text:
The Flâneurs
Meet Kalia Konstantinidou, Istoria and Parīlio, Greece
Meet the beautiful Kalia Eliopoulos; With her husband Antonis, they own the magical Istoria and Parīlio hotels on the islands of Santorini and Paros. She takes us on a journey, from the whitewash historical village to the legendary story of "the Italian Lady", she tells us about what inspired the design and what to expect when staying with them.
You and Antonis had had previous careers before entering the world of hospitality. How did you both meet and how did the first project come about?
We first met with my husband at a shipping event. Antonis was in the family shipping business at the time and I was working in PR for a think tank. On one of my husband's countless trips to Santorini, he decided to buy a rustic vineyard as his family's summer estate. He then had the bold idea of turning it into the first 5-star hotel of the island, the now renowned Vedema, a Luxury Collection Resort.
Can you tell us a little bit more about the Italian lady who owned the house before it became Istoria?
The house that used to lie on Istoria's grounds was owned by a lady that the locals would call 'the Italian lady' – who in fact is Greek but had moved to Italy almost 50 years ago. She's had a very interesting life and many stories to tell. When she was 16, the King of Greece had organized a big ball in the palace. She could attend it because her father worked for the king as he was in charge of the horses. The King of Jordan, who was 50 years old at the time, fell in love with her and wanted to marry her. Of course, he had many wives but that wasn't an issue. Her mother wanted to let her marry him but her father did not accept it. The pushy king was chasing her, so her father arranged a passport for her to enable her to travel around the Mediterranean. For four years she traveled to Turkey, Israel, Morocco, and Italy so that the king could not find her and that's how she ended up in Italy and she stayed there.
How are the islands of Santorini and Paros different from each other?
Santorini is our first and eternal love, while Paros is the island we used to spend our family vacation. Both islands boast exquisite natural beauty, rich culture, and gastronomy, and yet they are very unique and with a completely different vibe from each other. The volcanic landscape of Santorini on the one side is a major attraction because of its energy and mystery. A massive explosion that happened thousands of years ago at the Aegean sea, created the biggest Caldera in the world, now offering an amazingly beautiful scenery with breathtaking views – mostly famous for the sun-bleached houses hanging from the cliff, blue-domed churches, and legendary sunsets. On the other side, Paros is a place where unspoiled natural beauty and the slowed rhythms of Cycladic life prevail, the island embodies an authentic and organic Greek experience. The younger crowds come looking for a laidback holiday among its pristine sandy beaches and lively nightlife. Surfers come for its inviting waves, while families enjoy its many shallow, sheltered bays. Couples relish the picturesque villages and bougainvillea-lined alleyways. And nature lovers come to explore the island's wildlife and stunning geological formations.
Three things that people might not know about the hotels or the islands?
The premises of Istoria included 5 stables where the former owner used to keep her beloved horses. This long-forgotten story became the spark of inspiration for a collection of different ideas and images throughout the hotel, the most compelling of which is the arched entryways of the ground floor suites, decorated with water troughs and horse bridles, so reminiscent of the house and stables that used to stand there.
Another very interesting detail that few know is that in our effort to make the spaces as natural as possible, we have used in our spa actual black-volcanic sand from our beach on Perivolos.
Our interior architect has drawn inspiration from the surrounding nature for the design of Parīlio and most notably from the Koukounaries hill where one of the most ancient and important acropoleis of the Aegean was discovered. This pure piece of land inspired the Parīlio colour palette and this is notably reflected on the two rocks highlighting the impressive pool of the hotel. Moreover, the cross that the pool shapes is perfectly aligned with the main entrances of the hotel as well as the hill in the background.
What is your approach to hospitality?
Through our properties we aspire to offer our guests the Greek essence of distinctive hospitality, wishing them to fully disconnect and create their own story, while embracing the authentic Cycladic lifestyle and carefree state of mind. We hope that they will carry home with them cherished memories and stories they can share with the people they love.
What are you most proud of at the hotels?
It is the calming ambiance we have been able to create so far in our hotels. We have been able to design an accommodation experience that seems to decelerate time. Every corner is imbued with a sense of intimacy, relaxation, and comfort, for a prevailing atmosphere that feels like a home away from home. As soon as one steps in, they can immediately enter a world of solace and serenity.
What inspired the design?
For Istoria, we were profoundly influenced by the story of the house that used to lie on the grounds of the hotel. A deserted private residence neighboring a deep blue patch of the Aegean Sea that we transformed to a boutique hotel, aiming to form a new residential experience. The spaces are enhanced with an amalgamation of new and old materials and were intended to make guests feel as if they are living in an authentic Santorinian mansion, reminiscing the estate that once stood in its place.
For Parīlio, we were influenced by the traditional houses of Paros as well as the bright Cycladic sunlight and inspired by the local fortresses – like monasteries that one encounters on the island. The reception area, the first image that is captured by the visitor's eye, a modern sanctuary with narrow windows and a large entrance that is distinguished by a series of impressive arches, offers an overwhelming feeling of complete calmness. These images create a sense of time travelling to past decades, when life on the Cycladic islands was as pure as light itself.
What is the most important element for you when thinking about a hotel?
The islands' singular aura, abundant nature, traditional architecture, indigenous culture, and the deep blue of the sea that meets a clear blue sky; all of these elements together inspire us and try to always incorporate them in our hotels.
Do you have an object from either Istoria or Parilo that has a special place in your heart?
It is a replica of 'The Fat Lady of Saliagos' that is exposed at the lobby of Parīlio. The actual historical relic held in Paros' archaeological museum is the oldest ever found in the area and is at the heart of our hotel's symbolism. The ancient marble figurine has served as inspiration to the creation of our logo and identity. Symbolizing female beauty and power, and paying homage to the Aegean light and sea, the unassuming model personifies the key components that interlace to create Parīlio's unique brand of Cycladic simplicity.
What's your favourite spot on Paros? and on Santorini?
My favorite spot on Paros is the scenic village of Lefkes, characterized by the island's distinct white windmills, craggy quarries, and whitewashed properties. Lefkes was the island's first capital and the history there appears frozen in time, with rickety tavernas, leaning houses, and winding marble-paved streets and alleys all surviving and thriving centuries on.
My happy place in Santorini is Megalohori. This is where my family's house is located. An unspoiled, traditional little village where life has changed very little throughout the years. It's a carefree place where children can play in the streets and every neighbor is a friend. I also adore Perivolos beach. For Greeks, summer means lying on the beach, basking in the warm summer sun, and swimming in the cool waters of the Aegean. Perivolos beach, with its volcanic black sand and crystalline sea, is all that and more.
Who would be your dream guest?
My dream guest would fully unwind, immerse into our collection of the most curated experiences and embrace the true essence of the Cycladic way of life.
How do you think this crisis will impact the way we travel?
I believe we will travel more mindfully in the future, seeking for a true meaning in our voyage, wishing that in a way, it will play a transformative role in our existence. We will definitely spend more time in each destination, to fully immerse ourselves, while sustainability and the connection with the local communities will also play an important role to our travel choices.
What is your favourite hotel, restaurant and design address in the world?
Le Sirenuse in Positano, Maido in Lima and Bocci in Berlin.
Parhelion – Iosif Foskolos Artwork
Parilio £1,329
Exclusively designed artwork by Iosif Foskolos. Hand-knitted golden thread on canvas. Framed in natural wood. - In the "Parilio" project Iosif Foskolos was commissioned to work exclusively for the hotel artworks, working closely with the interior design studio. In those artworks the symbol of the sun plays a protagonist role. The name of the hotel is making both references to the combination of the words "Paros" and "Ilios" (Greek for sun), as also the atmospheric optical phenomenon of "Parhelion", where two bright spots appears in both sides of the sun through refraction. Those references, as also mystical, abstract symbols, maths and numerology associated with sun served as Iosif's main inspiration in most of the artworks. The use of gold coloured thread represents the sun rays, while the deep-blue colour is a homage to the Greek sea and the Greek islands. The symbol of the cross, found in some artworks, represents a Greek monastic aesthetic to come along with the minimalistic sanctuary interiors of the hotel.
Blue Sun – Iosif Foskolos Artwork
Parilio £665
Exclusevly designed artwork by Iosif Foskolos. Hand-knitted deep blue thread on canvas. Framed in white laquered wood. - In the "Parilio" project Iosif Foskolos was commissioned to work exclusively for the hotel artworks, working closely with the interior design studio. In those artworks the symbol of the sun plays a protagonist role. The name of the hotel is making both references to the combination of the words "Paros" and "Ilios" (Greek for sun), as also the atmospheric optical phenomenon of "Parhelion", where two bright spots appears in both sides of the sun through refraction. Those references, as also mystical, abstract symbols, maths and numerology associated with sun served as Iosif's main inspiration in most of the artworks. The use of gold coloured thread represents the sun rays, while the deep-blue colour is a homage to the Greek sea and the Greek islands. The symbol of the cross, found in some artworks, represents a Greek monastic aesthetic to come along with the minimalistic sanctuary interiors of the hotel.
Marie Winckler
15 of London's Best Restaurants for Valentine's Day, No Matter Your Plans
Whether you're with your significant other, besties or just riding solo.
Flân' Club: Lofos bar, Greece
On the outskirts of the suburban area of Kilkis, in the northern part of Greece, is an art bistro bar, where the design of high aesthetics meets ...
Make Yourself at Home: Cretan Malia Park, Crete Cretan Malia
Nestled amongst lush gardens, on the edge of the village of Malia, Cretan Malia Park sits peacefully by the bluest crystal-clear waters of the ...
Meet Agapi and Costantza Sbokou, Owners of Cretan Malia Park, Crete Cretan Malia
Agapi and Costantza Sbokou the two sisters behind Cretan Malia Park in Crete were born with the hospitality bug. Passionate, ambitious, and fearless, ...


text:
Andy Robertson explains why Liverpool FC are still hurting because of Man City
Manchester City pipped Jurgen Klopp's side to the Premier League title last season despite them amassing 97 points.
Thomas Bristow
Get the biggest City stories, analysis and transfer updates delivered straight to your inbox
Andy Robertson has revealed that losing out on the Premier League to Manchester City last season still hurts him and his Liverpool team-mates.
Last season's title race went to the final game with Pep Guardiola's side pipping Jurgen Klopp's by a single point, 98 to 97.
Liverpool's points tally would have been enough to win the title in 25 of the previous 26 Premier League seasons - City's Centurions campaign from the term prior being the only exception.
And Robertson has explained how achieving so much and performing so well but still finishing second took its toll. Winning the Champions League, however, did soften the blow.
"Our total of 97 points wins 95% of the Premier Leagues in previous years," he told the Liverpool Echo. "Unfortunately we were in the year where it wasn't enough.
"For us, that still hurts, missing out after such a fantastic season. It was eased by winning the Champions League and we can look back and say it was a successful season.
Dimitar Berbatov sends strong message to Liverpool FC in Premier League title race with Man City
Sane, Silva, Stones, Laporte - Man City injury latest and expected return dates
"Without the Champions League, though, we couldn't. It would have been a very long summer.
"We know how important every single game is because every point and every win and last-minute winner and clean sheet last season was so important. We fell short but pushed Man City all the way."
It is perhaps the hurt that has driven Liverpool on this season.
Klopp's men currently sit top of the table on 49 points, 10 ahead of closest challengers Leicester and 11 in front of City, with a game in hand on both of them.
It seems inconceivable to think Liverpool could throw away the lead now but City did overturn a ten-point gap last term, winning 14 games on the bounce at the end of the season to retain their title.
Liverpool FCJamie Carragher reveals worst Liverpool fear that Manchester United and Man City fans will loveLiverpool are now six points behind league leaders Manchester United and four behind Manchester City.
Erling Haaland's father speaks about Manchester United when asked about transfer
Erling HaalandThe father of Borussia Dortmund striker Erling Haaland has spoken about a potential transfer to Man United.

redpajama_set_name: RedPajamaCommonCrawl


For a long time now I've been trying to figure out a way to display my necklaces so I can actually remember to wear them! I jumped on the 'Statement Necklace' bandwagon as soon as I got my first one, and I've collected quite a few pieces since. Going into this project there were a couple of things I had in mind; I wanted the necklaces to be easily accessible, the project to be affordable, and the display to look nice enough to hang up on my wall. With all that in mind, I got started on my newest project!
I started off by using a stick for the base of the jewelry hanger. I used a decorative stick from IKEA, also known as HÖGFÄRDIG, which come in an assorted collection of colors. If you don't have an ikea near you, or if you want to make this project even more affordable, you can also use a tree branch from your backyard. You can leave the tree branch as is to have a more natural look or you can paint it any color you prefer.
Next, measure the stick to find the center. Use painters tape to tape the center of stick, which will provide a crisp line once the tape is removed. Only paint one side of the tape to give a color blocking affect. I used gold gild because I liked the color payoff, but if you are not able to get gild, acrylic paint will work as well. Feel free to substitute or customize your project according to personal preference.
It's time to screw in the hooks! The stick I used was hollow making it easier to screw the hooks in manually. If you use a stick or branch that is thick, you can use a screwdriver to help make a hole so it is easier to screw in the hooks. I screwed the first hook in the center and all other hooks two inches apart on each side.
Once all the screws are in, prepare to hang up the stick! Use a couple of nails and a hammer to secure the branch to the wall. I nailed each end and the center of the stick so the weight of the necklaces doesn't bring the whole thing down. In case you live in an apartment or dorm and are not allowed to nail anything to your wall, you can tie some string on each end of the stick and hang it on your wall with a couple of thumbtacks.
Once the display is pinned to the wall hang your necklaces and you're done!


text:
Individual Sole Trader Business BAS Agent Tax Agent Access Manager Super clearing house Non-resident
Register ? System maintenance and issues Access manager
Go to ato.gov.au
ATO Community
Tax Topics A-Z
Last search results
Public rulings
Practical compliance guidelines
Decision impact statements
ATO interpretative decisions
Law administration practice statements
Taxpayer alerts
Edited private advice
Public rulings and determinations
Accessing a known document
Doing a quick search
Doing an advanced search
Interpreting your search results
Providing the history of documents
Browsing to documents
About the Legal database
Back to browse20 related documents
View surrounding sectionsView surrounding sections Previous section | Next section
Corporations Act 2001
[ CCH Note: Pt 9.1A will be inserted by No 69 of 2020, s 3, Sch 2[11] (effective 22 June 2022 or a day or days to be fixed by Proclamation). Pt 9.1A will read:
PART 9.1A - DIRECTOR IDENTIFICATION NUMBERS
SECTION 1272 GIVING AND CANCELLING DIRECTOR IDENTIFICATION NUMBERS
The Registrar must, by notifying a person who has applied under section 1272A , give the person a director identification number if the Registrar is satisfied that the person ' s identity has been established.
The Registrar must make a record of the person ' s director identification number.
The Registrar may, by notifying a person, cancel the person ' s director identification number if:
(a) the Registrar is no longer satisfied that the person ' s identity has been established; or
(b) the Registrar has given the person another director identification number.
(a) at the time the person is given a director identification number under this section, the person is not an eligible officer; and
(b) the person does not, within 12 months after that time, become an eligible officer;
the person ' s director identification number is taken to have been cancelled at the end of the 12 month period.
SECTION 1272A APPLYING FOR A DIRECTOR IDENTIFICATION NUMBER
1272A(1)
An eligible officer may apply to the Registrar for a director identification number if the officer does not already have a director identification number.
The Registrar may direct an eligible officer to apply to the Registrar for a director identification number (whether or not the officer already has a director identification number).
A person who is not an eligible officer may apply to the Registrar for a director identification number if:
(a) the person intends to become an eligible officer within 12 months after applying; and
(b) the person does not already have a director identification number.
An application for a director identification number must meet any requirements of the data standards.
Note: A person may commit an offence if the person knowingly gives false or misleading information (see section 1308 of this Act and section 137.1 of the Criminal Code ).
SECTION 1272B ELIGIBLE OFFICERS
1272B(1)
An eligible officer is:
(a) a director of a company, or of a body corporate that is a registered Australian body or registered foreign company, who:
(i) is appointed to the position of a director; or
(ii) is appointed to the position of an alternate director and is acting in that capacity;
regardless of the name that is given to that position; or
(b) any other officer of a company, or of a body corporate that is a registered Australian body or registered foreign company, who is an officer of a kind prescribed by the regulations;
but does not include a person covered by a determination under subsection (2) or (3).
The Registrar may determine that a particular person is not an eligible officer . The Registrar must notify the person of the determination.
The Registrar may, by legislative instrument, determine that a class of persons are not eligible officers .
SECTION 1272C REQUIREMENT TO HAVE A DIRECTOR IDENTIFICATION NUMBER
1272C(1)
An eligible officer must have a director identification number.
Subsection (1) does not apply if:
(a) the officer applied to the Registrar under section 1272A for a director identification number:
(i) before the day the officer first became an eligible officer (or an eligible officer within the meaning of the Corporations (Aboriginal and Torres Strait Islander) Act 2006 ); or
(ii) if the regulations specify an application period - within that period, starting at the start of that day; or
(iii) within the longer period (if any) the Registrar allows under section 1272E , starting at the start of that day; and
(b) the application, and any reviews arising out of it, have not been finally determined or otherwise disposed of.
Note: A defendant bears an evidential burden in relation to the matters in subsection (2): see subsection 13.3(3) of the Criminal Code .
Subsection (1) does not apply if the officer became an eligible officer without the officer ' s knowledge.
Note: A defendant bears an evidential burden in relation to the matter in subsection (3): see subsection 13.3(3) of the Criminal Code .
An offence based on subsection (1) is an offence of strict liability.
Note: For strict liability , see section 6.1 of the Criminal Code .
A person who contravenes, or is involved in a contravention of, subsection (1) contravenes this subsection.
Note 1: Subsection (5) is a civil penalty provision (see section 1317E ).
Note 2: Section 79 defines involved .
SECTION 1272D REQUIREMENT TO APPLY FOR A DIRECTOR IDENTIFICATION NUMBER
1272D(1)
An eligible officer whom the Registrar has directed under subsection 1272A(2) to apply for a director identification number must apply to the Registrar under section 1272A for a director identification number:
(a) within the application period under subsection (2) of this section; or
(b) within such longer period as the Registrar allows under section 1272E .
The application period is the period of:
(a) the number of days specified in the direction; or
(b) if the number of days is not specified in the direction - 28 days;
after the day the Registrar gives the direction.
SECTION 1272E REGISTRAR MAY EXTEND APPLICATION PERIODS
1272E(1)
The Registrar may, on the application of an eligible officer, allow, as a longer period for applying to the Registrar under section 1272A for a director identification number:
(a) the period specified in the application; or
(b) such other period as the Registrar considers reasonable.
Note: An application for an extension of the period for applying can be made, and the period can be extended, after the period has ended: see section 70 .
The Registrar may, by legislative instrument, allow, for persons included in a specified class of persons, a longer period for applying to the Registrar under section 1272A for a director identification number.
SECTION 1272F INFRINGEMENT NOTICES
Subsections 1272C(1) and 1272D(1) are subject to an infringement notice under Part 5 of the Regulatory Powers Act.
Note: Part 5 of the Regulatory Powers Act creates a framework for using infringement notices in relation to provisions.
Infringement officer
For the purposes of Part 5 of the Regulatory Powers Act, each member of the staff of the Registrar who holds, or is acting in, an office or position that is equivalent to an SES employee is an infringement officer in relation to subsections 1272C(1) and 1272D(1) of this Act. Relevant chief executive
For the purposes of Part 5 of the Regulatory Powers Act, the relevant chief executive in relation to subsections 1272C(1) and 1272D(1) of this Act is:
(a) the person specified as the relevant chief executive in the Registrar ' s instrument of appointment under section 1270 ; or
(b) if there is no person specified - the Registrar. Matters to be included in an infringement notice
Subparagraph 104(1)(e)(iii) of the Regulatory Powers Act applies to an infringement notice relating to a contravention of subsection 1272C(1) or 1272D(1) of this Act as if that subparagraph did not require the notice to give details of the place of the contravention.
SECTION 1272G APPLYING FOR ADDITIONAL DIRECTOR IDENTIFICATION NUMBERS
1272G(1)
A person must not apply for a director identification number if the person knows that the person already has a director identification number.
Note: Failure to comply with this subsection is an offence: see subsection 1311(1) .
Subsection (1) does not apply if the Registrar directed the person under subsection 1272A(2) to make the application.
Subsection (1) does not apply if the person purports to make the application only in relation to Part 6-7A of the Corporations (Aboriginal and Torres Strait Islander) Act 2006 .
SECTION 1272H MISREPRESENTING DIRECTOR IDENTIFICATION NUMBERS
1272H(1)
A person must not intentionally represent to a Commonwealth body, company or registered body, as the director identification number of the person or another person, a number that is not that director identification number.
Note 2: Section 79 defines involved .]
PART 9.4 - OFFENCES
Division 2 - Offences generally
SECTION 1317 CERTAIN PERSONS TO ASSIST IN PROSECUTIONS
Where a prosecution in respect of an offence against this Act has been instituted, or ASIC is of the opinion that a prosecution in respect of an offence against this Act ought to be instituted, against a person (in this section referred to as the defendant ), ASIC may:
(a) if the defendant is a natural person - require any person who is or was a partner, employee or agent of the defendant; or
(b) if the defendant is a body corporate - require any person who is or was an officer, employee or agent of the defendant;
to assist in the prosecution, and the person who is so required must give all assistance in connection with the prosecution that that person is reasonably able to give.
ASIC must not make such a requirement as is mentioned in subsection (1) of a person who, in the opinion of ASIC, is or is likely to be a defendant in the proceedings or is or has been such a person's lawyer.
If a person to whom paragraph (1)(a) or (b) relates fails to give assistance as required by subsection (1), the person contravenes this section and, without affecting any penalty to which the person may be liable for the contravention, the Court may, on the application of ASIC, order the person to comply with the requirement within such time, and in such manner, as the Court orders.
In this section, agent , in relation to the defendant, includes a banker of the defendant and a person engaged as an auditor by the defendant, whether that person is an employee or an officer of the defendant or not.
View surrounding sectionsView surrounding sectionsBack to top
This information is provided by CCH Australia Limited Link opens in new window. View the disclaimer and notice of copyright.
About ATO
About ATO ? Toggle visibility of this section
Taxpayers' Charter
Related sites ? Toggle visibility of this section
Australian Business Register
Tax Practitioners Board
More related sites
Tools ? Toggle visibility of this section
Tax rates and codes
ATO app including myDeductions
Using ato.gov.au
Using ato.gov.au ? Toggle visibility of this section
Smarter searching
? See related links for ?
About information on this website
About calculators
The fight against tax crime
Tax evasion reporting form
Report avoidance schemes
Help for taxpayers
Help for taxpayers ? Toggle visibility of this section
Getting help with your tax
Do you need to lodge a tax return?
Appoint someone to act on your behalf
Tax help program
Help for small business
Norfolk Island tax and super
Law rulings and policy
Law rulings and policy ? Toggle visibility of this section
ATO advice and guidance
Getting it right ? Toggle visibility of this section
Your rights and obligations
Insight: Building trust and confidence
Correct a mistake or amend a return
Dispute or object to an ATO decision
Tax avoidance taskforce
Tax and Corporate Australia
Tax and individuals - not in business
Tax and small business
Australian Taxation Office �
Working for all Australians
Connect with us on... Facebook YouTube Twitter LinkedIn RSS
� Commonwealth of Australia
We are committed to providing you with accurate, consistent and clear information to help you understand your rights and entitlements and meet your obligations.
If you follow our information and it turns out to be incorrect, or it is misleading and you make a mistake as a result, we will take that into account when determining what action, if any, we should take.
Some of the information on this website applies to a specific financial year. This is clearly marked. Make sure you have the information for the right year before making decisions based on that information.
If you feel that our information does not fully cover your circumstances, or you are unsure how it applies to you, contact us or seek professional advice.
� Australian Taxation Office for the Commonwealth of Australia
You are free to copy, adapt, modify, transmit and distribute this material as you wish (but not in any way that suggests the ATO or the Commonwealth endorses you or any of your services or products).
Build: 2017.08.14.1


text:
Nampa Day Care License Suspended After Leaving Child in Park
Chalabala, Thinkstock
NAMPA, Idaho (AP) — Authorities have suspended a southwestern Idaho day care's state license after workers left a 3-year-old behind at a local park.
Little Angels Daycare/Preschool Operator Angelina Diordyuk told the Idaho Press Thursday that staff didn't realize that the child was missing until an employee saw a social media post from Nampa police about the girl found alone at the park.
Diordyuk and the staff took 14 children to a park last Friday. Staff counted the kids before they returned to their cars to make sure they were all there.
When they got back to the day care, staff assumed the girl was playing quietly like usual.
The Idaho Department of Health and Welfare suspended the day care's license four days after the incident for failing to report a missing child.
Diordyuk didn't expect that her license would be suspended for the mistake since she's never had any issues in her five years of running the day care, she said.
"It's my life and in one day it's all gone," Diordyuk said through tears. "I just can't believe it happened."
The suspension has caused the day care to lose its standing with the Idaho Child Care Program — which helps working families pay for child care through taxpayer-funded subsidies — for at least five years, said Chris Smith, a spokesman for the department.
The department is helping clients covered under the program find alternative care.
The suspension will be in effect at least until an investigation into possible child neglect is complete, Smith said.
Investigation findings are being sent to the Canyon County Prosecutor's Office, which will decide if any charges will be made, said Nampa Police Lt. Eric Skoglund.
The license will be permanently revoked if a charge is pressed, he said.
Filed Under: day care, idaho, nampa
Categories: Idaho News, Twin Falls News


text:
Periodicity not specified (38)
Ad Valorem Equivalent of Non-Tariff Measures
The ad valorem equivalent (AVE) of non-tariff measures (NTMs) is the uniform tariff that will result in the same trade impacts on the import of a product due to the presence of the NTMs. In other words, the AVEs represent the...
Data Type: Other Periodicity: Periodicity not specified Last Updated: Jan 23, 2019
What a Waste Global Database
What a Waste is a global project to aggregate data on solid waste management from around the world. This database features the statistics collected through the effort, covering nearly all countries and over 330 cities. The...
Data Type: Time Series Periodicity: Periodicity not specified Last Updated: Sep 20, 2018 Access Options: Download
Niger - Solar irradiation and PV power potential maps
Map with solar irradiation and PV power potential in Niger. The GIS data (AAIGRID and GEOTIFF) stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png)...
West Africa and India - Hydropower Dams
This database contains parameters gathered after an assessment of the technological potential for development of floating solar photovoltaic (PV) projects on existing hydropower dams and other reservoirs, starting with a pilot...
Data Type: Other Year: 2017 Periodicity: Periodicity not specified Last Updated: Apr 09, 2018 Publisher: Clara Ivanescu Access Options: Download
Niger - Electricity Transmission Network
The transmission network GIS datasets are obtained from the World Bank project - "Niger - Electricity Access Expansion Project". The Project Appraisal Document can be accessed at http://documents.worldbank.org/curated/en...
Data Type: Geospatial Periodicity: Periodicity not specified Last Updated: Mar 29, 2018 Publisher: Naichen Zhao Access Options: Download
Niger BOOST Public Expenditure Database
The Open Budgets Portal is the first effort to create a one-stop shop for budget data worldwide with the hope of bringing visibility to countries' efforts in this field, facilitating access and promoting use of spending data,...
Data Type: Time Series Year: 2003 - 2017 Periodicity: Periodicity not specified Last Updated: Mar 29, 2018


Hotel Bella Zurigo is equipped with numerous comforts.
A large Lobby, spacious dining room with air-conditioned, bar, veranda, TV room with satellite dish, lift, bicycles to ride along the shaded avenues fresh, free games for adults and children (table football, pinball, ping-pong).
For those who want to make cycling in wildmess, you can have the "lunch" and there is a locked storage where you can store your own bikes. Large private car parking with enough space for customers cars.


Boracay island in the Philippines is a world-class goal for every Traveler. The popular and excellent white shoreline with its delicate white sands continues to attract Tourists from everywhere throughout the world. Travelers are pulled into Boracay and the islands laid back life, with many attractions, activities and party atmosphere. However, there is something else entirely to do here than simply sunbathe, swim and move the night away.
This beautiful island of Boracay can offer its guests significantly more exercises and interests; ATV Hire and investigate the island with companions or family. Take a Paraw Sail on a Filipino sailboat at Sunset or Island Hopping tour with swimming, snorkeling, and fish feeding plus an outing included. Reach the skies when parasailing behind a speedboat. Experiment with Scuba diving magnificent coral Reefs. Substantially more for you to appreciate visiting an astounding, beautiful tropical island of relaxation, fun and delights in the Philippines. Luxury or Budget Resorts mixed with all types Accommodation abound the waterfront shores of Boracay.
Hop on a Bangka (Filipino boat) for your Boracay island hopping adventure, fun for everyone and a Family friendly tour. Explore beautiful Boracay and its' surrounding islands. Also, visit Crystal Cove for some exploring and Cliff Jumping, after a scrumptious lunch + 2-way Transfers from your hotel are also included in this day tour.
FREE roundtrip shuttle service from Station 1, 2 or 3 hotel/resort only to activity area.
Please prepare PHP 20.00/person (approx.cost US$1) snorkeling fee to be collected during the tour.
Please prepare PHP200.00/person (approx.US$4) Crystal Cove entrance fee to be collected during the tour. This is optional.
Important Note: Bring your own drinking water Tour itinerary and sequence may change depending on the current and weather. Free roundtrip shuttle service are for guests staying at Station 1, 2 and 3 hotels only.Notes: This is a shared tour. This shared Boracay Island Hopping Tour is done whole day and can't be mixed with other activities. Please prepare PHP 20.00/person snorkeling fee to be collected during the tour. Please prepare PHP200.00/person Crystal Cove entrance fee to be collected during the tour. This is optional.No free shuttle service pick up from Diniwid and Yapak areas. Indicate "Budget Mart at Dmall" on your booking as "Pick up / Drop off Hotel*" and proceed to Budget Mart at Dmall instead for the free pick up.
Mayonna S. Perante 2 months ago Highly recommended!! True to what is written in the itinerary. Madaling kausap and very responsive to any inquiries. Keep it up👍🏻👍🏻👍🏻..


The Hot Industries section of our site features links to information about insurance for industries that we see either having trouble finding insurance or that are popping up across the country. Of course, in both cases, we can help. We also highlight businesses that we see suddenly finding themselves in a position that requires new insurance.
Example #1 – We receive calls from people who are in a specific business but are having trouble finding the type of insurance that meets their needs. A great example is the number of notaries that contact who cannot find the amount of professional liability insurance they need for new contract requirements. We can help with high limits of errors and omissions insurance, up to $10,000,000. That's when you know it's time to talk to the Small Business Team.
Example #2 – Do you ever start to see a new type of business opening all around you? Think of all of the new specialty yogurt shops that you have noticed opening. We all know that they have tasty options, but can they find the right insurance to meet their needs? Once again, we are here to help by finding small businesses policies that can offer a large selection of coverages. Are you wondering what other businesses have been popping up? Think about your seasonal businesses below.
Example #3 – Seasonal industries often have trouble finding insurance, as many agencies and carriers target businesses that operate year round. We can help in these situations. Whether you have a pumpkin patch, Christmas Tree Lot, or a seasonal mall kiosk operator, you may find yourself needing insurance for just a season.


text:
using System;

namespace MyHealth.Client.Core.Services
{
    public interface IHockeyAppFeedbackService
    {
        void LaunchHockeyAppFeedback();
    }
}

redpajama_set_name: RedPajamaGithub


text:
Former Credit Controller Gets 5 Years For R1MLN Fraud
A convicted fraudster will be spending five years in a Cape Town prison after laundering over R1 million.
Anovuyo Mvula pleaded guilty on all 22 counts of fraud, money laundering and the contravention with the Electronic Communications and Transactions Act.
She was arrested in March and had since been convicted and sentenced in the Bellville Specialised Commercial Crime Court.
Mvula had been working as a credit controller for an international company while committing her crimes between January 2014 to December 2016.
The Hawks' Philani Nkwalase explained: "She made fraudulent transfers into her personal bank account. The stolen funds were supposed to be paid to existing suppliers and service providers."
Nkwalase said she was handed a 10-year sentence.
"Five years have been suspended with stringent conditions. Additionally, the accused's pension will be forfeited."
EWN
The post Former Credit Controller Gets 5 Years For R1MLN Fraud appeared first on iAfrica.com.


text:
Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods
William Michael Landau ,
* E-mail: landau@iastate.edu
Affiliation Department of Statistics, Iowa State University, Ames, Iowa, United States of America
William Michael Landau,
William Michael Landau Peng Liu
A central goal of RNA sequencing (RNA-seq) experiments is to detect differentially expressed genes. In the ubiquitous negative binomial model for RNA-seq data, each gene is given a dispersion parameter, and correctly estimating these dispersion parameters is vital to detecting differential expression. Since the dispersions control the variances of the gene counts, underestimation may lead to false discovery, while overestimation may lower the rate of true detection. After briefly reviewing several popular dispersion estimation methods, this article describes a simulation study that compares them in terms of point estimation and the effect on the performance of tests for differential expression. The methods that maximize the test performance are the ones that use a moderate degree of dispersion shrinkage: the DSS, Tagwise wqCML, and Tagwise APL. In practical RNA-seq data analysis, we recommend using one of these moderate-shrinkage methods with the QLShrink test in QuasiSeq R package.
Citation: Landau WM, Liu P (2013) Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods. PLoS ONE 8(12): e81415. https://doi.org/10.1371/journal.pone.0081415
Editor: Lin Chen, The University of Chicago, United States of America
Received: June 17, 2013; Accepted: October 12, 2013; Published: December 9, 2013
Copyright: © 2013 Landau, Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work is supported in part by the National Science Foundation Grant IOS-1127017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In the last five years, groundbreaking new RNA sequencing (RNA-seq) technologies have considerably improved studies in genetics that previously relied on microarray technologies. RNA-seq technologies have several advantages over microarrays, including less noise, higher throughput, and the power to detect novel promoters, isoforms, allele-specific expression, and a wider range of expression levels. So it is not surprising that RNA-seq has become ubiquitous in experiments that investigate the regulation of gene expression across different conditions, such as levels of a treatment factor, genotypes, environmental conditions, and developmental stages.
In a typical RNA-seq experiment, reverse transcription and fragmentation convert each RNA sample into a library of complementary DNA (cDNA) fragments, or tags. Next, a sequencing platform, such as the Illumina Genome Analyzer, Applied Biosystems SOLiD, Pacific Biosciences SMRT, or Roche 454 Life Sciences, amplifies and sequences the tags. After sequencing, a subsequence within each tag, called a read, is recorded. After the resulting collection of reads, or library, is assembled, the reads are mapped to genes in the original organism's genome. The number of reads in a library mapped to a gene represents the relative abundance of that gene in the library. The investigator typically assembles all the read counts of multiple libraries into a table with rows to indicate genes and columns to indicate libraries. Please consult references by Oshlack, Robinson, and Young [1] and by Wang, Li, and Brutnell [2] for details regarding sequencing technologies, gene mapping, and data preprocessing.
A central goal of RNA-seq experiments is to detect genes that are differentially expressed : i.e., ones for which the average number of reads differs significantly across treatment groups. Improving the detection of differentially expressed genes opens new ways to control organisms at the molecular level, advancing fields like agriculture engineering, personalized medicine, and the treatment of cancers, contributing to social welfare.
Some of the most popular new statistical methods that detect differentially expressed genes from RNA-seq data rely on the negative binomial (NB) probability distribution. If a random variable, , has an NB( , ) distribution –i.e., a negative binomial distribution with mean parameter and dispersion parameter – then the probability mass function (pmf), expected value, and variance of are:
Cameron and Trivedi [3] show that as , converges to the pmf of the Poisson( ) distribution, a distribution with mean and variance both equal to . So the dispersion parameter, , is a measure of the extra variance of that the Poisson( ) distribution does not account for.
In an RNA-seq dataset, the number of reads, , mapped to gene in library is treated as a random draw from an NB distribution. Here, is the unnormalized mean count of gene in library , and is the gene-wise ("tagwise") dispersion assigned to gene . In addition, the model assumes that , where is the treatment group of library , is the normalization factor of library , and is the normalized mean count for gene in each library of treatment group .
It is common practice to include in the model the library-wise normalization factors, , because counts in an RNA-seq data table may differ significantly across treatment levels for reasons other than the differential expression of genes. For instance, different RNA samples may be sequenced to different depths, making the libraries vary in size (total number of reads per library). To account for a possible variation in sequencing depth and other factors that may cause variation in library size, each column is assigned a normalization factor, , to be used in later analyses. There are several choices for the normalization factors. For instance, according to Si and Liu [4], taking each to be the 0.75 quantile of the counts in library is a simple method that performs relatively well. Another popular option is the method proposed by Anders and Huber [5], which divides each count by the geometric mean count of the corresponding gene and then takes the medians of the these scaled counts within each library. The Trimmed Mean of M Values (TMM) method by Robinson and Oshlack computes each normalization factor from the trimmed mean of the gene-wise log fold changes of the current library to a reference library [6].
With the above preliminaries taken care of, we now turn to the main issue of this article: the estimation of the dispersion parameters, . Each is a measure of the extra variance, relative to the Poisson ( ) distribution, of the read counts of gene . Since they control the variances of the counts, these 's play an important role in hypothesis tests that detect differentially expressed genes. Underestimating a dispersion parameter is equivalent to underestimating the variance relative to the mean, which may generate false evidence that a gene is differentially expressed. Inversely, overestimating a dispersion may cause a truly differentially expressed gene to go undetected. For the sake of accurately detecting differentially expressed genes, it is important to choose an effective method for estimating dispersion parameters.
First, we briefly review several popular dispersion estimation methods (implemented in freely-available R-language packages, AMAP.Seq, DSS, edgeR, and DESeq). We also touch on some popular tests for differential expression. Next, using a simulation study that draws fundamental information from real datasets, we compare the practical effectiveness of the methods in terms of the accuracy and precision of the point estimates and the effect on the performance of tests for differential expression. In the results and discussion sections, we discuss the distinguishing features of the most successful dispersion estimation methods.
Studies by Wu, Wang, and Wu [7] and by Yu, Huber, and Vitek [8] also include simulation-based comparisons of methods for estimating negative binomial dispersions from RNA-seq. However, the authors of these studies were primarily concerned with inventing and validating their own methods. Since we do not propose any new methods here, our study has less personal bias than otherwise. In addition, our comparison is broader in scope. Only three methods were compared in the article by Wu, Wang, and Wu, six were compared in the study by Yu, Huber, and Vitek, and both studies ignored alternate versions of these methods. On the other hand, we consider alternate versions of five classes of methods (for example, the "Common", "Tagwise" and "Trended" versions of the APL method described later), giving us a total of ten methods to compare. Considering alternate versions not only broadens the comparison, but also helps us isolate key features that help the good methods succeed. Lastly, our scheme for simulating datasets from the negative binomial model is based on real data and preserves observed relationships between the dispersions and the gene-specific mean counts. On the other hand, in the study by Wu, Wang, and Wu, dispersion parameters were simulated independently from the means. Yu, Huber, and Vitek use several simulation schemes and real datasets, but none of their realistic schemes simulates the negative binomial model, and according to these authors, the rest of the simulations favor either sSeq (their method) or DESeq (described later).
Existing Methods
Dispersion estimation methods.
In RNA-seq data analysis, we could apply methods based on counts for each gene separately to estimate model parameters, such as the Quasi-Likelihood (QL) method reviewed below. However, in RNA-seq data, there are typically tens of thousands of genes, but only a few counts per gene with which to estimate gene-specific parameters, a typical example of a "large small " scenario. In such cases, methods based on each gene separately are sub-optimal because they do not make use of most information contained in the whole dataset. Several methods proposed recently try to improve dispersion parameter estimation by using more information from the dataset. We review them after introducing the QL method.
The Quasi-Likelihood (QL) method.
The QL method estimates a dispersion parameter independently for each gene. This method [9] [4], implemented in the AMAP.Seq R package by Si and Liu, iteratively estimates the mean and dispersion as follows:
The MLE, , of each is obtained by maximizing the negative binomial log likelihood given and the read count, .
The tagwise dispersion estimate, , given , is computed via the quasi-likelihood (QL) technique reviewed by Robinson and Smyth [10].
The QL method estimates tens of thousands of dispersion parameters, but uses only a few read counts to compute each estimate. More sophisticated techniques make use of a larger number of read counts to estimate each . Specifically, they borrow information across genes and shrink the 's towards a common center. Each of the next four dispersion estimation methods applies some form of shrinkage.
The Weighted Quantile-Adjusted Conditional Maximum Likelihood (wqCML) method.
As explained by Robinson and Smyth [11], the wqCML method shrinks the dispersions towards a common value using a weighted likelihood approach. The method first calculates pseudo-data from the original data with a quantile-adjustment procedure so that all the library sizes become equal. Next, the method estimates each by maximizing the weighted likelihood, . Here, is the "common" log likelihood, the negative binomial log likelihood under the restriction that all genes share the same dispersion value, and is the negative binomial log likelihood on the pseudo-data conditioned on the sum of the pseudo-counts of gene . The tuning parameter, , represents the extent that the method "shrinks" individual tagwise dispersions towards the single dispersion given by the common likelihood. In practice, is calculated with the empirical Bayes rule described by Robinson and Smyth [11].
The wqCML method is implemented in the R package, edgeR, designed by Robinson, McCarthy, and Smyth and available at bioconductor.org [12], [13]. The user can choose between estimateTagwiseDisp(), which shrinks each dispersions towards a common estimate via wqCML, and estimateCommonDisp(), which estimates a single dispersion for all the genes by maximizing .
The Cox-Reid Adjusted Profile Likelihood (APL) method.
The wqCML method only applies to completely randomized designs with two treatment groups. In the APL method, McCarthy, Chen, and Smyth [14] extend wqCML's idea of shrinkage via weighted likelihoods to the framework of generalized linear models, which can handle more complex designs, potentially with multiple treatment factors and/or blocking factors. McCarthy, Chen, and Smyth [14] apply the loglinear negative binomial model given by , where is a vector of covariate values specifying the experimental conditions on library (i.e., is the 'th row of the design matrix), is the vector of experimental design parameters corresponding to gene , and is the total number of reads in library .
To estimate the 's, McCarthy, Chen, and Smyth [14] make use of the tagwise Cox-Reid adjusted profile likelihoods given by the loglinear model above instead of the ordinary negative binomial likelihoods in the wqCML method. The authors describe three different variations on the APL method, which use these adjusted profile likelihoods in different ways to achieve three different kinds of dispersion shrinkage. The "Common" variation sets all the dispersion estimates equal to the common value that maximizes the arithmetic mean of the APLs over all genes. The "Trended" variation, which estimates a different dispersion for each gene using adjusted profile likelihoods while modeling the 's as smooth functions of the gene-wise average read counts, heavily shrinks the dispersion estimates toward a common trend. The "Tagwise" variation shrinks each gene's dispersion estimate towards the common dispersion estimate of a set of neighboring genes. McCarthy, Chen, and Smyth [14] implement this method in the edgeR R package with the functions, estimateGLMCommonDisp(), estimateGLMTrendedDisp(), and estimateGLMTagwiseDisp().
The Differential Expression for Sequence Count Data (DESeq) method.
Like the wqCML and APL methods, the DESeq method by Anders and Huber [5] borrows information across genes to shrink the dispersion parameters. DESeq differs from the other methods mainly in that it uses directly normalized read counts and makes more use of the observed variance-mean relationship in the data.
Anders and Huber [5] reparameterize the negative binomial model in terms of the mean and variance, and further parameterize the variance in terms of the mean, normalization factor , and a new "raw variance parameter". They then use the normalized counts, , to compute the normalized negative binomial means and raw variance parameters for each gene-treatment group combination. The dispersions are calculated directly from these means and raw variance parameters.
As with the APL method, there are three variations on the DESeq method giving different ways to shrink the dispersions. The no-shrinkage variation transforms the estimated raw variance parameters directly into the estimated gene-wise dispersions without any shrinkage. The "Trended" variation performs a regression of the raw variance parameter estimates on the estimated means, and then computes the estimated dispersions from the fitted values on the trend. (In the DESeq R package, the implementation of the DESeq method, the user can choose between local and parametric regression to compute this trend. However, the parametric regression in the package is prone to failure and leads to poor point estimation test performance in our simulation study. Hence, only the local regression results are presented in this article.) The "Maximum" variation computes the maximum of each raw variance parameter estimate and its fitted value on the trend and then computes the dispersion estimates from these maxima. This last method of dispersion shrinkage is conservative, allowing overestimation of the dispersions, but guarding against underestimation in order to avoid false positives in tests for differential expression. The DESeq method is implemented in the R package, DESeq, available at bioconductor.org.
The Dispersion Shrinkage for Sequencing (DSS) method.
The Bayesian paradigm more naturally accommodates the notions of "borrowing information" and "shrinkage" than the Frequentist paradigm. Hence, the DSS method by Wu, Wang, and Wu [7], an empirical Bayes approach, is particularly elegant. Rather than explicitly force the dispersions to shrink to a common value or trend as in the wqCML, APL, and DESeq methods, the DSS method seamlessly incorporates shrinkage with a Gamma-Poisson hierarchical model in which the dispersion estimates naturally shrink towards a common log-normal prior. The hierarchical model in the DSS method is
Here, the gamma distribution is parameterized in terms of its mean, , and dispersion, , where is the reciprocal of the shape parameter. The marginal distribution of is NB( ), where as before. Each dispersion estimate is taken to be the mode of the conditional distribution of given . An adapted method of moments technique is used to estimate and . Wu, Wang, and Wu [7] implement this method in the R package, DSS, available at bioconductor.org.
Methods of testing for differential expression.
With a model specified and all the parameters estimated, we can test for the differential expression of genes. The simulation study in the next section uses the following five recently-proposed testing methods. The first two tests, found in the edgeR and DESeq packages, extend Fisher's exact test to data following negative binomial distribution. The next three tests, developed by Lund, Nettleton, McCarthy, and Smyth [15], are implemented in the QuasiSeq R package, available from the Comprehensive R Archive Network (CRAN). The QL test in the QuasiSeq package uses a quasi-negative binomial model in which a quasi-likelihood dispersion, , is assigned to each gene (separately from the negative binomial dispersion, ) for additional flexibility. The QL test executes a quasi-likelihood ratio test for the differential expression of each gene. The QLShrink test improves on the QL test by borrowing information across genes to estimate the 's. The QLSpline test extends the QLShrink test by using a spline fit to account for the relationship between the 's and the gene-wise means in the data.
The Simulation Study
This article presents a simulation study that puts the featured dispersion estimation methods to the test. We first generate pseudo-datasets for which the true negative binomial dispersion parameters and truly differentially expressed genes are known. Then, we apply the featured dispersion estimation methods and testing methods to the pseudo-data, compare the results to the truth, and measure the performance of the dispersion estimation methods in terms of point estimation and performance in testing for differential expression.
The underlying real datasets.
The simulation study uses two real RNA-seq datasets to generate the pseudo-datasets. The "Pickrell dataset" (Gene Expression Omnibus accession number GSE19480) comes from a study by Pickrell, Marioni, Pai, Degner, Engelhardt, et al. [16], who studied 69 lymphoblastoid cell lines derived from unrelated Nigerian individuals who were subjects in the International HapMap Project. The "Hammer dataset" (Gene Expression Omnibus accession number GSE20895) comes from a study by Hammer, Banck, Amberg, Wang, Petznick, et al. [17], who compared gene expression in the L4 dorsal root ganglia of control rats with those in rats with experimentally induced chronic neuropathic pain. Both of these datasets are publicly available at the Recount database at http://bowtie-bio.sourceforge.net/recount/ [18].
The top two panels of Figure 1 show the gene-wise log geometric mean counts and log dispersion estimates, estimated with the QL method, of each of the datasets. The Hammer dataset has higher mean counts and lower dispersions relative to the Pickrell dataset. Hence, tests for differential expression will, in general, have higher power when applied to the Hammer-generated pseudo-datasets than when applied to the Pickrell-generated pseudo-datasets. See the top two panels of Figure 2 for the relationship between the log quasi-likelihood dispersions and the gene-wise log geometric mean counts.
Figure 1. A look at the data.
Hammer data and Hammer-generated pseudo-data are in blue, while Pickrell data and Pickrell-generated pseudo-data are shown in black. The top two panels show the gene-wise log geometric mean counts and log dispersion estimates, estimated with the QL method, for the Hammer and Pickrell datasets. The bottom two panels plot the analogous quantities for example simulated pseudo-datasets, except that the log dispersions plotted are the true dispersions used to simulate the pseudo-counts and the gene-wise log geometric mean counts are the 's (log geometric mean counts from the real data) used in the simulations. The vertical bar at around in the plots of the log dispersions is an artifact of the QL method, which sets extremely low dispersions (i.e., dispersions of non-overdispersed genes) to a common minimum value.
Figure 2. Dispersion-mean relationships.
The top two panels show the relationship between the log QL-method-estimated dispersions and the gene-wise log geometric mean counts of the Hammer and Pickrell datasets. The bottom two plot the analogous quantities for example simulated pseudo-datasets, except that the log dispersions plotted are the true log dispersions used to simulate the pseudo-counts (i.e., the 's) and the gene-wise log geometric mean counts are the 's used in the simulations. Bins in these two-dimensional histograms are shaded by their log frequency.
Generating a pseudo-dataset.
For each one of the real datasets (Hammer or Pickrell), we first compute the two quantities, and , for each gene , where is the geometric mean and is the dispersion parameter estimated with the quasi-likelihood (QL) method. (All zero read counts are set to a small constant for the geometric mean calculation.) Then, we generate each pseudo-dataset with 10,000 genes as follows:
Randomly select 10,000 genes from one of the real datasets (Hammer or Pickrell) without replacement. The corresponding 10,000 pairs of and will be used as the the geometric mean expression level across treatments and true dispersion, respectively, of simulated gene .
Randomly select simulated gene to be either differentially expressed across the two treatments or equivalently expressed such that exactly 20% of the simulated genes are differentially expressed and the remaining 80% are equivalently expressed.
Set the log fold change across treatment levels, , to be zero for all equivalently expressed genes. In order to build a correlation structure into the differential expression pattern of the simulated genes, we draw the 's of all differentially expressed genes from a multivariate normal distribution with mean 0 and a block-diagonal variance-covariance matrix. Each of the 40 blocks is a 50 50 correlation matrix randomly drawn from a uniform distribution on the space of all possible correlation matrices. The study in this article used the rcorrmatrix() function in the ClusterGeneration R package to calculate the correlation matrices. Please see the reference by Joe [19] for the algorithm behind rcorrmatrix().
Compute the true mean expression level of simulated gene g for treatment levels and using
Randomly draw the pseudo-count of each simulated gene in library from a NB( , ) distribution, where is the treatment group of library .
Each gene in the pseudo-dataset should have at least one read to be included in the following analysis. Hence, if the pseudo-counts of simulated gene are all zero, we keep and redraw and , and then redraw the pseudo-counts as in steps 4 and 5.
Note that our method of choosing true mean and dispersion pairs builds an empirical dispersion-mean relationship into the simulated data. As Figures 1 and 2 show, the pseudo-datasets match the real datasets from which they were generated in terms of the distribution of the log gene-wise geometric mean counts, the distribution of the log dispersions, and the dispersion-mean relationship. In step 3, we simulate correlated differentially expressed genes because we expect some differentially expressed genes are dependent in real dataset. We also simulated datasets with 's from independent standard normal distributions, and the simulation results do not change significantly from what we present later in this article.
Simulation Code S1, the R code used to generate the pseudo-datasets and conduct the analyses, is available as supporting information. The code requires R version 2.15.3 and the R packages listed in Table 1.
Table 1. R Packages Required for the Simulation Study's Implementation.
Simulation settings.
Some pseudo-datasets were generated from the Hammer dataset, while others were generated from the Pickrell dataset. In addition, the number of libraries per treatment group varied from pseudo-dataset to pseudo-dataset. Hence, six "simulation settings", given in Table 2, were used. 30 pseudo-datasets were generated under each simulation setting.
Table 2. Simulation Settings.
Normalization.
Our simulation procedure was configured such that within each pseudo-dataset, the library sizes do not vary systematically. So when analyzing the simulated data, it would be reasonable to set all the library-wise normalization factors, , equal to 1. However, since practitioners use nontrivial normalization methods in the field, we borrowed a sophisticated normalization method: specifically, the one described by Anders and Huber [5]. As shown in unpublished work by Xiong and Liu [20], this popular normalization method performs on par with the ubiquitous 0.75 quantile and TMM methods described in the introduction, sometimes even surpassing these two alternatives. The results presented in this article were obtained using the method by Anders and Huber, and they agree with the results obtained from setting all normalization factors equal to 1.
Anders and Huber's method assigns each library-wise normalization factor, , to where is the total number of libraries. This article uses the implementation in Anders and Huber's DESeq package, which avoids dividing by zero by skipping genes whose geometric means, , are zero.
The methods implemented in edgeR the wqCML and APL dispersion estimation methods and an exact test for differential expression use adjusted library sizes instead of normalization factors. We borrow from the 's computed with DESeq to calculate these adjusted library sizes, where is observed size of library .
With 30 pseudo-datasets generated for each of 6 simulation settings, we apply each dispersion estimation method to each pseudo-dataset, and we use the dispersion estimates to test for the differential expression of genes. We use the true dispersions, the knowledge of which genes are truly differentially expressed, the dispersion estimates, and the test results to compare the dispersion estimation methods. We assess the quality of the methods in terms of the accuracy and precision of point estimation and performance in tests for differential expression.
Point Estimation
The overall quality of any point estimator can be measured in terms of its mean squared error. For each pseudo-dataset and each dispersion estimation method, we calculate the mean squared error of the transformed estimated dispersions,
Here, the 's are the true dispersions, and the 's are estimates computed with one of the methods described previously. The idea of transforming the dispersions by , which improves the robustness of MSE to the presence of outliers, was taken from an article by Robinson and Smyth [11].
Figure 3 displays the MSEs according to dispersion estimation method and simulation setting. The columns correspond to different dispersion estimation methods, and the rows correspond to different simulation settings. There are several types of dispersion shrinkage methods, which are indicated by the labels at the bottom of the figure. The "None" type indicates no shrinkage at all, which means dispersion parameters are estimated for each gene separately. "Common" denotes the methods that give all genes the same estimated dispersion parameter. "Trended" indicates the methods that fit each parameter to a common trend between dispersion and mean expression. "Maximum" refers to the variation of the DESeq method that effectively takes the maximum of the no-shrinkage estimate and the one obtained from the analogous "Trended" option. Lastly, "Tagwise" denotes the methods besides the DESeq "Maximum" method with a moderate level of shrinkage.
Figure 3. Mean squared error of the transformed dispersions.
Wu, Wang, and Wu [7] show a similar figure (their Figure 3) for the Tagwise wqCML method (called "edgeR" in their paper), the DESeq Maximum method, and the DSS method under their two simulation settings. Their figure suggests that under the MSE metric, the DSS method performs better than the Tagwise wqCML method, which in turn performs better than the DESeq Maximum method. Our results from simulation settings II though VI do not contradict this result. However, in simulation setting I, where sample sizes are extremely small, gene-specific mean counts are relatively low, and dispersions are relatively high (see Figure 1), this ranking is reversed.
Overall, the results for the Hammer-generated pseudo-datasets (simulation settings IV-VI) naturally group the dispersion estimation methods into three categories. The first group includes Maximum DESeq and the no-shrinkage DESeq methods. These methods produce the largest MSE, which is not surprising because the Maximum DESeq method is conservative and designed to obtain larger dispersion and because the no-shrinkage DESeq method applies a naive dispersion estimation technique for each gene independently. The next category includes the QL method alone, which performs better than the Maximum and no-shrinkage DESeq methods, but worse than others when the number of libraries is small (simulation setting IV). The other methods all perform similarly and form a group of MSE-best methods. This demonstrates that shrinkage indeed helps improve the point estimators by borrowing information across genes. However, too much shrinkage is detrimental, as the Common methods perform slightly worse than their Trended and Tagwise counterparts.
Parameter estimation is more challenging for the Pickrell-generated pseudo-datasets (simulation settings I–III) than the Hammer-generated pseudo-datasets (simulation settings IV–VI) because the counts are lower, dispersion is larger in general (Figure 1), and dispersion parameters have a wider spread. (Please note that Figure 1 uses a log scale for the horizontal axis.) MSEs are far greater for settings I–III than for settings IV–VI. In simulation setting I, where the number of libraries is small, the Trended APL, Tagwise APL, and Trended DESeq methods form a group of MSE-best methods. When the number of libraries increases, the DSS and Tagwise wqCML methods also perform well. Interestingly, the "Common" methods underperform, but the QL method is relatively good within settings II and III, which have larger sample sizes than simulation setting I. For these extremely varied dispersions (with a spread of observed in Figure 1), shrinking them toward a common value is not as good as estimating them separately. In all cases, the moderate shrinkage methods are never the worst methods and are often among the best ones.
Although useful for determining the overall quality of a point estimator, the MSE heuristic is only a single scalar computed for an entire dataset. It is also important to consider the way that estimation error varies with the magnitude of the true dispersions. Figures 4 and 5 plot the log estimated dispersions on the log true dispersions for several dispersion estimation methods for an example pseudo-dataset within each of simulation settings II and V. Analogous plots for simulation settings I and III are similar to Figure 4, and analogous plots for simulation settings IV and VI are similar to Figure 5.
Figure 4. Simulation setting II: estimated vs true dispersions for an example pseudo-dataset.
Dispersions with gene-wise log geometric mean counts below the median (log mean from 2.17 to 1.63) are shown in black, while those above the median (log mean from 1.63 to 10.6) are shown in light blue. Overlapping points are shown in dark blue. Results for simulation settings I and III are similar.
Figure 5. Simulation setting V: estimated vs true dispersions for an example pseudo-dataset.
Dispersions with gene-wise log geometric mean counts below the median (log mean from −2.17 to 4.49) are shown in black, while those above the median (log mean from 4.49 to 12.3) are shown in light blue. Overlapping points are shown in dark blue. Results for simulations IV and VI are similar.
Wu, Wang, and Wu [7] show a similar figure (their Figure 1) for the Tagwise wqCML method (called "edgeR" in their paper), the DESeq Maximum method, and the DSS method. The patterns in their figure approximately agree with our results for simulation settings I through III. However, for simulations IV through VI, our scatterplots of the DSS-estimated dispersions on the true dispersions show a lower truncation that is not present in the figure by Wu, Wang, and Wu. The DSS method may shrink dispersions particularly aggressively under these conditions.
According to Figures 4 and 5, the methods with the least shrinkage i.e., the QL and no-shrinkage DESeq methods exhibit the widest vertical spread about the identity line, but have patterns most closely resembling this line. Compared with the other methods, estimation error is high for these no-shrinkage methods, but maximally correlated with true dispersion magnitude. On the other hand, both of the trended methods (Trended APL and Trended DESeq) show the lowest vertical spreads, but the greatest systematic departures from the identity line. These trended methods systematically underestimate large true dispersions and systematically overestimate small true dispersions, even placing sharp upper and lower bounds on most of the estimated dispersions. Interestingly, the dispersions estimated with the moderate-shrinkage methods (DSS, Tagwise wqCML, Tagwise APL, and Maximum DESeq) show relatively high agreement with the true dispersions for simulation settings I-III (Pickrell-generated pseudo-data) but much lower agreement with the low true dispersions in simulation settings IV-VI (Hammer-generated pseudo-data). In practice, this reluctance to produce small dispersions may mitigate the false detection of differentially expressed genes.
Figures 4 and 5 show that some methods systematically overestimate low true dispersions and systematically underestimate high true dispersions. This behavior is exactly what we should expect from shrinkage. Intuitively, shrinkage pulls estimates towards some common focal point. As an immediate and important practical consequence for point estimation, low estimates should increase, high estimates should decrease, and the collective scatter of estimates should narrow. The practical consequences of shrinkage for significance testing, however, are not clear from Figures 4 and 5 alone. For real insight into hypothesis testing, we need the results of the next subsection.
Test Performance
Since the detection of differentially expressed genes is the major goal of most RNA-seq experiments, it is vitally important to measure and compare the direct impact of the dispersion estimation methods on the detection of differentially expressed genes, which is why the pseudo-datasets are generated such that each simulated gene is known to be either differentially expressed or equivalently expressed. Using this knowledge and the p-values obtained from the tests for differential expression, a receiver operating characteristic (ROC) curve is constructed for each pseudo-dataset/test for differential expression/dispersion estimation method combination.
An ROC curve is a graph of the true positive rate (TPR) of the detection of differentially expressed genes vs the false positive rate (FPR). In practice, we define FPR and TPR to be functions of the significance level, , of the tests for differential expression. Specifically, where FP( ) and TP( ), respectively, are the numbers of false positive and true positive detections at significance level . Also, EE and DE, respectively, are the true numbers of equivalently expressed and differentially expressed genes. In the pseudo-data, we know that EE is 8000 and DE is 2000. In fact, we know exactly which genes are differentially expressed, so we can calculate FP to be the number of equivalently expressed genes with p-values less than and TP to be the number of differentially expressed genes with p-values less than . Using a fixed range of values (the same for all ROC curves in this study), we calculate multiple points of the form, (FPR( ), TPR( )) and plot them on the x-y plane. To compensate for gaps in the x direction of the graph, we interpolate the points with a step function that lies beneath a simple linear interpolation, the latter of which may artificially inflate the AUC heuristic explained below.
In this study, we use the area under each ROC curve (AUC) as a relative measure of the quality of a test, where a high AUC indicates relatively good test performance. Here, each AUC is computed only for FPR so that testing situations are evaluated only at reasonable significance levels. Please note that AUC not only depends on the quality of a test, but also on the magnitude of differential expression. In Figures 6 through 11, the AUC values are small because in our simulations, the true log fold changes, , were taken from a standard normal distribution. As a result, the magnitude of differential expression was small for a significant fraction of truly differentially expressed genes.
Figure 6. Simulation setting I: areas under ROC curves.
Boxplots of AUC calculated based on 30 pseudo-datasets are shown for each combination of dispersion estimation method and test for differential expression.
Figure 7. Simulation setting II: areas under ROC curves.
Figure 8. Simulation setting III: areas under ROC curves.
Figure 9. Simulation setting IV: areas under ROC curves.
Figure 10. Simulation setting V: areas under ROC curves.
Figure 11. Simulation setting VI: areas under ROC curves.
Figures 6, 7, 8, 9, 10, and 11 show the relationships between AUC and dispersion estimation method for each test setting and simulation setting. We include the results of tests using the true dispersion parameters and use these results as the "gold standard" to evaluate all dispersion parameter estimation methods.
Table 2 of the paper by Yu, Huber, and Vitek [8] suggests that the Tagwise wqCML method (called "edgeR" in their paper) and the DESeq dispersion estimation method perform roughly equally well under the AUC metric. Our results agree, with the exception of the DESeq testing method in simulation settings I through III, where the DESeq dispersion estimation method performs worse than the Tagwise wqCML method.
Overall, the three tests in the QuasiSeq package are less affected by dispersion estimation than the edgeR and DESeq exact tests. These tests introduce gene-wise quasi-likelihood dispersion parameters to the negative binomial model, and the new parameters absorb some of the variability that would otherwise manifest solely in the negative binomial dispersions. The practical upshot is that all three QuasiSeq tests are relatively robust under both noisy data and poor estimation of negative binomial dispersions. The QL test is an extreme case, with little change in its AUC boxplots across the dispersion estimation methods, because it does not apply any special constraints to the quasi-likelihood dispersions. (On the other hand, the QLShrink test shrinks the quasi-likelihood dispersions using a common value, and the QLSpline test shrinks them using a fitted spline.) Unfortunately, the QL test also performs the worst among the five tests overall, making it a poor choice in practice despite its otherwise useful robustness. The QLSpline test is better than the QL test, and the QLShrink test is better still.
The rankings of the dispersion estimation methods are similar among the edgeR exact test, the DESeq exact test, and QLShrink test. Specifically, the DSS, Tagwise wqCML, and Tagwise APL – i.e., the moderate-shrinkage methods – are the best. Not only do these dispersion estimation methods perform well relative to other methods, but they are also extremely close to the true dispersions in terms of AUC. Methods with an extremely large or extremely small degree of dispersion shrinkage – i.e., the Trended, Common, and "None" modes of dispersion shrinkage – are subject to relatively poor performance in at least one of the simulation settings. Interestingly, the Maximum DESeq method also performs well in terms of AUC in several cases, although it does poorly with respect to MSE. This shows that accurate and precise estimation of dispersions and optimal test performance do not always go together.
Across all six simulation settings, when combined with moderate-shrinkage methods for dispersion, the best tests for differential expression are the edgeR exact test, the DESeq exact test, and the QLShrink test. These methods for testing perform roughly equally well, and they are better than other combinations of tests and dispersion estimation methods. In some cases, this difference in AUC between the two tiers of tests is dramatic. With the addition of the gene-wise quasi-likelihood dispersion parameter to the negative binomial model, the QLShrink test is more robust to changes in dispersion estimation method than the either of the exact tests, which is most noticeable for the Pickrell-generated pseudo-datasets (simulation settings I-III). In practice, we recommend using the QLShrink test because we expect the addition of quasi-likelihood dispersion parameters to make the QLShrink test more flexible than the edgeR and DESeq exact tests under departures from the negative binomial model.
It is challenging to estimate negative binomial dispersions from RNA-seq data due to the "small , large " problem. Methods that borrow information across genes are better than methods that estimate parameters independently for each gene. If we assume that the dispersion parameters are the same for all genes, then we can use the entire dataset to compute a precise estimate of a shared dispersion parameter. However, assuming a common dispersion for all genes is too unrealistic in practice. For example, the dispersion estimates for the the Pickrell dataset computed with the QL method range from about to about 8.35. We expected the optimal dispersion estimation methods to instead use a moderate degree of shrinkage: that is, to "borrow information" across genes, using the whole dataset to compute a common value, trend, or prior distribution for the dispersions, and then shrink individual gene-wise dispersion estimates toward this chosen anchor. Indeed, our simulation results show that the moderate-shrinkage methods – the DSS, Tagwise wqCML, and Tagwise APL methods – perform relatively well in terms of MSE and optimally in terms of the performance of tests for differential expression. Although DSS shrinks the dispersions towards a common prior, Tagwise wqCML shrinks them towards a common value, and Tagwise APL uses neighboring genes on a common trend for shrinkage, these three optimal methods perform roughly equally well in our simulations. Thus, the degree of dispersion shrinkage is more important than how this shrinkage is achieved.
These moderate shrinkage methods outperform the others in all five featured tests for differential expression. However, these tests do not perform equally well. The edgeR exact test, DESeq exact test, and QLShrink test outperform the other two. Furthermore, the performances of the edgeR and DESeq tests depend highly on the dispersion estimation method chosen, while the addition of a gene-wise quasi-likelihood dispersion parameter gives the QLShrink test extra robustness under the choice of dispersion estimation method. We expect this same flexibility to help the QLShrink test perform especially well under departures from the negative binomial model, so we recommend using the QLShrink method with either the DSS, Tagwise wqCML, or Tagwise APL method in practice.
Interestingly, the ranking of dispersion estimation methods according to MSE is not the same as the ranking according to AUC. A notable example is the Maximum DESeq method, which performs poorly in terms of MSE, but often performs extremely well in tests for differential expression. This behavior may result from the intentional overestimation of the dispersions, which contributes to a high MSE, but guards against false positives. In addition, methods with similar MSE may have very different AUC. For example, the Trended APL and Tagwise APL methods yield similar MSEs in simulation setting V, but the Tagwise APL method performs much better than the Trended APL method in the edgeR test (Figure 10). We do not have an explanation for this behavior, but we advise practitioners to think about point estimation and test performance separately when estimating negative binomial dispersions.
Simulation Code S1.
R code used to generate the pseudo-datasets and conduct the analyses. The software requirements can be found in Table 1.
We would like to thank Drs. Dan Nettleton, Dianne Cook, and Yaqing Si for their useful feedback. We would also like to thank Dr. Gordon Smyth of the Walter and Eliza Hall Institute in Australia for answering our questions.
Conceived and designed the experiments: WML PL. Performed the experiments: WML. Analyzed the data: WML PL. Contributed reagents/materials/analysis tools: WML. Wrote the paper: WML PL.
1. Oshlack A, Robinson MD, Young MD (2010) From rna-seq reads to differential expression results. Genome Biology 11.
2. Wang L, Li P, Brutnell TP (2010) Exploring plant transcriptomes using ultra high-throughput sequencing. Briefings in Functional Genomics 9: 118–128.
3. Cameron AC, Trivedi PK (1998) Regression Analysis of Count Data. Cambridge University Press.
4. Si Y, Liu P (2012) An optimal test with maximum average power while controlling fdr with application to rna-seq data. Biometrics 69: 594–605.
5. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biology 11.
6. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of rna-seq data. Genome Biology 11: 1275–1282.
7. Wu H, Wang C, Wu Z (2012) A new shrinkage estimator for dispersion improves differential expression detection in rna-seq data. Biostatistics 1: 1–24.
8. Yu D, Huber W, Vitek O (2013) Shrinkage estimation of dispersion in negative binomial models for rna-seq experiments with small sample size. Bioinformatics 29: 1275–1282.
9. Si Y (2012). Package 'amap.seq'. http://cran.r-project.org/web/packages/AMAP.Seq/AMAP.Seq.pdf.
10. Robinson MD, Smyth GK (2008) Small-sample estimation of negative binomial dispersion, with applications to sage data. Biostatistics 9: 321–332.
11. Robinson MD, Smyth GK (2007) Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23: 2881–2887.
12. Robinson MD, McCarthy DJ, Smyth GK (2009) edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139–140.
13. Robinson MD, McCarthy DJ, Chen Y, Smyth GK (2012). Package 'edger'. http://www.bioconductor.org/packages/2.10/bioc/manuals/edgeR/man/edgeR.pdf.
14. McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor rna-seq experiments with respect to biological variation. Nucleic Acids Research 40: 4288–97.
15. Lund SP, Nettleton D, McCarthy DJ, Smyth GK (2012) Detecting di_erential expression in rna-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology 11.
16. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, et al. (2010) Understanding mechanisms underlying human gene expression variation with rna sequencing. Nature 464: 768–72.
17. Hammer P, Banck MS, Amberg R, Wang C, Petznick G, et al. (2010) mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain. Genome Research 20: 847–60.
18. Langmead B, Frazee A (2012). Recount: A multi-experiment resource of analysis-ready rna-seq gene count datasets. http://bowtie-bio.sourceforge.net/recount/.
19. Joe H (2006) Generating Random Correlation Matrices Based on Partial Correlations. Journal of Multivariate Analysis 97: 2177–2189.
20. Xiong Y, Liu P (2012) Evaluation of normalization methods for differential expression analysis in RNA-seq experiments. Master of Science creative component, Iowa State University.
Simulation and modeling
Is the Subject Area "Simulation and modeling" applicable to this article?
Is the Subject Area "Gene expression" applicable to this article?
Binomials
Is the Subject Area "Binomials" applicable to this article?
Probability distribution
Is the Subject Area "Probability distribution" applicable to this article?
Is the Subject Area "cDNA libraries" applicable to this article?
Genomic libraries
Is the Subject Area "Genomic libraries" applicable to this article?
RNA analysis
Is the Subject Area "RNA analysis" applicable to this article?


text:
Greenwich Democrats endorse full slate of local candidates
Connecticut's All-America boys water polo players
Debate heats up over air rights with new Greenwich train station
Plan to replace bleachers at Cardinal Stadium hits an obstacle
Clay Art Center exhibit explores animals as metaphor for human
Bacon Brothers playing Greenwich benefit for Fairfield County's
ASOG Open Juried Show winners named
David Rafferty: The pretend American patriot
Sudell defends title at Joe Felder PGA Junior Championship
Greenwich gymnastics team takes fifth at New England
Brunswick's Riera wins State Open skiing title
After championship showing at NECA meet, GHS cheerleaders aiming
Interactive Brokers report lower returns
Greenwich-based Eldridge leads seven-figure e-sports investment
Outdoor bazaar returns to Greenwich Ave.
IRS data tracks drop in income tax liability
https://www.greenwichtime.com/news/article/Flash-flood-watch-posted-for-Fairfield-County-994.php
Flash flood watch posted for Fairfield County
Published 6:14 pm EDT, Monday, August 31, 2009
The National Weather Service has posted a flash flood watch and a hazardous weather outlook for this afternoon and tonight.
Thunderstorms, some of them severe, will rumble across western and southern Connecticut today and tonight, cause by a passing warm front, accompanied by a low-pressure disturbance.
The flash flood watch €" for all of Fairfield County €" will remain in effect through Saturday morning.
High tide today will occur at 8:06 p.m. in Bridgeport harbor, and at 8:10 p.m. in Stamford and Greenwich harbors.
Sunset today will occur at 8:10 p.m. There will be 14 hours and 23 minutes of sunlight today. Moonrise will occur at 4:38 p.m.
Today, there will be a slight chance of showers before 9 a.m., then occasional showers and possibly a thunderstorm between 9 a.m. and noon, then occasional showers and thunderstorms after noon. Some of the storms could produce gusty winds and heavy rain. High near 79. South wind between 6 and 11 mph. Chance of precipitation is 90 percent. New rainfall amounts between a half and three quarters of an inch possible.
Tonight: Occasional showers and thunderstorms, mainly before 3 a.m. Some of the storms could produce gusty winds and heavy rain. Low around 70. North wind around 6 mph. Chance of precipitation is 80 percent. New rainfall amounts between three quarters and one inch possible.
Saturday: Mostly sunny, with a high near 84. North wind 6 to 8 mph becoming south.
Saturday night: Mostly clear, with a low around 69. South wind between 3 and 7 mph.
Sunday: A chance of showers, with thunderstorms also possible after noon. Mostly cloudy, with a high near 78. South wind between 8 and 10 mph. Chance of precipitation is 40 percent.
Sunday night: A chance of showers and thunderstorms before midnight, then a slight chance of showers between midnight and 3 a.m. Mostly cloudy, with a low around 72. Chance of precipitation is 40 percent.
Monday: Partly sunny, with a high near 85.
Monday night: Partly cloudy, with a low around 70.
Tuesday: A chance of showers and thunderstorms. Mostly cloudy, with a high near 84. Chance of precipitation is 30 percent.
Tuesday night: A chance of showers and thunderstorms. Mostly cloudy, with a low around 69. Chance of precipitation is 30 percent.
Wednesday: Partly sunny, with a high near 81.
Wednesday night: Mostly cloudy, with a low around 65.
Thursday: Mostly sunny and a little cooler, with a high near 78. ۩
Cos Cob man charged in Belle Haven assault
Man charged with sex assault of minor rearrested in Greenwich
Greenwich resident Stephen Song wins World Series of Poker bracelet
Before heat wave, T-storms and showers moving in


text:
Home Breaking: Scott Mozarsky Named President of Bloomberg BNA's Legal Division
Breaking: Scott Mozarsky Named President of Bloomberg BNA's Legal Division
By Robert Ambrogi on November 1, 2016
I've just learned that Bloomberg BNA has named Scott Mozarsky as president of its Legal Division. He will report to Bloomberg BNA CEO and President Greg McCaffery and will be responsible for driving the growth of the company's legal products, including the flagship Bloomberg Law product.
Mozarsky replaces David Perla, who has reportedly decided to depart Bloomberg BNA in order to spend more time with his family at their home in the New York area.
See also: New President Mozarsky Discusses His Plans for Bloomberg's Legal Division.
Mozarsky, an attorney, joined Bloomberg BNA in 2014 and was the driving force behind the creation of its growing Cross Platform Division, which leverages content and market expertise to deliver a range of marketing and live event services to the company's clients.
Prior to joining the company, Mozarsky held a number of leadership roles during a 13-year tenure at UBM plc, including president of UBM Tech and chief commercial officer of PR Newswire.
He practiced law for seven years in private practice and served as general counsel of UBM. Mozarsky holds a B.A. from Williams College and a J.D. from Fordham University School of Law.


Step one: Identify a case and write a paper describing the case's focus.
Identifying a court case from SupremeCourtUS.com at http//www.supremecourtus.com/.
Determine whether the case supports judicial activism or judicial restraint. Write a paper supporting your thoughts . Be sure to provide three (3) supporting facts from the case.
Pick one (1) interest group whose agenda you like and one (1) you disagree with to research. Start by finding the homepage for each interest group on the internet. Conduct further web-based research to see what news sources have reported about the two interest groups, what some of their recent activities have been, which politicians they oppose, and so on.


Both positive and negative local effects of extraction are likely, so both governments and extraction companies should work towards minimising negative effects and/or compensating local households. Doing so effectively requires a thorough account of environmental and social effects, before extraction begins. It requires pro-active leadership by government and the engagement of all stakeholders. For companies, it is also in their own interest to be aware of their corporate social responsibility, because it will generate more local support for mining activities and fewer protests and delays. For governments, it is in their interest to care for the long-term development of extraction regions because deposits will eventually be depleted.
Government should identify the likely winners and losers from extraction activity and formulate a policy that balances the interests of the nation versus those of the directly affected local population. Moreover, it should consider postponing extraction if the environmental costs are too great under the current level of extraction technology, or if the local social costs are disproportionally borne by disadvantaged groups, such as when extraction takes place in poor areas. This requires the design of cost-benefit analyses, in which both direct impact and indirect impacts can be quantified. Direct impacts include infrastructure such as ports, pipelines, pumping stations, roads and railways, as well as on-site facilities, local labour market and environmental effects and risks. Indirect impacts include those that cross borders, such as air pollution, and those that compound over time, such as degradation of soils and biodiversity. Quantifying these is not easy but improved methodologies have become available.
Implementation of project decisions requires a suitable level of government capacity at all levels in terms of the number and quality of civil servants. This is required to ensure that agreements can be monitored and enforced, and corruption and waste can be avoided. Monitoring should involve all stakeholders to ensure it is independent and credible and should be written into law. At the same time, extraction contracts should be signed with companies that can demonstrate their ability to assess and mitigate the environmental and social impacts of extraction.
A paper from the Natural Resource Governance Institute (NRGI), that examines the challenges of generating lasting benefits from resource revenues (also available in the Guides section of this topic).
An analytical framework, developed by the International Council on Mining and Metals (ICMM), offering a practical toolkit to assess the impacts of mining at a project level (also available in the Library section of this topic).
A position paper prepared by the Collaborative Africa Budget Reform Initiative (CABRI), drawing on deliberations at the Accra Policy Dialogue and two keynote papers that were prepared to facilitate the dialogue.
In addition, case studies summarised in section 10 of this topic overview examine a wide range of practice and highlight key learnings from each case.