Background
==========

Pancreatic cancer is the fourth leading cause of cancer death in western societies, with a 5-year survival rate of less than 5% \[[@B1]\]. Systemic therapies are only modestly effective; however, there is emerging evidence that small groups of patients may respond well to specific treatments \[[@B2],[@B3]\]. Current therapeutic development is focused on targeting molecular mechanisms, and this has resulted in significant improvements in outcome for several cancer types (for example, crizotinib for *EML4-ALK* fusion-positive non-small cell lung cancer (NSCLC)). This approach is shifting the traditional organ-based classification of cancer towards a new molecular taxonomy, and creating opportunities to apply therapeutics for the treatment of cancers originating in other organs that harbour similar molecular anomalies. Such indications for extension of existing therapeutics is attractive; however, specific molecular phenotypes and diagnostic characteristics are complex and usually inadequately defined \[[@B4],[@B5]\]. The target population for cancers of organs, apart from where the therapeutic strategy was initially developed, often occur at low frequency, further adding to the challenge.

Emerging data from cancer sequencing initiatives such as the International Cancer Genome Consortium (ICGC) \[[@B6]\] and The Cancer Genome Atlas (TCGA) \[[@B7]\] are unveiling a vast heterogeneity of molecular aberrations in cancer. Pancreatic ductal adenocarcinoma (PDAC), the predominant form of pancreatic cancer, is particularly heterogeneous, and apart from a few notable exceptions, which have not been successfully targeted, most genetic aberrations have a frequency of 2% or less \[[@B8]-[@B10]\].

Trastuzumab, a monoclonal antibody that targets the *HER2* receptor, is an effective therapy for *HER2*-amplified breast cancer, and was recently extended to the treatment of *HER2*-amplified gastric cancer \[[@B11]\]. In addition, borderline signals have been seen in clinical trials of semi-selected patients with NSCLC \[[@B4]\], and case reports describe exceptional responses to trastuzumab in other cancers with *HER2* amplification, such as cholangiocarcinoma \[[@B12]\]. Defining *HER*2 amplification as a biomarker of trastuzumab responsiveness is integral to targeting appropriate populations for therapy. Although *HER2* overexpression and amplification has been assessed in PDAC (see Additional file [1](#S1){ref-type="supplementary-material"}), standardized diagnostic assays have on the whole not been applied, and the roles of emerging diagnostic approaches such as genomic sequencing are yet to be explored. As a consequence, the diagnostic criteria and prevalence of *HER2* amplification in PDAC remain unclear. Although preclinical studies support potential efficacy of trastuzumab in PDAC \[[@B13],[@B14]\], clinical trials have been hampered by non-standardized assays and a consequent lack of focus on appropriate subgroups \[[@B15],[@B16]\]. With the current poor survival, low therapeutic responsiveness, and vast molecular heterogeneity of PDAC, even if a relatively low proportion were found to be *HER2*-amplified, targeting by *HER2* amplification represents an attractive potential therapy.

In this study, we identified *HER2* amplification in diagnostic specimens of PDAC using single nucleotide polymorphism (SNP) arrays and whole genome sequencing. We defined the characteristics and prevalence (2.1%) of *HER2* amplification in a large cohort of patients with resected PDAC using standardized reference laboratory assays. We found that *HER2*-amplified PDAC has an atypical pattern of metastatic spread with a predilection for lung metastasis and local recurrence, rather than liver metastases. Assessment of *HER2* amplification across 16 cancer types suggested a prevalence of at least 22,000 cases per annum in the USA (excluding breast and gastric cancer), suggesting that a molecular recruitment strategy may make it feasible to test anti-HER2 therapies in less common cancer types.

Methods
=======

Ethics approval
---------------

Ethics approval for acquisition of data and biological material was obtained from the human research ethics committee at each participating institution, conducted in accordance with the National Statement on Ethical Conduct in Human Research 2007 and the Declaration of Helsinki. Consent was obtained from prospectively recruited patients for genomic sequencing through the Australian Pancreatic Cancer Genome Initiative (APGI) as part of the ICGC. Consent was waived by Human Research Ethics Committees for retrospectively acquired data and material under an approved protocol (see Additional file [2](#S2){ref-type="supplementary-material"}).

Genomic sequencing, copy number, and mRNA expression analysis
-------------------------------------------------------------

Patients were prospectively recruited to the APGI \[[@B17]\] for genomic sequencing as part of the ICGC, and details of sample acquisition and processing have been described previously \[[@B9]\]. Briefly, samples focused on primary operable non-pretreated PDAC. Tissue was prepared by either full face frozen sectioning or the ends being excised and processed in formalin, then representative sections were reviewed by at least one pathologist to verify presence of carcinoma in the sample to be sequenced, and to estimate the percentage of malignant epithelial nuclei in the sample relative to stromal nuclei. Nucleic acids were extracted from fresh frozen tumour and normal tissue pairs, and whole genome and exome sequencing was performed using a combination of long mate pair and paired-end approaches. DNA copy number was assessed using specific microarrays (HumanOmni1-Quad BeadChip, Illumina Inc., San Diego, CA, USA). Primary tumour mRNA expression was assayed using human microarrays (HT-12 V4; Illumina) (GEO accession GSE36924). Gene expression profiles were classified into intrinsic breast cancer subtypes using the PAM50 classifier \[[@B18]\].

Defining prevalence and diagnostic criteria for *HER2* amplification
--------------------------------------------------------------------

Formalin-fixed, paraffin wax-embedded diagnostic material from a cohort of 469 patients who had undergone operative resection for PDAC was accrued from 12 institutions associated with the APGI between 1990 and 2012. Subsets of this cohort have previously been published \[[@B19],[@B20]\], and detailed characteristics are presented (see Additional file [3](#S3){ref-type="supplementary-material"}: Table S2).

HER2 immunohistochemistry (IHC) and *in situ* hybridization (ISH) was performed in a national reference HER2 diagnostic testing laboratory. Scoring for *HER2* protein expression and amplification was based on criteria recommended for breast and gastric carcinoma (Table [1](#T1){ref-type="table"}) \[[@B21]\].

######

**IHC and FISH scoring for the detection of*HER2*amplification**^**a**^

  **Her2 IHC score**   **IHC scores**    ***HER2*FISH**
  -------------------- ---------------- ---------------- -----
  3+                   7 (1.5%)                7           0
  2+                   27 (5.8%)               3          24
  1+                   59 (12.6%)              0          59
  0                    376 (80.2%)             0          376

Abbreviations: *FISH* fluorescence *in situ* hybridization; *IHC* immunohistochemistry; *PDAC* pancreatic ductal adenocarcinoma.

^a^The criteria used for scoring HER2 by immunohistochemistry and *in situ* hybridization in PDAC were as follows. Her2 IHC score criteria (modified from Hofmann *et al*\[[@B21]\]): 0, no staining of any pattern or intensity; 1+. weak discernable membrane staining; 2+, mild to moderate complete or basolateral membrane staining; 3+, strong, complete, or basolateral membrane staining. *HER2* ISH criteria: non-amplified, Her2 count \<4 and Her2:cep17 ratio \<2; amplified, Her2 count ≥4 and Her2:cep17 ratio ≥2.

Results
=======

In the initial set of 50 patients recruited to the APGI, SNP arrays detected a case of *HER2* amplification (Figure [1](#F1){ref-type="fig"}A), with associated high *HER2* mRNA expression (see Additional file [4](#S4){ref-type="supplementary-material"}). *HER2* amplification was verified using standardized laboratory assays currently used for breast and gastric cancer at an *HER2* diagnostic reference laboratory. This case was analyzed further using whole genome sequencing, copy number analysis, and mRNA expression profiling.

![***HER2*-amplified pancreatic ductal adenocarcinoma. (A)** Single nucleotide polymorphism (SNP) array showing amplification on chromosome 17 containing the *HER2* locus (boxed). **(B)** Inverted amplification on chromosome 17, spanning *HER2* and truncating *MED1* and *TOP2A* at either end (chr17:37,565,271-38,554,848). **(C)** Circos plot showing mutations and structural rearrangements of patient 9 (Table [2](#T2){ref-type="table"}). Genes with non-silent substitutions are shown on the outer wheel. The SNP array data are shown on the subsequent plots (copy number predictions and B-allele frequency of probes). Structural rearrangements are indicated with lines inside the circle: deletions (green), inversions (yellow), intra-chromosomal rearrangements (blue), foldback inversions (light orange), and amplified inversions (dark orange). Genes with structural variation are shown in blue if deleted and green if interrupted.](gm482-1){#F1}

Genomic characteristics of *HER2*-amplified pancreatic ductal adenocarcinoma
----------------------------------------------------------------------------

Whole genome and exome sequencing identified several classes of mutation including single nucleotide variants, small insertions and deletions (indels), and chromosomal rearrangements (see Additional file [5](#S5){ref-type="supplementary-material"}). Chromosomal rearrangements included a region of loss on chromosome 18 containing the *MAPK4*, *DCC*, *SMAD4*, and *ELAC1* genes, and three complex amplification regions: an inverted amplification of a region of chromosome 17, spanning *HER2* and truncating *MED1* and *TOP2A* at either end (chr17:37,565,271-38,554,848) (Figure [1](#F1){ref-type="fig"}B), and two overlapping fold back inversion events on chromosome 9: a 1.5 Mb event within a 1.9 Mb event, disrupting *UBAP2*. Exome sequencing identified 23 somatic mutations affecting 21 genes, including *KRAS*^p.G12V^ and *TP53*^p.Q317^, and 2 mutations in both *TOP2A* and *RYR2* (Figure [1](#F1){ref-type="fig"}C).

We performed gene set enrichment analysis using all genes affected by mutation, amplification, copy number alterations (GISTIC2.0 peaks *P* \< 0.25), and disruptions caused by structural rearrangements (see Additional file [6](#S6){ref-type="supplementary-material"}). There were three strong biological themes: 1) genes from the *HER2*-amplicon (13 genes; *P*\<1 × 10^-7^); 2) a broad cancer theme driven by *KRAS*, *TP53*, and *EPHA5* (13 gene sets; *P*\<0.001); and 3) an immune signature, driven by type I interferons, *IFNA2*, *INFA6*, *INFA13* (7 gene sets; *P* \< 0.001), which are involved in recognition of viral infection and neoplasia (see Additional file [7](#S7){ref-type="supplementary-material"}).

Intrinsic subtype analysis using expression microarray data from the APGI cohort (n = 90) \[[@B9]\] classified each PDAC sample using the PAM50 classifier, which captures five breast cancer subtypes: *HER2*-amplified, luminal A, luminal B, basal, and normal-like \[[@B18]\]. The *HER2*-amplified patient with PDAC identified above clustered with the *HER2*-amplified intrinsic subtype, with a confidence of 93%. This was driven by high expression of *HER2*, *GRB7*, and *FOXA1*, and low expression of *KRT17* and *MIA* (see Additional file [8](#S8){ref-type="supplementary-material"}).

*HER2* amplification in pancreatic ductal adenocarcinoma
--------------------------------------------------------

In order to define the clinical and histopathological characteristics, diagnostic criteria, and prevalence of *HER2* amplification in PDAC, we assessed a cohort of 469 patients with resected PDAC using IHC and *in situ* hybridization (ISH). IHC for Her2 protein expression showed 3+ staining in 7 cases (1.5%) (Figure [2](#F2){ref-type="fig"}A), 2+ staining in 27 (5.8%), 1+ in 59 (12.6%) and no staining in 376 (80.2%) (Table [1](#T1){ref-type="table"}). All 7 cases that exhibited Her2 3+ staining were found to be *HER2*-amplified on silver ISH and fluorescence ISH (FISH) with 100% concordance between the two methods (Figure [2](#F2){ref-type="fig"}B,C). Only 3 of 27 cases that were Her2 2+ on IHC were found to be *HER2*-amplified (11%). No cases of 1+ or negative Her2 IHC were *HER2*-amplified, and the overall prevalence of *HER2* amplification was 2.1% (10 of 469 cases). All the amplified cases showed a tendency for the *HER2* signal to form clusters. One case was found to have intra-tumoral heterogeneity by IHC and ISH, with distinct *HER2*-positive and *HER2*-negative areas. As *HER2* amplification was only present when Her2 IHC produced 2+ or 3+ staining, IHC screening followed by ISH in cases of 2+ and 3+ IHC staining may be an appropriate and cost-effective approach for the detection of *HER2*-amplified PDAC (see Additional file [9](#S9){ref-type="supplementary-material"}).

![**Clinicopathological features of*HER2*-amplified pancreatic ductal adenocarcinoma (PDAC). (A)** Her2 immunohistochemistry showing 3+ staining, and corresponding *HER2* amplification on **(B)** silver and **(C)** fluorescence *in situ* hybridization. Imaging results of patient 9 (Table [2](#T2){ref-type="table"}) showing **(D)** cerebral and **(E)** lung metastases, and **(F)** absence of liver metastases.](gm482-2){#F2}

Clinical features of *HER2*-amplified pancreatic ductal adenocarcinoma
----------------------------------------------------------------------

Of the ten patients with *HER2* amplified tumours, nine died of PDAC, and one died of respiratory failure as a result of chronic obstructive pulmonary disease with no evidence of cancer recurrence. The pattern of metastatic relapse of the *HER2*-amplified cases was characterized by a lack of liver metastases. None of the eight cases with a documented site of recurrence had liver metastases (one patient had recurrence, and for another patient the site of recurrence was not reported). Of these eight patients, four had lung metastases (one of whom also had brain metastases (Figure [2](#F2){ref-type="fig"}D-F), and two also had local recurrence, although cerebral imaging was only performed in one patient), while the remaining four patients had peritoneal and local recurrences only, although imaging of the thorax was not performed in two of these patients (Table [2](#T2){ref-type="table"}). The higher prevalence of lung metastasis (in the absence of liver metastases) in *HER2*-amplified tumours was significantly different to that of non-*HER2*-amplified tumours (*P* = 0.0022; Table [3](#T3){ref-type="table"}), as was the rate of recurrence at any site without liver metastases (*P* = 0.0028; Table [3](#T3){ref-type="table"}). *HER2* amplification was not associated with any other clinicopathological characteristics such as age (*P* = 0.5467), sex (*P* = 0.7520), tumour stage (*P* = 0.4495), grade (*P* = 0.1259), or size (*P* = 0.4695), or disease-specific survival (*P* = 0.2502; Table [3](#T3){ref-type="table"}). Histopathologically, there were no specific features to distinguish *HER2*-amplified tumours from non-*HER2*-amplified tumours. All *HER2*-amplified cases were moderately differentiated, and most had a diffuse or at least focally macroglandular architecture.

######

**Clinicopathological characteristics of*HER2***-**amplified PDAC**

  **Patient**   **Her2 IHC score**   ***HER2*ratio**   ***HER2*count**   **Overall survival (months)**   **Cause of death**    **Pattern of recurrence**  **Comments**
  ------------- -------------------- ----------------- ----------------- ------------------------------- -------------------- --------------------------- -------------- ------- ------- ------- ------------------------------------------------------
  1^a^          3+                   2.45              6                 13.6                            PDAC                            **-**            ND             **-**   **+**   **+**   Laparotomy showed peritoneal and local recurrence
  2             3+                   7.5               13                43.5                            COPD                            **-**            ND             **-**   **-**   **-**   CT at 43 months showed no recurrence
  3             3+                   4.12              10                5.0                             PDAC                            **-**            ND             **-**   **+**   **-**   Local recurrence
  4^b^          2+                   2.5               5.4               36.5                            PDAC                            **+**            ND             **-**   **+**   **+**   Ascites/pleural effusions, no liver metastases on CT
  5             3+                   4.38              8.1               18.6                            PDAC                             ND              ND             ND      ND      ND      Site not documented
  6             2                    3.56              7                 10.1                            PDAC                             ND              ND             **-**   **+**   ND      R2 resection
  7             3+                   2.7               4.5               41.6                            PDAC                             ND              ND             **-**   **+**   **+**   No liver metastases on CT; peritoneal recurrence
  8             2+                   5.32              9                 10.9                            PDAC                            **+**            ND             **-**   **+**   **-**   No liver metastases on CT
  9^c,d^        3+                   2.95              6.2               35.0                            PDAC                            **+**            **+**          **-**   **-**   **-**   No liver metastases on CT
  10^e^         3+                   2.86\*            6.3               27.0                            PDAC                            **+**            ND             **-**   **-**   **-**   No liver metastases on CT

Abbreviations: + present; - absent; *COPD* chronic obstructive pulmonary disease; *CT* computed tomography; *FISH* fluorescence *in situ* hybridization; *IHC* immunohistochemistry; *ND* not determined; *PDAC* pancreatic ductal adenocarcinoma.

^a^Received adjuvant 5-fluouracil.

^b^Received adjuvant gemcitabine.

^c^Only case 9 received palliative chemotherapy (gemcitabine + erlotinib) and also received adjuvant gemcitabine.

^d^Case that was whole genome sequenced.

^e^Case 10 showed heterogeneous staining.

######

**Clinicopathological characteristics of*HER2*amplified and*HER2*non-amplified cases**

  **Clinicopathological characteristics**     **Total cohort, n = 469**   **HER2 amplification**    ***P*-value**
  ------------------------------------------ --------------------------- ------------------------ ------------------ -------------
  Sex, n (%)
   Male                                               241 (51)                    6 (60)               220 (51)        0.7520^b^
   Female                                             228 (49)                    4 (30)               210 (49)
  Age, years
   Mean                                                  66                         64                    66           0.5467^c^
   Median                                                68                        69.5                  67.5
   Range                                              28 to 88                   47 to 73              28 to 87
  AJCC stage, n (%)
   1a                                                 17 (3.6)                    1 (10)               16 (3.5)        0.4495^d^
   1b                                                 25 (5.3)                    1 (10)               24 (5.2)
   2a                                                132 (28.1)                   3 (30)               129 (28)
   2b                                                279 (59.5)                   4 (40)               275 (60)
   4                                                  16 (3.4)                    1 (10)                15 (3)
  T stage, n
   T1                                                    32                         1                     31
   T2                                                    64                         2                     62
   T3                                                    372                        7                    365
   T4                                                     1                         0                     1
  N stage, n
   N0                                                    180                        6                    174
   N1                                                    289                        4                    185
  AJCC grade, n
   1 (well differentiated)                               36                         0                     36           0.1259^d^
   2 (moderately differentiated)                         305                        10                   295
   3 (poorly differentiated)                             125                        0                    125
   4 (undifferentiated)                                   3                         0                     3
  Tumour size, mm
   \<=20                                                 105                        3                    102           0.4695^b^
   \>20                                                  364                        7                    357
  Vascular invasion, n
   Present                                               222                        2                    220           0.1106^b^
   Absent                                                247                        8                    239
  Perineural invasion, n
   Present                                               339                        8                    331           0.7336^b^
   Absent                                                130                        2                    128
  Tumour location, n
   Head                                                  381                        9                    372           0.6961^b^
   Others                                                88                         1                     87
   Margins
   Clear                                                 301                        7                    294           1.0000^b^
   Involved                                              168                        3                    165
  Outcome
   Follow-up, months                                 0.03 to 240               5.0 to 43.6           0.03 to 240       1.0000^b^
   Median follow-up, months                              16                         23                    16
   Death: PDAC                                           369                        8                    361
   Death: other                                          32                         2                     30
   Death: unknown                                        16                         0                     16
   Alive                                                 49                         0                     49
   Lost to follow-up                                      3                         0                     3
  Cancer-specific survival, mean ± SD
   Length, months                                    20 ± 19.43                 28 ± 19.65            20 ± 19.55      0\. 2502^c^
  Recurrence
   Present                                               261                        8                    253
   Absent                                                78                         1                     77
   Unknown                                               130                        1                    129
  Pattern of recurrence, n (% of total n)
   Lung without liver metastasis                  22 (8.4% of 261)             4 (50% of 8)        18 (7.1% of 253)    0.0022^b^
   Any recurrence without liver metastasis        127 (49% of 261)            8 (100% of 8)        119 (47% of 253)    0.0028^b^
  Adjuvant therapy, n
   Yes                                                   175                        3                    172
   No                                                    289                        7                    282
   Unknown                                                5                         0                     5

AJCC, American Joint Committee on Cancer; PDAC, pancreatic ductal adenocarcinoma.

^a^AJCC Cancer Staging Manual, seventh edition.

^b^Fisher's exact test.

^c^Unpaired *t*-test.

^d^χ^2^ test.

Since the completion of this study, we have begun prospectively screening patients with PDAC for *HER2* amplification using this diagnostic approach, and identified a further two individuals (of eleven) with *HER2* amplification. The first was a primary resected cancer with no evidence of recurrent disease to date at 6 months. Subsequently, whole genome sequence analysis on this patient's tumour confirmed mutation in KRAS ^*p.G12V*^. The second patient had an atypical pattern of metastatic disease at diagnosis, with a small primary pancreatic tumour on imaging, histologically confirmed peritoneal disease in the pelvis, and nodules in the lungs. Because of the unusual pattern of disease, PDAC was verified using a targeted exome panel of tissue from this patient's peritoneal disease, which showed mutations in *KRAS*^*p.G12V*^ and *SMAD4*, further supporting the diagnosis of PDAC.

*HER2* amplification in other cancer types
------------------------------------------

The low prevalence of HER2 amplification in PDAC and many other cancer types makes clinical trials challenging. Analysis of genomic sequence data for 16 other cancer types (by TCGA, ICGC and other large-scale genomic efforts) identified an incidence of 0.5% to 13%, which equates to over 54,000 new cases of *HER2*-amplified cancers per year in the USA alone (see Additional file [10](#S10){ref-type="supplementary-material"}). Excluding breast and gastric cancer, we estimate an incidence in excess of 22,000 *HER2* amplified cancers per annum. Recruiting patients based on *HER2* amplification rather than organ of tumour origin would make trials of anti-HER2 therapies feasible in less common cancer types. In order to identify these 22,000 patients, 740,000 patients would need to be screened using IHC, and of these, all 2+ and 3+ IHC cases (\~8%) would require validation using ISH. This approach may circumvent the challenges of previous clinical trials based on organ of origin recruitment, in which there have been mixed results but some efficacy in individuals (see Additional file [11](#S11){ref-type="supplementary-material"}). An orthogonal approach by which a primary molecular classification is used and patients are sub-classified based on organ of origin may be more tractable.

Discussion
==========

In this study, we found that *HER2*-amplified PDAC has a prevalence of 2%, is detectable using contemporary genomic approaches, is associated with a clinical phenotype characterized by metastatic spread predominantly to the lungs and peritoneum with local recurrence, and can metastasize to the brain but tends to avoid the liver. It bears molecular similarities to *HER2*-amplified breast cancer, and is yet to be adequately assessed for potential responsiveness to anti-HER2 therapy. Multiple studies in large cohorts have shown that *HER2*-amplified breast cancers more commonly metastasize to the lung and the brain \[[@B22]\]. In PDAC, clinical trial data indicate that the first site of distant metastases occurred most commonly in the liver (50%), and occurred in the lung in 9% of the cases \[[@B23]\]. Autopsy studies have reported that 80% of distant metastases are to the liver, which occurred either alone or in combination with peritoneal and or lung metastases, while metastases sparing the liver made up the rest, and occurred in the peritoneum, lung, adrenal glands, and lymph nodes \[[@B24]\]. Cerebral metastases were not found in these studies. In our cohort of 469 patients, the incidence of lung metastases without liver metastases was 8%, comparable to previous studies. We detected only one case with brain metastases (0.2%), although only this single patient was investigated specifically for brain metastases. If we include all 10 *HER2*-amplified cases with documented metastatic disease, none had evidence of liver metastases (*P* = 0.0028), and the rate of lung metastases was 50% (*P* = 0.0022). These data suggest that *HER2*-amplified PDAC may have a distinct clinical phenotype, and that liver metastases are not determined by physical factors such as portal blood flow, but by the pathophysiology of disease.

These findings have significant clinical implications. First, the detection of small lung nodules should not delay the diagnosis of metastatic disease originating in the pancreas or at relapse if the liver and other sites are clear, particularly in known *HER2*-amplified cases. Second, if an individual is known to have an *HER2*-amplified PDAC, then monitoring for disease progression in non-traditional sites such as the lung and the brain, with vigilance for neurological symptoms may be prudent. Finally, there is potential for anti-HER2 therapies in this subset of patients.

*In situ* hybridization studies in our reference laboratory identified *HER2* amplification only in PDACs with high protein expression by IHC (score 2+/3+). Therefore, a reasonable and cost-effective approach to universal *HER2* screening is to initially test all cases with IHC and then perform secondary ISH testing only on cases with 2+/3+ staining, as was initially performed for breast cancer. Using this approach, 8% of PDAC (2+ and 3+ cancers) will require *HER2* ISH assessment, and of these one-quarter will be amplified.

In the current study, in-depth genomic analysis, apart from *HER2* amplification, did not reveal any features that are atypical of PDAC, with mutations of *KRAS* and *TP53* and loss of *SMAD4* found, although the inherent heterogeneity of PDAC makes it difficult to draw conclusions about the other mutations detected. mRNA expression profiles clustered with *HER2*-amplified breast cancer, suggesting that *HER2* may be an important driver of carcinogenesis in this subgroup of PDAC.

It is interesting to note that all three *HER2*-amplified cases with available genomic data harboured the *KRASp.G12V* mutation. This mutation is less common than the p.G12D mutation, and accounts for 32% of *KRAS* mutations in PDAC versus 40% for p.G12D \[[@B9]\]. Given the small numbers of *HER2*-amplified cases, further studies of larger cohorts will be required before it can be determined if this association is sufficiently robust to be used diagnostically or targeted therapeutically.

Two clinical trials have assessed targeted trastuzumab therapy in PDAC \[[@B15],[@B16]\]. Both are single arm phase II trials used in combination with gemcitabine \[[@B15]\] and capecitabine \[[@B16]\]. Although the latter performed *HER2* FISH for 2+ expressing cases, the former did not, and neither verified the 3+ IHC cases by FISH. In addition, these were not standardized assays performed in reference laboratories, and resulted in a HER2 positive rate of over 10%. This likely overestimation underpowered the trials by over 80%, making a negative result uninterpretable.

Conclusion
==========

*HER2* amplification occurs in 2.1% of PDAC cases, and is associated with an atypical pattern of metastatic disease. A number of cancers of different organs including NSCLC, ovarian cancer, cholangiocarcinoma, and PDAC have well-defined low-prevalence *HER2* amplification. Testing anti-*HER2* therapies may not be feasible in organ groups because of this low prevalence and the likely heterogeneous response rates. However, these studies could be approached using novel adaptive clinical trials testing personalized therapeutic strategies (for example,: BATTLE \[[@B25]\], I-SPY \[[@B26]\], FOCUS 4 \[[@B27]\] and IMPaCT \[[@B3]\]), or using a molecular taxonomy or 'biotype' that recruits *HER2*-amplified cancers irrespective of the organ in which the tumour arises (often referred to as 'basket' trials) \[[@B28]\], with specific attention to diagnostic criteria for patient recruitment, particularly as more effective anti-*HER2* therapies emerge.

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

APGI: Australian Pancreatic Genome Initiative; FISH: Fluorescence *in situ* hybridization; ICGC: International Cancer Genome Consortium; IHC: Immunohistochemistry; ISH: *In situ* hybridization; NSCLC: Non-small cell lung cancer; PDAC: Pancreatic ductal adenocarcinoma; SNP: Single nucleotide polymorphism; TCGA: The Cancer Genome Atlas.

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

JW has received honoraria received from Roche; RS has received clinical research funding received from Therapeutic Innovation Australia and Cancer Council New South Wales; DG has an uncompensated consultant/advisory role with Celgene, Bayer, and Pfizer, and has received clinical research funding from Celgene, Pfizer, and Amgen; AM is on the Roche HER2 advisory board in Australia, and had received honoraria from Roche. The remaining authors declare that they have no competing interests.

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

AC, NW, MJC, AJG, DKC, JW, MP, ALJ, AMN, CT, VC, LC, JH, JSS, ALM, SMG, and AVB were responsible for the concept and design of the study. All authors participated in collection and assembly of data, and AC, NW, MJC, AJG, DKC, JW, KK, MP, DG, ALM, SMG and AVB were involved in data analysis and interpretation. AJ, APGI, LC, HW, JS, AG, DKC and AVB were involved in provision of study materials or patients: All authors were involved in manuscript writing and final approval of the manuscript.

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

###### Additional file 1: Table S1

Literature reports of *Her2* positive rate in pancreatic ductal adenocarcinoma by immunohistochemistry and *in situ* hybridization techniques.

######

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###### Additional file 2: Supplementary methods

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###### Additional file 3: Table S2

Clinico-pathological characteristics of the cohort.

######

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###### Additional file 4: Figure S1

HER2 mRNA expression relative to cohort.

######

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###### Additional file 5: Table S3

Single nucleotide variations (SNVs) and small insertions or deletions (indels) within a patient with HER2 amplification, identified by whole exome sequencing, in MAF format. **Table S4.** Single nucleotide variations (SNVs) and small insertions or deletions (indels) annotated and compared with data from dbSNP, COSMIC, TCGA Breast, Biankin *et al*. \[[@B9]\] and Jones *et al*. \[[@B10]\]. Non-synonymous mutations were assessed for functional effects by two algorithms, Polyphen2 and SIFT; scores close to 1.0 and 0.0, respectively, are predicted to be probably damaging. These data have already been imported into COSMIC, thus many of the COSMIC identification numbers are unique only to this patient. **Table S5:** Genes affected by structural variation, as determined by whole genome sequencing of tissue from a patient with HER2 amplification. **Table S6:** Structural and copy number aberrations in a patient identified with HER2 amplification. Listed here are the genes affected by structural variation (SV) as determined by whole genome sequencing, or copy number altered, as determined by single nucleotide polymorphism (SNP) array analysis. Genes of interest were defined as those genes affected by SV that were expected to have a functional effect (that is, genes that were truncated, amplified (but not duplicated) or deleted); genes whose copy number was found by SNP array analysis to be greatly altered, that is, reduced to 0, or the algorithm's maximum of four copies (see Methods); and genes that were affected by copy number variation and that overlapped the statistically significant copy number altered regions, as determined by GISTIC 2.0 analysis (see Biankin *et al*., \[[@B9]\]), reported here by their q-value.

######

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###### Additional file 6: Table S7

Genomic alterations within a patient identified with HER2 amplification. Genes of interest were defined as those affected by non-synonymous single nucleotide variation (SNV); small insertions or deletions (see Additional file [5](#S5){ref-type="supplementary-material"}: Table S3); genes affected by structural variation (SV) as determined by whole genome sequencing, or copy number variation (CNV) as determined by SNP array analysis (see Additional file [5](#S5){ref-type="supplementary-material"}: Table S6).

######

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###### Additional file 7: Table S8

MSigDB analysis of all genes affected by mutation, tructural variation (SV) and copy number variation (CNV) (from Additional file [6](#S6){ref-type="supplementary-material"}: Table S7).

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###### Additional file 8: Figure S2

Gene expression profiles from 90 pancreatic cancer primary tumors, classified into five intrinsic breast subtypes by the PAM50 classifier. The HER2-amplified patient is highlighted by a white box; genes overexpressed or underexpressed in this patient are highlighted in red and green, respectively.

######

Click here for file

###### Additional file 9: Figure S3

Algorithm for diagnostic testing for *HER2* amplification.

######

Click here for file

###### Additional file 10: Table S9

Incidence of and deaths from *HER2*-amplified cancers in the USA. Extrapolating from the frequency of *HER2* amplifications identified from current projects from The Cancer Genome Atlas (TCGA) and our own International Cancer Genome Consortium (ICGC) pancreatic ductal adenocarcinoma (PDAC) project, with the estimated number of new cancer cases in the USA in 2012. From the 1.37 million expected cases, we would expect 54,000 (3.94%) new cases of HER2-amplified cancer. Excluding breast and stomach, for which *HER2* diagnostic assays are common practice, we expect 740,000 new cases, 22,000 of which will be *HER2*-amplified. Extrapolating from the number of immunohistochemistry (IHC) and *in situ* hybridization (ISH) assays to identify *HER*2-amplified PDAC (see Additional file [9](#S9){ref-type="supplementary-material"}: Figure S3), to identify these additional cases, we would expect to perform 742,000 IHC and 117,000 FISH assays, of which 22,000 will be confirmed (that is, will have IHC staining of 2+ or 3+ and be ISH-positive). **Figure S4.** Prevalence of *HER2* amplification (≥4 copies) in cancer: data from The Cancer Genome Atlas (TCGA), via the cBio portal, and the International Cancer Genome Consortium (ICGC) \[[@B9]\].

######

Click here for file

###### Additional file 11: Table S10

Clinical trials of *HER2*-amplified cancers, excluding breast and stomach (gastric) cancers.

######

Click here for file

Funding/support
===============

The work was supported by the National Health and Medical Research Council of Australia (NHMRC; 631701, 535903, 535914); Cancer Council NSW (SRP06-01; ICGC09-01; SRP11-01); Australian Government: Department of Innovation, Industry, Science, Research and Tertiary Education (DIISRTE); Australian Cancer Research Foundation (ACRF); Queensland Government (NIRAP); University of Queensland; Cancer Institute NSW (06/ECF/1-24, 09/CDF/2-40, 07/CDF/1-03, 10/CRF/1-01, 08/RSA/1-15, 10/CDF/2-26,10/FRL/2-03, 06/RSA/1-05, 09/RIG/1-02, 10/TPG/1-04, 11/REG/1-10, 11/CDF/3-26); Garvan Institute of Medical Research; Avner Nahmani Pancreatic Cancer Foundation; R.T. Hall Trust; Jane Hemstritch in memory of Philip Hemstritch; Gastroenterological Society of Australia (GESA); Royal Australasian College of Surgeons (RACS); Sydney Catalyst Translational Cancer Centre; Royal Australasian College of Physicians (RACP); Royal College of Pathologists of Australasia (RCPA) and the St Vincent's Clinic Foundation.

Acknowledgments
===============

We thank all the members of the Australian APGI (<http://www.pancreaticcancer.net.au/apgi/collaborators>) for their continuing support with provision of biospecimens and clinical data. We also thank Mary-Anne Brancato BSc, Michelle Thomas BSc, Sarah Rowe BSc, and Mona Martyn-Smith BSc for maintenance of the APGI database and biospecimen resource.


How Does Obama fulfill the "deceiver" archetype?

Primary tabs

By the "deceiver" archetype, I refer to inter-personal, social, spiritual and exopolitical archetypes, not to any specific individual. The "deceiver" archetype refers to an archetypal social, spiritual, and exopolitical role that an individual may play in society. Some archetypes such as “Antichrist” and “False Prophet” are archetypes that have been set out in prophetic texts. Hermeneutics is the art of interpreting prophetic texts and hermeneuticists in the recent historical era have attempted to interpret to Revelations to frame Napoleon Bonaparte and Adolph Hitler, respectively, as fulfilling the prophesized archetype of the "Deceiver". The "deceiver" archetype embodies duality consciousness (“I win - you lose”) with an emphasis on deception, and is the obverse of Unity Consciousness ("We are all One") embodied by Christ consciousness.

The individual publicly known as “Barack Hussein Obama” is a reasonable candidate for fulfilling the "deceiver" archetype on at least the following grounds, prima facie evidence for which is set out in this article:

1. He is practiced in institutional deception since adolescence;

2. As U.S. President he has covertly participated in and coordinated false flag operations causing ecocide and genocide through environmental warfare; bioweapons; and ionizing radiation.

3. He was elected to the highest political position of U.S. President by mass deception.

4. As U.S. President he has covertly participated in and served as "public Capstone" for false flag strategic deception operations intended to deceptively deprive U.S. citizens of their rights under the Bill of Rights including their right to bear arms, and ultimately to depopulate and enslave the U.S. population.

5. He covertly and intentionally serves as an archetypal "deceiver" asset of an international war crimes racketeering organization intent on installing a global police state known as The New World Order, and he owes primary allegiance to the New World Order crime organization.

Each of the above five (5) aspects of the deceiver archetype of Barack Hussein Obama is documented in extenso (some might say ad nauseam) in the article below.

"Identify the Perpetrator": Why it is important to expose the deceiver "Barack Hussein Obama" publicly

"Identify the Perpetrator" is a succesful strategy that individuals and groups can use when liberating themselves of abuse by a perpetrator whom they had difficulty identifying and naming not only publicly but even to themselves.

"Friendly Fascism" is a methodology that I analyzed as a Futurist at Stanford Research Institute in the mid-1970s as the system that the new world order would attempt to implement in the United States at this time. "Friendly Fascism" is the programmatic name for the integrated mind control police state that is now being rolled out in the United States, as well as in other jurisdictions around the planet. In the United States variety, "Barack Hussein Obama" serves a key role as the "friendly leader". "Barack Hussein Obama" is to "Friendly Fascism" what Adolph Hitler was to Nazism in the 1930s.

"Friendly fascism" includes mass mind control technologies ranging from HAARP to synthetic terror events like the Newtown and Boston Marathon that create a "consensual trance" in the population, so the population feels it is imposing "friendly fascism" as part of its democratic process.

In this context, The consensual trance in the United States (and in the world) is still such that "Barack Hussein Obama" is to "Friendly Fascism" what Adolph Hitler was to Nazism in the 1930s. The consciousness of the typical citizen who is in a "consensual trance" assumes that the ecocidal events, the genocides like Fukushima, the false flag operations like Hurricanes Isaac and Sandy, the false flag psyops like Sandy Hook Elementary and the Boston Marathon are occur as "an act of God", natural events, or random, senseless acts of terrorists or deranged individuals in the case of false flag "terror events". In fact, all of these events are intentional acts of state terror put into operation and/or coordinated under plausible deniability around the chief executive officer of that state, in this case "Barack Hussein Obama."

Under the "consensual trance", these false flag events are, where feasible, so designed with "Barack Hussein Obama" as the Capstone who comes in after the tragedy and offers the benediction of the state that will protect the citizens, while concealing the forensic evidence establishing that it is state terror under the plausibale deniability coordination of "Barack Hussein Obama" that creates the catastrophe in the first place.

Why it is reasonable to hypothesize that "Barrack Hussein Obama" fulfills the social archetype of arch-deceiver?

Comments

The entire George W. Bush administration was involved in the same types of activities. Bush and Cheney were much more fitting of the title "deceiver". And so were all the republican administrations before them. But everyone seems to forget or ignore the past as they see fit.

No difference in the administrations. C & B were seen as siniter, esp. C., and the press did not favor them or shrill for them much. At least we knew who they were. B.O. is a total masquerade and fraud, since the cover of Time in 2004 where his qualification for American president seemed to be that he wasn't really very American. Fraud alert. But you don't need to be intuitive. Just rationally there are so many obvious obfuscations that only mass control and a controlled media can explain it.

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---
abstract: 'In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces error for the image processing (used in image guided surgery), but also reduces the visibility of the surgeons. In this paper, we propose to enhance the laparoscopic images by decomposing them into unwanted smoke part and enhanced part using a variational approach. The proposed method relies on the observation that smoke has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained unwanted smoke component is then subtracted from the original degraded image, resulting in the enhanced image. The obtained quantitative scores in terms of FADE, JNBM and RE metrics show that our proposed method performs rather well. Furthermore, the qualitative visual inspection of the results show that it removes smoke effectively from the laparoscopic images.'
address: |
    $^{1}$ Norwegian Colour and Visual Computing Lab, Norwegian University of Science and Technology, Norway.\
    $^{2}$ L2TI-Institut Galilée, Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France\
    $^{3}$The Intervention Centre, Oslo University Hospital, Oslo, Norway.\
    $^{4}$The Department of Informatics, University of Oslo, Oslo, Norway.
bibliography:
- 'strings.bib'
- 'refs.bib'
title: A SMOKE REMOVAL METHOD for LAPAROSCOPIC IMAGES
---

Laparoscopic images, smoke removal, dehazing, variational, quality.

Introduction {#sec:intro}
============

As one of the most important intra-operative data modality, laparoscopic images’ quality is of vital importance for navigation systems and for the operating surgeons [@stoyanov2012surgical]. The artifacts during laparoscopic images specificities include smoke, blood, dynamic illumination conditions, specular reflections, *etc* [@sdiri2016adaptive]. Smoke significantly reduces the contrast and radiance information for large areas of the scene. Computer vision algorithms’ performance and surgeons’ visibility would inevitably suffer from this degradation. Therefore, smoke removal in laparoscopic images becomes necessary to improve the image guided surgery conditions and to provide a better operation field visualization.

To the best of our knowledge, there is only a few recent works related to laparoscopic desmoking [@kotwal2016joint; @baid2017joint; @tchakaa2017chromaticity; @luo2017vision]. In these papers, the image desmoking problem is considered as a dehazing problem which has been studied for many years in the literature [@tan2008visibility; @he2011single]. In such problem, the atmospheric scattering model presented by Eq. (\[scattering\]) describes the formation of a hazy image and is widely used in computer vision [@narasimhan2002vision].

$$\label{scattering}
\mathbf{I}(x)=\mathbf{J}(x)t(x)+\mathbf{A}(1-t(x)),$$

where $\mathbf{I}$ is the observed intensity, $\mathbf{J}$ is the scene radiance representing the haze-free image, $t$ is the medium transmission map considered to be inversely related to the scene’s depth, and $\mathbf{A}$ is the airlight which is usually a constant as it is the global atmospheric light which is location independent.

However, while haze related to scene depth, smoke concentration is a local phenomenon which does not depend on the scene depth, but rather depends on the position of the tip of the thermal cutting instrument. Moreover, in laparoscopic images, the light source is provided from the instrument which is not evenly distributed, and the organ surface is not a Lambertian surface. These properties violate the assumptions underlying Eq. (\[scattering\]), which makes it inappropriate for laparoscopic images.

In this paper, we propose a novel laparoscopic images smoke removal method. More precisely, instead of resorting to the classical physical atmospheric model, we propose another model where the degraded image is separated to two parts: the weighted smoke part and the desmoked one that should be recovered. To estimate the smoke, our approach relies on two easily verifiable assumptions: smoke has a low contrast and low inter-channel differences.

The remainder of this paper is organized as follows. In Sec. \[sec:rel\_works\], a review of image dehazing as well as laparoscopic image desmoking methods are given. Sec. \[sec:method\] describes our proposed approach by defining the energy function and the optimization procedure. Finally, in Sec. \[sec:result\], experimental results are presented and some conclusions are drawn in Sec. \[sec:conclustion\].

RELATED WORKS {#sec:rel_works}
=============

Recently, some works were proposed for desmoking in laparoscopic images [@kotwal2016joint; @baid2017joint; @tchakaa2017chromaticity; @luo2017vision]. In [@kotwal2016joint], the authors formulated a joint desmoking and denoising problem as a Bayesian inference problem based on probabilistic graphical model. This work is then extended in [@baid2017joint] for desmoking, denoising and specularity removal. In [@tchakaa2017chromaticity], an adapted dark-channel prior combined with histogram equalization method is presented. In [@luo2017vision], a visibility-driven fusion defogging framework is proposed.

While there is few works related to laparoscopic images smoke removal, a similar problem referred to as image dehazing has also been studied in the literature. Many of the image dehazing works use the atmospheric scattering model and rely on the estimation of the transmission map $t$ or the depth map of the images and the airlight alternatively [@zhu2015fast; @he2011single; @tarel2009fast]. He *et al.* propose the dark channel approach based on a statistical observation from outdoor haze-free images: for most of the haze-free natural images, pixel values are very low for at least one channel [@he2011single]. The transmission map $t$ computed by this prior together with an estimated $\mathbf{A}$ calculated from the detected most haze-opaque region of the image are applied to invert Eq. (\[scattering\]), resulting in a haze free image. This is a well-known efficient approach and lots of recent methods are proposed based on it [@tchakaa2017chromaticity; @xu2012fast].

Some works have been also developed without estimating transmission or depth maps. Tan *et al.* [@tan2008visibility] tried to enhance the haze image directly by maximizing the local contrast under an airlight smooth constraint. In [@ancuti2013single], a multi-scale fusion dehazing method is proposed by deriving a white balance and contrast enhanced inputs.

In [@galdran2014variational], a variational contrast enhancement framework for image dehazing with a modified gray-world assumption is proposed. Later in [@galdran2015enhanced], an improved version is presented, where a saturation term is added to the variational cost function aiming to maximize the contrast and saturation together. In [@galdran2017fusion], Galdran *et al.* further improved their work by enhancing faraway regions where normally have more fog and preserving nearby low-fog regions. Those methods do not rely on a physical atmospheric model, but try to maximize contrast and saturation. However, the modified gray-world assumption and the assumption that pixels’ intensity is related to depth are violated in laparoscopic images.

Proposed smoke removal approach {#sec:method}
===============================

Variational techniques have attracted a considerable attention over the last years in signal/image processing literature. They have been found to be among the most powerful techniques in different fields such as enhancement, restoration, super resolution and disparity/motion estimation from a sequence of images. For this reason, we propose here a variational approach to remove smoke in laparoscopic images. More precisely, an energy function is first defined and then minimized (i.e optimized) via an augmented Lagrangian method, as we shall address next.

Energy function
---------------

Due to the aforementioned limitations of the atmospheric scattering model in laparoscopic images, we propose to consider another model where the degraded laparoscopic image is assumed to follow this decomposition strategy: $$\label{model}
\mathbf{I}^{c}=\mathbf{J}^{c}+\alpha^{c} \cdot \mathbf{F}^{c},$$ where $c\in\{R,G,B\} $ indicates the RGB channels, $\mathbf{I}$ is the obtained degraded image by laparoscopic camera, $\mathbf{J}$ contains the color image information, $\mathbf{F}$ is the unwanted smoke component and $\alpha^{c}$ is a scalar weight for every channel. Thus, the smoked images $\mathbf{I}$ is separated into two parts: the smoke part $\mathbf{F}$ and the enhanced one $\mathbf{J}$.

Based on the observations that smoke part’s variation is smooth and the RGB channel differences are low as a result of the whitish property of smoke, we propose to estimate the smoke part by minimizing the following energy function: $$\label{cost}
E=\frac{\lambda }{2}\left \| \mathbf{F}-\mathbf{I} \right \|^{2}+\left \| \mathbf{F}_{TV} \right \|_{2},$$ where $\mathbf{I}$ is the degraded color image in the RGB color space, $\mathbf{F}$ is the smoke part to be estimated, $\lambda$ is a scalar to adjust weights between the two terms of the equation, and $\left \| \mathbf{F} _{TV}\right \|_{2}$ is an isotropic total variation (TV)-norm which is given by: $$\label{tv}
\left \| \mathbf{F} _{TV}\right \|_{2}=\sum_{i} \sqrt{\beta _{x}^2[\mathbf{D}_{x}\mathbf{F}]_{i}^{2}+\beta _{y}^2[\mathbf{D}_{y}\mathbf{F}]_{i}^{2}+\beta _{c}^2[\mathbf{D}_{c}\mathbf{F}]_{i}^{2}},$$ where $\beta_{x}$, $\beta_{y}$, $\beta_{c}$ are three scalar parameters to balance the weights between the gradient of the color image and the inter-channel differences. $\mathbf{D}_{x}$, $\mathbf{D}_{y}$, $\mathbf{D}_{c}$ are the forward differential operators along the three dimensions. Thus, we have $\mathbf{D}_{x}\mathbf{F}=\mathbf{F}(x+1,y,c)-\mathbf{F}(x,y,c)$, $\mathbf{D}_{y}\mathbf{F}=\mathbf{F}(x,y+1,c)-\mathbf{F}(x,y,c)$, and $\mathbf{D}_{c}\mathbf{F}=\mathbf{F}(x,y,c+1)-\mathbf{F}(x,y,c)$. Note that $(x,y,c)$ represents the pixel coordinates of the color image with horizontal and vertical directions $(x,y)$ and channel direction $c$. Using matrix-vector notation, $[\mathbf{D}_{d}\mathbf{F}]_i$, with $d \in \{x,y,c\}$, denotes the $i$-th component of the one dimensional vector obtained from $\mathbf{D}_{d}\mathbf{F}$.

The first term in Eq. (\[cost\]) aims to keep the similarity between the estimated smoke part and the input degraded image. The second total variation norm part represents the properties of the smoke part: low contrast and low inter-channel differences.

After the estimation of global smoke $\textbf{F}$, the smoke free image $\textbf{J}$ is then calculated as: $$\label{final}
\mathbf{J}^{c}=\mathbf{I}^{c}-\alpha^{c} \cdot \mathbf{F}^{c},$$ where $\alpha^{c}$ is defined as the mean values of the estimated smoke image over the RGB channels.

Optimization method
-------------------

The energy function minimization problem can be solved by employing the augmented Lagrangian method [@chan2011augmented], which will be described in the following. The function, given by Eq. (\[cost\]), is split by introducing an intermediate new variable $\textbf{u}$: $$\label{spliting}
\begin{split}
&\displaystyle{\min_{\textbf{F}}  \quad \frac{\lambda }{2}\left \| \mathbf{F}-\mathbf{I} \right \|^{2}+\left \| \mathbf{u} \right \|_{2}}, \\
&\textrm{\textit{s. t.}} \quad \mathbf{F}_{TV}-\mathbf{u}=0.
\end{split}$$ The augmented Lagrangian for Eq. (\[spliting\]) is:

$$\label{split}
    L_{\rho}(\mathbf{F},\mathbf{u},\mathbf{y})=\frac{\lambda }{2}\left \| \mathbf{F}-\mathbf{I} \right \|^{2}+\left \| \mathbf{u} \right \|_{2}+\mathbf{y}^{T}(\mathbf{F}_{TV}-\mathbf{u})+\frac{\rho}{2}\left \|\mathbf{F} _{TV}- \mathbf{u}\right \|^{2},$$

where $\rho$ is a non-negative constant parameter called penalty parameter and $\mathbf{y}=[\mathbf{y}_{x}^{\top},\mathbf{y}_{y}^{\top},\mathbf{y}_{c}^{\top}]^{\top}$ is the Lagrange multipliers vector and $\mathbf{u}=[\mathbf{u}_{x}^{\top},\mathbf{u}_{y}^{\top},\mathbf{u}_{c}^{\top}]^{\top}$. Then, the alternating direction method (ADM) [@boyd2011distributed] is used to solve the following minimization sub-problems iteratively:

$$\label{subproblem}
    \begin{split}
    \textbf{F}^{k+1}&:=\operatorname*{argmin}_{\textbf{F}} L_{\rho}(\textbf{F},\textbf{u}^{k},\textbf{y}^{k}),\\
    &=\operatorname*{argmin}_{\textbf{F}} \frac{\lambda }{2}\left \| \mathbf{F}-\mathbf{I} \right \|^{2}+(\mathbf{y}^{k})^{\top}(\mathbf{F}_{TV}-\mathbf{u}^{k})+\frac{\rho}{2}\left \|\mathbf{F} _{TV}- \mathbf{u}^{k}\right \|^{2},  \\
        \textbf{u}^{k+1}&:=\operatorname*{argmin}_{\textbf{u}} L_{\rho}(\textbf{F}^{k+1},\textbf{u},\textbf{y}^{k}),\\
    &=\operatorname*{argmin}_{\textbf{u}} \left \| \mathbf{u} \right \|_{2}+ (\mathbf{y}^{k})^{\top}(\mathbf{F}_{TV}^{k+1}-\mathbf{u})+\frac{\rho}{2}\left \|\mathbf{F} _{TV}^{k+1}- \mathbf{u}\right \|^{2},  \\
        \textbf{y}^{k+1}&:= \textbf{y}^{k}+\rho(\textbf{F}_{TV}^{k+1}-\textbf{u}^{k+1}).
    \end{split}$$

By introducing the operator $\textbf{D}=[\beta _{x}\textbf{D}_{x}^{\top},\beta _{y}\textbf{D}_{y}^{\top},\beta _{c}\textbf{D}_{c}^{\top}]^{\top}$, the $\textbf{F}$-minimization subproblem leads to the following solution: $$\label{fproblem}
\mathbf{F}=\mathcal{F}^{-1}[\frac{\mathcal{F}[\lambda \mathbf{I}+\rho\mathbf{D}^{\top}\mathbf{u}-\mathbf{D}^{\top}\mathbf{y}]}{\lambda+\rho(\left | \beta_{x}\mathcal{F}[\textbf{D}_{x}]\right |^{2}+\left |\beta_{y}\mathcal{F}[\textbf{D}_{y}]\right |^{2}+\left|\beta_{c}\mathcal{F}[\textbf{D}_{c}]\right |^{2} )}],$$ where $\mathcal{F}$ is the Fourier transform operator. Then, the $\textbf{u}$ minimization subproblem results in: $$\label{uproblem}
\mathbf{u}_{x}=\mbox{max}\left \{  \mathbf{v}-\frac{1}{\rho},0 \right \} \cdot \frac{\mathbf{v}_{x}}{\mathbf{v}},$$ where $\mathbf{v}_{x}=\beta_{x} \textbf{D}_{x}\textbf{F}+(\frac{1}{\rho})\textbf{y}_{x}$. Similar definition is applied to $\textbf{v}_{y}$, $\textbf{v}_{c}$, and $\textbf{v}=\mbox{max} \{ \sqrt{\left|\textbf{v}_{x}\right|^{2}+\left|\textbf{v}_{y}\right|^{2} +\left|\textbf{v}_{c}\right|^{2}}, \epsilon  \}$ with $\epsilon $ a small constant. In a similar way, $\textbf{u}_{y}$ and $\textbf{u}_{c}$ are determined to obtain the vector $\mathbf{u}$. More details about these solutions can be found in [@chan2011numerical].

EXPERIMENTAL RESULTS {#sec:result}
====================

In vivo procedure datasets [@giannarou2013probabilistic; @ye2017self], taken from Hamlyn Centre Laparoscopic / Endoscopic Video Dataset Page[^1], are used for validation. *Dataset1* has 96 smoked images and *Dataset2* contains 4031 images. In order to show the benefits of the proposed method, we will compare it to the following recent ones [@he2011single; @galdran2015enhanced; @galdran2017fusion]. The first one is the atmospheric model based image dehazing method with dark channel prior [@he2011single]. This method will be designated by DCP. It is important to note here that similar approach has been investigated in [@tchakaa2017chromaticity] to remove smoke by adding thresholding or refining steps. However, the later approach has not been considered in our evaluation because of its sensitivity to different parameters which should be empirically selected for input smoked images of the large experimental dataset. The second one, which will be denoted by E-VAR, corresponds to an enhanced variational approach developed in [@galdran2015enhanced]. Finally, the third one, designated by F-VAR, is a fusion-based variational technique [@galdran2017fusion]. The parameters setting used in this experiment are: $\lambda=1$ for Eq. (\[cost\]), $\beta_{x}=\beta_{y}=\beta_{c}=1$ for Eq. (\[tv\]) and $\rho=5$.

![image](img192.png){width="3.3cm"}

![image](DCP192.png){width="3.3cm"}

![image](VARF192.png){width="3.3cm"}

![image](VARE192.png){width="3.3cm"}

![image](OUR192.png){width="3.3cm"}

![image](4003.png){width="3.3cm"}

![image](DCP4003.png){width="3.3cm"}

![image](VARF4003.png){width="3.3cm"}

![image](VARE4003.png){width="3.3cm"}

![image](OUR4003.png){width="3.3cm"}

![image](3366.png){width="3.3cm"}

(a)

![image](DCP3366.png){width="3.3cm"}

(b)

![image](VARF3366.png){width="3.3cm"}

(c)

![image](VARE3366.png){width="3.3cm"}

(d)

![image](OUR3366.png){width="3.3cm"}

(e)

![image](fade.png){width="1.1\linewidth"}

(a)

![image](JNBM.png){width="1.1\linewidth"}

(b)

![image](e.png){width="1.1\linewidth"}

(c)

**Quantitative evaluation:** Examples of three images are shown in Fig. \[fig:res1\](a). As the ground-truth information for a smoked laparoscopic image is not available, we propose to employ two no-reference image quality metrics and another metric that compares the visibility of edges before and after smoke removal. For the purpose of evaluating the ability of smoke removal, a referenceless Fog Aware Density Evaluator (FADE) has been used to evaluate the perceptual fog density [@choi2015referenceless; @fog]. A lower FADE value means a lower perceptual fog density. Besides, a just noticeable blur based no-reference objective image sharpness metric (JNBM) [@ferzli2009no] is used to evaluate the perceptual sharpness. A higher value means higher perceptual sharpness or lower blurriness. Further more, we employ a metric, proposed by Hautière *et al.* [@hautiere2011blind], which aims to assess the ability of restoring edges (RE) that are not visible in **I** but are in **J** (obtained after smoke removal). This metric will be designated by RE. A higher RE value means a better edge restoration.

Tables \[result1\] and \[result2\] show mean and standard deviation of the scores of the different approaches for *Dataset1* and *Dataset2*. Fig. \[fig:res2\] illustrates the scores of the three metrics on *Dataset2*. All the three metrics show better scores for our approach. In terms of FADE metric, the DCP method removes smoke well. However, it scarifies the perceptual quality as shown in Fig. \[fig:res1\]  (b) as a result of the constant airlight assumption. E-VAR removes more smoke than F-VAR. F-VAR’s results indicate that there are still high smoke density in the images. Our proposed method’s smoke density is the lowest. The proposed approach removes the smooth smoke component of the image resulting in a contrast enhanced image, which has the best scores for JNBM and RE.

**Qualitative evaluation:** In this part, we evaluate the different methods subjectively. Fig. \[fig:res1\] illustrates the results for three laparoscopic images from the two datasets. It can be observed, from Fig. \[fig:res1\](b), that the DCP method can remove the smoke effectively but causes an unnatural color change in the desmoked images. Moreover, as shown in Fig. \[fig:res1\](c), the smoke is not well removed by the F-VAR approach as smoke is independent of depth in laparoscopic images and this approach tries to preserve image information on nearby region under the assumption that the concentration of the haze is related with depth. E-VAR [@galdran2015enhanced] method relies mildly on the physical model, leads to the fine result shown in Fig. \[fig:res1\](d). Finally, Fig. \[fig:res1\](e) shows that our proposed method allows to remove smoke effectively, leading to an output image with enhanced contrast.

Therefore, all the obtained results confirm the benefits of the proposed desmoking method for laparoscopic images.

Conclusion {#sec:conclustion}
==========

Unlike most of the natural image dehazing methods which rely on a physical model, we propose in this paper a variational desmoking method for laparoscopic images. The aim is to remove the smoke from the scene, thus to improve the image guided surgery condition as well as the surgeons’ visibility. Quantitative and qualitative evaluations are performed and show that the proposed approach reduces the smoke effectively while preserving the important perceptual information of the image. Further work should include a more robust prior about the smoke.

[^1]: http://hamlyn.doc.ic.ac.uk/vision/


Due to the fact that not only my team and I like but also many of our customers like to mix boilies and pellets, I had the idea of launching the Action packs. Besides our micro crumb the mix contains different pellets, boilies in different sizes and crushed ones. To cut it short a better attraction cannot be achieved. Before your fishing session add a bit of one of our liquids to the mix and you´re ready to go!


Brendon

Brendon is a village in Devon, England, close to the border with Somerset within the Exmoor National Park. The village is  southeast of Lynton and  west of Minehead in the East Lyn Valley. It is located just off the A39 and is on two long distance footpaths, the Coleridge Way and the Samaritans Way South West. The Church of St Brendon is  from the village and was built in 1738, possibly with building material brought from another site.

The village
The Church of St. Brendon was built in 1738, possibly using the stones from the original church at Cheriton. It lies midway between Cheriton and Brendon, about two miles (3 km) from each, and has a tower and four bells. The church was further restored in the nineteenth century and houses a Norman font. There is a sundial above the porch dated 1707.

The village is located just off the A39 and its population was 159 in 2001. The parish of Brendon is roughly square in shape and is defined by the East Lyn River to the north, the Hoaroak Water to the west and the Badgworthy Water to the east; a tributary of the latter, the Hoccombe Water defines part of its southern boundary. Brendon Common occupies a part of the moorland area which characterises the south of the parish. Badgeworthy Water is crossed by the 17th century packhorse Malmsmead Bridge.

History
The name contains two Old English place-name elements, the first – from brom – referring to the plant broom and the second – from dun – which tends to signify a fairly extensive and flat hill or upland expanse. It is recorded in the Domesday Book as Brandone. It is not connected with the Brendon Hills in Somerset, the name of which has a different origin.

White's Devonshire Directory (1850) describes Brendon as follows:
A small village 1½ miles SE of Lynton & 15 miles W of Minehead is in a picturesque valley of the river Lyn, has in its parish 271 souls & 6733 acres of land including Leeford hamlet & a large tract of moorland on the borders of Somersetshire where the rivers Exe, Lyn and Barle have their sources. F. W. Knight Esq. is the Lord of the mannor owner of most of the soil, and patron of the rectory ... valued in 1831 at £148. The Rev. T. Roe of Oare, Somerset is the incumbent, and has 57a, 2r, 22p, of glebe. The parsonage is a small cottage, and the church [St. Brendon] is an ancient structure with a tower and 4 bells.

Brendon is home to the Exmoor Folk Festival, and is both on the route of the Coleridge Way and the Samaritans Way South West.

Since 2012, Brendon has formed part of the civil parish of Brendon and Countisbury, having previously been a civil parish in its own right.

References

External links

 Brendon Valley

Category:Villages in Devon
Category:Former civil parishes in Devon
Category:North Devon


by ray goodlass

By the time this column appears much water may have passed under the bridge of our energy security, electricity supply and prices debate, but at the time of writing I am struck by one particular aspect of it, namely the collapse of the political consensus that held sway over the last twenty years or so that privatisation of the electricity retail market would lead to lower prices.

And a very good thing too, for evidence shows that privatisation leads to price gouging and deterioration of service levels, as so clearly demonstrated by what has happened to vocational education, child care, job centres, Sydney airport, and many other services.

With regards to electricity pricing, this aspect of neo-liberal deregulated laissez-faire capitalism has elements of the ‘Emperor’s New Clothes’ about it, and though thankfully some are now seeing through this myth Malcolm Turnbull, Josh Frydenberg & Co fail to see it, unfortunately. They have focussed solely on supply augmentation, with part-solutions such as Snowy Hydro 2.0, which will be beset with problematic issues, all they can do is call for gas companies, and the state governments that regulate drilling, to produce more gas, which can only mean coal seam gas, produced by fracking, just when we thought that one had been firmly put in its box.

Only South Australia is prepared to go look at a public service approach, though as Greens Senator for South Australia Sarah Hanson-Young said: “All kudos to the South Australian state government for standing up to fill the gap left by the missing-in-action “Innovation Prime Minister” Malcolm Turnbull, but they’ve backed the wrong horse in funding a gas-fired power plant and letting rip on fracking.”

Back to electricity pricing. In the latest Grattan Institute report, Price Shock: Is the retail electricity market failing consumers?, Tom Woods, its Energy Program Director, provides evidence that in the electricity retail sector the anticipated price reductions have not happened, and innovation has been very slow in coming.

The privatisation of Australia’s electricity retail markets dates back to 1993. The ensuing decade saw a raft of reforms that initially delivered increases in productivity, lower prices and business innovation. But in the decade after that, this progress became much harder to sustain.

The idea was for states to create regulated monopolies in electricity transmission and distribution (poles and wires), while deregulating the retail side (the supply of gas and electricity to customers).

The competition in electricity generation largely delivered lower wholesale prices through the National Electricity Market (NEM), but not at the retail level.

Yet so far there have been few genuine innovations in electricity pricing. The most common tactic has been a discount for paying on time or by direct debit, although consumers are often frustrated when they discover that at the end of their contract they lose the discount even if they continue to pay the same way.

Products that offer different prices for electricity use at different times of the day have been slow to appear. These products have the potential to deliver major savings, yet the industry has failed to deliver them in a way that makes them easy for customers to understand and adopt.


hich is smaller: 606/47 or 8?
8
Which is greater: 1 or 8/602013?
1
Which is greater: -811 or -796?
-796
Which is smaller: -59614 or -10?
-59614
Which is smaller: 2/198997 or 1?
2/198997
Which is smaller: -13746/41 or -334?
-13746/41
Is -2/17 not equal to 448992?
True
Is -5 <= 0.27545?
True
Is -5 bigger than 1430.5?
False
Is 0.204 greater than or equal to 21/8?
False
Are -8018 and -8018 non-equal?
False
Which is greater: -212/57 or 3/88?
3/88
Which is greater: -3981 or -2486?
-2486
Which is bigger: 28988 or 28980?
28988
Which is smaller: 0.28617 or -2/11?
-2/11
Is -279 < 111?
True
Is 1 <= -9/21383?
False
Which is bigger: -9/7 or -33336?
-9/7
Which is bigger: -3/112840 or 1?
1
Is 0 less than 47/14591?
True
Is -6/712235 smaller than 1?
True
Which is smaller: 220.7 or -3/2?
-3/2
Is -5/39 < -478?
False
Which is smaller: -324859 or -2598875/8?
-2598875/8
Is 7/309835 < 0?
False
Which is smaller: 3751 or 3348?
3348
Which is bigger: 257/6 or -2/1829?
257/6
Is -428 at least as big as -389?
False
Is 542 < 406?
False
Which is smaller: 816503 or 1633003/2?
1633003/2
Which is smaller: 0 or -6/924625?
-6/924625
Are -327687 and -327692 equal?
False
Is 2/49 less than -0.2041?
False
Which is greater: -12563 or -100499/8?
-100499/8
Are 862 and -393 unequal?
True
Which is smaller: -27934 or -0.1?
-27934
Is -1329036 <= -1329035?
True
Which is greater: 17725 or -0.1?
17725
Is 91 smaller than -6394?
False
Which is greater: -2 or -217/137?
-217/137
Which is bigger: -114390/49 or -2334?
-2334
Which is bigger: -7 or -6524.8?
-7
Do -8525 and -8525 have the same value?
True
Which is bigger: 69887 or 69894?
69894
Which is smaller: -17250 or -17252?
-17252
Which is smaller: 116078 or 5/3?
5/3
Is 2/18247485 != 0?
True
Which is smaller: -7 or -32468?
-32468
Which is smaller: 167538 or 167531?
167531
Which is bigger: 132910/13 or 10223?
132910/13
Which is bigger: 1822 or 1753?
1822
Is 1 at least as big as -151/20894?
True
Is -1.99 greater than -13.4?
True
Which is bigger: 1 or 41/1206?
1
Which is bigger: 487462 or 487464?
487464
Is -43137 greater than 19?
False
Is 38 greater than or equal to 19779?
False
Which is greater: 12/2165 or -1?
12/2165
Is 379333 < 379333?
False
Is 185692 > -1/4?
True
Which is smaller: 205545 or 205546?
205545
Is -36514 bigger than -36511?
False
Is -5648 greater than or equal to -39534/7?
False
Is 158861 less than or equal to 3?
False
Which is bigger: -4 or 80061?
80061
Which is smaller: -4/1031615 or 0?
-4/1031615
Is -802598 >= 1?
False
Which is smaller: 557049 or 557050?
557049
Do 1756791 and -1 have the same value?
False
Which is bigger: 0 or -258/33199?
0
Which is smaller: 34000/3 or 11334?
34000/3
Which is greater: -702506/5 or -140501?
-140501
Is 1279 not equal to 710?
True
Is -1 >= -280/26153?
False
Which is bigger: -8555 or -8558?
-8555
Is -367/10 > 4.5?
False
Is 32283 at least 32362?
False
Which is smaller: 43761 or 43760?
43760
Which is smaller: 16604 or 16619?
16604
Which is bigger: 2/9 or -20551?
2/9
Is 102.54 less than -14?
False
Are -43568 and -43573 unequal?
True
Which is smaller: -1849 or -1543?
-1849
Which is greater: -900.7 or 1?
1
Which is bigger: -930 or -933?
-930
Which is bigger: -5/22293 or 0?
0
Is 1/1115842 >= 1?
False
Which is greater: 0 or -184.48?
0
Is 0 smaller than 1/84221?
True
Are -412 and -7002/17 equal?
False
Which is smaller: 1 or 437/328?
1
Is -951 bigger than -929?
False
Does 3002 = 3004?
False
Which is smaller: 1016411 or 1016412?
1016411
Which is smaller: -4026/5 or -3/7?
-4026/5
Do 25791 and 4 have the same value?
False
Which is smaller: -2/4559 or 0?
-2/4559
Is 245 greater than or equal to 53/58?
True
Which is greater: 0.4 or 2/94569?
0.4
Which is greater: -2 or -260/267?
-260/267
Is 1549 equal to 1546?
False
Do 5/549 and -97 have the same value?
False
Is -41228 less than or equal to -41229?
False
Is -1117 smaller than -1117?
False
Which is smaller: -136 or -155?
-155
Which is smaller: 12/9155 or -0.1?
-0.1
Is 0 < 23/16067?
True
Are -0.012132 and -3 equal?
False
Which is greater: -389 or 294?
294
Which is smaller: 181 or 3232?
181
Is 2/91429 >= -1?
True
Which is smaller: -1 or -1/433206?
-1
Which is smaller: -39/3191 or 1?
-39/3191
Is -13167 not equal to 34?
True
Is 3 at most -3/239374?
False
Which is greater: -478/2595 or -1?
-478/2595
Is -70270/9 equal to -7808?
False
Is 3/106577 less than 1?
True
Is -441 at most 80?
True
Is 0.09705 smaller than 0?
False
Is -865 greater than -0.005?
False
Is -9/13 less than -20/47?
True
Which is bigger: -18736 or 0.2?
0.2
Which is smaller: -9 or -6656/815?
-9
Which is greater: 1 or 112/387?
1
Which is greater: 1652/179 or 8?
1652/179
Does -1 = -7/476537?
False
Which is greater: 4/173073 or -1?
4/173073
Which is bigger: -27755 or -27702?
-27702
Is -2/61 smaller than 6/161?
True
Is -811 <= -872?
False
Which is smaller: 1467567 or 1467569?
1467567
Which is smaller: -12385 or 2?
-12385
Do 450023 and 0 have the same value?
False
Do 39 and 285.1 have the same value?
False
Is 7156/21 less than 342?
True
Which is smaller: 0 or -43/5206?
-43/5206
Do 1 and -486/37661 have different values?
True
Is -26/2017 less than or equal to -1?
False
Is 1 smaller than -1/222828?
False
Which is smaller: 1 or 179/1320?
179/1320
Is 1 smaller than -306/2921?
False
Is -52/241 smaller than -9/8?
False
Which is bigger: 92722 or 92725?
92725
Are -1 and 3/240275 equal?
False
Which is greater: 414.37 or 4?
414.37
Are -2327 and 59 equal?
False
Which is bigger: -7.5 or 790?
790
Is -2/9 < 10608/287?
True
Which is bigger: 3670 or 3699?
3699
Which is bigger: 459/5341 or -1/2?
459/5341
Are 10 and 2838/329 equal?
False
Which is greater: -3211 or -3206?
-3206
Which is smaller: -15 or -2573/157?
-2573/157
Which is greater: -950018 or -950020?
-950018
Which is smaller: -241 or -3139/13?
-3139/13
Which is bigger: 11 or 48/5?
11
Which is smaller: 0 or 45860?
0
Which is bigger: 11/57164 or 1/6?
1/6
Which is smaller: 7/269498 or 1?
7/269498
Which is bigger: 3831655 or 3831652?
3831655
Does 31416 = -2.18?
False
Is -15419/4 at least as big as -3854?
False
Which is smaller: 5079 or 0.46?
0.46
Which is smaller: -175082 or -175081?
-175082
Does -336418 = -336417?
False
Which is smaller: 18/2351 or 1?
18/2351
Which is smaller: -48 or -1438?
-1438
Which is smaller: 493/55 or 9?
493/55
Which is bigger: -55/48703 or 0?
0
Does -143953 = -143954?
False
Which is smaller: 1086 or 1005?
1005
Are 1/1860893 and 1 non-equal?
True
Which is bigger: -27/124606 or 1?
1
Which is smaller: 18 or 73246?
18
Which is greater: 2972 or 2942?
2972
Are 448 and 7618/17 non-equal?
True
Which is bigger: -1/3 or -2859876?
-1/3
Which is smaller: 2515/162 or 16?
2515/162
Is 46753 greater than or equal to -1/10?
True
Which is greater: 873 or -0.15?
873
Is 596174 greater than 596168?
True
Are -111419 and -111418 unequal?
True
Is 12 equal to 1499/114?
False
Is 2/3 greater than 275724?
False
Are -103999 and -103999 non-equal?
False
Which is greater: 0.0517 or -355?
0.0517
Which is smaller: -0.0444 or -7?
-7
Is 1 less than 866529?
True
Is 15/42247 less than or equal to 0?
False
Which is smaller: 38416 or 38417?
38416
Is -755551 equal to 4?
False
Which is smaller: -4 or 18595?
-4
Is 0.002 != 25.9?
True
Which is smaller: 9 or 699/82?
699/82
Is -312 smaller than -75?
True
Is 6826 greater than or equal to 20476/3?
True
Which is smaller: 1/3361008 or -1?
-1
Is 166417 equal to 166417?
True
Is -1/8 not equal to 4528?
True
Is 208878 equal to 208878?
True
Which is bigger: -380 or 0.174?
0.174
Which is bigger: -5 or 3267?
3267
Which is greater: 6253 or 1024?
6253
Which is greater: 0.0527 or 22?
22
Which is smaller: 0 or -57/191284?
-57/191284
Which is smaller: 165698/3 or 55232?
55232
Which is smaller: 1240093 or 1240091?
1240091
Which is greater: 700.02 or 0?
700.02
Is -147/2 at least as big as -169/2?
True
Is -5/8 at most 93406?
True
Which is greater: 2/360277 or 1?
1
Is 20/20687 at least as big as -1?
True
Which is smaller: -21711 or 0.3?
-21711
Is 14344.3 greater than -0.18?
True
Which is greater: 12 or 1997/164?
1997/164
Is 29 less than or equal to 1294/45?
False
Is 619/1801 greater than 0?
True
Which is greater: 145716 or 145717?
145717
Which is smaller: 300166 or 300168?
300166
Is 0 > -6/174833?
True
Which is smaller: -9794 or -284020/29?
-9794
Is -3.38 <= -12.8


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LeeCounty

Great location for a home or small business only blocks from downtown. 4 bedrooms/1.5 baths. Property has been used as an office with a special use permit. Updated interior with newer laminate floors and windows, original brick fireplace.

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The overall thesis of this Program is that hemoglobin (Hb) provides a well-defined, tractable "molecular workbench" which can be used to develop the rules by which oligomeric protein complexes undergo allosteric regulation. A central goal is to test and further develop a predictive stereochemical model that was formulated during the last funding period. The model describes how specific residues propagate ligation-induced changes in heme structure to the subunit-subunit interfaces and thereby promote the transitions from a low affinity T structure to higher affinity T-like structures, and finally to a range of R quaternary structures. The specific aims of this Project are to capitalize on advances in electron-nuclear double resonance (ENDOR) spectroscopy to address two basic issues. i) How are the critical interfaces coupled to ligand binding events at the hemes: What structural perturbations are induced at the liganded and liganded hemes of Hb ligation intermediates, and how do they function to modulate ligand affinity. ii) By measuring the actual distances between the heme-iron and critical heme-pocket residues, we will be able for the first time to accurately probe the tertiary-structure switch points of the Hb ligation pathway, and in so doing will suggest ways to test and refine the stereochemical allosteric model.


An early handball allowed Ross County to take the lead from the spot through Josh Mullin, only for Niall McGinn to pull the Dons Level 5 minutes later.
A free-kick delivery from Ryan hedges found its way into the net to give Aberdeen the lead followed by a curled finish from Andrew Considine to seal another 3-points for Derek McInnes.


FBI should stop spying on war protesters

November 28, 2003

Freedom Newspapers

When is the Bush administration going to stop violating Americans’ civil rights?
According to The New York Times, the FBI has “collected extensive information on the tactics, training and organization of anti-war demonstrators and has advised local law enforcement officials to report any suspicious activity at protests to its counterterrorism squads.”
The Times attributed the report in part to a “confidential bureau memorandum.”
The FBI insists it only monitored groups that supposedly might cause violence. But the memo also contained information such as “how protesters have … (used) the Internet to raise money,” the Times reported.
Unless a group specifically advocates violence or has committed it, the FBI should stop its spying. Spying on war protesters brings back the chilling memory of FBI abuses under the late J. Edgar Hoover, such as spying on civil-rights leader and anti-Vietnam War activist Martin Luther King.


Clearing the 223 WYLDE Confusion

Clearing the Caliber Confusion: .223 Wylde vs. 5.56 NATO

Though they share identical case dimensions, the .223 Remington and 5.56 NATO are slightly different—and the .223 Wylde makes the most of both of these loads.

Brad Fitzpatrick

The 5.56×45 NATO broke cover in 1957 as a military cartridge, and since that time it has served in multiple campaigns and conflicts around the globe. The 5.56×45 offered a lot—it was a flat-shooting, fast cartridge with minimal recoil and minimal weight (read: you can carry a lot of them). So great was the 5.56 that it soon followed in civilian garb as the .223 Remington, a cartridge that has remained viable and popular since its release date.

There are some that will tell you that a .223 and a 5.56 are the same cartridge. Well, in terms of case dimension, that is true. So, how do they differ?

The short answer is that they differ with regard to pressure and chamber dimensions. Pressures in the 5.56 cartridge are higher than the .223, and as a result the chamber of the 5.56 is different as well. There’s more throat length in a 5.56 barrel—about .077 inches—and the angle of the throat is different to accommodate increased pressures. It is, therefore, alright to fire a .223 cartridge in a 5.56 chamber, but going the other way can cause pressure problems. Simply put, the .223 doesn’t perform as well in 5.56 chambers as it could. If you have two cartridges that are so similar in external case dimensions why not have one chamber that makes the best of both loads?

Enter Bill Wylde. Bill had the idea to create a chamber that would serve the 5.56 and the .223 Remington equally well. The .223 Wylde has the same chamber angling as the standard 5.56 chamber, so there’s no problem with pressures, and it also has a .2240 freebore diameter. The result? You have a chamber that is sufficient to handle the hotter 5.56 load without concerns about pressure and you get the gilt-edge accuracy that’s common in many quality .223 rifles.

The .223 Wylde Chamber Dimensions

Is there a compelling reason to switch to a Wylde chamber? Well, the most obvious reason is that you can fire .223 ammo without giving up accuracy and 5.56 ammo without worrying about excess pressure. Sure, you can fire .223 ammo all day from a 5.56 without worrying about pressure problems thanks to generous chamber size, but if you really want to tighten those groups that .2240 freebore diameter helps. In fact, .223 Wylde chambers are known for extreme accuracy, which is better on the whole than what you can expect from a standard 5.56×45 chamber.

Better accuracy, more versatility with ammo—so what’s the downside? Well, right now that can be cost and availability. The gains that the .223 Wylde provides haven’t prompted a whole bunch of companies to swap to that chamber, but there are certainly .223 Wylde-chambered target rifles out there. Do you need a Wylde? No, but it will help your long game and will make your AR rifle even more versatile than it is now.


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