates some amazing memories.

“I have allowed myself to dream and think about what it would be like to win it. But you’ve got to rein that in. It is important to think about winning it, but we have to focus on how we are going to do that and not just the result.

“Both clubs really want to win it and that shows how competitive the Superleague has become. That makes it even more special. Whichever team wins has had to beat some very good teams to do it. We are nervous and excited. It’s a massive game and so hard to predict the outcome. We just have to stay focused on what we have done well so far and not get too distracted by the hype. It’s just a usual training week.”

Thunder won their semi-final against Bath 53-46 and have only lost one game from 15 this season – against Surrey Storm, who were successful 55-49 in Manchester back in February. That defeat was avenged on April 16 by 64-56.

Both sides know each other well. England veteran turned coach Tamsin Greenway will have done her homework on Thunder’s patterns, plays and tactics. But, equally, Thunder coach Dan Ryan has kept on top of Storm’s improvements.

“We’ve won one each, but they’ve improved throughout the season,” Bayman said. “We’re on top of that improvement in their gameplan. And we will put out the best combinations and gameplay to prevent them from what we do well. Our away form has been good this season and we’re hoping that carries on.

“It doesn’t get any easier, I am just as nervous. But you have to acknowledge it and use it to your advantage. You can’t shy away from nerves, you embrace the situation. You have to attack the game and enjoy the level of excitement, not dwell on fear and anxiety. As captain I make sure everyone is settled down and on the right path. The temptation is to get over hyped, so we must remain calm and treat it like any other game – even though it’s not! It’s about acknowledging what we’ve done to get to this point, go through our processes, work as a team, play for each other. That’s when we play best.”

The Superleague has gone from strength to strength in recent years, both on and off court with greater strength and depth in the eight teams, and improved media coverage to boot. As a result participation levels have soared. Superleague matches are sold out and teams now have the welcome problem of searching for larger venues to accommodate an ever-growing fanbase.

And thanks to improved television, young girls now have role models to look up to. “I didn’t used to think of myself as a role model, but now when I think about it – yes, I am. But that is responsibility, not pressure. It’s about being accessible, letting our fans see us and be able to talk to us. The fanbase means a massive amount to us, and from where the Superleague started, you’d never believe it. Young girls tell us we are their inspiration and that is so good to hear. It makes you humble. We are doing this anyway, but it’s nice they are on board and able to see it and experience it with you.”

A stronger Superleague, buoyed by more fans, more TV coverage and bigger investment, will only contribute to head coach Tracey Neville’s grand plan to make England the no 1 team in the world ahead of the 2018 Commonwealth Games and the 2019 World Cup in Liverpool. Until now, they have always been the bridesmaids to Australia and New Zealand. And Bayman recalls in painful detail the heartbreak of the Commonwealth Games in Glasgow in 2014, when England finished fourth.

“It was a crushing defeat, to lose that semi-final against New Zealand by one,” said the Bath University graduate. “For one of the first times, England went out there to win it, rather than simply wanting a medal. We knew from the group stages we were good enough to win it. It was a process of acknowledging how close we were. Eventually you see that as a positive. It’s so easy to get drawn in to the thinking that we lost. But if you look at our performances, we had some great performances in 2014. At some point you have to bin the result and just take the positives. And that helped us to winning bronze at the World Cup.

“Straight after Glasgow, everyone took some time off and a lot of people were struggling. I went away with Jo [Harten], Serena [Guthrie] and Pam [Cookey] to Vegas and we didn’t speak about netball for three weeks. It was a time to do some healing and look at it from an outside perspective. You realise it’s still what you want. It’s still a game and I play it because I enjoy it.

“I’ve learnt a lot in my career, and most of all I’d tell myself to relax. I was always very worried about people’s perceptions of me, how good people thought I was, how people saw me as a player. Those worries are irrelevant, it doesn’t matter. It’s about the job you are doing for your team, how your team-mates perceive you. My desire to please people was always inhibiting, but now I am much more comfortable with the player I am and how I play the game. I can’t change who I am.”

Sky Sports’ biggest ever season of netball concludes with live coverage of the Vitality Netball Superleague Grand Final on Saturday, May 7, Sky Sports 5, 4.30pm

ABOUT THE AUTHOR

Laura Winter is a sports journalist, presenter and event host. She worked in sports communications for the International Rowing Federation for two years, before working and training as a journalist in Gloucestershire, covering a variety of sports including rugby, boxing, football, and triathlon. She then turned freelance at the end of 2014 and is part of the team who founded Voxwomen, a women’s cycling show that seeks to give the female elite peloton the coverage they deserve. Laura’s latest articles.


Okay, first off your going to want to pour the tomatoes into a large bowl and crush them by hand.. removing the hard stem area and any bits of skin that may have been left on.
Mince the onion and give the garlic and basil a coarse chop.
Begin heating the olive oil on low and start sautéing the onion with a few sprinkles of salt for a few minutes until it begins to soften. Then, add in the garlic, red pepper, bay leaves, oregano, and fresh basil. Saute all of that for a couple more minutes but be careful not to brown or burn the garlic.
Add in the tomatoes and 1 teaspoon of sugar to start as well as a 1/2 teaspoon of salt.
Simmer this uncovered over low heat for 45 minutes to 1 hour... or until it reaches your desired thickness.
When it finished cooking your going to need to season it to your taste (I usually work in 1/2 teaspoon increments of sugar and 1/4 - 1/2 teaspoon increments on salt).
Remember, you can always add more but you cannot take it back out.
Also, for a thicker and richer sauce you can add a tablespoon or 2 of tomato paste.


idiom: universal

idiom: iphone

idiom: ipad

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idiom: mac

idiom: car

author: xcode


Facts

The Sheriff's Office was called to conduct a welfare check on the children, ages 2 and 7, at Empire Tattoo, 5024 14th St. W., Bradenton. The report says the children were living with their father inside the business, where investigators found “numerous” used syringes and other drug paraphernalia on the floor and strewn about inside within easy reach of the children.

The children's father had left a few minutes earlier, the report says, and he left the two in the care of Rick Rowland, 39, who is an employee.

Rowland was arrested for child neglect. No other details have been released.

BRADENTON - A Manatee County Sheriff's Office report says two children were found living at a local business in “deplorable conditions.”

The Sheriff's Office was called to conduct a welfare check on the children, ages 2 and 7, at Empire Tattoo, 5024 14th St. W., Bradenton. The report says the children were living with their father inside the business, where investigators found “numerous” used syringes and other drug paraphernalia on the floor and strewn about inside within easy reach of the children.

The children's father had left a few minutes earlier, the report says, and he left the two in the care of Rick Rowland, 39, who is an employee.

Rowland was arrested for child neglect. No other details have been released.


In our #5 spot, is BriteSmart. This product contains only two main ingredients and a proprietary blend. There is no artificial flavors, preservatives or starches. There are a number of noted side effects and there is no guarantee of any kind offered.

EDITOR'S RATING

24 User Reviews

QUALITY RATING

AVERAGE

Introduction

This is a review for the product BriteSmart. This product contains only two main ingredients and a proprietary blend which is still pending. There is no artificial flavors, preservatives or starches. The manufacturer assures consumers that their formula has been tested and is natural and safe to use. However, there are some noted side effects. There is no guarantee of any kind offered. Read on to find out more details.

Ingredients

Acetyl-L-Carnitine

Ceretrophin

Biotin

Alpha Lipoic Acid

Rhodiola Rosea Extract

Vinpocetine

Huperzine A

Silicon Dioxide

Shellac

Vegetable Magnesium Stearate

Calcium Carbonate

Croscarmellose Sodium

Vegetable Stearic Acid

Microcrystalline Cellulose

Tricalcium Phosphate

Dextrin

Dextrose Monohydrate

FD&C Blue #2 Aluminum Lake

Hydroxypropyl Methylcellulose

Soy Lecithin (Soy Beans)

Polyethylene Glycol

Titanium Dioxide

According to the product’s label, there is no artificial flavors, preservatives or starches in this supplement.

Dosage

According to the label, the user is supposed to take three tablets a day with food and a full glass of water.

Possible Side Effects

There are some noted side effects including nausea, vomiting, constipation, diarrhea, insomnia, and dry mouth. The manufacturer states that users should start out with a small dose of BriteSmart to gauge how the body will react to it. Women who are pregnant should not take this supplement and anyone who is currently taking prescription medications should consult a physician before taking it. The manufacturer’s website states that if the user is schizophrenic, has bipolar disorder, hemophilia, epilepsy, they should not take this product.

Price

Via the company’s website, the price for this product is currently $59.95.

Guarantee

According to the information that is listed on the manufacturer’s website, there is no guarantee of any kind offered for this product. There are very few online customer reviews.

Conclusion

This product contains only two main ingredients and a proprietary blend which is still pending. This herbal blend is being marketed by the manufacturer as a product that helps with increasing brain function. However, there is very little information available that explains how the formula actually works. There are a number of well-known ingredients in this product such as Vinpocetine and Huperzine. However, there are many ingredients listed that we found no real connection to brain boosting at all. There are very few online customer reviews for this product so it is difficult to say how well this product performs or how prominent the side effects are. But in any case, we are wary of a product that includes so many side effects, together with no money back guarantee, we had to place this at the bottom of our top picks.

Editor's Choice

Our Top Picks

*Results for individuals will vary, depending on existing health factors, lifestyle and level of fitness. The information contained on this site is intended to educate only and is in no way, a substitute for medical advice that your doctor or healthcare provider can offer, with whom you should always consult with before making any dietary changes. Information within should not be used for diagnosis, treatment or prevention of any disease. Testimonials and results contained within may not be an implication of future results. Testimonials on this site are based on the experiences of a few people and you may not have similar results. These statements have not been evaluated by the Food and Drug Administration.


TEMPE, Ariz. – Chone Figgins, who spent eight years with the Angels as a valuable utility man and lineup spark plug, will sign a one-day contract with the team on Monday so he can officially retire as an Angel.

Figgins, 38, was with the Angels from 2002 to 2009, including an All-Star appearance in his final season. He hit .291 with a .363 on-base percentage with the Angels. For most of his Angels’ career, he hit first or second.

A fan favorite, Figgins played second, third, shortstop and all three outfield positions with the Angels. In 2009, Figgins was the Angels everyday third baseman, when he hit .298 with a .395 on-base percentage.

“Chone was a player who absolutely loved to play the game,” Manager Mike Scioscia said in a statement. “He played the game aggressively and fearlessly. That’s why he was a big part of what we did in 2002 and up through 2009.

“For not ever having really played third base, getting the opportunity and then saying yes I’ll play and doing so at such a high level, shows you how competitive what a great player he was. Whether we put him in to steal a base in a key situation or he was playing every day third base, Chone took the challenge to heart and put everything he had into it. I wish Chone and his family the very best ahead.”

Figgins, who then signed a four-year, $36-million deal with the Seattle Mariners, was never again able to reach that level of productivity after leaving the Angels.

The Mariners released him with a year left on his contract. The Miami Marlins signed him and released him in spring training, and he didn’t play in 2013. In 2014, he appeared in 38 games with the Dodgers.


Cytogenetic study of acute lymphoblastic leukemia and its correlation with immunophenotype and genotype.
Among 72 Chinese patients with acute lymphoblastic leukemia (ALL), 50 had clonal chromosomal abnormalities. Structural abnormalities were detected in 42 patients: these included t(9;22) in 9, t(1;19) in 6, t(4;11) in 5, del(11)(q23) in 4, and del(6q) in 4. Adults had a higher incidence of t(9;22) and t(1;19) but a lower incidence of t(4;11) and hyperdiploid greater than 50 karyotype than children. A significant difference was also noted in white blood cell (WBC) count among various karyotypic groups. Patients with chromosomal abnormalities t(9;22), t(1;19), t(4;11) and del(11) (q23) had a shorter complete remission duration as compared with patients free of these abnormalities. Immunophenotyping was performed on 69 patients. All patients with t(9;22), t(1;19), and t(4;11) had B-lineage ALL restricted to certain stages of maturation: groups III and IV, groups IV and V, and group II, respectively (according to the classification of Foon and Tood). Among patients with t(9;22), t(4;11), and del(11)(q23), which have been considered to be associated with acute mixed-lineage leukemia, one each, respectively, showed myeloid antigen expression on the leukemic blasts (My+ ALL). No cross-lineage rearrangements of immunoglobulin (Ig) or T-cell receptor (TCR) genes were detected in these karyotypic subgroups of patients who underwent gene analysis.


/*
 * LoggerConnection.h
 *
 * BSD license follows (http://www.opensource.org/licenses/bsd-license.php)
 *
 * Copyright (c) 2010-2018 Florent Pillet <fpillet@gmail.com> All Rights Reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 * Redistributions of  source code  must retain  the above  copyright notice,
 * this list of  conditions and the following  disclaimer. Redistributions in
 * binary  form must  reproduce  the  above copyright  notice,  this list  of
 * conditions and the following disclaimer  in the documentation and/or other
 * materials  provided with  the distribution.  Neither the  name of  Florent
 * Pillet nor the names of its contributors may be used to endorse or promote
 * products  derived  from  this  software  without  specific  prior  written
 * permission.  THIS  SOFTWARE  IS  PROVIDED BY  THE  COPYRIGHT  HOLDERS  AND
 * CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
 * NOT LIMITED TO, THE IMPLIED  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 * A  PARTICULAR PURPOSE  ARE DISCLAIMED.  IN  NO EVENT  SHALL THE  COPYRIGHT
 * HOLDER OR  CONTRIBUTORS BE  LIABLE FOR  ANY DIRECT,  INDIRECT, INCIDENTAL,
 * SPECIAL, EXEMPLARY,  OR CONSEQUENTIAL DAMAGES (INCLUDING,  BUT NOT LIMITED
 * TO, PROCUREMENT  OF SUBSTITUTE GOODS  OR SERVICES;  LOSS OF USE,  DATA, OR
 * PROFITS; OR  BUSINESS INTERRUPTION)  HOWEVER CAUSED AND  ON ANY  THEORY OF
 * LIABILITY,  WHETHER  IN CONTRACT,  STRICT  LIABILITY,  OR TORT  (INCLUDING
 * NEGLIGENCE  OR OTHERWISE)  ARISING  IN ANY  WAY  OUT OF  THE  USE OF  THIS
 * SOFTWARE,   EVEN  IF   ADVISED  OF   THE  POSSIBILITY   OF  SUCH   DAMAGE.
 *
 */
#import <Cocoa/Cocoa.h>

@class LoggerConnection, LoggerMessage;

// -----------------------------------------------------------------------------
// LoggerConnectionDelegate protocol
// -----------------------------------------------------------------------------
@protocol LoggerConnectionDelegate
// method that may not be called on main thread
- (void)connection:(LoggerConnection *)theConnection didReceiveMessages:(NSArray *)theMessages range:(NSRange)rangeInMessagesList;
@end

@interface NSObject (LoggerConnectionDelegateOptional)
// method always called on main thread
- (void)remoteDisconnected:(LoggerConnection *)theConnection;
@end

// -----------------------------------------------------------------------------
// NSLoggerConnection class
// -----------------------------------------------------------------------------
@interface LoggerConnection : NSObject <NSCoding>

@property (retain) id <LoggerConnectionDelegate> delegate;

@property (nonatomic, retain) NSString *clientName;
@property (nonatomic, retain) NSString *clientVersion;
@property (nonatomic, retain) NSString *clientOSName;
@property (nonatomic, retain) NSString *clientOSVersion;
@property (nonatomic, retain) NSString *clientDevice;
@property (nonatomic, retain) NSString *clientUDID;

@property (nonatomic, readonly) NSData *clientAddress;			// depends on the underlying protocol
@property (nonatomic, readonly) NSMutableSet *filenames;		// pool of unique file names
@property (nonatomic, readonly) NSMutableSet *functionNames;	// pool of unique function names

@property (nonatomic, readonly) NSMutableArray *messages;
@property (nonatomic, assign) int reconnectionCount;			// when a reconnection is detected (same client, disconnects then reconnects), the # reconnection for this connection
@property (nonatomic, assign) BOOL connected;
@property (nonatomic, readonly) BOOL restoredFromSave;
@property (nonatomic, assign) BOOL attachedToWindow;

@property (nonatomic, readonly, retain) NSMutableArray *parentIndexesStack;	// during messages receive, use this to quickly locate parent indexes in groups
@property (nonatomic, readonly) dispatch_queue_t messageProcessingQueue;

- (id)initWithAddress:(NSData *)anAddress;
- (void)shutdown;

- (void)messagesReceived:(NSArray *)msgs;
- (void)clientInfoReceived:(LoggerMessage *)message;
- (void)clearMessages;

- (NSString *)clientAppDescription;
- (NSString *)clientAddressDescription;
- (NSString *)clientDescription;

- (BOOL)isNewRunOfClient:(LoggerConnection *)aConnection;

@end

extern char sConnectionAssociatedObjectKey;


JAMMU, Oct 20: In an unending series of tragic mishaps in hilly districts, 21 passengers Thursday lost their lives and 45 others were injured when an overloaded 42-seater passenger bus fell into a deep gorge in Reasi district while a woman and her ...


Q:

Unix command line utilities for Windows x64?

There are many packages of UNIX command line utilities for 32-bit Windows. Now that the mingw-w64 Windows compiler is stable, I was expecting similar utilities to appear for 64-bit Windows (e.g. "GnuWin64"). However, I'm not finding them. Where are they?

A:

As I've mentioned on SuperUser one … two … three times:
The tools in the SFUA utility toolkit, which run in the Subsystem for UNIX-based Applications that comes right there in the box with Windows 7 Ultimate edition and Windows Server 2008 R2 (For Windows XP, one can download and install Services for UNIX version 3.5.), come in both AMD64 and IA64 flavours as well as x86-32.  This toolkit has a large number of command-line TUI tools, from mv and du, through the Korn and C shells, to perl and awk.  The programs run in Windows NT's native proper POSIX environment, rather than with emulator DLLs (such as cygwin1.dll) layering things over Win32 as other toolsets do.

A:

SFUA utilities are not the same as the GNU ones.  They are largely incompatible, and only implement a very small subset of the Posix functions.
There are few utilities that would see any gain from Win64 memory addressing are the newer ones related to compression.  Most of those have Win64 builds available on their project site.  Simply use that version instead.
As for the libraries, if you are using them in a project, I recommend building the dll yourself if one is not available on other pages.  I recommend against updating the dll libraries that came with other projects.  There is a reason they shipped the version they did.  Simply get an updated version of the whole package if you need to address an issue in the dll.

A:

Cygwin started providing 64-bit versions a few month ago(July 2013)


Q:

Odd Javascript behavior for array of strings

Someone pointed this weird array result in JavaScript and I was hoping someone could explain why this happens.
If I have an array like so:
var arr = ["10", "10", "10", "10"];

And I try to map those "10"'s to integers like so:
arr.map(parseInt);

The result will be an array with the values: [10, NaN, 2, 3] (I ran all of this code in Chrome's console)
I expected the result to be: [10, 10, 10, 10]
I understand that JavaScript can parse integers from string into different numbering systems, like binary, which I think explains where the 2 and 3 three are coming from, kind of. But why would parseInt change to parsing them in different bases? Could someone please explain why this is the result I get?

A:

Array.prototype.map accepts a callback which takes 3 arguments:

The current element
The index of the element
The array that this function was called on

parseInt takes up to two arguments:

The string to parse
(Optional) The radix i.e. the base of the number.

So in passing parseInt to map, you essentially get the following calls:
parseInt(arr[0], 0); // if 0, same as default
parseInt(arr[1], 1); // Parse as base 1
parseInt(arr[2], 2); // Parse as base 2 or binary
parseInt(arr[3], 3); // Parse as base 3

An easier and more effective way of accomplishing what you're trying is to use Number:

var arr = ["10", "10", "10", "10", "23", "74"];
console.log(arr.map(Number));


Free Pet PortaBottle Water Bottle- Freebies

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Free Pet PortaBottle Water Bottle

Never again will you have to carry a bowl and water for your pet on long hikes or other fun filled excursions. The PortaBottle conveniently merges a water trough and bottle into one convenient package! Simply flip the trough out and fill it with the water bottle. The wide mouth bottle-top design lets you put ice into the water bottle as well.


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To create the Ruination, the strongest Cuban-seed ligeros were taken from the top growing regions of Nicaragua and Honduras and chosen based on quality, combustion, appearance, and level of flavor and aroma. These leaves were then introduced to an extensive fermentation and aging process for optimum flavor, prior to being blended within a beautiful Habano Ecuador ligero wrapper. This leaf is gorgeous....dark, oily, reddish in color, and thick in nature. Expect a generous dose of spice off the bat, followed by a deep series of oak, earth, toast, sweet wood, and leather throughout the slow and super-cool burn. The finish is long, while the robust aroma fills the room as you approach the full-bodied, full-flavored nub.


Introduction

This article demonstrates how BoofCV's Android library greatly simplifies working with the Camera2 API, processing camera images, and visualizing the results. Android development and the Camera2 API are both very complex, e.g., as demonstrated by Google's Camera2Basic example. That example covers about 1/2 of what you need to do.

With BoofCV, you just need to tell it what resolution you want, implement an image processing function, and (optionally) write a visualization function. BoofCV will select the camera, open the camera, create a thread pool, synchronize data structures, handle the Android life cycle (open/close the camera properly, stop threads), convert the YUV420 image, correctly align input pixels to screen pixels, and change camera settings upon request.

What we are going to discuss in this article is writing your own auto-focus routine. While not as sexy as say using the latest in Deep Learning (e.g., classifying all objects, aging your face), this article is about being practical and making something that works. Doesn't matter how amazing your computer vision algorithm is if the app is constantly crashing! Once you understand how this code works, you can combine it with other algorithms to say focus the camera on just dogs and not people.

If you want to see additional Android computer vision examples written using this library, checkout the BoofCV demonstration app:

APK on Google Play Store

The Auto-Focus Algorithm

How built in auto-focus behaves varies by manufactures and in computer vision applications, my experience is that they tend to focus on anything but what you want them to. To get around this problem, we are going to roll our own auto-focus by manually controlling the camera's focus and finding the focus value with the maximum image "sharpest". The sharpness of an image is determined by its gradient intensity. The image's gradient is its x and y spatial derivatives. The gradient's intensity can be defined in different ways. Here, we use the Euclidean norm, i.e., sqrt(dx**2 + dy**2) .

The auto-focus algorithm is a finite state machine, summarized above. It exhaustively goes through all focus values to select the one with the maximum edge intensity. Once the maximum edge intensity is found, it fixes the focus. You can start the process over by tapping the screen.

Source Code Highlights

At this point, you should check out the source code on Github and explore the code a bit. We will step through critical steps in setting up your own project and important lines in the source code itself. It's assumed that you already know a bit about Android development and some steps are skipped for the sake of brevity.

Step One: Create a New Project in Android Studio

This article is labeled as intermediate so I'm assuming you can do this without pictures and a video. The minimum SDK has to be 22. This is the first version in which a nasty bug in Camera 2 API was fixed.

Step Two: Dependencies

Add the following to the dependencies field in app/build.gradle.

dependencies { ['boofcv-android', 'boofcv-core'].each { String a -> implementation group: 'org.boofcv', name: a, version: '0.32-SNAPSHOT' } }

You also need to exclude a few transitive dependencies because Android includes their own version and there will be a conflict.

configurations { all*.exclude group: "xmlpull", module: "xmlpull" all*.exclude group: "org.apache.commons", module: "commons-compress" all*.exclude group: "com.thoughtworks.xstream", module: "commons-compress" }

Then let Android Studio synchronize your Gradle files.

Step Three: Android Permissions

Inside app/src/main/AndroidManifest.xml, you need to grant yourself access to the camera:

< uses-permission android:name =" android.permission.CAMERA" / > < uses-feature android:name =" android.hardware.camera2.full" / >

Step Four: MainActivity: OnCreate()

You should now open up the MainActivity.java file. Notice how this class extends VisualzieCamera2Activity. That's the activity you should extend if you wish to render something on the screen. Otherwise, you can extend SimpleCamera2Activity if you just needs the camera frames.

Check out the onCreate() function. A lot is going on in this function; getting camera permissions, specifying the type of output image, what resolution you want, and turning off double buffering. Let's go through line by line.

TextureView displays the raw camera preview. FrameLayout for drawing visuals. We also add a touch listener so that the user can restart the focus algorithm. More on that later.

TextureView view = findViewById(R.id.camera_view); FrameLayout surface = findViewById(R.id.camera_frame); surface.setOnClickListener( this );

Here, we request access to the camera from the user. This has been covered extensively throughout the web, so look at the source code for details on how this is done.

requestCameraPermission();

BoofCV needs to know what image format we desire. Here, we tell it to give us a gray scale 8-bit image.

setImageType(ImageType.single(GrayU8. class ));

Cameras typically support numerous resolutions. BoofCV will handle selecting a resolution for us, but we should tell it something about our requirements. What the line below does is tells BoofCV to find an image resolution which has approximately that many pixels. More complex logic is possible by overriding the selectResolution() function.

targetResolution = 640 * 480 ;

BoofCV will also automatically render the captures frame. In this case, we don't want to do that and just show a raw camera feed. So let's turn that behavior off.

bitmapMode = BitmapMode.NONE;

Finally, we tell it to start the camera and to use the following surface and view for visualization.

startCamera(surface,view);

Step Five: Camera Control

By default, the camera is configured to be in full auto mode. We don't want that so we override the default configureCamera() function with our own logic. BoofCV defined configureCamera() and it's not a built in Camera2 API function. It takes a lot more work to do this using the Camera2 API directly.

The in code comments below describe what each block of code is doing. In essence, it's where the finite state machine above is implemented.

@Override protected void configureCamera(CameraDevice device, CameraCharacteristics characteristics, CaptureRequest.Builder captureRequestBuilder) { captureRequestBuilder.set(CaptureRequest.CONTROL_AF_MODE, CaptureRequest.CONTROL_AF_MODE_OFF); Float minFocus = characteristics.get(CameraCharacteristics.LENS_INFO_MINIMUM_FOCUS_DISTANCE); switch ( state ) { case INITIALIZE:{ if ( minFocus == null ) { Toast.makeText( this , " manual focus not supported" , Toast.LENGTH_SHORT).show(); state = State.UNSUPPORTED; } else { focusBestIndex = 0 ; focusBestValue = 0 ; focusIndex = 0 ; focusTime = System.currentTimeMillis()+FOCUS_PERIOD; state = State.FOCUSING; captureRequestBuilder.set(CaptureRequest.LENS_FOCUS_DISTANCE, 0f); } } break ; case PENDING:{ focusIndex++; if ( focusIndex < FOCUS_LEVELS ) { focusTime = System.currentTimeMillis()+FOCUS_PERIOD; captureRequestBuilder.set(CaptureRequest.LENS_FOCUS_DISTANCE, minFocus*focusIndex/(FOCUS_LEVELS- 1 )); state = State.FOCUSING; } else { captureRequestBuilder.set(CaptureRequest.LENS_FOCUS_DISTANCE, minFocus*focusBestIndex/(FOCUS_LEVELS- 1 )); state = State.FIXED; } } break ; } }

Step Six: Image Processing

BoofCV provides its own image processing function that automatically converted the streaming YUV420_888 image into one which you understand. It has also launched a thread for you to process images inside of. This means you can take as long as you want without crashing! Frames are simply discarded if your image processing can't keep up with the camera's feed.

@Override protected void processImage(ImageBase image) { GrayU8 gray = (GrayU8)image; derivX.reshape(gray.width,gray.height); derivY.reshape(gray.width,gray.height); intensity.reshape(gray.width,gray.height); GImageDerivativeOps.gradient(DerivativeType.SOBEL,gray,derivX,derivY, BorderType.EXTENDED); GGradientToEdgeFeatures.intensityE(derivX, derivY, intensity); edgeValue = ImageStatistics.mean(intensity); }

Step Seven: Drawing

Normally, you will need to create a call back for the SurfaceView , but this is also handled for you. You do still need to override the onDrawFrame function below. The code below is fairly standard Android drawing which is why it isn't highlighted. If you want to see how you can use BoofCV render the actual image being processed (not the live video feed) and align visuals to the exact pixel, see the QR Code example.

@Override protected void onDrawFrame(SurfaceView view, Canvas canvas) { super .onDrawFrame(view, canvas); .... }

Step Eight: Responding to User Touches

In onCreate() , we added a touch listener. This allows the user to restart the auto-focus. The code below shows how this is done. By waiting until the auto-focus is in the FIXED state, we don't need to worry about if the camera is busy or not, simplifying the code.

@Override public void onClick(View v) { if ( state == State.FIXED ) { state = State.INITIALIZE; changeCameraConfiguration(); } }

Using Other Computer Vision Libraries

In this example, we used BoofCV to do the image processing. There's no reason you can't use other libraries like OpenCV or your favorite deep learning library (e.g., Torch) instead. What you will need to do is convert the BoofCV image into an image that the other library will understand. That's much easier to do than obtain the image in the first place! You have full access to the raw byte arrays and image characteristics (e.g., width , height , stride ) in BoofCV images.

Additional Examples

BoofCV's repository includes a few additional minimalist Android examples.

Conclusion

We were able to create a fully functional computer vision app on Android which manipulates the camera settings in library, while processing images and displaying information to the user. This was done without extensive knowledge of the Android life cycle, how the Camera2 API works, or know what YUV420 is enabling you to go down to business and create cool computer vision applications with minimal fuss!

History


Q:

How to serialize an object in windows phone 7

How to convert an object to JSON data in windows phone. In web application I have used following code
 JavaScriptSerializer serializer = new JavaScriptSerializer();
 string stringData = serializer.Serialize(object);

I want to get the same output as that of the above code in windows phone 7.

A:

JavaScriptSerializer is not supported on Windows Phone. An alternative is to use JSON.NET (you can add it via NuGet).
The code will then look like this:
string stringData = JsonConvert.SerializeObject(object);


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