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Reviewed and updated 14th March 2022
High Grade – the forgotten patient group?
When reading articles in the mainstream media, found in medical publications; and even listening to doctors speak about my disease, it's clear that the focus is on the term "Neuroendocrine Tumours" or NET for short. Many websites of advocate foundation organisations and specialist scientific organisations, all still use the term "NET" in their naming. I too am guilty of having a large Facebook site falling into this category. It's little wonder that those with high grade disease can often feel like the forgotten patient group. Clearly all the aforementioned organisations support all patients regardless of grade, but it's true to say that the naming and general use of terminology continues to fall behind. It's also true that the term NET remains applicable to the majority of patients and that many use it as a convenience when they actually mean all types including Neuroendocrine Carcinoma. Nonetheless, context remains an important part of overall understanding and inclusivity – words and acronyms matter.
However, High grade or Grade 3 is no longer just Neuroendocrine Carcinoma (NEC). Things have changed since 2017 and have been reinforced in 2019.
What are Neuroendocrine Neoplasms?
Neuroendocrine neoplasms (NENs) are without doubt a heterogeneous (i.e. diverse) bunch of tumours with a common phenotype (i.e. the physical appearance or biochemical characteristic). However, there are two fundamentally different groups of NENs: well-differentiated, low-proliferating NENs, called neuroendocrine tumours (NETs), and poorly differentiated, highly proliferating NENs, called neuroendocrine carcinomas (NECs). The difference between well and poorly differentiated has been described as a 'dichotomy', most likely due to the origin from different neuroendocrine progenitor cells (i.e. source cells). Should the term Neuroendocrine Neoplasm be used more? Yes, probably. But should we perhaps also ask if ENETS and NANETS will change their names to ENENS and NANENS?
This revised classification is not recent as many are currently suggesting. These changes were covered in my Staging and Grading article produced in early 2017 and confirmed Neuroendocrine Neoplasms (or NENs) was the overarching term for all types of neuroendocrine disease. See graphic below, recently updated to incorporate 2019 updates.
Traditionally, any proliferation score over 20% on the Ki-67 proliferation index (or over 20 mitoses/10 HPF on the Mitotic Index or see Lung NENs here) would have been deemed a Neuroendocrine Carcinoma. However, in the pancreas, NETs and NECs may overlap in their proliferation index, making the distinction between them difficult and leading to treatment uncertainties.
In 2017, the Endocrine 'Blue Book' of cancer classification systems introduced a new pancreatic NET category based on a Grade 3 tumour which is well differentiated (i.e. cancer cells look more like normal cells and tend to grow and spread more slowly). While all classifications for all NENs recognise the existence of the two major groups (NET and NEC), there are proposals to develop common NEN classification across all the 'Blue Books' and future versions will reflect these changes. The most interesting change will be in the Lung classification because high grade NENs can be small cell or large cell and it's probably the most controversial grouping.
Interestingly, ENETS guidelines already use the term across the board in their 2016 series (i.e. in advance of the 2017 changes). These changes are part influenced by the results of the NORDIC NEC study which showed that although patients with a Ki67 <55% were less responsive to platinum-based chemotherapy (i.e. drug names ending in 'platin' such as Cisplatin, Carboplatin, Oxaliplatin), they had a longer survival, and concluded that not all NEC should be considered as one single disease entity. Also, worth noting that the NORDIC NEC study covered many different areas of the anatomy, not just the pancreas. Some of the rationale for the division of grade 3 into well differentiated NETs and poorly differentiated carcinomas is that some grade 3 tumours which are classified into this category according to the Ki67 index percentage, have been recognised to behave more like grade 2 NETs rather than aggressive carcinomas. The inference is that there could be treatment and prognostic significance (advantages) if a patient is a Grade 3 NET.
These changes were reinforced in the 2019 update – read here
The situation with high-grade Lung Neuroendocrine Neoplasms is quite different than it is with Gastroenteropancreatic NENs (GEPNENs). Read more about Lung here (or click on the picture).
MANEC vs MiNEN
Added for completeness because most of these mixed cell tumoursinvolve a Grade 3 NEN. This mixed and rare neoplasm type has traditionally been related to NEC but in 2017 the nomenclature change to a new term was necessary to reflect the fact that some of the tumours involved were not carcinomas or adenocarcinomas but rather were well differentiated tumours or even adenomas (i.e. benign). Previously known as Mixed AdenoNeuroendocrine Carcinoma (MANEC), they were renamed to Mixed Neuroendocrine Non-Neuroendocrine Neoplasms (MiNEN).
MiNEN are neoplasms with two distinct neuroendocrine and non-neuroendocrine cell populations. They can be morphologically classified into three entities: collision, composite, and amphicrine MiNEN. Currently, both components composing a MiNEN must represent at least 30% of the whole tumour. Diagnosis of MiNEN is usually facilitated by the presence of at least one well-differentiated component which may be the Neuroendocrine or Non-Neuroendocrine component. However, the two components may be difficult to identify with conventional morphological techniques, particularly when they are poorly differentiated, and their identification may require additional immunohistochemical techniques. MiNEN usually originate from organs that contain neuroendocrine cells and in which "classical" NENs are known to develop, such as pancreas, appendix, colon, and to a lesser degree small intestine. Other locations in my source document includes Oesophagus, Stomach, Bilary Tract and Gallbladder, Duodenum and Ampulla of Vater and Rectum.
Molecular signatures
One of the study areas which will help with accurate diagnoses of well or poorly differentiated NENs is the use of molecular signatures. This is an advancing science and something which may also be utilised in other areas of NENs. The abstract below is extremely interesting which will help experts update guidelines to include pathology checks on the genetic abnormalities in the TP53 and Rb1 genes in cases of G3 NENs. This study also supports the key differences in terms of the more common locations of G3 NET vs the more common locations of NEC.
Kasajima, A., Konukiewitz, B., Schlitter, A.M. et al. An analysis of 130 neuroendocrine tumors G3 regarding prevalence, origin, metastasis, and diagnostic features. Virchows Arch (2021). https://doi.org/10.1007/s00428-021-03202-6
"Limited data exist on high-grade neuroendocrine tumors (NETs G3) which represent a new category among neuroendocrine neoplasms (NEN). We analyzed NETs G3 in a consultation series regarding prevalence, origin, metastasis, and diagnostic problems. Based on the WHO classification of digestive system tumors, 130 NETs G3 (9%) were identified in 1513 NENs. NET G3 samples were more often obtained from metastatic sites (69%) than NET G1/G2 samples (24%). NET G3 metastases presented most frequently in the liver (74%) and originated from the pancreas (38/90, 42%), followed by the lung (9%), ileum (7%), stomach (3%), rectum (1%), and rare sites (2%) such as the prostate and breast. The primaries remained unknown in 15%. NETs G3 had a median Ki67 of 30% that distinguished them from NECs (60%), though with great overlap. The expression of site-specific markers, p53, Rb1, and SST2 was similar in NETs G3 and NETs G1/G2, except for p53 and Rb1 which were abnormally expressed in 8% and 7% of liver metastases from NET G3 but not from NET G1/G2. NETs G3 were frequently referred as NECs (39%) but could be well distinguished from NECs by normal p53 (92% versus 21%) and Rb1 expression (93% versus 41%) expression. In conclusion, NETs G3 are frequently discovered as liver metastases from pancreatic or pulmonary primaries and are often misinterpreted as NEC. p53 and Rb1 are powerful markers in the distinction of NET G3 from NEC. Rarely, carcinomas from non-digestive, non-pulmonary organs with neuroendocrine features may present as NET G3."
NEC vs NET
Having researched widely, I believe there are 8 key differences between NET and NEC:
Grade – NEC is by default Grade 3/high grade so there is no requirement to use either term – Neuroendocrine Carcinoma suffices, NETs however, can be Grade 1, 2 or 3.
Differentiation – all NETs are well differentiated by default, NECs are poorly differentiated by default. Important difference when the Ki67/Mitotic Count indicates Grade 3.
Aggressiveness – Most NETs tend to be indolent or slow growing while NECs tend to be aggressive and faster growing. However, Ki67 and/or mitotic count is an aggressiveness measurement tool. Genetic profiles can also be a guide but this is beyond the purposes of this article but may be explored in subsequent parts. It follows that NECs normally have a worse prognosis in comparison to NETs.
Hormone Secretion – NETs can produce peptide hormones that may be associated with hormonal syndromes. NECs usually fail to express hormones or produce hormonal syndromes.
Somatostatin Receptors – A NET is much more likely to express somatostatin receptors which can influence treatments such as somatostatin analogues and peptide receptor radiotherapy (PRRT)
Hereditary Syndromes – NETs are much more likely to be associated with hereditary syndromes such as Multiple Endocrine Neoplasia (MEN).
Platinum Based Chemotherapy – NETs are less likely to show a good response to platinum based chemotherapy which can often be the first line treatment for NEC.
Primary Locations – can be vastly different in terms of commonality and therefore provide clues to investigators. Common locations for NEC include: Lung, Esophagus, Colon, Urogential Organs and Skin – with the exception of Lung, these are very rare locations in NETs. Conversely, rare/very rare locations for NEC but common in NET include: Rectal, Small Intestine, Appendix, Stomach, Pancreas.
Treatment for G3 Well Differentiated NETs
The optimal first-line management in NET G3 and sequencing therapies remains a challenge awaiting future trials taking into consideration the unique characteristics of this category. Guidelines are still being developed. The reason treatment options for well differentiated G3 NET are not fully tied down is due to lack of data and the small population size of the patients. As more studies and trials are complete, this will start to firm up. We are also waiting on data from ongoing PRRT trials (e.g. NETTER-2), particularly in the case of well differentiated G3 NET.
If you have a G3 well differentiated NET and are reading this, please consult with your specialist for the latest therapy options and clinical trials where you are.
However, I offer you two studies, one from US and one from UK (these may be displayed differently due to licensing):
Treatment Outcomes of Well‐Differentiated High‐Grade Neuroendocrine Tumors
Recent classification of neuroendocrine neoplasms has defined well‐differentiated high‐grade neuroendocrine tumors (NET G3) as a distinct entity from poorly differentiated neuroendocrine carcinoma. The optimal treatment for NET G3 has not been well‐described. This study aimed to evaluate metastatic NET G3 response to different treatment regimens.
This was a retrospective study of patients with NET G3 within the Mayo Clinic database. Patients' demographics along with treatment characteristics, responses, and survival were assessed. Primary endpoints were progression‐free survival (PFS) and overall survival. Secondary endpoints were objective response rate (ORR) and disease control rate (DCR).
Treatment data was available in 30 patients with median age of 59.5 years at diagnosis. The primary tumor was mostly pancreatic (73.3%). Ki‐67 index was ≥55% in 26.7% of cases. Treatments included capecitabine + temozolomide (CAPTEM) (n = 20), lutetium 177 DOTATATE (PRRT; n = 10), Platinum‐etoposide (EP; n = 8), FOLFOX (n = 7), and everolimus (n = 2). CAPTEM exhibited ORR 35%, DCR 65%, and median PFS 9.4 months (95% confidence interval, 2.96–16.07). Both EP and FOLFOX showed similar radiographic response rates with ORR 25.0% and 28.6%; however, median PFS durations were quite distinct at 2.94 and 13.04 months, respectively. PRRT had ORR of 20%, DCR of 70%, and median PFS of 9.13 months.
Among patients with NET G3, CAPTEM was the most commonly used treatment with clinically meaningful efficacy and disease control. FOLFOX or PRRT are other potentially active treatment options. EP has some activity in NET G3, but responses appear to be short‐lived. Prospective studies evaluating different treatments effects in patients with NET G3 are needed to determine an optimal treatment strategy.
Implications for Practice
High‐grade well‐differentiated neuroendocrine tumors (NET G3) are considered a different entity from low‐grade NET and neuroendocrine carcinoma in terms of prognosis and management. The oral combination of capecitabine and temozolomide is considered a good option in the management of metastatic NET G3 and may be preferred. FOLFOX is another systemic option with reasonable efficacy. Similar to other well‐differentiated neuroendocrine tumors, peptide receptor radionuclide therapy seems to have some efficacy in these tumors.
Citing information:
Treatment Outcomes of Well-Differentiated High-Grade Neuroendocrine Tumors
Alex J Liu 1, Benjamin E Ueberroth 1, Patrick W McGarrah 2, Skye A Buckner Petty 3, Ayse Tuba Kendi 4, Jason Starr 5, Timothy J Hobday 2, Thorvardur R Halfdanarson 2, Mohamad Bassam Sonbol 6
DOI: 10.1002/onco.13686
Well-differentiated gastroenteropancreatic G3 NET: findings from a large single centre cohort
Neuroendocrine neoplasms are known to have heterogeneous biological behavior. G3 neuroendocrine tumours (NET G3) are characterized by well-differentiated morphology and Ki67 > 20%. The prognosis of this disease is understood to be intermediate between NET G2 and neuroendocrine carcinoma (NEC). Clinical management of NET G3 is challenging due to limited data to inform treatment strategies. We describe clinical characteristics, treatment, and outcomes in a large single centre cohort of patients with gastroenteropancreatic NET G3. Data was reviewed from 26 cases managed at Queen Elizabeth Hospital, Birmingham, UK, from 2012 to 2019. Most commonly the site of the primary tumour was unknown and majority of cases with identifiable primaries originated in the GI tract. Majority of cases demonstrated somatostatin receptor avidity. Median Ki67 was 30%, and most cases had stage IV disease at diagnosis. Treatment options included surgery, somatostatin analogs (SSA), and chemotherapy with either platinum-based or temozolomide-based regimens. Estimated progression free survival was 4 months following initiation of SSA and 3 months following initiation of chemotherapy. Disease control was observed following treatment in 5/11 patients treated with chemotherapy. Estimated median survival was 19 months; estimated 1 year survival was 60% and estimated 2 year survival was 13%. NET G3 is a heterogeneous group of tumours and patients which commonly have advanced disease at presentation. Prognosis is typically poor, though select cases may respond to treatment with SSA and/or chemotherapy. Further study is needed to compare efficacy of different treatment strategies for this disease.
Lithgow, K., Venkataraman, H., Hughes, S. et al. Well-differentiated gastroenteropancreatic G3 NET: findings from a large single centre cohort. Sci Rep 11, 17947 (2021). https://doi.org/10.1038/s41598-021-97247-x
Treatment for Neuroendocrine Carcinoma (i.e. poorly differentiated)
Extrapulmonary (i.e. anywhere except the lungs) poorly differentiated neuroendocrine carcinomas (NEC) can originate in the gastrointestinal tract, bladder, cervix, and prostate. These high-grade malignancies are characterized by aggressive histological features (high mitotic rate, extensive necrosis, and nuclear atypia) and normally a poor clinical prognosis. They are rarely functional and rarely express somatostatin receptors.
Pulmonary Neuroendocrine Carcinomas comprise small and large cell variants, quite often managed by Lung Multidisciplinary Teams (MDT) rather than Neuroendocrine MDTs, particularly the former.
Information on treatment is scant in guidelines but tends to involve cytotoxic chemotherapy at first and second line in many cases. Therapy can be guided by the stage (e.g. locally advanced (locoregional) or metastatic). Most poorly differentiated neuroendocrine carcinomas are locally advanced or metastatic at presentation. First-line systemic chemotherapy with a platinum agent as a combo (e.g. cisplatin and etoposide) is recommended for most patients with metastatic-stage disease but response durations are often short. Sequential or concurrent chemo and/or radiation is recommended for patients with loco-regional disease. In patients with localized tumors undergoing surgical resection, adjuvant treatment (chemotherapy with or without radiation) is warranted in most cases. This extract is from NANETS guidelines, and it also generally reflects the content of the NCCN Guidelines (page PDNEC-1).
As you would expect in the most aggressive types of NEN, there are many clinical trials available.
If you have a NEC and are reading this, please consult with your specialist for the latest therapy options and clinical trials where you are.
It will be useful to see what changes to the above will be once we have the output from the NETTER-2 clinical trial which is focussed on Grade 2 and Grade 3 well differentiated. And also, any decision to automatically apply molecular signature checks to all G3 NENs.
In addition to my own knowledge gained over the years of researching and writing, the following resources were key in establishing the facts used in compiling this article:
1. Sorbye H, Welin S, Langer SW, Vestermark LW, Holt N, Osterlund P, et al: Predictive and prognostic factors for treatment and survival
in 305 patients with advanced gastrointestinal neuroendocrine carcinoma (WHO G3): the NORDIC NEC study. Ann Oncol 2013; 24: 152–160
2. de Mestier L, Cros J, Neuzillet C, Hentic O, Egal A, Muller N, Bouché O, Cadiot G, Ruszniewski P, Couvelard A, Hammel P: Digestive System Mixed Neuroendocrine-Non-Neuroendocrine Neoplasms. Neuroendocrinology 2017;105:412-425. doi: 10.1159/000475527
3. Koppel G: Neuroendocrine Neoplasms: Dichotomy, Origin and Classifications. Visc Med 2017;33:324-330. doi: 10.1159/000481390
4. Rindi G, Klimstra DS, Abedi-Ardekani B, et al. A common classification framework for neuroendocrine neoplasms: an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert consensus proposal. Mod Pathol. 2018;31(12):1770–1786. doi:10.1038/s41379-018-0110-y
5. Alex J Liu, Benjamin E Ueberroth, Patrick W McGarrah, Skye A Buckner Petty, Ayse Tuba Kendi, Jason Starr, Timothy J Hobday, Thorvardur R Halfdanarson, Mohamad Bassam Sonbol – Oncologist 2021 May;26(5):383-388. doi: 10.1002/onco.13686. Epub 2021 Feb 8.
6. Well-differentiated gastroenteropancreatic G3 NET: findings from a large single centre cohort Lithgow, K., Venkataraman, H., Hughes, S. et al. Well-differentiated gastroenteropancreatic G3 NET: findings from a large single centre cohort. Sci Rep 11, 17947 (2021). https://doi.org/10.1038/s41598-021-97247-x
7. Treatment Outcomes of Well-Differentiated High-Grade Neuroendocrine Tumors Alex J Liu 1, Benjamin E Ueberroth 1, Patrick W McGarrah 2, Skye A Buckner Petty 3, Ayse Tuba Kendi 4, Jason Starr 5, Timothy J Hobday 2, Thorvardur R Halfdanarson 2, Mohamad Bassam Sonbol 6
General Clinical Trials Disclaimer
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided in the clinical trials document. It's very important to check the trial inclusion and exclusion criteria before making any contact. If you need questions, the articles here is very useful Questions to Ask About Clinical Trials | Cancer.Net
The inclusion of any trial within this blog should not be taken as a recommendation by Ronny Allan.
5 thoughts on "The Heterogeneity of Grade 3 (High grade) Neuroendocrine Neoplasms"
NANETS is producing new classification guide for NETS and MEN. Expected to be released January 2020
Great article, Ronny! Thanks for bringing this information to the masses! Looking forward to part 2!
Ronny, your blog is one of my first go-to places when I want to update myself on the latest NET therapies. I've just had my first Galium PET scan and will soon start PRRT. Your blog means that I'm going into treatment much better informed than if I'd randomly searched the web or tried to wade through numerous scientific articles. Thank you for doing that for me. Thank you for being there.
Jo chen
Hi. What's your Ki?
Excellent article Ronny! Thank you. ☺


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It's no big deal. I don't mind helping you out.


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Q: How would one implement a data structure with test-driven development in mind? In higher-level languages such as Java or Python, we don't usually need to implement our own data structures since the languages provide their own libraries for them.
So, for example, how would we start writing a data structure for a set with test-driven development in mind?
How would you create the tests first?

A: A common misconception about TDD is that TDD drives the entire design of a software project, that a complete software architecture naturally emerges from writing tests and getting them to pass.
That's not how TDD works.  You still have to design your software.
So, to begin writing a data structure for a Set using TDD, you would first determine what sort of data will be stored, how that data might be represented in memory conceptually, and then figure out what sort of behaviors you will need to store, retrieve and manipulate that data.
That should be more than enough information to begin writing tests for methods and code that implement your Set data structure.

redpajama_set_name: RedPajamaStackExchange


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text:
Q: Draw curve between two points I would like to know how people draw the graphs like below.
I have multiple location points.
I need to connect two locations by a curve. (Not by line) Any idea how to achieve this? In MATLAB I did not find any in-built function.

A: With the latest version of MATLAB (R2015b) you can use the new 'graph and network' facilities. It offers different kind of layouts maybe you will find some of them appropriate for your problem.
s = [1 1 1 2 2 3 3 4 5 5 6 7];
t = [2 4 5 3 6 4 7 8 6 8 7 8];
G = graph(s,t);
h = plot(G,'Layout','layered');


Hey all you Cardinal Fans, Tuesday, October 25th, is the last chance to catch a look at the future stars of Cardinal Football. The 7th, 8th, and 9th grade teams play Oologah at Sallee Field. Games begin at 5:00pm.
Hope to see you there to watch an exciting Bedlam series between these two rivalries.
Members of the NE Oklahoma Chapter of Newborns In Need met October 11, 2011 with Cherokee Nation officials at the Pecan Porch in Catoosa, OK to discuss current needs of the Cherokee Nation Indian Child Welfare organization. The group meets once or twice annually to determine how best NIN can serve the Cherokee Nation in the NE OK area, in addition to several other hospitals, shelters, crisis centers, etc.
L-R: Greater OK NIN President Joann Drye, CNICW Program Mgr Hettie Charboneau, CNICW Program Mgr Sandra Edgmon, NIN member Sue Ellison, CNICW Program Mgr Lou Stretch, and NIN Sec'y/Editor Linda Pfeffer.
Collinsville High School will host the 2011 ASA Entertainment High School Tour. The anti-tobacco high school tour is in it's 12th year educating the youth of today about the dangers of tobacco use.
See top athletes shred the half-pipe.
Tour sponsors are The Campaign for Tobacco Free Kids and the US Marine Corps.
Mary Wood looks on as new member Velma Touriainer displays her latest quilt project at the October 11, 2011 meeting of the Collinsville Patchworkers. Janice Coke presented a program on loom knitting, plus gave a demonstration on a new way to cut bias strips for quilt binding. The next meeting will be November 8 at the Library, and is open to the public.


structure with responsibilities pdf. 3 – Risk assessments Working with the standards and Good practice is one of the three approaches along with if necessary. Source: Assessment – a brief guide to controlling risk in the workplace. Available at: http:// (accessed ).
HSE has published a guide to Risk Assessment [ 8 ] which identifies the key steps. Depending upon the level of harm and the probability, the risk is labelled as High, Medium or Low. Good Practice and Pitfalls in Risk Assessment. HSE successfully prosecuted all parties, the Designer, the Contract Company and the Public Park Authority, for failing to protect the health and safety of both workers and the public. Carrying out a detailed Quantitative Risk Assessment without first considering whether any relevant good practice was applicable, or when relevant good practice exists.
The reason for placing the responsibility with the organisation creating the risk is that it is impossible for HSE to be expert in the operation of every technology and workplace, especially at the rate of technology development.
Here, the risk consequence level of harm has its probability derived from a number of factors. The group did not have the structural engineering expertise to cover the stability of the structure and did not undg163 evidence from the Designer or the Contract Company that the appropriate load calculations had been undertaken and verified, and that the structure would be appropriately secured to ensure stability.
The Nimrod aircraft had been in service for over 30 years and had been modified twice. Subsequent maintenance records showed evidence of fuel leaks that had happened before but these were not acted upon.
It is important to note that 'Sanctions include fines, imprisonment and aww. Consider for example a small manufacturing process where Team A take raw material and prepare it for initial fabrication by Team B.
This is illustrated in Fig. The case studies illustrate some of the common failing in undertaking risk assessments. HSL undertook an investigation and determined that the structure was tethered by 22 ropes and pegs distributed around the perimeter of the structure. This article has been cited by other articles in PMC. Using "reasonably practicable" allows HSE to gog goals for organisations, rather than being pre scriptive.
The corroded pipe is shown in Fig. The Risk Graph of Fig. The Nimrod Review Report [ 11 ] stated that the failure was one of 'leadership, culture and priorities'. Three case studies are now presented to illustrate the consequences of not undertaking a suitable and sufficient risk assessment. There are no short cuts. The best results from these tools are achieved when a team of people undertake the risk assessment together.
The Health and safety at Work etc. For example; had the structure been designed to withstand wind conditions? Had engineering calculations been undertaken to determine the loading that various weather conditions would idng163 on the structure?
The Nimrod Review report. In the great majority of cases, HSE can make this judgement by referring to existing 'industry good practice' that has been established uttp a process of discussion with in dustry stakeholders to achieve a consensus about what is 'SFAIRP'.
HSL then investigated the risk assessments that had been undertaken asking many questions. A hsse and sufficient risk assessment called a Safety Case for large, high hazard plant was not completed following the modifications. All three parties, the Designer, the Contract Company and the Public Park Authority, had legal responsibility for the health and safety of the staff who would operate the structure and pubnns public who would visit the park. How the risk is managed is to be determined by those who create the risk.
The paper concludes with an analysis of the common failings of risk assessments and key actions for improvement. Had these calculations determined the type strengthnumber and positioning of tethers and pegs? Risk graph [ 10 ].
The best way for this organisation to risk assess their manufacturing process is first indg16 have each Team A-D assess their own work and then for representatives of each Team A-D to form a new team to complete a 'whole process' risk assessment using both the information on the individual risk assessments and considering the interactions between processes.
In all cases, there was a loss of life that was completely avoidable if the risk www had been completed properly and acted upon. Investigation of the historical context indicated that the pipes were installed correctly and to the appropriate standard of the time the pipes were installed in What new actions are required to further reduce the risk? However, the focus of the group's risk assessment was event management in terms of crowd management, people flow through the structure etc.
The investigation identified that fuel leaked out of a coupling during the in-flight refuelling. Introduction Prior to the UK had spent the preceding years generating a large number of Health and Safety laws focused on individual industries, and even individual regions of the country. There was also a failure of the parties involved to communicate to one another about possible risks. Find articles by Karen Russ. Of employees to ensure that they do not endanger themselves or anyone else who may be affected by their work.


Product prices and availability are accurate as of 2019-04-14 11:25:32 EDT and are subject to change. Any price and availability information displayed on http://www.amazon.com/ at the time of purchase will apply to the purchase of this product.
My Home Barista are delighted to present the brilliant Breville BDC600XL YouBrew Drip Coffee Maker.
With so many available these days, it is great to have a brand you can recognise. The Breville BDC600XL YouBrew Drip Coffee Maker is certainly that and will be a perfect purchase.
For this great price, the Breville BDC600XL YouBrew Drip Coffee Maker is highly recommended and is always a popular choice amongst most people. HWI/Breville USA have added some great touches and this means good value.


Pinterest has quickly become everyone's favorite go-to platform for finding ideas and tidbits of information on all sorts of different topics. Its visual interface makes it easier and more fun than ever to keep everything organized by pinning things to the right board.
Some of the early Pinterest users that consistently kept pinning all sorts of great content over the years have been able to grow their followings by the millions. And for many webmasters out there on the increasingly crowded and noisy internet, Pinterest is the big web traffic driver they depend on to keep them in business.
The following Pinterest users have longstanding reputations for posting exceptional content on a pretty regular basis. Have a look and give them a follow if their pins pique your interest!
At 13 million followers, Joy Cho is one of the top most-followed Pinterest users. She's a Los Angeles-based designer, blogger and food enthusiast who has worked with big brands like Target and Urban Outfitters.
You're bound to come across something that fits your creative taste by browsing through her 65 different boards. Virtually all of her boards are lifestyle categories like travel, food, home and beauty.
What really makes her boards stand out are the types of categories that are so unique. Her Mini Travels board, for example, is a great one to follow if you're interested in tips, gear, and ideas for traveling with kids.
While some of her boards don't exactly include hundreds or thousands of pins you can look forward to browsing through, you can count on Joy for stressing quality over quantity.
Interior designer Maryann Rizzo is all about gorgeous home decor, architecture, landscaping and everything else that has to do with making your home as beautiful as possible.
She definitely didn't attract 9 million followers by pinning mediocre content. If you take a look through her 268 boards, you'll notice that a huge chunk of them are centered around various appealing design categories, neatly broken down into all sorts of separate components for easy browsing.
Need ideas for furniture of a particular color? Maryann's Painted Furnishings board can help you out with that.
What about furniture that has more of a wooden, natural look? You can check out her Au Natural Furnishings board for more ideas there.
By now, you should be aware that Pinterest is an extremely visual, image-based social platform. For photographers and photography enthusiasts, it means more opportunities to show off and view more beautiful photos!
Bekka Palmer is a Brooklyn-based photographer who takes the third top spot on all of Pinterest, with 8 million followers. The boards closest to the top of her profile focus mainly on photography, and as you scroll down, you get to see more pins of helpful lifestyle sources like food, gardening, and apparel.
Bekka only has 63 boards, but they're all packed full of amazing pins. Her top boards have hundreds of pins, and as you get into more of the lifestyle stuff, you'll find really cool boards like Scissors and Casa.
Lifestyle content can really encompass almost anything, but the folks at PoppyTalk have really nailed the most exciting and essential areas.
PoppyTalk is a blog that's been around for over a decade and mainly features content on design, DIY, handmade and vintage content. You'll find over 21,000 incredible pins scattered across 133 boards.
Unlike a lot of the other popular Pinterest users in this list who are big designers and bloggers, Jane Wang doesn't offer a whole lot of detail about who she is in her bio section. It's not entirely clear whether she's a penguin living in Antarctica or not, but it's okay if she is, because she really knows how to pin great stuff!
Jane has over 7 million followers and 48,000 pins to discover. That's a lot of browsing and pinning to do.
Editorial and commercial photographer Bonnie Tsang has a bit of a different vibe on her Pinterest profile, offering her followers a variety of pins represented by very clear, crisp and simple photos on her 25 boards. Overall, her boards look a lot less "busy" than a lot of other users make their boards out to be.
Being the sixth most followed user on the whole platform and named one of the "Top 30 Pinterest Curators to Follow" by Time Magazine, you can bet that her pins are of the highest quality—despite lacking some of the color and detail that a lot of the other stuff people tend to pin.
With over 7 million followers and more than 33,500 pins scattered across all her boards, Evelyn's Pinterest profile is hard to pass up after a short glance at all the gorgeous images and board titles she has.
And if you have dreams of traveling to all sorts of new places all over the world, her boards are perfect for you! Most of them are neatly organized entertainment and travel categories at the top, which then flow nicely into photography and lifestyle collections like food and fashion.
Bookworms should check out her Books Are Portable Magic board for reading inspiration and title ideas followed by her travel-specific boards for each continent.
Molly Pickering only has around 12 boards on her Pinterest profile, but that's no major problem for her 7 million followers. Her boards mainly focus on cool photography and images with a few that are geared toward lifestyle categories sprinkle in there as well.
Her biggest board is her fashion board, with over 1,000 pins. And if you're into interior design, her en la casa board is worth looking at too.
Are you a fan of simple imagery and great lifestyle ideas? Then you need to check out Pejper on Pinterest—a Swedish lifestyle blog run by two women.
Most images pinned by Pejper share the common theme of having a white or light background combined with high-quality photography. Unlike other Pinterest users who love massive detail and lots of color, Pejper's boards allow you to get a fast glimpse of what you can expect to find on each board and what they're all about.
Their unique way of curating the best images and sources has been enough to earn them almost 7 million followers, with popular lifestyle content consisting of everything from jewelry and wooden objects to black and white photography and fashion accessories.
Last in the top 10 most-followed Pinterest users is HonestlyWTF—a great little blog run by Erica Chan Coffman, who scours the web to feature the best in DIY, art, home decor, beauty and more.
Right off the bat, you'll notice all the great boards there are for fashion and interior design categories. All pinned images are clear, colorful and interesting without being too overpowering.
With 97 boards featuring more than 27,000 pins, what sets HonestlyWTF apart is Erica's dedication to curating extraordinary content. You'd probably have a heck of a time trying to find this stuff anywhere else!
Check out their incredible DIY board or their food board to get a glimpse of how different and amazing their pins are.


We give more. By demanding less.
pounds of material are protected by our anodes all over North America!
of the energy used by our manufacturing plant comes from a clean and renewable source of energy!
It's weird to see an elephant here, isn't it?
Well, not at all! In fact, we should have put 90 000 elephants here.
Each year, more than 8 million water heaters are changed in North America. It represents 1 000 000 000 pounds of material, which is the equivalent of 90 000 elephants! It's Huge.
With your help, we can greatly reduce this number!
Save Your Hot Water Tank Now!
Even after all that, we knew we could do even more!
Since January 2019, we have decided to cancel some of our CO2 emissions by planting trees in some developing countries with the help of Eden Projets. We are proud of our progress year after year and we continue to find new ways to improve!
Our goal in 2019: Plant a total of 5000 trees!


9:00 p.m. - 3:00 a.m.
Jay Gatsby was a visionary - a dreamer and a showman with a clear mission. For the busy people of Zurich, this should be the greatest party of the year. The entire city magically united in happiness and euphoria.
1922. The city was pulsing. Everyone had heard of the mysterious millionaire, Jay Gatsby and his spectacular plan. As a man of the world, he knew the tremendous value of his inner circle. A colourful spectrum of the greatest performers of their time. What if, for only one extraordinary night, he could unite them into an ensemble truly spectacular?
Party like Gatsby presents the "Spectacle Extraordinaire". Dress in your finest attire and feel the vibrant pulse of the Golden Twenties. A world aimed to amaze, amuse and entertain you. Become part of Jay Gatsby's inner circle, while dancing the night away with your friends, creating everlasting memories.
Great event, with brilliant bands and performers.
Great party! Had a lot of fun, learnt some dancing steps as well.
All in all worth the money and definitely going again.


text:
asian · asian pacific islander · bai ling · Crank: High Voltage · Filipino Americans · film · gerry maravilla · Jason S. Mordeno · Manny Streetz · News · seed and spark · short films · the crow · Aug 08, 2014
Cross (APIA Short Film) Crowd Funding
The boxing film genre is a time-honored staple of American cinema. There's something about the agony and triumph in this particular sports genre that taps into the heart of the American spirit. Despite this, boxing films starring Asian or Pacific Islander Americans actors are virtually non-existent.
Dir. Gerry Maravilla wants to change that. He's commenced a crowd funding campaign for his upcoming short film, Cross, on the Seed and Spark platform. Cross portrays the life of a Filipino American (Jason S. Mordeno) as he struggles to pay his mother's ever accumulating medical expenses. With his family and dreams of becoming a professional fighter on the line, he ventures into the lawless underbelly of San Fernando's Valley's backyard boxing.
Maravilla is a San Fernando Valley native and Giant Robot previously featured his music videos on its website. He agrees with the long held grievance that Asian and Pacific Islander Americans are underrepresented in the media and hopes his short film will serve as stepping stone towards more progress on this frontier.
Other cast and crew include B. Rich Adams as executive producer; Melanie DiPietro and Leo Oliva as producers; and Caitlin Kelly as the script supervisor.
Although the script and production plans are already completed, its crowd funding is still far from finished. As of August 8th, its goal of $15,000 is still 40% complete.
But there's still time!
You can donate here on the film's Seed and Spark page. Television and radio personality Manny Streetz of 102.7 KISS-FM has thrown his support behind the project. More recently, Actress Bai Ling (The Crow, Crank: High Voltage) lent her support for Cross.
Cross in this rendition is a short film being prepared to shown to financiers and investors. Upon its completion, its producers will submit to film festivals across the country before it reaches online distribution. Backers who support the film at a certain level will receive the added benefit of a digital copy and/or a private streaming video. Once its festival circuit has completed, it will be readily available for anyone and everyone on Seed and Spark's webpage. The finished product will be used to pitch and acquire further financing towards a feature length film. Hurry up and donate to cinematic history in the making!
For more information about Cross follow them on Facebook, Twitter, or visit their Seed and Spark page.
You can follow Gerry Maravilla and Jason S. Mordeno on Twitter through @gerrymaravilla and @j_sm__. Their Seed and Spark crowdfunding campaign ends on August 17th, 2014.
You can also meet Maravilla and Mordeno at a meeting for "#WeOwnThe8Th," a communal gathering for Asian American Pacific Islander artists and entrepreneurs and other like-minded individuals. It's open to all those who wish to attend. (It's a potluck, so feel free to bring food or drinks to share with other attendees).
#WeOwnThe8th Meeting
Friday, August 8th at 7:00 PM
The Great Company
1917 Bay St., Los Angeles, CA

redpajama_set_name: RedPajamaCommonCrawl


WASLI. Scenes from Segovia. There is a need to increase the awareness of the deaf community and to explore the misconception that interpreter training can be done in 5 days (as was in the former Yugoslavia). There is the first group of interpreters coming to the end of their 3 year training course and over the course there has been 7 international guests training on our programme include Dr Napier who looked at the code of ethics and how to work with interpreters amongst other things. Sign Language is still not recognised by the government; however the Prime Minister has set up a working party to look at this.


text:
SBAZ
In The Spotlight: Gwen Hillis
Gwen Hillis. Photo by Haute Photography.
You are such a longstanding supporter of the arts in Arizona and Board Chair Emerita. What inspired such generosity, passion and commitment to ensuring that Ballet Arizona thrives and grows?
The dancers have been my biggest inspiration over the past 20+ years that I have been involved with Ballet Arizona. Their incredible talent, passion and dedication to their art has inspired and challenged me to do whatever I could to support them.
Why do you give?
The arts are a pillar in a vibrant community. It has taken almost 40 years to build Ballet Arizona to the outstanding dance company it is today. It wouldn't have been possible without the support of generous donors, foundations and our local government. I feel we have a responsibility to protect their investment and build on what they have contributed to ensure Ballet Arizona's future.
What ballets classic or contemporary works have inspired you the most?
I don't think any work of art could have an impact on me like Topia. It is such a unique and breathtaking experience. The power and elegance of both the dancers and the music in the desert setting at sunset, literally takes your breath away. I Iove how Topia amazes and touches everyone who sees it. It has a universal appeal that few ballets can match.
What are you most looking forward to next season?
I love Twyla Tharp's Sinatra Suite. I love musicals and I love ballet and this combines the two for me. Our dancers bring just the right touch of grace and theatre to their performance and it is such fun. I plan to see it more than once and as a Prima Circle member I will probably take advantage of seeing it in rehearsal too.
You and Ib Andersen are being honored at our upcoming Dance With Me Gala…
It is very special to be honored with Ib. I was the Board Chair 20 year ago when we hired Ib and few people know better than I the miracles he has performed.
Before Ib, we could almost sell you a Season row not just a Season ticket in the little Herberger Theatre. This year, The Nutcracker broke a record with a sold out performance at Symphony Hall! Ib has also hired a wonderful team that helps create the magic on stage including; Leonor Texidor who is responsible for the fabulous costumes, Director of Production, Michael Panvini, and of course Maria Simonetti (the glue) who now leads The School of Ballet Arizona.
I think our gala is the most fun gala in town. It really is a party, not a fundraiser with the torture of paddle raising and a live auction. The involvement of the dancers really makes it unique and fun. You might have a fascinating dinner partner from Cuba or if you've ever dreamed of dancing with a star this is your night. Our dancers are the best sports and will dance all night with guests.
It was so much fun to see Nayon Iovino's Mambaz…WOW! The combination of the jazz band on stage and the dancers' performance rocked the Orpheum Theatre.
Interested in Ballet Arizona's Dance With Me Gala? Click here for more information!
Behind-The-Scenes of A Midsummer Night's Dream Life Changing Support


text:
\section{Introduction}

These past years have seen the rise of computational methods to detect, track, qualify, and quantify how a word's sense -- or senses -- change over time.
These tasks are critical challenges that are relevant to a range of NLP fields, including the study of
historical semantic change. The successful outcome of semantic change detection is relevant to any diachronic textual analysis, including machine translation or normalization of historical texts \citep{BollmannEtal17}, the detection of cultural semantic shifts \citep{kutuzov-etal-2017-tracing} or applications in digital humanities \citep{Tahmasebi17}.
However, currently, the best-performing models  \citep{DiachronicWordEmb,kulkarni2015statistically,Schlechtwegetal19} require a complex alignment procedure and have been shown to suffer from biases \citep{Dubossarsky-EMNLP17}. This exposes them to various sources of noise influencing their predictions; a fact which has long gone unnoticed because of the lack of standard evaluation procedures in the field.

We examine the modeling approach of \textit{Temporal Referencing} (TR) which avoids post hoc alignment and is applicable to any vector space learning technique.  We show that it (i) is less affected by noise and (ii) clearly outperforms state-of-the-art alignment models on a synthetic change detection task. The task is based on data from a synchronic corpus into which we artificially inject lexical semantic change (LSC) in a controlled and semantically principled way. We further evaluate the models on a manual testset of diachronic LSC and examine their properties.

In this paper, we focus on skip-gram with negative sampling (SGNS) models \cite{Mikolov13b} and PPMI \cite{Levy2015}  and make use of TR to share context information across time periods, while learning individual embeddings for a target word in each time period. We evaluate models in two ways: on the one hand, through the comparison of model performance between semantically changing and stable words. This is achieved through the synthetic introduction (and removal) of polysemy, mimicking \citet{schutze-1998,kulkarni2015statistically,rosenfeld2018deep}. We differ from previous work by creating those changes in a more structured way, and for many time points. The second type of evaluation put forward is a study built on a smaller number of words manually classified as changed or stable.

Our contributions are the following:
\vspace{0.1cm}
\begin{compactitem}
\item \textbf{Noise Reduction:} We avoid post hoc alignment by TR and show that it outperforms other models and is robust to noise.
\item \textbf{LSC Simulation:} We propose a systematic and principled method of injecting semantic change in a controlled fashion.
\item \textbf{Evaluation:} We evaluate (i) by testing for noise reduction in a control condition, (ii) on large and controlled artificial data and (iii) on a manually annotated LSC testset.
\item \textbf{Framework:} The above comprises a framework to test \textit{any} model of semantic change for their levels of noise and sensitivity in detecting simulated semantic change.
\end{compactitem}

\section{Related Work}
\label{sec:relatedwork}

\paragraph{Models of LSC Detection} Computational approaches to semantic change detection can be divided in different families:  count-based semantic spaces \citep{sagi2009semantic,Gulordava} and more recently based on neural embeddings \citep{TempAnal-NeuralLangModel, basilediachronic, kulkarni2015statistically,DiachronicWordEmb}; graph-based models \citep{Tahmasebi17, WSE-Mitra,Mitra2015NLE}; and finally topic-based \cite{NovelSenses,
wang2015sense,frermann2016bayesian,hengchen2017does,perrone2019gasc}.
Recently, we have seen dynamic embeddings with the main aim to circumvent alignment, and share data across time points, thus reducing data volume requirements. Using different base embeddings, SGNS \cite{bamler17}, PPMI \cite{Yao-2018}, and Bernoulli embeddings \cite{RudolphB18-dynamicEmbforLangEvo}, the results show that sharing data is beneficial regardless of the method.\footnote{For an extensive survey of computational approaches to lexical semantic change, we refer the readers to \citet{tahmasebi2018survey}, and to \citet{kutuzov-etal-2018} for a specialized focus on diachronic word embeddings.} Temporal Referencing has been applied first in the field of term extraction \citet{ferrari2017detecting} and recently been tested for diachronic LSC detection \cite{Schlechtwegetal19}.

\paragraph{Evaluation} Due to a lack of proper evaluation methods and datasets, all papers above have performed different, non-comparable evaluations.
Previous evaluation procedures mainly tackle a few words: case studies of individual words \citep{Wijaya,Jatowt:2014:FAS:2740769.2740809,hamilton-etal-2016}, or a comparison between a few changing and semantically stable words \citep{NovelSenses,schlechtweg-EtAl:2017:CoNLL}. Other works focus on the post hoc evaluation of their respective models \citep{kulkarni2015statistically,Eger-SemChange}. Importantly, \citet{Dubossarsky-EMNLP17} proposed to use a control condition to mitigate the absent of validated evaluation methods and datasets.

\paragraph{Control Condition}\label{sec:controlcondition}
Evaluating empirical results often demands comparing these under a control condition in order to maintain that these are indeed valid and are not the result of unwanted confounding factors. A control condition directly follows from a specific research hypothesis, and therefore must resemble the original condition in any aspect, except the variable of interest that is being hypothesized about. For example, \citet{Dubossarsky-EMNLP17} attested that a shuffled diachronic corpus is a proper control condition to test models for semantic change, under the hypothesis that such models indeed capture semantic change and not something else. They concluded that any degree of semantic change that is reported by a model on the shuffled corpus may only be related to noise, instead of a true semantic change. Similarly, we propose to test the noise levels associated with different semantic change models using a shuffled historical corpus, and evaluate their true degree of semantic change by comparing their results to the original historical corpus. Importantly, there are many ways to create control conditions, and the synthetic lexical semantic change proposed in Section \ref{sec:SynLSC} contains another type of control condition, that is based on artificially induced semantic change.

\section{Models}
\label{sec:models}

\paragraph{Embeddings} A common method in LSC detection is to learn low-dimensional semantic vector spaces (embeddings) for specific time periods and then align spaces for consecutive time periods with an orthogonal mapping which minimizes the distances between the time-specific vectors for all words \citep{DiachronicWordEmb}. Given two consecutive time periods $a$, $b$, and corresponding text corpora $C_a$, $C_b$, we learn two vector spaces $A$, $B$. Orthogonal Procrustes analysis can then be applied to find the optimal mapping matrix $W^{*}$ such that the sum of squared Euclidean distances between $B$'s mapping $BW$ and $A$ is minimized:
\begin{equation*}
W^{*} = \arg\min_{W} \| BW-A \|^{2}.
\end{equation*}
The optimal solution for this problem is given by an application of Singular Value Decomposition \citep{artetxe2017acl}.\footnote{$W$ is constrained to be orthogonal. $A$ and $B$ are first length-normalized and mean-centered and their rows are reduced to the intersection of the vocabulary of $C_a$ and $C_b$ for finding the mapping.} The degree of LSC of a word $w$ is then measured with the cosine distance \citep{salton1986introduction} between $w$'s vectors in $A$ and $BW^{*}$ ($B$'s mapping). This approach has been found to outperform other LSC detection methods in various studies \citep{DiachronicWordEmb,kulkarni2015statistically}. It has the advantage of not assuming that words keep the same meaning over time. A presumable downside of this approach is expected noise from the alignment, i.e., it may not be possible to align all words to each other that have similar meanings, because the spaces were learned independently.

\paragraph{PPMI} Another method to learn time-specific semantic vector space representations $A$, $B$ is to store count-based co-occurrence information for each word in a high-dimensional sparse matrix and then apply Positive Pointwise Mutual Information (PPMI) weighting \cite{Levy2015}. In such a matrix each column stores the co-occurrence statistics with a specific context word. This has the advantage that $A$ and $B$ can be aligned straightforwardly, because many context words occur as columns in both $A$ and $B$ and can hence be mapped onto each other. Mapping $A$ and $B$ to a common coordinate axis then corresponds to intersecting their columns \citep{DiachronicWordEmb}. This has the advantage of avoiding the complex alignment procedure for embeddings, but also loses their performance advantages \cite{marcobaroni2014predict,Levy2015}.

\paragraph{Temporal Referencing}\label{sec:tr}
Temporal Referencing (TR) is an alternative to learning individual word representations for different time periods, which avoids alignment using a procedure radically simpler than proposed for dynamic embeddings. TR is potentially applicable to every vector space learning method.  We treat all time-specific corpora $C_a$, $C_b$, ..., $C_n$ as one corpus $C$ and learn word representations on the full corpus. However, we first replace each target word $w\in C_t$ with a time-specific token $w_{t}$.\footnote{In our case, $t$ is a decade. E.g., in the corpus for 1920 we replace each occurrence of \textit{computer} with the string \textit{computer$_{1920}$}.}
This temporal referencing of $w$ is only performed when it is a target word, when the word is considered a context word, it remains unchanged. Following this procedure, we learn one single space that contains a vector for each target-time pair $w_{t}$, which may  be compared directly without the need for alignment.
Besides the considerable advantages of avoiding alignment and being applicable to count-based and embedding methods, it presumably lowers data requirements (because context words are collapsed, and thus shared, across corpora). Accordingly, we assume TR to produce smoother change values. As various other models, TR relies on the assumption that the semantics of the context words stays relatively stable over time.

\section{Synthetic Lexical Semantic Change}
\label{sec:SynLSC}

We aim to simulate semantic change under controlled settings, while keeping the corpus as natural as possible.\footnote{Hence, the target words' frequencies were not matched, but rather stayed natural.} We call this procedure \textit{sense injection}. We increase the semantic material of a recipient word $w^r$ in subsequent subcorpora by injecting contexts from a donor word $w^d$. The context of the recipient word (illustrated as Sense 1 in Figure \ref{fig:synChange}) stays as it is in the corpus. The first subcorpus contains only contexts from the recipient $w^r$ and all the contexts of the donor $w^d$ are removed. In the next time period we  add 25\% of the contexts of $w^d$, with donor word replaced by the recipient word. In each subsequent corpus, an additional 25\% of the donor word are injected until the last time periods contain equal amounts of contexts from the donor and recipient. As a result, seen from the recipient $w^r$, the last time periods have double the amount of contexts as in the first time period $|w^r({t_n}) +w^d({t_n})| = 2*|w^r({t_1})|$.

Note that due to the polysemous nature of words (each is usually associated with more than one sense), we preferred to \textit{add} the donor words' contexts instead of simply \textit{replacing} the existing contexts of the recipient words with the contexts of the donor words. This is because the former involves a single source of synthetic lexical semantic change, while the latter involves two sources (the removal of contexts associated with different senses of a recipient word, as well as the added contexts associated with the senses of a donor word). As a result, this procedure yields less noisy examples of synthetic lexical semantic change.

We differ between cases where recipient and donor are related (e.g. \textit{maker} $\rightarrow$ \textit{creator}, Fig. \ref{fig:synChange}a) and unrelated (e.g. \textit{shoulders} $\rightarrow$ \textit{horde}, Fig. \ref{fig:synChange}b), following e.g., \citet{pilehvar2013paving}. This procedure is aimed to give us insight into how much novel semantic material is needed for our methods to detect semantic change. Our hypothesis is that cases where the donor word is unrelated to the recipient word should be simpler to detect compared to those that are in close relation. It is linguistically motivated to choose semantically related words to simulate sense change; those are the most difficult cases of sense change, and a likely procedure of semantic change introducing polysemy \cite{Blank97XVI}.

Finally, to simulate the same increase in frequency, we repeat the sense injection for a set of control words. In this case recipient and donor word are the same $w^r = w^d$. This creates the same increased frequency of the recipient word $|w^r({t_n})| = 2*|w^r({t_1})|$ as the above, but without any added semantic information because the control word keeps its original contexts.

      \begin{figure}[t]
        \center{\includegraphics[width=1.0\linewidth]  {3.png}}
        \caption{\label{fig:synChange}  Increase in semantic material for a word by means of sense injection. I.: new injected sense is related to the existing sense. II.: new injected sense is unrelated.}
      \end{figure}

\section{Experimental setup}
\subsection{Corpora}\label{sec:corpora}
For Experiment 1 (Sec.~\ref{subsec:experiment1}) we used COHA \citep{davies2002corpus}, of which we restrict ourselves to decadal bins spanning from 1920 to 1970 so as to have a comparable number of tokens for each time slice.
For Experiment 2 (Sec.~\ref{subsec:experiment2}) we used COCA \citep{davies2008corpus}, of which we remove the spoken and academic genres in order to maintain a more similar usage context of words.
As a control setting, we created shuffled versions of the same corpora with the same periods, and straightforwardly followed \citet{Dubossarsky-EMNLP17}.

\subsection{Synthetic semantic change}
\label{Synthetic semantic change}
For related words, we used the Noun-Noun pairs in SimLex-999 \cite{hill2015simlex} as a starting point. However, even semantically unrelated pairs in SimLex were deemed somewhat related by our annotators, and therefore we kept only 10 of those. We created the rest of the list of unrelated words as follows:
we randomly sampled 300 lowercased nouns\footnote{The filtering was carried out on the basis of the output of NLTK \citep{bird2009natural}'s $pos\_tag()$ function.} from our corpus, which we assembled into 150 pairs.
We then asked three annotators to independently go through the list of generated pairs and determine whether they were semantically related or not.
All 150 pairs were deemed semantically unrelated by at least 2 annotators. Only 5 pairs had a disagreement but were qualified as border line cases by the disagreeing annotator, and kept.
This procedure yielded 356 word pairs in total, of which 196 were related and 160 were not related.

\subsection{Model training}
We tested two models in our experiments: (i) low-dimensional embeddings learned with SGNS and (ii) high-dimensional sparse PPMI vectors. Each of these were tested with their respective alignment method (AL) and with Temporal Referencing (TR) as described in Section \ref{sec:models}, leaving us with four models to compare:
    \begin{center}
    \begin{tabular}{ll}
      SGNS$_{\text{AL}}$   &  SGNS$_{\text{TR}}$\\
       PPMI$_{\text{AL}}$  & PPMI$_{\text{TR}}$\\
    \end{tabular}
    \label{tab:my_label}
    \end{center}
In order to avoid that replaced target words co-occur with other target words in TR we used the implementation of \citet{Levy2015}, allowing us to train SGNS and PPMI on extracted word-context pairs instead of the corpus directly. For this, we iterated over corpus $C_t$ such that for each token $w$ and for each of its context words $c$ within a symmetric window we extracted the word-context pair:
($w_t$,$c$) if $w$ is a target word and ($w$,$c$) otherwise.

In this way, we guarantee a target word is never replaced and treated as context of any other word. For TR, SGNS and PPMI were then trained on these extracted pairs. For AL, we extracted only regular word-context pairs ($w$,$c$) and trained SGNS and PPMI on these. LSC is measured for all four models via cosine distance.\footnote{Find a full implementation of the pipeline at \url{https://github.com/Garrafao/TemporalReferencing}.} (See Appendix \ref{sec:parameter} for preprocessing and hyper-parameter details.)

\section{Evaluation}
To test our methods we performed three main experiments, comparing the performances of TR to the existing state-of-the-art diachronic model alignment.
In the first experiment, we compare the models' performance under control conditions that address complementary (potential) weaknesses.
The second experiment tests different synthetic change types and assesses whether better models improve detection of lexical semantic change, in a controlled setting.
Finally, we test our methods on a manually created testset on a genuine corpus, and manually inspect the results.

\subsection{Experiment 1: Model comparison}
\label{subsec:experiment1}

In this experiment, we trained each model on two corpora, one genuine diachronic corpus with natural semantic change, and one shuffled where the diachronic change is distributed equally across all time periods (see Sec. \ref{sec:corpora}).  We study the average change of cosine distance as a proxy for semantic change. Following \citet{Dubossarsky-EMNLP17} we consider the average cosine distance (\textit{\textbf{acd}}) trained on the genuine corpus to correspond to true semantic change + noise. In contrast, the average cosine distance on the shuffled corpus corresponds to pure noise. Therefore, the difference between the two equals to true signal, or in other words, true lexical semantic change.

Importantly, we are interested in investigating, and hopefully mitigating, possible sources of the noise that might be found in some of the models. Specifically, we hypothesize that the alignment procedure adds considerable noise to the \textit{acd}, and plan to test how TR can alleviate some of that noise. Moreover, TR is assumed to contribute not only by circumventing the alignment, but also by producing more stable context vectors due to the increased amount of data on which they are trained.\footnote{We differ between \textit{stable} vectors that do not change despite the randomness involved in training between multiple runs, and \textit{accurate} vectors give a good representation of meaning. Note that when we use the term \textit{stable word} we mean stable in meaning over time.} Therefore, we first tease-apart these factors using the following comparisons between the different models.
\vspace{0.1em}
\begin{compactenum}
\item For all models, we consider the difference in average cosine distance between genuine and shuffled conditions ($acd_{genuine} -acd_{shuffled}$) as being inversely proportional to the amount of noise that the original model unknowingly captures. Hence, the larger the difference, the less noisier (and better) the model is. We consider this to be an approximation of the \textit{true semantic change}.
\item Focusing on the differences between the two PPMI models allows us to test the independent contribution of TR in providing more accurate context vectors because the intersection of the PPMI vectors are inherently aligned.
\item Focusing on the SGNS models conflates the potential benefits from more accurate context vectors with the disadvantage of Procrustes alignment (which is necessary for SGNS$_{\text{AL}}$ but not for SGNS$_{\text{TR}}$).
\item The difference between the last two would allow us to evaluate the independent contribution of these two sources on the (presumably) less noisy SGNS$_{\text{TR}}$ model scores.
\end{compactenum}

\paragraph{Results (experiment 1)}

We start analyzing the true semantic change for each of the models (PPMI$_{\text{AL}}$ to PPMI$_{\text{TR}}$ and SGNS$_{\text{AL}}$ to SGNS$_{\text{TR}}$) over the corpus. In Figure \ref{fig:my-label}, we can see that temporal referencing introduces less noise throughout the 5 decadal comparisons. For both PPMI and SGNS, the true semantic change increases for the TR models compared to the aligned.

\begin{figure}[h]
\center{\includegraphics[scale=0.5]        {fig2d_1.png}}
\caption{\label{fig:my-label} Comparison of aligned embedding spaces and temporal referencing using both the genuine and the shuffled corpora. High difference in cosine distance indicate less noise captured by the model. }
\end{figure}

Importantly, Table \ref{table:avgcosgrouped} shows that for the PPMI models, Temporal Referencing has a much smaller improvement over the aligned model (.005) compared to the SGNS models (.026) (all reported differences are statistically significant, t-test $p<.01$). Temporal Referencing influences the PPMI models only by creating more stable context vectors. In contrast, for the SGNS models the introduction of Temporal Referencing circumvents the use of alignment in addition to creating more stable context vectors. Therefore, the results support our hypothesis that TR has two complementing factors that improve prior models; firstly, it avoids the need for alignment altogether (and the noise that usually comes with it), and secondly, it produces more stable context vectors due to the increased volume of data when using the full corpus.

\begin{table}[h]
\caption{Difference in average cosine distance between genuine and shuffled conditions (true semantic change) for each method, collapsed over the 5 time bins (1920--1970) in COHA.}
\label{table:avgcosgrouped}
\centering
\begin{tabular}{lllc}
\toprule
& Align & TR & $\Delta$\\
\cmidrule(l){2-4}
SGNS& 0.033& 0.059 & 0.026\\
PPMI &0.028&0.033 & 0.005\\
\bottomrule
\end{tabular}
\end{table}

\paragraph{Smoothness of Temporal Referencing} We further analyzed the nature of the progression of the cumulative semantic change that words exhibit over time. Under the assumption that words change their meaning in a systematic way, it follows that words' semantic change would increase over the years.
Therefore, an ecologically valid model of semantic change should show that the words change more as the time interval for comparison increases, for the vocabulary as a whole. In contrast, if a model captures stochastic fluctuations in the words' vectors instead of true semantic change, then such a shift in the distribution will be less prominent.

We plot the distribution of the words' cosine distances with increasing time intervals (relative to 1920) for both SGNS models in Figure~\ref{fig:histograml}. Both models show a gradual transition from left (smaller change scores) to right (larger change scores). This corroborates our basic assumption that words change more as the time interval for comparison increases. Crucially, Temporal Referencing shows a more constant cumulative progression of cosine distances over time in contrast to alignment where decadal cosine distance distributions seem to be more volatile. We follow \citet{bamler17} in interpreting these results as attesting for the relatively high noise factor in the SGNS$_{\text{AL}}$ over the SGNS$_{\text{TR}}$.

\begin{figure}[t]
\center{\includegraphics[scale=0.4]        {fig3f.png}}
\caption{\label{fig:histograml} Smoothed histograms of word distances for the two SGNS models. For the TR model, we see a more constant cumulative shift which is reflected by the overlap between the distributions as well as by differences in their means (dashed vertical lines).}
\end{figure}

Overall, the different analyses converge to the same conclusion: Temporal Referencing is a better model for capturing a word's semantic information from diachronic text because it introduces less noise. Next, we will investigate if a less noisy model is also better at detecting semantic change.

\subsection{Experiment 2: Synthetic semantic change}
\label{subsec:experiment2}

This experiment aims to see how well our methods can find different synthetic change types.
In order to minimize natural semantic change in the dataset, we made use of the synchronic dataset COCA which we randomly shuffled, and simulated a diachronic corpus for which we have 7 time-bins.
We randomly assigned a seventh of COCA to each of our artificial time periods, labeled $t_1$ to $t_{7}$. Sentences in which either word of the synthetic semantic change pairs (see Sec. \ref{sec:SynLSC}) or their corresponding control words appeared were held out. These sentences were subsequently added back to COCA according to the procedure outlined in Section \ref{sec:SynLSC}, which enabled us to control for the fixed ratio incremental steps between the recipient and donor words (i.e., changes to the injection ratio were made only for $t_2$-$t_3$, $t_3$-$t_4$, $t_4$-$t_5$, and $t_5$-$t_6$, while $t_1$-$t_2$ and $t_6$-$t_7$ had no such changes).

All four models were trained on the 7 synthetic time-bins exactly as in Experiment 1. The target words were the 356 words with synthetic lexical semantic change and their 356 control words that were matched with the same frequency increase but otherwise are considered semantically stable. For each target word, the cosine distances between two consecutive synthetic time-bins were computed, resulting in 6 change scores per word.

We analyze the peak distribution of the individual words. We defined the peak position of each word as its vector $argmax$ (the position in which it shows the maximum cosine distance).
In order to evaluate the models' ability to truly detect semantic change, we formulate a naïve binary classification task based on the words' peak positions. For each word, if the peak is in position 2--5, we classify it as changed, and otherwise as stable and measure accuracy and F1-score.

\paragraph{Results} Figure \ref{fig:avgSC} shows the \textit{acd} of the four models for the change and stable words separately, according to the different sense injection ratios. The two plots differ markedly. For the semantic change words (upper plot), all four models show a noticeable peak when the new sense was first injected (step 2), followed by a steady decrease in \textit{acd} until step 6. In contrast, the stable words only show the steady decrease starting from step 1, without any noticeable peaks. This decrease probably stems from the target words' increased frequency that can lead to more accurate word embeddings \cite{HellrichH16-Coling}. Because peaks in \textit{acd} are interpreted as points were semantic change was the most profound, the results support the models' ability to detect synthetic semantic changes.

  \begin{figure}[h]
    \center{\includegraphics[width=1.0\linewidth]   {fig4.png}}
    \caption{\textit{acd} at different sense injection steps for the four models. Steps without sense injection are shaded. \label{fig:avgSC} }
  \end{figure}

Although the majority of peaks for the semantic change words fall in step 2, as expected by the \textit{acd} analysis above, words had their peaks in other step positions as well (see Appendix~\ref{sec:peak_analysis}).\footnote{We also ran experiments with moving the time point when the first change was injected and the results mimic those presented here.}

Table \ref{tab:exp2accuracy} reports accuracy and F-scores for the four models in the binary classification task. As clearly seen, all four models perform better than chance even under these very rudimentary conditions (finding the $argmax$ of a vector of length 6). Crucially, SGNS$_{\text{TR}}$ outperforms the rest of the models, and especially SGNS$_{\text{AL}}$ that shows the worst performance. These results corroborate our hypothesis from Experiment 1 that noise is negatively influencing task performance. By alleviating the noise factor that exists in SGNS$_{\text{AL}}$ (due to alignment), SGNS$_{\text{TR}}$ is able to show substantial gains in this binary classification task.

\begin{table}[h]
    \centering
    \small
      \caption{Accuracy (averaged, and split into individual classes) and F1-scores for semantic change detection. For stable words (control words), peaks at 1 and 6 steps are correct. For change words, peaks at steps 2--5 are correct. We see that all methods find unrelated change better than related change, and that SGNS$_{\text{TR}}$ outperforms the other methods.}
    \label{tab:exp2accuracy}
    \begin{tabular}{l l l l l@{}}
    \toprule
    & PPMI$_{\text{AL}}$ & 	PPMI$_{\text{TR}}$	& SGNS$_{\text{AL}}$ & SGNS$_{\text{TR}}$\\
    \cmidrule{2-5}
Stable  &	0.52&	0.54&	0.37	&\textbf{0.57}\\
Unrelated	&0.83&	0.83&	0.86&	\textbf{0.91}\\
Related&	0.73&	0.73&	0.78&	\textbf{0.78}\\
\midrule
 Mean acc.&	0.65&	0.66&	0.59&	\textbf{0.70}\\
F1-score & 0.69& 0.69 & 0.67& \textbf{0.74}\\
\bottomrule
    \end{tabular}
\end{table}

\paragraph{Discussion} Table \ref{tab:exp2accuracy} shows that SGNS$_{\text{TR}}$ gains its performance advantage over SGNS$_{\text{AL}}$ mainly from a better classification of the stable words ($0.37$ vs. $0.57$). In order to understand this better, we inspect their mean cosine distance curves only for stable words in Figure \ref{fig:dominik}. SGNS$_{\text{TR}}$'s curve clearly declines, while SGNS$_{\text{AL}}$'s curve declines much less and is more volatile. We attribute the decline of both curves to the diminishing noise that comes from the continuous increase in frequency of the control words \citep{Dubossarsky-EMNLP17}. It seems that this diminishing frequency noise is counteracted by the alignment noise, yielding a flatter curve for SGNS$_{\text{AL}}$. The latter increases SGNS$_{\text{AL}}$'s chance to have peaks in one of the center injection steps producing false positives in our classification task. However, this property may also have a positive influence on SGNS$_{\text{AL}}$ in related LSC detection tasks \citep{Schlechtwegetal19}.

\begin{figure}[t]
\center{\includegraphics[scale=0.6]{fig_dominik.png}}
\caption{\label{fig:dominik} Mean cosine distance curves for SGNS$_{\text{TR}}$ and SGNS$_{\text{AL}}$.}
\end{figure}

\subsection{Experiment 3: WSC testset}

So far, the results have been based on either a large random sample to show general tendencies for the language in the corpus as a whole, or synthetically injected semantic change.  In this part, we test the behavior of our methods on a small, manually created testset for semantic change. We use the Word Sense Change Testset \cite{WSCtestset} that consists of words and the different associated change events, for the time span 1785 -- 2010. In this experiment, we ignore the sense changes and consider only words as changed or stable, and restrict our change words to those that have change events between 1920 and 1970.\footnote{As an example, the word \textit{car} is considered stable since its change event occurred before 1920. }
In total we have 13 changed and 19 stable words (excluding words with a total frequency $\leq 100$).

\begin{table}[h]
\caption{\textit{acd} for WSC testset. Var $\in (0.0-0.01)$.  CH = changed word, ST = stable word, DIFF = difference between ACD for change and stable in percent.  \label{tab:ACD-WSC}}
\centering
\small
\begin{tabular}{lllll}
\toprule
&\multicolumn{2}{c}{SGNS} & \multicolumn{2}{c}{PPMI}\\
& Align & TR & Align & TR\\
\cmidrule(l){2-3}
\cmidrule(l){4-5}
CH &0.47  & 0.31   & 0.86  & 0.86 \\
ST & 0.34   &0.21 & 0.71  &  0.73 \\
\midrule
DIFF & 38\% & 50\% & 20\% & 17\%\\
\bottomrule
\end{tabular}
\end{table}

In Table \ref{tab:ACD-WSC} we see \textit{acd} of each model on the changed and stable words.
We find that for all methods, SGNS$_{\text{AL}}$, SGNS$_{\text{TR}}$, PPMI$_{\text{AL}}$ and PPMI$_{\text{TR}}$, the \textit{acd} for the changed words is statistically significantly higher (p values $\leq 0.01$) than for the stable words which nicely corresponds to intuition; words with true semantic change should have vectors that differ more than words without change.
The mean difference between the stable and the changed words, that gives us some notion of how well the two different classes are separated, is highest for SGNS$_{\text{TR}}$. Because of the limited size of the testset, the results are indicative rather than conclusive and we continue with a manual analysis of the nearest neighboring words.

We carry out a qualitative evaluation for the closest neighbors for \textit{computer} (see Figure~\ref{fig:knn-comp-paper}), a word we expect to have changed after the invention of the digital computer in the 1940s, for the SGNS aligned version and SGNS with Temporal Referencing.
SGNS$_{\text{AL}}$ has only a few words in common in 1950--1970, and while the digital computer is showing here, there are few overlapping words. The time periods 1920--1940 have no common words.  In comparison, the SGNS$_{\text{TR}}$ show clear patterns. We see a clear break between 1940 and 1950, without any overlapping word, and a pattern between 1950--1970;  the closest words are the other \textit{computer$_{\textit{1940--1970}}$}.\footnote{The closest words in 1920--1940 have high cosine distances and are thus not very related. Still, for each \textit{computer$_{\textit{time}}$}, the other vectors of \textit{computer} are among the neighbors, meaning that despite sparsity and little overlap in context,  some structure is found.}  This is exactly the pattern that we expected to see using the sense injection; stable senses can be distinguished from changing senses by their relationship to the other temporally referenced vectors.

     \begin{figure}[t]
       \center{\includegraphics[width=1.0\linewidth]  {computer-NN.png}}
      \caption{\label{fig:knn-comp-paper}Nearest neighbors for \textit{computer}. Upper part SGNS$_{\text{AL}}$, lower part SGNS$_{\text{TR}}$. A larger rendering of this figure is available in Appendix~\ref{sec:appComp}.}
     \end{figure}

Next, we study a word for which we expect no sense change, namely \textit{ship} (see Appendix~\ref{sec:appShip}).
The SGNS$_{\text{AL}}$ show a fairly low \textit{acd}, but still there are large differences in the top neighboring words. The SGNS$_{\text{TR}}$ show what we expect; the most similar words are the other \textit{ship$_{\textit{1920--1970}}$}, and over time we see that the `self-similarity' decreases. For almost all decades, the most similar words are \textit{ship} from the decade before and after. The lower words also help describe the meaning of \textit{ship}, as a \textit{boat} and later also as a \textit{spaceship}. The pattern of stability is much more clear for SGNS$_{\text{TR}}$ than SGNS$_{\text{AL}}$ and holds for most other stable words as well.

For the word \textit{tape}, that has a change in dominant sense (or an addition of another strong sense) with the addition of the music tape to adhesive tape, we see the same patterns as for ship, but the bottom words contain \textit{ribbon, paper, adhesive} for 1920--1940 and \textit{recorder, recording, stereo} in 1950--1970.\footnote{Find all nearest neighbour lists at \url{https://github.com/Garrafao/TemporalReferencing/tree/master/data}.}

For both the real change in
Table~\ref{tab:ACD-WSC} and the  synthetic  change in Table \ref{tab:ACD-synth}, we find that SGNS$_{\text{TR}}$ is best at differentiating between the stable and the change classes for both datasets (50\% for WSC and 26\% for synthetic change).

\begin{table}[t]
\caption{\textit{acd} for synthetic change. Var $\in (0.0-0.01)$.   \label{tab:ACD-synth}}
\centering
\small
\begin{tabular}{lllll}
\toprule
&\multicolumn{2}{c}{SGNS} & \multicolumn{2}{c}{PPMI}\\
& Align & TR & Align & TR\\
\cmidrule(l){2-3}
\cmidrule(l){4-5}
CH &0.46  & 0.33  & 0.86  & 0.87 \\
ST & 0.37   &0.26 & 0.83  &  0.83 \\
\midrule
DIFF & 24\% & 26\% & 4\% & 4\%\\
\bottomrule
\end{tabular}
\end{table}

\section{Conclusions and future work}
In this paper, we have empirically tested the temporal referencing method for lexical semantic change. We train one vector space model over the whole corpus, and thus share information of the context words while training individual vectors for each target word and time period. We compare two commonly used models, namely PPMI and SGNS because of their properties; the PPMI model is count-based and does not require alignment across time, while the SGNS model has shown state-of-the-art results in previous work.

We find that the SGNS model trained with Temporal Referencing contains significantly less noise than the standard SGNS for which an alignment is necessary. In comparison, for the PPMI model where no alignment is needed, Temporal Referencing also significantly reduced the noise level, but to a lesser extent.

Next we evaluated whether the noise reduction carries over performance on a synthetic lexical semantic change detection task. We simulated change in a controlled and semantically principled way, using sense injection and showed that words with semantically related and unrelated semantic change can be differentiated from control (stable) words that are not sense injected, but increase in frequency in the same way as the changed words. SGNS with Temporal Referencing outperforms the other methods in correctly classifying the words to the two classes (change vs. stable).

Finally, we evaluated on a small, handcrafted set of change and stable words and found that SGNS with Temporal Referencing gives the largest separation between words that undergo semantic change and those that stay stable over time. In particular, we observe a similar behavior between this smaller testset and the synthetic sense injection, supporting our sense injection method as a good proxy for isolating and studying lexical semantic change.

Our results support the following conclusion; \textit{trained on a diachronic corpus, SGNS with Temporal Referencing will capture more true semantic change.}
In the future, we plan to evaluate Temporal Referencing against the related dynamic embedding models on an annotated empirical lexical change dataset with multiple languages. We also plan on testing how well Temporal Referencing deals with corpora that are too small for alignment-based methods, hopefully opening new avenues of quantitative research.

\section*{Acknowledgements}
The authors would like to thank Dr. Barbara McGillivray for her encouragement, and the anonymous reviewers for their helpful comments and suggestions.
This work has been funded in parts by the University of Helsinki (research visit grant C1/2019 from the Faculty of Arts, to SH), by the project \textit{Towards Computational Lexical Semantic Change Detection} supported by a project grant (2019–2022; dnr 2018-01184, to NT), the Centre for Digital Humanities at University of Gothenburg, the Konrad Adenauer Foundation and the CRETA center funded by the German Ministry for Education and Research (BMBF), and the Blavatnik Postdoctoral Fellowship.

redpajama_set_name: RedPajamaArXiv


text:
Buried Prey
Sandford, John
Large Print - 2011
In 1985, Davenport was a young cop with a reputation for recklessness. He was working the case of a missing young girl until his bosses ultimately declared the case closed . It has haunted him ever since. Now that he has a chance to investigate it all over again, one thing is becoming increasingly clear: it wasn't just the bodies that were buried...it was the truth.
Publisher: Waterville, Me. : Thorndike Press, 2011.
Read more reviews of Buried Prey at iDreamBooks.com
apoe2003 Aug 08, 2017
i highly recommend this series. It is beautifully written and will keep you wanting to read more.
PimaLib_ChristineR Jan 18, 2017
Lucas Davenport #21 takes Davenport into the cold case files when the bodies of two girls who went missing in the 1980s show up in a demolished house.
ilovewhippets Jan 06, 2015
Nothing to write home about! It is an OK book.
RicardoEdm Apr 25, 2014
Lucas Davenport is the man!
StarGladiator Nov 30, 2013
Great read, jumping back to the past of Davenport, the cop, but soon-to-be detective. Lucas is the honest cop, full of honor and integrity we wished we'd meet, not all the corrupt and lazy cops we have met, or read about in the news on a daily basis. (In real life there are honest cops, but they don't last long, e.g., Paul Schraga of LA's Rampart precinct, in the aftermath of the Sen. Bobby Kennedy assassination.)
Krajzel Jan 30, 2012
I think John Stanford is an amazing author. Love the Lucas Davenport series, this one is a real page turner!
debwalker Jun 11, 2011
"There's a simple reason why all of John Sandford's Lucas Davenport crime novels hit the best-seller list: They're good. That's because Sandford creates in Davenport a plausible, if slightly super-human, detective, the supporting cast of cronies and villains is well fleshed-out and the stories are often first-rate. This time, we venture into Davenport's past as evidence of an old crime that occurred while he was a young cop, and that haunts him still, is found at a construction site. A homeless man was accused of killing two young girls. Davenport has always doubted his guilt, and new evidence not only bears him out, but indicates not only that the killer is still at large, but that these were not his only victims."
emerge May 11, 2011
This series is about as close to a sure thing as you can get.
When a pair of bodies are accidently exhumed, a case from the distant past comes back to haunt Lucas, one that he always felt guilty about. Through flashbacks, we get to see how he first hooked up with many of detectives that have become regulars in this series. But one of them won't survive re-opening the investigation.
Even though he's pushing 50 (and expecting a new baby), we get to see vintage Davenport....driven, determined & thoroughly ticked off as he attempts to avenge the murder of his friend & colleague.
bt1000009864322 Feb 25, 2011
can't wait for this next story!!
D041622235 Sep 16, 2011
Snatched girls show up buried since 1985 when a Minneapolis block of bldgs is demolished. Lucas Davenport, local hockey hero's, first detective case.
Camp, John
Davenport, Lucas (Fictitious Character) — Fiction.
Private Investigators — Minnesota — Minneapolis — Fiction.
Cold Cases (Criminal Investigation) — Fiction.
Serial Murders — Fiction.
Serial Murder Investigation — Fiction.
Minneapolis (Minn.) — Fiction.
Large Type Books.


text:
Written by / Agency / Source: Innovative Interfaces, Inc.
Innovative and 3M Partner for Powerful eBook Integration in Encore ES
NewswireToday - /newswire/ - Emeryville, CA, United States, 2013/06/25 - Innovative Interfaces, Inc. and 3M announced a partnership that will integrate the 3M™ Cloud Library into the Encore discovery platform - 3M.com / III.com.
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02/5/15 9:13 AM
The latest addition to the long running Mobile Suit Gundam series has been confirmed for worldwide streaming as well as a full Blu-Ray release as well. Mobile Suit Gundam The Origin will begin airing later this month on February 28th on Daisuki.net as a premium (paid) stream, starting with the Epsiode 1 : "Blue-Eyed Casval". This new OVA (original video animation) series is set in the Universal Century timeline, based on the popular manga of the same name.
Later on this year in April, a Collector's Edition Blu-Ray of the first episode is to be released as a worldwide 'export' edition from Japan , which will be available in the UK via Anime Limited for £69.99. A subtitled trailer for the first episode of the OVA has also been uploaded to give a teaser on the upcoming Gundam anime.
————— Original Press Release Below —————-
Anime Consortium Japan to announce premium rental streaming of Mobile Suit Gundam The Origin
Worldwide streaming and pre-order of the limited Blu-ray Disc Collector's Edition release
FOR IMMEDIATE RELEASE February 4, 2015
Anime Consortium Japan Inc., the Japanese company for official anime streaming is excited to announce the streaming of Gundam the Origin, the latest anime OVA series of the popular Gundam franchise.
About Mobile Suit Gundam The Origin:
Episode 1 "Blue-Eyed Casval"
Universal Century 0068, Side 3 – The Autonomous Republic of Munzo. Zeon Zum Deikun attempts to declare complete independence of Munzo from the Earth Federation Government, while he preaches the evolutionary potential of humans who have advanced into outer space. Deikun however suddenly falls to his death in the middle of his speech at parliament.
Upon Deikun's death, Jimba Ral spreads word of a House of Zabi conspiracy – but despite such efforts the power and sphere of influence of House of Zabi, led by Degwin Sodo Zabi, only seem to escalate.
While we witness for the first time the untold convulsions of Universal Century history, Casval and Artesia, just bereaved of their father, must face destinies which will be just as tumultuous as the very era itself.
Created by Yoshikazu Yasuhiko, character designer and animation director for the 1979 TV hit "MOBILE SUIT GUNDAM"; "MOBILE SUIT GUNDAM THE ORIGIN" is a mega-hit manga boasting a total of 10-million copies printed. The manga is now being adapted to an anime, which is produced by SUNRISE Inc.
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And for more Gundam official information, visit: http://www.gundam.info
Streaming will start on the anime streaming platform DAISUKI ( http://www.DAISUKI.net ) on February 28 (JST) and will be offered as a premium streaming (paid). Available languages will be Japanese and English dub; selectable subtitles include English, French, Korean, Traditional Chinese (Taiwan), Traditional Chinese (Hong Kong) and Simplified Chinese.
Gundam The Origin will be available world-wide (except of Japan and China). Further details about the premium streaming will be announced on the website.
Furthermore, starting on February 4, users can pre-order the physical release Mobile Suit Gundam The Origin Volume 1 Blu-ray Disc Collector's Edition (Export Version). This limited special version comes with full of gorgeous bonus features with Japanese and English audio, and with subtitles in various languages and will be released on April 24, 2015. Pre-oders will be accepted from worldwide.
DAISUKI.net Gundam The Origin page: http://www.daisuki.net/gundamtheorigin/
© SOTSU, SUNRISE
About Anime Consortium Japan Inc.
Anime Consortium Japan Inc. was established on November 7, 2014. The new company is designed to develop the platform that will provide streaming service of official Japanese anime content and operate e-commerce site to offer anime character-related goods to overseas fans.
The major shareholders are BANDAI NAMCO Holdings Inc., Cool Japan Fund Inc., ASATSU-DK INC., Aniplex Inc., TOEI ANIMATION Co., Ltd., SUNRISE Inc., TMS ENTERTAINMENT CO.LTD, NIHON
AD SYSTEMS, INC. (NAS), and DENTSU INC. as of December 25, 2014.
Anime Consortium Japan (ACJ) merged with Daisuki and is taking over their business and remodeling and upgrading all services.
DAISUKI.net is currently streaming titles such as SWORD ART ONLINE 2, M3 the dark metal, Persona 4 the Golden ANIMATION, KILL la KILL, SUSHI NINJA, BUDDY COMPLEX and more. Visit http://www.daisuki.net to see the full selection of titles.


Well, I just thought I would post a probably not very helpful warning. I got a virus from a link posted for streaming videos. I don't remember what the website was, and I don't even remember which subreddit I read about it on. I had thought it was something science fiction-y, but I can't find it on any of the ones I've looked at, but I had to delete the partition I was in and create a new one, and I'm tired now.
Sorry I couldn't be more specific.