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text:
Robert Weissman: Let's cut the military budget,…
Robert Weissman: Let's cut the military budget, just don't call it 'defund'
U.S. Marine Corps F-35 Lightning II Joint Strike Fighters from Naval Air Station Pensacola, Florida fly off the coast of Northwest Florida May 15, 2013, off the coast of Northwest Florida. (U.S. Air Force photo by Master Sgt. Donald R. Allen)
By Robert Weissman |
There is an excellent argument that the "defund" trope has become so politicized that it now gets in the way of, rather than advances, policy advocacy of any stripe.
But that doesn't mean we should reflexively dismiss the underlying idea that government funds should be shifted away from wasteful or counterproductive purposes and redirected to beneficial ones.
Case in point: The United States spends far too much on the Pentagon. The country would be more secure, safer, healthier and more just if we shifted some of the nearly $1 trillion in Pentagon spending to priority domestic and human needs.
Let's start with the first half of the equation: The Pentagon budget is far too big.
The United States spends far more on its military than other nations do. U.S. military spending is more than the next nine countries combined; it is 12 times the amount Russia spends.
Yet demands persist from the military-industrial complex to spend ever more. For the coming fiscal year, the Biden administration requested an increase in Pentagon spending to more than $800 billion.
Congress is poised to add even more to that total — with some saying the U.S. should spend a trillion dollars on the military budget.
The lobby to spend more, more and more on the Pentagon is completely disconnected from any legitimate national security needs.
Not only does the United States vastly outspend other nations but it also doesn't effectively manage what it does spend. The Pentagon is unable to pass an audit, and Pentagon spending is replete with waste and fraud. The Pentagon identifies more than $100 billion in administrative waste in its budget.
The Pentagon throws unfathomable sums at needless and/or ineffective weapons. According to the Project on Government Oversight, the defective and dysfunctional F-35 program will cost more than $1.7 trillion over its projected 50-year lifespan.
The Pentagon squanders tens of billions on overpriced contractors to perform jobs that should be done by government employees. One spare parts maker reports a 3,800 percent profit level.
Indeed, you can better understand the size of the Pentagon budget not through the lens of national security but as a reflection of the lobbying prowess of military-industrial contractors, who collect more than half of all Pentagon spending.
Now to the second half of the equation: We have a long list of urgent needs in this country that we don't address for lack of funding.
Take one specific example: Congress increased Pentagon spending last year by $28 billion over the Biden administration's request. That same amount could instead have been spent to scale up global vaccine production and end or at least diminish dramatically the pandemic.
Did the extra $28 billion for the Pentagon do anything to make us safer?
By contrast, is there any doubt that our national security would have been massively improved if we had helped vaccinate the world — an investment that may have prevented the omicron variant from ever coming into existence?
To take another example, there is no greater threat to national security right now than the fast-worsening climate crisis. In the historic Inflation Reduction Act, Congress agreed to spend $40 billion a year to reduce carbon emissions. That's less than one-twentieth of the annual Pentagon budget!
Moreover, the Inflation Reduction Act spending is wholly paid for with new tax revenues. By contrast, Congress routinely throws billions more at the Pentagon without worrying about how the costs will be covered.
The list of transformative, 100%-paid-for investments rejected in the Inflation Reduction Act includes universal daycare, a tax credit that reduced child poverty by one-third, dental and hearing coverage for seniors, free community college and more.
Why does it make more sense to tolerate waste, fraud and military contractor profiteering at the Pentagon when so many pressing needs go unattended?
It does not.
Let's skip the tired arguments about "defunding" and instead focus on what really matters: It's time to end wasting money on the bloated Pentagon budget.
We will make our nation stronger and better by shifting some of the Pentagon's excessive funding to other needs.
Robert Weissman is president of Public Citizen. This column was provided by InsideSources.
Robert Weissman
Naomi Ishisaka: Teens carry a threat to mental health in their pockets
Veronique De Rugy: Is bipartisanship good? That depends
Carl P. Leubsdorf: Debt ceiling showdown is probably not what Congress imagined in 1917
Editorial: Reasons why we struggle in the war on drugs

redpajama_set_name: RedPajamaCommonCrawl


Difference between revisions of "Bolshoi Circus"
[[File:Bolshoi_Circus_by_night.jpeg|right|thumb|330px|The Bolshoi Circus]]The "Great Moscow Circus on Vernadsky Avenue" ("Bolshoi Moscowskiy Tsirk na Vernadskogo" in Russian) was built under the reign of Leonid Brezhnev as First Secretary of the Communist Party of the USSR, and opened its doors on April 30, 1971. It is located on Vorobiovy Hills (formerly Lenin Hills), near Moscow University, at 24 Vernadsky Prospect (Avenue). Built by Yakov Belopolsky—one of the busiest official architects of the Brezhnev era—in cooperation with E. Bulikh, S. Feoksitov and V. Khavin (who may have been more familiar with the technical demands of such a structure), it is, with its 3,350-seat house and its numerous and specialized facilities, the world's largest circus building.
[[File:Bolshoi_Circus_by_night.jpeg|right|thumb|400px|The Bolshoi Circus]]The "Great Moscow Circus on Vernadsky Avenue" ("Bolshoi Moscowskiy Tsirk na Vernadskogo" in Russian) was built under the reign of Leonid Brezhnev as First Secretary of the Communist Party of the USSR, and opened its doors on April 30, 1971. It is located on Vorobiovy Hills (formerly Lenin Hills), near Moscow University, at 24 Vernadsky Prospect (Avenue). Built by Yakov Belopolsky—one of the busiest official architects of the Brezhnev era—in cooperation with E. Bulikh, S. Feoksitov and V. Khavin (who may have been more familiar with the technical demands of such a structure), it is, with its 3,350-seat house and its numerous and specialized facilities, the world's largest circus building.
Its unique technical fittings make it an exceptional tool for the presentation of all sorts of large-scale circus productions. The building includes a rehearsal ring, located backstage, behind the ring entrance, in a space vast and high enough to accommodate a large flying act; a stage above the ring entrance, which can connect to the ring with a giant telescopic staircase that unfolds automatically; the possibility for aerialists to access their apparatus from the ceiling; and several rooms at various temperatures to accommodate the upkeep of exotic animals.
The building's heavy machinery was conceived with custom systems and elements especially designed for this project, and it became difficult to maintain or fix when needed, since there are no existing replacement parts. Furthermore, the building is located far from Moscow's center, and after the fall of the Soviet regime—and its system of corporate group sales—it had proved difficult to fill to capacity the 3,500 seats of a circus that is particularly expensive to maintain. Although the Bolshoi Circus still offerered circus shows of the highest quality, its old downtown competition, "The Circus on Tsvetnoi Boulevard," today [[Circus Nkulin]], had reclaimed its crown as Moscow's—and Russia's—premier circus.


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Grad Newton to Discuss His Metamorphosis
How did Eastern Kentucky University's "worst" student become an award-winning war reporter and, now, a senior producer for a provocative documentary series on HBO.
Jeff Newton will take his audience along for his life's journey when he presents "Oh, How I Miss T-Bombs: Ramblings on Global Conflicts from EKU's Worst Student and Best War Reporter." The event, on Wednesday, April 13, at 6:30 p.m. in the Grand Reading Room of John Grant Crabbe Main Library, is sponsored by the Friends of the Library and the EKU International Alumni Association. It is free and open to the public.
A member of EKU's Hall of Distinguished Alumni, Newton served as editor of The Eastern Progress in 1990-91. He left Eastern a few credits shy of graduation before returning to complete a bachelor's degree in journalism in 1997. He began shooting, editing and broadcasting war stories from the Middle East in 2001, armed only with a mini-cam and a laptop, making him one of the first "backpack journalists," covering combat zones as a free-lance journalist. He went on to serve 13 years as a producer for TV's "60 Minutes," covering some of the world's most volatile conflicts, endangering his own life so that viewers could make sense of some of significant current events. In August 2015, Newton joined HBO's "Vice" as a senior producer for stories and content.
Newton is a two-time Emmy Award recipient and a winner of the Edward R. Murrow Award from the Radio Television Digital News Association, the Alfred I. duPont Award presented by Columbia University's Graduate School of Journalism, and the David Kaplan Award from the Overseas Press Club of America.
A reception will follow Newton's talk. Parking will be available in the Adams House Lot on Lancaster Avenue (at the intersection of Lancaster Avenue and Crabbe Street).
By the way, the T-Bombs referenced in Newton's speech title was T Bombadils, at one time a popular student hangout in downtown Richmond.
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Next Saturday, Connecticut fashion will be walking in the dirt when Faschinn presents a runway show at The Dirt Salon in Hartford.
Chinyerre McPherson, the brains behind the Faschinn clothing line, will be combining with The Dirt Salon to present All White All Light on Saturday.
Although she has been part of showcases before, McPherson said this is the first time where she has been part of a runway show where she was the sole designer.
All White All Light will be held at The Dirt Salon, 50 Bartholomew St., Hartford from 5 to 9 p.m. on Saturday, July 1. Dress code is all white.
A selection of Faschinn's fashion show at Hartford Fashion Week last year. Faschinn shows her collection at All White All Light on Saturday.
Separator image Posted in Fashion and tagged with All White All Light, Chinnyere McPherson, FasChinn, fashion, fashion show, model, The Dirt Salon.


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Help For Researching German Roots: German-Related Resources and Strategies
Teresa Koch-Bostic April 30, 2022
Finding the ancestral town of your German ancestors is typically not easy—especially for those of us with family members who arrived before the twentieth century and no oral history or home sources still exist. Few if any US records may state their exact place of birth. Most US citizenship records for German immigrants before 1900 fail to give clues to origins beyond the country they no longer have allegiance to. Census records dangle before us the country of origin as Germany, Switzerland, or Austria but only confirm that Germanic ancestry was reported to the census enumerator.
Complicated German Geography
After more than two decades of research I found in an 1872 NYC Lutheran Church marriage register that my immigrant ancestor, Richard Koch, was from Bamberg, Germany. But where in Bamberg still eludes me. Bamberg is located in the administrative region of Upper Franconia (Oberfranken), in the Free State of Bavaria (Bayern), the largest German state by land area and home to a fifth of Germany's population. Bamberg is both an independent city and a district; the district surrounds the city but does not include it. The Bamberg district covers 451 square miles made up of four towns, eight market towns, twenty-four municipalities, and twelve collective municipalities. The city of Bamberg is the largest medium-size city in Bavaria, with 54.6 square miles.
It's a fairly daunting task to research all this area, each jurisdiction with multiple church books and civil records to seek out and search, with little to go on to narrow down the task. But at least I have a few new clues. The baptismal ledger of St. Luke's Lutheran Church in Manhattan includes Richard's parents' names along with Bamberg as the location of his birth. My earlier searches within Bamberg City were on faded microfilm with no results. I will continue to look for sources I can access from here in New York and review every set of Lutheran church records as they come online for each area within Bamberg City and Bamberg District to find Richard Wilhelm Koch, with parents Georg and Johanna.
This case reinforces two thoughts: keep searching religious records, since they are one of the best sources of parentage and locale; and understand the geography you are working in.
Teresa Koch-Bostic


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Q: using Delphi message queue to avoid repeatedly triggering expensive operations While playing with master-detail on relatively heavy tables, I was pleasantly surprised to notice the details section did not refresh immediately during scrolling.
Instead, the refresh only got triggered when I stopped heavily scrolling.
This is as opposed to programmatically execute a SetRange in the After-Scroll event, which was obviously getting triggered at every single scroll.
For simplicity in this question, we can assume that 'custom_operation' is a SetRange, though it need not be limited to this, and could be any other operation.
I've been thinking about how this could be working internally, and the Delphi message queue comes to mind:
I cannot seem to locate the article describing the queue's priority, but it described how  messages in the queue have different priority depending on their type (hardware triggers, timer, postmessage..., forgot the order exactly).
Could it be that master-detail internally uses the message queue by posting a message asking for a setrange, but then realize that there are more scroll events happening, thus removing old 'custom' messages and reposting a new one?  That way, only when there are no more hardware messages, will the 'custom' message be handled.
Being able to delay the custom_operation until the user stopped doing something would be useful.
How would one go about achieving this? Thanks!
Yes, I know. threads can be also used to make the user-experience smoother, but avoiding the custom_operation in the first place would save unnecessary network traffic.

A: I would use a TTimer. I would restart (Stop then start) the timer from the scroll event handler. If the event doesn't come fast enough, the timer will call his OnTimer event handler where you do the operation.
I have done that in a TDelayedEdit component deriving from TEdit. The timer is restarted from the OnChange event. If the user keeps typing, the timer never reach the end but once the user stop typing for as long as the timer interval, then the timer trigger his OnTimer event and my component triggers his OnChange event.

redpajama_set_name: RedPajamaStackExchange


With media reports of suicide among lesbian, gay, bisexual and transgender (LGBT) youth becoming increasingly common, communities are grappling with the urgent need to identify interventions to prevent suicide and suicidal behavior among LGBT youth. However, until today, no "Best Practices" have been identified or designated to help reduce vulnerability and risk among LGBT children, youth or adults.
Although recent media attention has focused primarily on the impact of bullying on self-harm behavior. suicide is typically the result of many interacting factors including depression, substance use and despair, in addition to environmental stressors. These complex interactions underscore the need for comprehensive suicide prevention strategies and interventions to change the environment at the systems level. For LGBT youth, family systems are especially important.
For the past decade, the Family Acceptance Project (FAP) at SF State University, under the direction of Dr. Caitlin Ryan, has been studying the impact of family acceptance and rejection on suicide risk among LGBT youth. Published findings from these studies point to the critical role of families in both contributing to and protecting against suicidal behavior and risk among LGBT young people. FAP's multi-disciplinary team has been developing resources, interventions and strategies to help diverse families reduce risk and to promote their LGBT children's well-being.
The first of these resources – a multi-lingual, multi-cultural series of family education booklets – Supportive Families, Healthy Children: Helping Families Support their LGBT Children – have been designated as the first "Best Practice" resources for suicide prevention for LGBT youth and young adults by the national Best Practices Registry for Suicide Prevention.


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Do always call to check match coverage before travelling by calling Pete on 07595 843802……….


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rendezvous weymouth
Look up rendezvous weymouth in Wiktionary, the free dictionary.
rendezvous weymouth Sunglasses allow better vision in bright daylight, and may protect one's eyes against damage from high levels of ultraviolet light. Typical sunglasses are darkened for protection against bright light or glare; some specialized glasses are clear in dark or indoor conditions, but turn into sunglasses when in bright light. Most sunglasses do not have corrective power in the lenses; however, special prescription sunglasses can be ordered. Specialized glasses may be used for viewing specific visual information (such as stereoscopy) or 3D glasses for viewing three-dimensional movies. Sometimes glasses with no corrective power in the lenses are worn simply for aesthetic or fashion purposes. Even with glasses used for vision correction, a wide range of designs are available for fashion purposes, using plastic, wire, and other materials. Part of the reason for the surge in enquiries is that Euroglaze's website currently ranks on page 1 of Google searches for REHAU frames. This is attributed to the fact that the company's web partner has updated the website's architecture, switched the security protocols and kept pace with Google's changing algorithms on SEO. Owing to the property's location in heart of the Yorkshire Dales, it was also vital that the new windows and patio doors offered superior thermal efficiency and acoustic performance as well as ease of operation and an attractive appearance. Commenting on the recent renovations, Ms Stephens said: "I am absolutely delighted with the work that has been carried out and Dortech's customer service and professional installation has been second to none. "We're obviously delighted to be featured in Love Your Home and Garden. It's very pleasing to be recognised as a leading provider of aluminium windows and doors – but it's just as satisfying to be supporting good people going through hard times. Torquay's Fenster Fabrications is set to feature in an upcoming episode of ITV 1's hit home improvement show Love Your Home and Garden.
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In 1-quart glass dish, combine onions and margarine. Cook 2�3 minutes until onions are soft. Stir. Add beef broth and cook on high for 2�3 minutes. Stir. Pour into large soup bowl. Cover mixture with bread slice and sprinkle with cheeses. Cook, uncovered, for 45 seconds on high until cheese is melted. Serves 1.


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In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection.
The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in model quality is obtained (as shown with a Wilcoxon signed rank test yielding p-values below 10-15). The average increase in TM score is 0.016, the maximum observed increase in TM-score is 0.29.
In template-based protein structure prediction alignment is known to be a bottleneck limiting the overall model quality. Here we show that a combination of systematic alignment variation and modern model scoring functions can significantly improve the quality of alignment-based models.
Protein structure prediction by comparative modeling and/or fold recognition consists of three largely independent steps: (1) Postulating the structural similarity of the target protein sequence with a known template structure on the basis of a significant alignment score between the two protein sequences. (2) This or a different alignment serves as a basis for model construction. In this process residues in the target sequence that are aligned to residues in the template structure are mapped on the corresponding coordinates in the structure. (3) Finally, unmapped regions are filled in, breaks in the backbone are mended, and the overall model is refined.
Thus the quality of the alignment in the second step has an essential impact on the quality of the resulting model. The continual benchmarks in the biannual CASP assessment of protein structure prediction methods witness that there is significant progress in identifying suitable templates , due in part to the introduction of profile-profile alignment methods [2–5] and the sophisticated construction of profiles . While CASP assessors found little improvement in the predicted models , they found steady progress in alignment quality over the years .
The optimal alignment resulting from an algorithm with a specific optimized parameter setting is not always the best choice for model creation. Jaroszewski et al. have set up a computational experiment in which they sample a huge conformational space (size up to 1010) of alternative alignments by combining an approach of varying parameters (such as gap penalties and substitution matrices) with an iterative approach of penalizing previously visited regions of the sample space . The study states that there exist alignments surpassing the original alignments in quality for about 50% of the protein pairs. Contreras-Moreira and coworkers  as well as John and Sali  propose genetic algorithms for constructing a large number of alternative alignments by recombining an initial set of alignments. A common problem of these approaches is the selection of the alignment allowing for the construction of the final model.
Recently, a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs) [12–14]. MQAPs are computer programs that receive as input a 3D model of a protein structure and produce as output a real number representing the quality of the model . We will refer to this number as the model score. In contrast to model evaluation programs, like GDT , MaxSub , or TM-score , which assess the quality of the model by comparing it to the native structure, MQAPs do not compare to the native structure. Instead, they estimate the quality of a proposed model without knowledge of the native structure. Unlike scoring functions in sequence-to-structure alignment and to physical energy functions, MQAPs operate on an intermediate level – they are more flexible than a sequence-to-structure alignment function as the dynamic programming paradigm used in alignment computation imposes the requirement of prefix optimality which is not required in MQAPs. MQAPs aim at scoring the quality of predicted models. Typically, MQAPs use one or more different statistical potentials, representing information coded in protein structures [19, 20, 12, 13]. Different MQAPs were recently tested in CAFASP-4 as meta-selectors for pinpointing high quality models from the ensemble of models proposed by different automated servers [15, 13, 21] proving that MQAPs are highly effective selectors.
In this manuscript we propose and validate a protocol for improving alignments in step (2) of comparative modeling or fold recognition. Optimization is achieved by generating alternative alignment-based models for a target sequence and selecting the most promising model using an MQAP.
Ensembles of alternative alignments are generated with the state-of-the-art profile-profile alignment method Arby [22, 23] by varying parameters. Apart from the Arby default, we suggest two different procedures for generating alternative alignments: PVS varies the parameters in the profile-profile alignment method slightly, whereas PVH varies the parameters heavily. Each procedure reports an ensemble of distinct alignments. For each alignment a model is constructed (see Methods for details, as well as Table 1 for an overview of the parameters used in PVS and PVH).
The parameters used in the different model generating procedures.
Generates the default Arby models , exactly one for each target. Default parameters are: gap insertion 14.7, gap extension 0.37, substitution matrix Blosum62, Henikoff position-specific sequence weighting, relative weight of secondary structure is 0.24.
Generates models resulting from alignments with parameters multiplied by a factor varying from 0.5 to 1.5 with a step width of 0.25. This results in an ensemble of alternative models for each target.
Generates models resulting from alignments with parameters multiplied by a factor from 0.2 to 2.4 with a step width of 0.2. This results in an ensemble of alternative models for each target.
The ensembles of alternative models typically contain models with higher quality as well as models with lower quality than the standard Arby model. The FRST  MQAP program is applied to scoring the quality of the models. By choosing the model with the best model score according to the FRST potential, we can select a promising model for each target. These selected models are potentially improved with respect to the Arby default model. Additionally, we developed an SVM-based selection mechanism. A support vector machine (SVM) is trained on the model scores and on the FRST partial potentials for recognizing the models with increased quality.
The performance of the protocol is evaluated by comparing the chosen models to the previously withheld native structures. The comparison is performed with the model evaluation program TM-score , its score reflecting the "real" quality of the models. The TM-score always lies in the interval (0,1], where the upper limit stands for a model perfectly superposable with the structure. This allows for comparing the quality of the generated and selected models with the quality of the default Arby models and for assessing the significance of the selection process.
The protocol was evaluated on a set of 1612 target sequences with known structures (see Methods). For each target t we computed the Arby default model d(t) and exercised the two model generation procedures PVS and PVH resulting in two ensembles of models E PVS (t) and E PVH (t) per target. Summary statistics of the number of models per target are given in Table 2.
Summary statistics for the model generation procedures.
First, we analyze the quality of the model generation procedures. The key ideas are to count per target the number of models with increased quality, and to measure the average difference of model quality with respect to the default model in terms of TM-score.
Similarly, we consider the relative frequency fptE,<(t) of models with a quality below that of the Arby default models.
We define an indicator function whether a better model for a target t exists in the ensemble E(t).
The frequency fpt was defined per target and its average over all targets is f p t ¯ = 1 n ∑ f p t MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdchaWjabdsha0baacqGH9aqpdaWcaaqaaiabigdaXaqaaiabd6gaUbaadaaeabqaaiabdAgaMjabdchaWjabdsha0bWcbeqab0GaeyyeIuoaaaa@3A9A@ . While fpt describes the frequency of better models per target, f b ¯ = 1 n ∑ f b MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdkgaIbaacqGH9aqpdaWcaaqaaiabigdaXaqaaiabd6gaUbaadaaeabqaaiabdAgaMjabdkgaIbWcbeqab0GaeyyeIuoaaaa@3780@ reflects the fraction of targets that have a model with a quality above the Arby default within the ensemble of constructed models.
When selecting models randomly, an average quality improvement of q i r ¯ = 1 n ∑ q i r MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabdkhaYbaacqGH9aqpdaWcaaqaaiabigdaXaqaaiabd6gaUbaadaaeabqaaiabdghaXjabdMgaPjabdkhaYbWcbeqab0GaeyyeIuoaaaa@3AA2@ is obtained. When selecting models optimally, an average quality improvement of q i b ¯ = 1 n ∑ q i b MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabdkgaIbaacqGH9aqpdaWcaaqaaiabigdaXaqaaiabd6gaUbaadaaeabqaaiabdghaXjabdMgaPjabdkgaIbWcbeqab0GaeyyeIuoaaaa@3A62@ is obtained, imposing a theoretical upper bound to what is feasible with MQAP selection on the alignments generated as proposed. For the two procedures PVS and PVH generating alignment-based models these numbers are listed in Table 3.
How good are the generated models? Description of the distributions of the TM-score quality. f p t ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdchaWjabdsha0baaaaa@30EC@ <and f p t ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdchaWjabdsha0baaaaa@30EC@ > are the relative frequencies of models per target with a TM-score below and above Arby default, respectively, q i r ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabdkhaYbaaaaa@30F0@ is the improvement in TM-score when choosing models randomly. f b ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdkgaIbaaaaa@2F5F@ is the relative frequency of targets for which a better model exists, q i b ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabdkgaIbaaaaa@30D0@ is the best theoretically possible improvement for the given ensemble of models.
In order to visualize the distributions of model quality, in Figure 1 for each target t the TM-score of the default Arby model s plotted versus the TM-score improvements of the models constructed for that target. The scatter plots in Figure 1 along with Table 3 clearly indicate that better models are generated for a large fraction of the targets. Summing up, the above-mentioned procedures generate models with a better quality than the Arby default models, but identification of the improved models among the generated models is a hard task as analyzed in the next section.
Overview of model quality improvement with respect to the model difficulty. Left for PVS, right for PVH analogously. Each dot corresponds to a model where the x-coordinate is the TM-score of the corresponding default Arby model and the y-coordinate is the TM-score improvement with respect to this default model. Smoothed quantile lines are shown for the 10% (lower dashed), 50% (middle), 90% (upper dashed) quantiles of the models within a sliding window of size 0.15. Black lines represent all models, red lines represent the models selected using FRST, green lines represent the models selected using the SVM approach. For the smoothing evaluations are made at 1000 equidistant points and the resulting quantiles are smoothed with a lowess function (local linear scatter plot smoother). Interpretation: The TM-score of the Arby default gives an indication of how difficult it is to find the right template for a target. For the selection methods random, FRST, and SVM, this plot shows the potential improvement with respect to difficulty of the target. For PVH, more models are generated below default. For both PVS and PVH, the SVM selection performs better than FRST selection, and FRST performs better than random.
In the following, we analyze how well the model selection procedure works on the models generated with procedures PVS and PVH. The key ideas are to count for how many targets an improved model is selected, and to measure the quality improvement with respect to the default model in terms of TM-score. We perform the analysis for the selection based on the FRST potential and then repeat it analogously for the SVM based selection.
since lower frst is better. In the supplementary material (see additional file supplement) we analyze the FRST partial potentials in more detail.
These functions indicate whether the model selected by the MQAP is of higher, equal, or lower quality than the Arby default.
qim E,frst (t) = TM(s E,frst (t)) - TM(d(t)).
Across all targets, f i m ¯ > = 1 n ∑ f i m > MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdMgaPjabd2gaTbaadaWgaaWcbaGaeyOpa4dabeaakiabg2da9maalaaabaGaeGymaedabaGaemOBa4gaamaaqaeabaGaemOzayMaemyAaKMaemyBa02aaSbaaSqaaiabg6da+aqabaaabeqab0GaeyyeIuoaaaa@3CC9@ is the fraction of targets whose models improve when choosing models using the FRST MQAP. We measure the average improvement in model quality as q i m ¯ = 1 n ∑ q i m MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabd2gaTbaacqGH9aqpdaWcaaqaaiabigdaXaqaaiabd6gaUbaadaaeabqaaiabdghaXjabdMgaPjabd2gaTbWcbeqab0GaeyyeIuoaaaa@3A8E@ .
A summary of the results when selecting models according to the frst potential is given in Table 4.
How well does model selection work? Description of distributions when selecting models according to the FRST potentials and the SVM. n i ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabd6gaUjabdMgaPbaaaaa@2F7D@ is the relative frequency of targets for which a selection procedure suggests improved models. f i m ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdMgaPjabd2gaTbaaaaa@30D0@ <, f i m ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdMgaPjabd2gaTbaaaaa@30D0@ =, and f i m ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdMgaPjabd2gaTbaaaaa@30D0@ >, are the relative frequencies of selected models with decreased, equal, or increased TM-score quality, respectively. min qim and max qim are the minimal and maximal quality improvements achieved per target. q i m ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabd2gaTbaaaaa@30E6@ is the average quality improvement over all targets. qim is the average quality improvement for the targets that the selection procedure suggests improved models for.
ni E,frst (t) = [frst(s E,fsrt (t)) <frst(d(t))].
The set of targets for which model selection proposes candidates with estimated improvement consists of n· n i ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabd6gaUjabdMgaPbaaaaa@2F7D@ = ∑ni targets. On this candidate set, we denote the average improvement in model quality as q i m ¯ = 1 n ⋅ n i ¯ ∑ t q i m ( t ) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaadaaqaaiabdghaXjabdMgaPjabd2gaTbaacqGH9aqpdaWcaaqaaiabigdaXaqaaiabd6gaUjabgwSixpaanaaabaGaemOBa4MaemyAaKgaaaaadaaeqaqaaiabdghaXjabdMgaPjabd2gaTjabcIcaOiabdsha0jabcMcaPaWcbaGaemiDaqhabeqdcqGHris5aaaa@440B@ .
The fim> and qim numbers exhibit a noticeable increase in model quality with respect to random selection of models (cf. f p t ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdchaWjabdsha0baaaaa@30EC@ <and q i r ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabdkhaYbaaaaa@30F0@ values in Table 3).
More importantly, comparing models resulting from the selection process to the Arby default by applying a paired Wilcoxon signed rank test, we find for model generation procedure PVS that the models selected according to frst are significantly better than the Arby default (with a p-value of 0.002). For the model generation procedure PVH, the models selected with frst alone are neither significantly better nor worse than the default, demonstrating that it is hard to select better models when generating more low-quality models.
Selection of models constructed with model generation procedure PVS results in an average quality improvement of q i m ¯ E P V S , f r s t MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaadaaqaaiabdghaXjabdMgaPjabd2gaTbaadaWgaaWcbaGaemyrau0aaSbaaWqaaiabdcfaqjabdAfawjabdofatjabcYcaSiabdAgaMjabdkhaYjabdohaZjabdsha0bqabaaaleqaaaaa@3C6B@ = 0.0031 and works better than selection of models constructed with model generation procedure PVH with an average quality improvement of q i m ¯ E P V H , f r s t MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaadaaqaaiabdghaXjabdMgaPjabd2gaTbaadaWgaaWcbaGaemyrau0aaSbaaWqaaiabdcfaqjabdAfawjabdIeaijabcYcaSiabdAgaMjabdkhaYjabdohaZjabdsha0bqabaaaleqaaaaa@3C55@ = 0.00068.
For generating procedure PVS, the selection according to FRST suggests an alternative model for 51% of the targets; 53% of these suggested targets are improved according to TM-score. For generating procedure PVH, alternative models are suggested for 70% of the targets; 48% of these suggested targets are improved according to TM-score.
Based on the FRST scores, an SVM was trained to choose high quality models as described in the Methods section. The values fim svm , qim svm , and ni svm are calculated analogously to the previously defined fim frst , qim frst , and ni frst , by replacing frst in these formulas with the negative SVM decision values.
The results produced with selecting models according to the SVM decision values are summarized in Table 4. For PVS, an overview is given in Figure 2.
(Left) Average increase in TM-score, for ranges of difficulty. Targets are binned according to the TM-score of the default Arby model. Within each bin the average increase in quality qim is plotted. Bins are enumerated horizontally, the two outer bins were concatenated with their neighbors as each contained less than 100 target samples. Models are selected from PVS using the SVM. For comparison the average increase in quality obtained on this benchmark set by performing loop modeling is 0.003. (Right) Maximum increase in TM-score, for the same ranges of difficulty. The maximum increase in quality max qim within each bin is visualized as a line above the box representing the average increase (which is the same as on the left side, just the scale is different).
Compared to the FRST potentials, the n i ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabd6gaUjabdMgaPbaaaaa@2F7D@ sum are smaller (i.e. fewer targets were suggested for alteration, see Table 4). The SVM more effectively avoids changing models for the worse. This is visible in Figure 1 and also reflected by noticeably smaller average numbers of models with decreased quality ( f i m ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdAgaMjabdMgaPjabd2gaTbaaaaa@30D0@ <values, see Table 4). The overall average improvement in TM-score model quality ( q i m ¯ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaadaqdaaqaaiabdghaXjabdMgaPjabd2gaTbaaaaa@30E6@ , qim) increases.
Applying a paired Wilcoxon signed rank test, we find for both generation procedures that the models selected by the SVM are significantly improved with respect to the Arby default (p-values below 10-15). The SVM selected models are also significantly improved with respect to the FRST selection process (with p-values below 10-5).
For generating procedure PVS, the SVM-based selection suggests an alternative model for 40% of the targets; 64% of these suggested targets are improved according to TM-score. For generating procedure PVH, alternative models are suggested for 58% of the targets; 61% of these suggested targets are improved according to TM-score.
Jaroszewski et al.  show their method to produce significantly better alignments in about half of test cases, for a benchmark set of 742 protein pairs and make no statement regarding the likelihood of selecting such a solution from the ensemble of alternative alignments generated. To this end, they generate an average of 733 alignments per target-template pair with improved solutions in 34% of the test cases (average of 49 alignments). Our method is able to generate improved alignments for 59% of the test cases (PVH, Table 3) with only 13 alignments on average. The 55-fold decrease in the number of evaluated alignments compared to the method of Jaroszewski et al., while maintaining at least comparable increments in alignment quality, implies that we are exploring regions in the space of alignments that are densely populated with high-quality solutions, making the method practical for improving fully automated fold recognition servers such as Arby . This is important when comparing our method to other approaches like ROBETTA , or the work of John and Sali  or Contreras-Moreira and coworkers , where improved model generation requires several orders of magnitude more alignments to be evaluated.
Improved alignments have to be selected from the ensemble of alternatives with an MQAP program in order to be useful. Neither Jaroszewski  nor Chivian  make quantitative statements about the selection of improved solutions. The results of John and Sali or in silico recombination are not directly comparable, as they generate and select the solutions iteratively. Our data show that the selection of improved alignments is a difficult task. A random selector would actually deteriorate the overall performance of the method. Generating more models does not necessarily help the selection process. Especially if more models below default quality are generated (as with generating procedure PVH), avoiding to select worse models is more difficult. Thus the error rate can increase and the overall performance can decrease if more low-quality models are generated.
Here we show that the proposed protocol, including model generation and SVM-based selection, significantly improves model quality (p-values below 10-15 using a Wilcoxon signed rank test). With the model generation procedure PVS, using the SVM-based selection the proposed method achieves a close to optimal average TM-score improvement of 0.016 and a maximal observed increase in TM-quality of max qim = 0.29 This has to be related to typical fold recognition targets, where the TM-scores for large portions of the predictions lie in the range of 0.1 to 0.4 for hard targets and from 0.4 to 0.8 for easier targets.
To emphasize the relevance of the proposed method in practical use, we compare the quality improvement of the proposed protocol with the quality gain obtained by loop modeling alone. The average quality increase in TM-score incurred by using our protocol amounts to 0.016, which is a factor of five above the quality gain obtained by loop modeling alone 0.003. The quality increase for our protocol is computed as the difference between the TM-scores of the MQAP selection from varied models with modeled loops minus the Arby default with loops modeled. The model quality obtained by loop modeling alone is computed as the difference of the TM-score of the Arby default with loops modeled minus that of the Arby default without loops modeled (see additional file supplement).
We have presented an approach for improving structure prediction models that goes in a different direction from the one recently proposed by Pettitt et al. . Whereas they have evaluated the possibility to choose better templates with an MQAP program, we show that it is possible to generate and select better alignments for a fixed template with an MQAP program. The two approaches can be combined and will improve automated servers such as Arby.
As this seems a promising approach in a competitive field, we will continue to work on the topic in two directions: First, generation of models with a high likelihood of improving the quality and second improving the selection process. For the latter, the numbers on the SVM performance clearly indicate that the current linear combination of the partial potentials in FRST can be improved.
The 3D protein structure model that we construct for a target protein is based on an alignment with a template structure. The method described here is independent of the strategy for template identification. With a given target and template as input, we compute a default alignment using profile-profile alignment with log-average scoring and parameters as tuned for the Arby server [22, 23]. Namely these parameters are: substitution matrix Blosum62, gap insertion 14.7, gap extension 0.37, and a relative weight of secondary structure to sequence information of 0.24.
In addition to the Arby default alignment, we propose two procedures (PVS, PVH)for generating alternative alignments for a target in analogy to the parametric approach of Jaroszewski et al. . The alternatives are computed by a global profile-profile alignment method, using parameters multiplied with a factor varied inside the range from a lower to an upper bound. The parameters varied are gap-insertion, gap-extension, and the relative weights of amino-acid and secondary structure profiles. The two procedures differ with respect to the ranges of the factors. Each procedure reports alignments that occur multiply for different parameter settings only one time, resulting in an ensemble of distinct alignments.
For each alignment a model is built as follows. Loop modeling of insertions and deletions is performed, using the LOBO program . Conserved (i.e. identical) residues and their side chains are copied from the template structure. The non-conserved residues and their side chains are positioned and optimized by SCWRL3.0 .
The quality of the model is then estimated using the FRST MQAP program , which computes four potentials, namely a residue-specific all-atom distance potential  (rapdf), a solvation potential (solv), a hydrogen bonding potential (hydb), and a torsion angle potential (tors). These four potentials are linearly combined into the frst energy score (with factors 2.5, 500.0, -50.0, and 350.0, respectively ). This leaves us with the frst score as an estimate of the quality of each constructed model.
We can select the best alignment-based model for each target, by choosing the model with the lowest energy score according to the frst potential. These selected models are potentially improvements over the default model (constructed according to the default alignment). In the supplement we additionally analyze selection according to the partial contributions rapdf, solv, hydb, and tors of the frst potential (see additional file supplement). The FRST MQAP program places a strong emphasis on the torsion angle component . Since each residue can either increase or decrease the overall score, there is no correlation between the number of gaps in a model and the overall score.
For 95% of the targets in the benchmark set of this paper, the FRST MQAP can distinguish the native structures from the Arby default models. Similarly, the performance of FRST on selecting the native structure from the models generated with procedures PVS and PVH is 95% and 94%, respectively.
If, additionally, the native structure of the target is known, using the model evaluation programs GDT , MaxSub , and TM-score , we can compute scores (GDT, MS, TM), reflecting the "real" quality of the model in terms of structural similarity between model structure and target structure.
In general the quality measures GDT, MS, and TM correlate well: The correlation coefficients between quality measures for all models produced are cor GDT,MS = 0.99, cor GDT,TM = 0.93, and cor MS,TM = 0.93 (see supplement, Table 1). Overall the analysis yields similar results for all three quality measures. As the TM-score has the advantage of being independent of the size of the protein, we restrict our presentation to the analysis of the TM-score.
Overall, a moderate negative correlation cor TM,frst = -0.43 of the quality measure TM-score with the frst score can be observed. It has to be pointed out that the correlation of the frst score across all targets is not as relevant as its selection capabilities per target.
We train a Support Vector Machine (SVM) for selecting models with higher TM-score than the TM-score of the default model. The binary labels used for each model are TM-score-increase and TM-score-decrease with respect to the default Arby model. As features we use the frst, rapdf, solv, hydb, tors values of each model and the corresponding default model as well as the differences of these scores between model and default. For each target, the best model is selected based on the SVM decision value . Models with a negative SVM decision value remain unchanged with respect to the Arby default. As SVM implementation, the R package e1071  based on libsvm  is employed. As parameter tuning showed only negligible changes in classification accuracy, standard parameters and a radial basis function kernel are used.
For the validation of our approach, the improvement of the proposed models over the default Arby models was evaluated. Target sequences were taken from a representative set of SCOP 1.65 domains  with at most 40% sequence identity as provided by the Astral compendium [33, 34]. As a basis for the alternative models, in this study, one template was chosen for each target: With log-average scoring and default parameters as listed in Table 1[22, 23], the target was compared against the rest of the domains in the Astral 40% set and the top ranking hit was chosen as template. Our analysis was restricted to targets which have a template with at least 25% sequence identity, evaluating the proposed method for targets from the homologous fold recognition category. These criteria specify 1765 targets, each with one template. For 153 (8.7%) of these 1765 targets, some of the necessary computations failed. We excluded those targets, which leaves us with n = 1612 targets, for which we have all relevant scores available.
The training and validation of the support vector machine is performed using five-fold cross-validation. In order to ensure that there are no models for the same target in the training as well as in the testing set, during the cross-validation successively models for one fifth of the targets (not: one fifth of the models) are removed from the training set and used for testing.
As the pairwise sequence identity between targets is below 40% according to selection criteria it is guaranteed that models in the test and training sets are sufficiently distinct.
In order to assess the effect of the choice of k in k-fold cross-validation, a ten-fold cross-validation was also performed, yielding results identical to one digit precision in Table 4 (data not shown, the figures in the article refer to k = 5).
They draw their names from the respective relative frequencies.
I.S. is funded by DFG (grant Le 491/14). S.C.E.T. is funded by a "Rientro dei cervelli" grant from the Italian Ministry for Education, University and Research (MIUR). We thank Giorgio Valle and Alessandro Albiero for insightful discussions as well as Lars Kunert, Francisco S. Domingues, and Jörg Rahnenführer for valuable comments on the manuscript. This research was performed in the context of the BioSapiens Network of Excellence (EU grant no. LSHG-CT-2003-503265).
S.C.E.T, S.T., and I.S. conceived the experiment, I.S. and S.T. performed the experiment, I.S., O.S., and T.L. analyzed the results, and I.S., T.L., and S.C.E.T. wrote the final manuscript, which all authors have approved.


Wild Cave Tours are guided tours into caves, either in undeveloped parts of a show cave or in a nearby "wild" cave. The tour is very similar to a real caving tour, there is no light and no paths. But there is a guide, normally most of the equipment is provided, and it costs a fee.
Most caving clubs make very similar tours for new members. Normally there is no fee, despite the members fee. So it is much better to look for a caving club nearby, become member, and do some caving. But this trips offer people a chance to get a taste of what caving is like, before they join a caving club.
Most of those tours are connected to a show caves. Some of them are made by adventure companies, where caving is another extreme sport beneath bungy jumping and canyoning. I heard many bad critics about those tours, especially because of the sometimes very poor knowledge of the guides. So have a good look at the company and the guy!


text:
Philip H. Clark
Musings on Life & Literature
Cultural Criticism
Admirable People
Joseph Frank's Dostoevsky: The Miraculous Years, 1865-1871
by Philip Clark on September 20, 2020
With this fourth book in Joseph Frank's five-volume biography of Dostoevsky, we approach the apex of Dostoevsky's career as a writer, for in the five-year period between 1865 and 1871, he will write three masterpieces in rapid succession: Crime and Punishment, The Idiot, and Demons – all of them conceived and executed under extreme financial pressure. These are indeed "miraculous" years, and the best compliment I can pay Joseph Frank is that his efforts in this fourth volume do them justice, illuminating for us both the difficult personal circumstances under which Dostoevsky composed and the furious ideological battles that gripped the Russian intelligentsia in the closing decades of the 19th century. The censorship of the tsarist authorities – though frequently inept – nonetheless directed most political and philosophical energies into literature, and each of the above-mentioned novels constituted Dostoevsky's salvos in a pitched ideological battle that would conclude only after his death, in the October Revolution of 1917. Frank's contention, throughout these biographies, has been that Dostoevsky's writings cannot fully be appreciated shorn of their context within Russian history, and in this fourth volume he makes his strongest case yet.
In 1865, Dostoevsky was, in his own words, "the unhappiest of mortals." His brother Mikhail, his closest friend and confidant, as well as his business partner in the operation of their jointly-owned monthly magazine, Time, had passed away the previous year, as had Dostoevsky's first wife, and out of a sense of obligation, he assumed financial responsibility for both his stepson Pasha and Mikhail's widow and children – a decision that would condemn him to penury for the next five years. Frank does an admirable job reconstructing the anxiety and strain under which Dostoevsky laboured, forever having to borrow money to pay off debts, or the interest on his debts, and his few financial triumphs inevitably followed by the open-handed requests of Pasha and his brother's widow, eager for their share of his success. History has not forgotten Dostoevsky's gambling addiction – his frequent and costly visits to casinos, risking money he did not have – but Frank depicts these as desperate bids to rid himself, in a single lucky roll, of the accumulated debts burdening his existence. Consider, by way of illustrating his desperation, the composition of the novella The Gambler. Desperate for funds, Dostoevsky accepted a three-thousand ruble advance for the publishing rights to his completed works, as well as the delivery of a new work, no later than November 1st, 1866. Per the terms of the contract, a failure to deliver this new work on time would grant the unscrupulous publisher rights to all of Dostoevsky's future fiction, royalty-free, for the next nine years. Only desperation could drive him to such a devil's bargain, but as happened so often in his life, his greatest triumphs emerged from his moments of greatest despair: not only did he complete The Gambler on time, but the stenographer he hired to assist him, a novice 18 years his junior, would become his second wife.
Anna Grigoryevna Snitkina, just 27 years old when she first met Dosteovsky, was unusually tolerant of his eccentricities and shortcomings. After all, he was in his forties, a widower, epileptic, broke, and singularly devoted to a craft that required irregular sleep and regular isolation, and whose financial rewards were negligible even in the best of times. Here, for example, is Dostoevsky's description of his work routine in Dresden:
I rise at one o'clock in the afternoon […] because I work at night. I work from three to four [in the afternoon]. I take a walk for a half-hour to the post office, and I return through the Royal Garden. We eat dinner. At seven, I take another walk and return always by the same route. I then have some tea, and at half-past ten I start to work until 5 o'clock in the morning. I go to bed and fall asleep as six o'clock sounds. That's my life, complete.
Worse, still, when they are inevitably forced abroad, to escape the constant threat of creditors knocking on their door, Dostoevsky's weakness for gambling is revealed to her, and Frank recounts the sad scene, repeated with alarming regularity, of Dostoevsky returning to her late at night, filled with tears and self-recriminations, having to confess that he has gambled away their few remaining funds – a blunder that necessitated further humiliating appeals to friends and acquaintances, and a deepening of their debts. Their self-imposed exile – first in Germany, later in Switzerland, and finally in Italy – likely added to their stresses, rather than alleviating them, for not only did Dostoevsky find separation from his native land oppressive and stifling, disconnecting him from what he regarded as the fount of his imaginative powers, but their poverty won them no friends and often forced them to downgrade their living situation to accommodate their meagre means. Given these huge strains, Dostoevsky's prodigious output during this period seems all the more miraculous (a constant refrain, in his letters to friends, expresses how envious he is of Turgenev and Tolstoy, that they can compose while financially secure), but Frank gives us a glimpse at the source of his energies by describing the growing radicalism of the new generation of Russian writers, and Dostoevsky's horror at their conclusions.
Dostoevsky, now in his 40s, belonged to an older generation, the generation who came of age during the turbulent period of the 1840s, but this younger generation, coming of age in the 1860s, regarded their elders with disdain. Impatient at the progress that had been made, they turned to more radical objectives, and more radical means for achieving them, and the literary journals of the 1860s are filled with denunciations of former liberal heroes like Alexander Herzen and Ivan Turgenev, now recast as milquetoast moralizers, too friendly with the status quo to be relied upon to force the necessary changes upon Russian society. The literary critic Nikolay Chernyshevsky, whose novel What Is to Be Done? was a thinly-veiled call for revolution written while Chernyshevsky was already in prison, provides a perfect example of the radical youth. In his novel, he describes the ideal revolutionary as an ascetic, single-mindedly devoted to the cause of revolution, and that idea was incarnated in the person of Sergey Nechayev, author of the Catechism of a Revolutionary (still widely read today, in revolutionary circles), in which he echoed and amplified Chernyshevsky's definition of the ideal radical:
The revolutionary is a doomed man. He has no private interests, no affairs, no sentiments, ties, property nor even a name of his own. His entire being is devoured by one purpose, one thought, one passion – the revolution. Heart and soul, not merely by word but by deed, he has severed every link with the social order and with the entire civilized world; with the laws, good manners, conventions, and morality of that world. He is its merciless enemy and continues to inhabit it with only one purpose – to destroy it.
Nechayev united thought with action in 1869, when he and his revolutionary comrades murdered one of their own, for the crime of dissenting, and attempted to hide his body under the frozen waters of a lake. The discovery of the body and uncovering of the murder became an international scandal, and shocked all of Russia, and Dostoevsky's response was to immortalize Nechayev in the fictional figure of Pyotr Verkhovensky, the nihilistic conspirator whose machinations drive the plot of Demons.
As has been his custom thus far, Frank devotes large sections of his biography to close readings of Dostoevsky's fiction, and the three novels covered here add significantly to the bulk of this book, but these sections are particularly valuable, for Frank is a careful and clever reader, and he uncovers connections between even the most mundane of passages and Dostoevsky's larger themes. When he arrives at Demons, he adopts the format of a trial, analyzing the book to answer a charge levelled at Dostoevsky in his own lifetime, whether or not he had fairly represented the radicals and their aims. This section, in particular, demonstrates how thoroughly familiar Frank was with 19th century Russian history, and how astutely he has managed to understand Dostoevsky's work. Having presented his case, arguing that indeed Dostoevsky understood the radical ideology, and even better than most of its proponents, he offers us a final tribute to Dostoevsky's genius, in the form of a conversation recorded more than a half-century after his death, after the infamous Twentieth Party Congress of the Soviet Union, in which the crimes of Stalin were first officially discussed. Here are the memories of Yury Karyakin, a literary critic and cultural historian, in the wake of these momentous revelations:
For me, and most of my friends, this was a veritable earthquake. But someone close to me (now dead), a classic Russian intelligent, a typical Stepan Trofimovich Verkhovensky, with a doctorate in chemistry, a professor, seeking relief from personal and political troubles in Dostoevsky, Leskov, and Chekhov, or departing every Thursday to the House of Teachers […] – said to me with a sorrowful smile: "But you know, all this is in The Devils. I was almost arrested in '36 because I read that novel. Someone denounced me."
These were frightening and enlightening nights: we read The Devils and the notebooks for the novel (we managed to get them) … We read, and did not believe our eyes: all this we knew, had believed it all, all this we recalled only too well. We read and interrupted each other almost on every page: "It can't be. How could he have known all this?"
I cannot read the above passage without my skin crawling, for the same shock of recognition hit me upon my first reading of Demons, even though my experience of the Soviet Union is purely historical. Dostoevsky understood the ideology so thoroughly that he could see, with perfect clarity, where it would take Russia were it ever to be implemented, and Demons is therefore the work of a Russian Cassandra, a prophetic warning destined to be ignored.
Dostoevsky: The Miraculous Years ends with Dostoevsky's return to his native soil, having become both a husband and a father, and though his financial difficulties are no less great upon his return, his literary reputation has been hugely improved, first by the undeniable triumph of Crime And Punishment, and later by the furious reaction provoked by Demons. His artistic and philosophical development has reached new heights, and even were his life to conclude prematurely, at this exact point, his case for literary immortality would be undeniable. And yet there is more still to come; his mission is not yet complete.
I am a lover of poetry and prose, an aspiring writer and literary critic, and a secular humanist with a strong belief in the ideals of the Enlightenment. I make no claims to special wisdom or insight; I offer merely the benefit, if benefit there be, of my reflections.
A.E. Housman
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At St. John's we believe that movement is essential for a complete education experience for the students. Students will learn better if they are allowed to move. Therefore, we provide opportunities to move during recess and gym class.
Students can extend their skill and understanding of the game by participating in the extra-curricular sports, competing against other area Christian schools. St. John's participates in a Bay-Midland County Lutheran School League consisting of 6 schools. St. John's athletic program is managed by our Athletic Director, Mr. Michael Hiddings, who teaches Grades 5-8.
SCHOOL MASCOT – WE ARE THE EAGLES!!!


text:
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Arbitration Decisions
Landmark Litigation Cases - Complex & Interesting
Walsh v. Mobil Oil (2008), 296 D.L.R. (4th) 178 (Alta. C.A.) (appeal from Q.B.)
Alberta's first female "landman" found to be victim of gender discrimination and retaliation by Mobil Oil.
K. v. Canada (Human Rights Commission), 2009 FC 495, K. v. Greyhound Canada, 2007 CHRT 34
Race & age discrimination and elements of proof
S.M.A. v. Canadian Security Intelligence Service (2000) (SIRC)
Successfully challenged CSIS' allegations that client was a "terrorist"; SIRC (Review Committee) See also S.M.A. v. Minister of Citizenship & Immigration (Sept. 2000) Application for mandamus compelling Minister to land client; violation of section 7 Charter Rights
Starlight v. Onespot (Nov 2, 1998) (Alta. C.A.)
Criteria for grant of Ex parte order silencing defendant in defamation cases; allegations of financial mismanagement and corruption on Tsuu T'ina Band forwarded to Minister of Indian Affairs; leaked to public; breach of federal privacy legislation; public accountability for taxpayer funds
Vriend A.G. v. Alta. [1998] S.C.J. No. 29 (SCC) appealed from [1996] 5 W.W.R. 617 37 Alta. L. R. (3d) 364 (Alta.C.A.)
Exclusion of "sexual orientation" in Individual's RightsProtection Act violates s.15 of the Charter and was "read in"
(constitutional remedy) to provide access to Human RightsCommission process (Submissions on behalf of Alberta Civil Liberties Association)(Co-Counsel, Brian Edy)
Friends of Athabasca v. PHAAB & Al Pac [1996] 4 W.W.R. 604, 181 A.R. 81 (Alta. C.A.) appeal from [1994]
6 W.W. R. 267 (Q.B.)
Waste management approval - public interest standing for environmental groups
Grant v. Canada A.G (1995) 125 D.L.R. (4th) 556 (F.C.A.) aff'd (1994) 81 F.T.R. 195 (F.C.T.D.)(Reed J.)
Sikh RCMP officer found to have a constitutional right to wear turban and government's decision to accommodate him was required to protect freedom of religion:
"The defendants and the intervenors, particularly the able argument of Ms. Chotalia for the Alberta Civil Liberties
Association, turn the plaintiffs' argument respecting discrimination on its head. They argue that the Commissioner's decision was designed to prevent discrimination occurring to Khalsa Sikhs. As such they argue that that decision offends none of the provisions of the Charter, indeed that it is required by section 15 of the Charter." para 103
Felipe v M.E.I. [1994] Imm 5296-93 (F.C.T.D.)Judicial review challenging medical inadmissibility provisions being then s. 19(1)(a)(ii) Immigration Act on basis of violation of s. 15 Charter – applicant contracting breast cancer in Canada and being subject to removal; See alsoTuringan v M.E.I. (1993) 72 F.T.R. 316 (F.C.T.D.)(A.C.J. Jerome) Judicial review – judgment establishing the unique nature of foreign domestic worker program; duty of fairness
Kuich v. Commissioner of NWT [1994] N.W.T.J. No. 14 (NWT S.Ct.) (De Weerdt J.)
Maintained tortious conspiracy action against the defendant Commissioner even though plaintiff also using the arbitration process (see also J. Vertes decision of July 1995); successful action for conspiracy & defamation re: false allegations of sexual harassment by group of unionized claimants
Learning Disabilities Association v. Edmonton Public School Board (1994) 120 D.L.R. (4th) 407 (C.A.)
"Kindergarten case" - judicial review under School Act of a school board decision allocating school funds towards
ECS (early childhood education)
R. v. Thompson [1989] A.J. No. 192 (Q.B.)
Judgment that s. 643.1 of Criminal Code held to violate s. 7 & 11(d) of Charter - use of videotape evidence in sexual offences
See also R. v. Thompson, 1989 ABCA 234; see also R. v. Thompson, 1989 ABCA 234
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Identifies the presence of distant metastatic involvement of the brain at the time of diagnosis.
Code information about brain metastases only (discontinuous or distant metastases to brain) known at the time of diagnosis. This field should not be coded for involvement of the spinal cord or other parts of the central nervous system.
Brain involvement may be single or multiple.
Information about brain involvement may be clinical or pathologic.
Code this fields whether or not the patient had any preoperative systemic therapy.
Use code 8 for Hematopoietic, Reticuloendothelial, Immunoproliferative aned Myeloproliferative Neoplasms, and Hodgkin and non-Hodgkin Lymphoma.
Use code 9 when it cannot be determined from the medical record whether the patient specifically had brain metastases; for example, when CS Mets at DX is coded as carcinomatosis but the brain is not specifically mentioned as a metastatic site. Also use code 9 when it is not known whether the patient had any distant metastases.
This item was first collected in 2010. Because of delays in some hospitals in implementing registry software updates, data may be incomplete for 2010 diagnoses.This item was discontinued in 2016. It was replaced by the FORDS manual variable METS_AT_DX_BRAIN in 2016.


Psalm 2:10 Therefore be wise, O kings; be admonished, O judges of the earth.
Now then, you kings, act wisely! Be warned, you rulers of the earth!
Therefore be wise, O kings; be admonished, O judges of the earth.
Be smart, all you rulers, and pay close attention.
So now, you kings, do what is wise; you rulers of the earth, submit to correction!
Now therefore, you kings, be wise; receive correction, you judges of the earth.
Now, you kings, act wisely. Be warned, you rulers of the earth!
Now therefore, O ye kings, be wise; Be admonished, ye judges of the earth.
Understand now therefore, O ye kings: receive chastening, ye judges of the earth.
Be wise now therefore, O you kings: be instructed, you judges of the earth.
Now therefore understand, ye kings: be instructed, all ye that judge the earth.
Verse 10. - Be wise now therefore, O ye kings. The remainder of the psalm contains the advice of the psalmist to the rebels of vers. 1-3, and to all who may be inclined to imitate them. "Be wise," he says," be prudent. For your own sakes desist from attempts at rebellion. Jehovah and Messiah are irresistible. Ye will find it "hard to kick against the pricks.'" Be instructed, ye judges of the earth. "Be taught," i.e., "by experience, if ye are not wise enough to know beforehand, that opposition to God is futile." Compare the advice of Gamaliel (Acts 5:38, 39).


text:
Q: Nested locks of the same Thread causing a dead lock I have a small class (Counters) which wraps a dictionary of objects (Counter).
Here is a simplified example (with my debugging prints...)
import threading
import time
import logging
import random

logging.basicConfig(level=logging.DEBUG, format='(%(threadName)-9s) %(message)s',)

class Counter(object):

    def __init__(self, start : int = 0):
        self.lock = threading.Lock()
        self.value = start

    def increment(self):
        logging.debug('Waiting inc - %s', threading.current_thread().name)
        self.lock.acquire()
        try:
            logging.debug('Acquired inc - %s', threading.current_thread().name)
            self.value = self.value + 1
        finally:
            logging.debug('Released inc - %s', threading.current_thread().name)
            self.lock.release()

    def lastValue(self) -> str:
        logging.debug('Waiting lastValue - %s', threading.current_thread().name)
        self.lock.acquire()
        try:
            # return the last seen time in mysql format:
            logging.debug('Acquired lastValue - %s', threading.current_thread().name)
            return f" value -> {self.value}"
        except Exception as e:
            logging.error(e)
        finally:
            logging.debug('Released lastValue - %s', threading.current_thread().name)
            self.lock.release()

    def getAsDict(self, with_log=False) -> dict:
        logging.debug('Waiting getAsDict - %s', threading.current_thread().name)
        self.lock.acquire()
        try:
            logging.debug('Acquired getAsDict - %s', threading.current_thread().name)
            return {
                "counted" : self.lastValue(),
            }
        except Exception as e:
            logging.error(e)
        finally:
            logging.debug('Released getAsDict - %s', threading.current_thread().name)
            self.lock.release()

class Counters:

    def __init__(self,):
        self.lock = threading.Lock()
        self.store = {}

    def add(self, name : str) -> None:
        # add a counter object to the list:
        logging.debug('Waiting add - %s', threading.current_thread().name)
        self.lock.acquire()
        try:
            logging.debug('Acquired add - %s', threading.current_thread().name)
            self.store[name] = Counter(0)
        finally:
            logging.debug('Released add - %s', threading.current_thread().name)
            self.lock.release()

    def remove(self, name : str) -> bool:
        # remove a counter from the dictionary:
        with self.lock:
            if name in self.store:
                del self.store[name]
                return True
            return False

    def get(self, name) -> Counter or None:
        with self.lock:
            return self.store.get(name, None)

    def getAll(self) -> dict:
        logging.debug('Waiting getAll - %s', threading.current_thread().name)
        self.lock.acquire()
        try:
            logging.debug('Acquired getAll - %s', threading.current_thread().name)
            ret = {}
            for name, counter in self.store.items():
                print(counter.getAsDict())
                ret[name] = counter.getAsDict()
            return ret
        except Exception as e:
            print(e)
        finally:
            logging.debug('Released getAll - %s', threading.current_thread().name)
            self.lock.release()

When I call the getAll() method I get stuck / deadlock in lastValue.
To my best knowledge python allows nested lock acquiring and in this case this is the problematic call path that causes the deadlock:

*

*getAll    (1 lock on Counters)

*getAsDict (2 lock on Counter)

*lastValue (3 lock on Counter) - Dead here

It can also be observed when running one thread:
def worker(c):
    for i in range(1):
        r = random.random()
        n = random.randint(1, 500)
        #random name:
        name = f"counter_{n}"
        logging.debug('Counter [%s] Sleeping %0.02f', name, r)
        time.sleep(r)
        c.add(name)
        c.get(name).increment()
    logging.debug('Done')
    result = c.getAll()
    logging.debug('Result: %r', result)

if __name__ == '__main__':
    counters = Counters()
    for i in range(1):
        t = threading.Thread(target=worker, args=(counters,))
        t.start()

    logging.debug('Waiting for worker threads')
    main_thread = threading.current_thread()
    for t in threading.enumerate():
        if t is not main_thread:
            t.join()

The output is:
(MainThread) Waiting for worker threads
(Thread-7 (worker)) Counter [counter_129] Sleeping 0.55
(Thread-7 (worker)) Waiting add - Thread-7 (worker)
(Thread-7 (worker)) Acquired add - Thread-7 (worker)
(Thread-7 (worker)) Released add - Thread-7 (worker)
(Thread-7 (worker)) Waiting inc - Thread-7 (worker)
(Thread-7 (worker)) Acquired inc - Thread-7 (worker)
(Thread-7 (worker)) Released inc - Thread-7 (worker)
(Thread-7 (worker)) Done
(Thread-7 (worker)) Waiting getAll - Thread-7 (worker)
(Thread-7 (worker)) Acquired getAll - Thread-7 (worker)
(Thread-7 (worker)) Waiting getAsDict - Thread-7 (worker)
(Thread-7 (worker)) Acquired getAsDict - Thread-7 (worker)
(Thread-7 (worker)) Waiting lastValue - Thread-7 (worker) <-- DEADLOCK

What am I missing?

A: You need a reentrant lock, threading.RLock(). With threading.Lock(), if a thread tries to acquire a lock it's already holding, it deadlocks.


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text:
SpaceX will deploy a network of a million terrestrial antennas for Starlink
BY Flyn Braun	 March 23, 2020 7:02 pm GMT+0300 April 6, 2020 10:15 am GMT+0300 458 Views
SpaceX will deploy a network of a million terrestrial antennas for Starlink. This is written by CNBC.
The United States Federal Communications Commission (FCC) gave SpaceX official permission to Elon Musk to create a network of a million terrestrial antennas for the Starlink project.
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Tags: Elon Musk, SpaceX


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There are various formats that are all appropriate, you can select one amongst lots of styles and styles from the photo below. If you assume the layout of the file style is not interesting you could seek other layouts in this article.
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text:
The 1935–36 Indiana Hoosiers men's basketball team represented Indiana University. Their head coach was Everett Dean, who was in his 12th year. The team played its home games in The Fieldhouse in Bloomington, Indiana, and was a member of the Big Ten Conference.

The Hoosiers finished the regular season with an overall record of 18–2 and a conference record of 11–1, finishing 1st in the Big Ten Conference.

Roster

Schedule/Results

|-
!colspan=8| Regular Season
|-

References

Indiana
Indiana Hoosiers men's basketball seasons
1935 in sports in Indiana
1936 in sports in Indiana

redpajama_set_name: RedPajamaWikipedia


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Water is very important in all aspects of our daily life. We clean with it, wash in it, water the garden with it and the list just goes on. By conserving water, you can play a part in helping our often drought stricken land as well as helping to save money in the family's household budget.
Minimise evaporation by watering during the early morning hours when temperatures are cooler and winds are lighter. Remember to periodically check your sprinkler system valves for leaks and keep sprinkler heads in good shape.
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