text:
Jessica L. Van Dalen | Professionals | Faegre Baker Daniels
http://www.faegrebd.com/-/media/images/professionals/v/jessicavandalen.jpg
Jessica L. Van Dalen
jessica.vandalen@FaegreBD.com
D +1 317 237 1054
Jessica Van Dalen works with clients to prepare and prosecute patent applications. Jessica practices before the U.S. Patent and Trademark Office as well as government entities in Japan, Europe, Canada, Mexico, China and Australia. She collaborates with clients on licensing agreements, portfolio analysis, opinion work related to infringement and patent invalidity and post-grant proceedings. A former engineer, Jessica has particular expertise in mechanical, material and chemical arts technology.
Past Experience
During law school, Jessica worked as a summer associate at Faegre Baker Daniels and summer clerk at an Indianapolis firm.
Before law school, Jessica served as a co-op engineer with Raybestos Products Co., gaining experience in heavy-duty friction materials used for automotive and agricultural vehicle applications. She also worked as an engineering research assistant at Purdue University, focusing on adhesive formulation and testing.
Indiana University Maurer School of Law Intellectual Property Clinic — Adjunct Faculty Member
Intellectual Property Litigation & Dispute Resolution Patent
U.S. District Court for the Northern District of Indiana
U.S. District Court for the Southern District of Indiana
Indiana University Maurer School of Law
J.D. Indiana Journal of Global Legal Studies (executive business editor) (2010)
B.S. in Materials Science & Engineering (2007)
Other Perspectives
Updates September 2017
Ready for Takeoff? How to Launch Your Commercial Drone
Media Mentions September 2015
Jessica Van Dalen Connects With Inventor Via New Maurer School Website
New Pro Bono Opportunities for Indiana Patent Lawyers
Panelist, Indiana University Maurer School of Law Center for Intellectual Property Research, September 2015
Leadership & Community
Civic Activities
Indiana University Maurer School of Law IP Clinic — Advisory Board
Old Northside Association — Board of Directors, 2015-present
Old Northside Foundation — Board of Directors, 2015-16
Indianapolis Public School 39 — Volunteer
Society of Women Engineers — Member; Assist with outreach events for middle school girls throughout Indiana
YMCA of Greater of Indianapolis — Steering Committee, 2015-present
Indiana Super Lawyers — Rising Stars, Intellectual Property, 2019
School of Materials Science Engineering, Purdue University — Outstanding Materials Engineer, 2018
Indiana Bar Foundation — Pro Bono Publico, 2017
Faegre Baker Daniels — Pro Bono Honor Roll, 2014-17
Indiana University School of Law — Women's Law Caucus, president
Purdue University — College of Engineering Outstanding Senior for Materials Sciences and Engineering


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I live in Southern Cali, on Topanga Canyon Blvd in Topanga, and am considering constructing a 6 ft, by 80+ft sound barrier, preferably out of transparent material.
Wondering if anyone can pass along any names/companies that specialize in this, as well as an experiences.
Suggest you call Granahan Construction Co. at: 949 280-6220. They have completed various soundproofing jobs throughout Southern California.
Contact QUILITE International in LA.
They offer a see through PC wall system that might meet your needs.
Does anyone know of any sound contractors in New Mexico? I have searched but haven't come up with anything. I hate to waste money on something that won't work and would like to get some professional advice.


6: how many official complaints were made regarding the use of the CS spray and what were the outcomes of those complaints?
I am writing to inform you that we have searched our incident logs regarding protests and none mention the use of CS gas by police. We have also spoken to our Public Order training liaison who also holds no relevant data.


text:
Richard Thomas Church CBE (26 March 1893 – 4 March 1972) was an English writer, poet and critic; he also wrote novels and verse plays, and three volumes of autobiography.

Early life
Church was born on 26 March 1893 in Battersea, in south-east London. He went to Dulwich Hamlet School in Dulwich. The second son of Thomas John Church and Lavina Annie Orton Church. His mother was distantly related to the novelist  George Eliot but kept quiet about this because of her bohemian lifestyle. His father was a sorter for the General Post Office and his mother was a schoolteacher who suffered ill-health and died in 1910 when he was only seventeen. After leaving school at sixteen, he started work as a clerk in the Customs and Excise branch of the Civil Service. In his first volume of autobiography he recounts the physicality of his father, the intelligence of his mother, his resourceful older brother, privations, and the difficult relationship of his ill-matched parents.

His first book of poems, The Flood of Life,  was published in 1917 when he was 24, but he remained in the Civil Service until 1933, when he left to write full-time at the age of 40.

Career
Church became a respected journalist and reviewer, and wrote extensively on country matters. His first poetry appeared in Robert Blatchford's Clarion, and he contributed verse to periodicals for the rest of his life.

His first post as a literary editor was with the New Leader, organ of the Independent Labour Party. He was director of the Oxford Festival of Spoken Poetry during the 1930s. His much-anthologised World War I poem "Mud" first appeared in Life and Letters, January 1935.

The first volume of Church's autobiography, Over the Bridge (1955), was awarded the Sunday Times Prize for Literature while the novelist Howard Spring described it as "the loveliest autobiography written in our time," pointing out that the writer had "found life full of enchantment, and how not the least of its enchantments was its challenge." The second volume, The Golden Sovereign, appeared in 1957. That year Church was named a Commander of the Order of the British Empire by Queen Elizabeth II.

Mystical experience
While young, Church had a mystical experience at a convalescent home, which he recounted in his autobiography, Over the Bridge, and which was also recounted by the British occultist writer Colin Wilson. Looking out of some French windows, Church saw a gardener chopping down a dead tree. What struck Church after a while was that the sight of the axe hitting the tree and the sound of the axe hitting the tree were not synchronised. The sound was delayed. At first he did not believe his own powers of perception, but after concentrating his vision and hearing, he came to the conclusion that he was experiencing an error in the laws of physics. He came to the conclusion – which would remain with him for the rest of his life – that "time and space are not absolute. Their power was not law."  He experienced an incredible freedom in this epiphany. "(...) I was free. Since time and space were deceivers, openly contradicting each other, and at best offering a compromise in place of law"

After this epiphany another soon followed. From where he stood he sensed that "(...) my limbs and trunk were lighter than they seemed, and that I had only to reduce them by an act of will, perhaps by a mere change of physical mechanics, to command them off the ground, out of the tyranny of gravitation". He then left the ground and glided "about the room" some twelve or eighteen inches above the floor. He returned to the ground only to take off once more.

Personal life
Church married three times: firstly to  Caroline Parfett in 1915, with whom he had three daughters,  this marriage ending in divorce. He married his second wife, Catherina Schimmer on 19 November 1930, and the couple had a son before her death in 1965. He married his third wife, Dorothy Beale, a widow, on 25 February 1967.

He had a great love for the Kent countryside and this is reflected in much of his writing. He published an anthology of works on Kent.

Death
He and Dorothy initially lived at The Old Stable, they subsequently moved to The Priest's House at Sissinghurst Castle in Cranbrook where he died suddenly, aged 78, on 4 March 1972.

Works

Poetry collections
The Flood of Life (1917)
Hurricane (1919)
Philip (1923)
The Portrait of the Abbot (1926)
The Dream (1927)
Theme with Variations (1928)
Mood without Measure (1928)
Mary Shelley (1928)
The Glance Backward (1930)
News from the Mountain (1932)
Apple of Concord (1935)
Twelve Noon (1936)
The Solitary Man (1941)
Twentieth-Century Psalter (1943)
The Lamp (1946)
Collected Poems (1948)
Selected Lyrical Poems (1951)
The Inheritors (1957)
North of Rome (1960)
The Burning Bush (1967)

Novels
Oliver's Daughter (1930)
High Summer (1931)
The Prodigal Father (1933)
The Porch (1937)
The Stronghold (1939)
The Sampler (1942)
The Cave (1951) AKA Five Boys in a Cave.
Dog Toby. A Frontier Tale (1953)
 The dangerous years (1956)
 The Nightingale (1958)
The Crab-Apple Tree (1959)
Prince Albert (1963)
The Room Within (1940)
The White Doe (1968)
Little Miss Moffatt: a confession (1969)
The French lieutenant: a ghost story (1971)

Autobiography
Over the Bridge (1955)
The Golden Sovereign (1957)
The Voyage Home (1964)

Other books
Calling for a Spade (1939) Essays on country themes.
 Plato's Mistake (1941)
 , essays on contemporary writers
A squirrel called Rufus (1941) For children.
 Green Tide (1945) Essays, mainly on country themes.
 British authors : a twentieth-century gallery with 53 portraits (1948)
A window on a hill [1951]. Essays, mainly on country themes
Books and Writers (Robert Lynd) Foreword by Richard Church (1952)
The prodigal: a play in verse (1953)
 Down River (1957) For children.
 A country window; a round of essays (1958)
 Small moments. Decorated with wood-engravings by Joan Hassall (1957) Essays.
 The bells of Rye. Front. by Michael Hubbard (1960) For children.
 Calm October, essays (1961)
 The growth of the English novel (1961)
 A stroll before dark : essays (1965)
 The royal parks of London. With drawings by Victor Cooley (1965)
 Portrait of Canterbury (1968)
 Speaking aloud (1968)
 The wonder of words (1970)
A harvest of mushrooms: and other sporadic essays (1970)

Notes

External links

 Richard Church Papers at the John Rylands Library
 Richard Church Collection at the Harry Ransom Center
 Richard Church bibliography

People from Dulwich
1893 births
1972 deaths
English male poets
20th-century English poets
20th-century English male writers
English World War I poets
Presidents of the English Centre of PEN

redpajama_set_name: RedPajamaWikipedia


An early entrant in the distinguished "crazy Vietnam veteran" genre, "Running Gun Blues" features an ex-soldier turned serial killer. Bowie sounds unhinged in the verses, tries for menace in the choruses, going on about cracking the heads of "gooks." It's satire fit for (and seemingly written by) a squalid 13-year-old boy.
Mick Ronson offers amends—beefing up the D-C-G riff that Bowie first offers on his acoustic, locking in with Tony Visconti's bass to ride out the track. It's no use, as the track's nothing but cheap, loud burlesque with "social commentary" pretensions. Angela Bowie recalled that her husband wrote the lyrics to "Running Gun Blues" over an afternoon when he kept being interrupted to do interviews, and it shows ("for I promote oblivion/and I'll plug a few civilians"). As rancid as it is forgettable.
Recorded 18 April-22 May 1970; never performed again by Bowie, or anyone else.
Top: Lt. William Calley goes on trial, November 1970.


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How do I install Node.js application on my hosting?
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text:
Q: Very simple question about logical operator in C lang I have a question about C lang. There are logical operators in C like & and  |.
I used it in this code:
#include <stdio.h>
int main()
{
    printf("%d",(1>2)||(2==2);
    printf("%d",(1>2)|(2==2);
}

I know why the printed value is 1,  but I don't know why the compilers didn't get errored.
Are there no problems that the number of logical operators is one or two?
(eg: ||, | get the same value)
Are there any rules about the number of logical operators that use in print functions?

A: You did not specify the error the compiler raises but each printf command is missing an enclosing bracket:
#include <stdio.h>
int main()
{
    printf("%d",(1>2)||(2==2));
    printf("%d",(1>2)|(2==2));
}

A: In C, x | y performs an individual logical "or" operation on each bit of x with the corresponding bit of y, while x || y performs a logical "or" operation on the whole value: the result is zero if both values are zero and one if either x or y is non-zero.
For example, if the bits of x are 0110 0111 and the bits of y are 0011 0001, then the bits of x | y are 0111 0111, and x || y is 1.
In your code, 1>2 produces the value 0, and 2==2 produces the value 1. For these values, 0 | 1 produces 1 (because the bits are 00…00 ORed with 00…01, which produces 00…01, which is 1) and 0 || 1 produces 1 (because one of the operands is non-zero, so 0 || 1 produces 1).
This is unrelated to whether they are used in a printf call or elsewhere.

redpajama_set_name: RedPajamaStackExchange


text:
Light::Light(LightType _type)
{
	type = _type;
}

Light::~Light()
{
}

void Light::SetType(LightType _type)
{
	type = _type;
}

Light::Light()
{

}

void Light::AddToManager()
{
	//const Transform* transform = GetGameObject()->GetTransform();
	//m_World2Local = transform->GetWorldToLocalMatrixNoScale();
	//m_WorldPosition = transform->GetPosition();
	GetLightManager().AddLight(this);

}

void Light::RemoveFromManager()
{
	GetLightManager().RemoveLight(this);
}

redpajama_set_name: RedPajamaGithub


With its highly experienced team in the sector, PDS Makina's automation group continuously applies the innovations of current technology and cooperates with the leading companies in the sector in terms of quality and performance. Thus, it is assertive in machine manufacturing.
Our company applies the automation solutions for production lines within the scope of turn-key manufacturing and carries out the technological improvements and revisions of operating production lines on-site.


text:
Alten Road Cottage
Kinder House
Civic Trust Auckland
Special Anniversary Heritage Tour of the former 1YA Building
Saturday | June 1, 2019
Gus Fisher Gallery, 74 Shortland St, Auckland Central.
The Gus Fisher Gallery was originally the premises of New Zealand's first ever television broadcast in 1960. Join us on the anniversary of this broadcast for a special tour of the building's heritage.
This is a free event – see the Gus Fisher Gallery website here:
Nestled in the heart of Auckland city centre's CBD, Gus Fisher Gallery is a contemporary arts centre that showcases local and international art through boldly curated shows. Housed in a 1934 Grade I listed heritage building that was formerly home to Radio 1YA and TVNZ, the gallery's programme will reflect the pioneering spirit of its building's history through a vibrant artistic programme that features contemporary art with a strength in artist's film and video from 2019.
Operating on the fourth floor of the Kenneth Myers Centre, heritage features such as a neo-Romanesque façade and Art Deco interiors enable a striking contrast to the contemporary art exhibitions, as well as opportunities for artists to create site-specific responses.
The gallery temporarily closed for essential refurbishments in late 2018, and re-opened on 5 April 2019 with a brand new exhibitions and events programme that extends beyond the gallery's walls with a dynamic public programme for Auckland's audiences.
Register for Event: Special Anniversary Heritage Tour of the former 1YA Building
© 2020 Civic Trust Auckland. All Rights Reserved

redpajama_set_name: RedPajamaCommonCrawl


text:
Banny () is a rural locality (a khutor) in Lychakskoye Rural Settlement, Frolovsky District, Volgograd Oblast, Russia. The population was 68 as of 2010.

Geography
Banny is located in center of Frolovsky District, 23 km northeast of Prigorodny (the district's administrative centre) by road. Blagodatny is the nearest rural locality.

References

Rural localities in Frolovsky District


text:
Jack's Angels Foundation
Awareness, Support and Research for DIPG
About DIPG
CCTalkRadio
Jack's Story
Hearts for Jack
3rd Annual Festival 2015
Angel Awards 2016
RELAY for RESEARCH
Skip for Jack!
Art for Jack 2015 Performing Artists
Jack's (very) BIG Music Benefit 11/24/13
Art for Jack
Project 'Angel Box'
Shop with iGive
Home Call to Action Relay for Jack: Local Communities come together in support of National DIPG Awareness Week
Relay for Jack: Local Communities come together in support of National DIPG Awareness Week
jackangel 24 May, 2016 30 May, 2016 Call to Action, editorial, Newsflash, Press Release
May 24, 2016, Santa Clarita, CA—HRes586 was introduced to the House of Representatives Energy and Commerce Committee on January 13, 2016, by Congressman Steve Knight (R-CA-25) the day after the Moonshot Initiative was introduced as part of the State of the Union Ceremonies January 12, 2016. HRes586 designates the fourth week of May, which is Brain Tumor Awareness Month, as National DIPG Awareness Week in honor of the thousands of mostly very young children who have faced, and continue to face death bravely and with uncompromising certainty as the result of this devastating disease. Diffuse intrinsic pontine glioma, or "DIPG", is not new to the medical community, but represents the limitations of our current science in the area of neurology, the brain and central nervous system, and in pediatric oncology, childhood cancer. DIPG is perhaps the most aggressive cancer and most evasive of known treatment. Unfortunately, treatment protocol and prognosis for the disease has changed little in the 50 years since Neil Armstrong's daughter died of it in 1962. With uncanny regularity, approximately 350 new cases present themselves each year in the United States alone, and with the same regularity they perish with a median survival time of 9 months with treatment.
Local activist for children with brain cancer Janet Demeter, CEO of Jack's Angels Foundation for DIPG Awareness and Research, took on the mantle of fighting for these children diagnosed with DIPG and their families after losing her son Jack to DIPG in July of 2012. "It was the darkest experience of my life," says Janet when referring to the diagnosis of her son in October of 2011, "All of the sudden it was as if we were on another planet. When we asked why there were no therapies developed for the disease, the answer was the same no matter where we turned, 'the numbers aren't great enough for investors.' We felt as freakish as if we'd been struck by lightning, and this was somehow the justification for the lack of research activity for children like my son. I didn't realize there were six other children being treated at that hospital for DIPG that year."
After Jack died, Janet came to realize the fact that DIPG is not a "rare brain tumor", but the 2nd most common pediatric brain tumor, the leading cause of cancer-related death in children. "When I learned that DIPG is responsible for roughly 80% of pediatric brain tumor deaths annually I was furious. That families are expected to just accept that their child's life is worthless to research investors is the motivation for my activism for these kids and their families. HRes586, a simple Awareness Resolution, does something very powerful. It asks for elevated consideration for low-survival rate cancers—urgency, and years of life lost—children, in the research grant process with NCI. Currently, DIPG is like death-row for children, and no matter how much we scream that there's a house burning down, no one seems to care for fear of offending a bigger building."
Jack's Angels and other DIPG foundations have planned their main events to coincide with National DIPG Awareness Week. "Relay for Jack!", scheduled for this Saturday, May 28 2016, boasts all-day family activities and Relay at Vasquez Rocks County Park in Agua Dulce, CA, bringing together the communities of Santa Clarita, Agua Dulce, and Acton in support of National DIPG Awareness. Congressman Steve Knight, Assemblyman Scott Wilk, and candidate for 2017 California Assembly Dante Acosta will all be in attendance for Jack's Angels 2016 Angel Awards, honoring those who have made Jack's Angels mission possible. Assemblyman Wilk received the 2014-15 "Man of the Year" Award for his contribution to DIPG Awareness in 2014 with ACR151, the precedent-setting "DIPG Awareness Week" in California also designated as the 4th week of May. This year, Demeter has asked the Assemblyman to assist in presenting the same award for 2016 to Congressman Steve Knight for introducing HRes586. Other honorees include Sandra Beck, local media specialist, Lillian Smith of the Country Journal, and Vasquez High School ASB for their annual Powderpuff Football Game, all having contributed consistently in an essential way to Jack's Angels mission.
"Relay for Jack!" supports a pre-clinical research project for DIPG called "Jack's 12 Pebbles" at the Children's Cancer Therapy Development Institute, a new non-profit biotech company devoted to eradicating childhood cancer in all its forms. The Relay includes a 2 ½ mile course, a ½ mile course, and children's course. Activist runner for children with cancer Everett Smith from Redlands, CA, who joined Demeter in the 200 mi "Run for HRes586" in April, has pledged to run 40 mi for the running event. Information about "Jack's 12 Pebbles" research project and other event information including can be found at www.relayforjack.org. For more information about HRes586, visit http://hres586.com.
Jack's Angels Foundation
32520 Wagon Wheel Rd.
Agua Dulce CA 91390
Jacksangels1@gmail.com
www.jacksangels.org
2016 Angel Awards, 4th week of May, Acton CA, Agua Dulce CA, Assemblyman Scott Wilk, California's 25th Congressional District, CC-TDI, Chad and Jack's DIPG Resolution, childhood cancer, childhood cancer awareness, childhood cancer talk radio, Children's Cancer Therapy Development Institute, Congressman Steve Knight, Dante Acosta, diffuse intrinsic pontine glioma, DIPG, dipg research, Everett Smith, HRes586, hres586.com, Jack, Jack's 12 Pebbles, Jack's Angels Foundation, Janet Demeter, Lillian Smith, Mayor Pro-tem Santa Clarita Dante Acosta, Military Mom Talk Radio, National DIPG Awareness Week, Neil Armstrong, neurology, pediatric neuro-oncology, pediatric oncology, Powderpuff Football Game, Relay for Jack supports DIPG research, Relay for Jack!, Sandra Beck, Santa Clarita CA, terminal prognosis, Terry Sonntag, the Country Journal, Vanessa Wilk, Vasquez High School, Vasquez High School ASB, Vasquez Rocks County Park. Bookmark.
CCTR Presents Kathy Riley and We Can Pediatric Brain Tumor Network
CCTR Interviews Morgan Platt, 12 yrs. old, 5-year survivor of GBM
DIPG WON'T WAIT.
https://jacksangelsfoundation.com/wp-content/uploads/2020/12/JaceW_12-3-2020.mp4
Arms Wide Open Childhood Cancer Foundation, with Mike Gillette!
2020 Ends on a High Note with Hope on the Horizon
Letter to Speaker Pelosi, Dec. 22, 2020
AmazonSmile makes it Easy!
Jack's Angels is fully volunteer-powered and relies solely on donations--please help! ALL amounts are helpful.
Jack's Angels Facebook page
Regional Cancer Moonshot Summit Report – Jack's Angels Foundation on 'Champion of Change in Precision Medicine' Amanda Haddock, President of DragonMaster Foundation
LETTERS for DIPG Awareness
Organizational Report
PR-outreach
Promote publication
Response Invitation
Shared Articles
Contact Jack's Angels
Jack's Angels Foundation, 32520 Wagon Wheel Rd., Santa Clarita, CA 91390; 661-977-3125 office, 818-400-2724 mobile; email: jacksangels1@gmail.com
Jack's Angels Inc is a 501(c)3 Charitable Organization, #46-1320003


text:
Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism
Sebastien Levy1, 2, 3,
Marlena Duda1, 2,
Nick Haber1, 2 and
Dennis P. Wall1, 2Email author
Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by ADOS can achieve clinically acceptable levels of accuracy. Here we expand on this initial work to build sparse models that have higher potential to generalize to the clinical population.
We assembled a collection of score sheets for two ADOS modules, one for children with phrased speech (Module 2; 1319 ASD cases, 70 controls) and the other for children with verbal fluency (Module 3; 2870 ASD cases, 273 controls). We used sparsity/parsimony enforcing regularization techniques in a nested cross validation grid search to select features for 17 unique supervised learning models, encoding missing values as additional indicator features. We augmented our feature sets with gender and age to train minimal and interpretable classifiers capable of robust detection of ASD from non-ASD.
By applying 17 unique supervised learning methods across 5 classification families tuned for sparse use of features and to be within 1 standard error of the optimal model, we find reduced sets of 10 and 5 features used in a majority of models. We tested the performance of the most interpretable of these sparse models, including Logistic Regression with L2 regularization or Linear SVM with L1 regularization. We obtained an area under the ROC curve of 0.95 for ADOS Module 3 and 0.93 for ADOS Module 2 with less than or equal to 10 features.
The resulting models provide improved stability over previous machine learning efforts to minimize the time complexity of autism detection due to regularization and a small parameter space. These robustness techniques yield classifiers that are sparse, interpretable and that have potential to generalize to alternative modes of autism screening, diagnosis and monitoring, possibly including analysis of short home videos.
Autism screening
Autism diagnosis
Sparse machine learning
Autism spectrum disorder (ASD) is a developmental neuropsychiatric disorder characterized by impairments in social interaction, communication (both verbal and non-verbal), and restricted, repetitive behaviors. The most recent estimates by the Centers for Disease Control indicate that autism affects one in 68 children in the USA and is much more common in males, affecting as many as one in 42 boys as compared to one in 189 girls [1]. The average age of diagnosis for ASD is 4.5 years, though parents often identify developmental concerns within the child's first year of life, even as early as 6 months of age [2], and clinicians report that reliable diagnoses can be made as early as age 2 [3].
Currently, the diagnosis of autism is based on behavioral symptoms alone. A typical diagnostic appointment consists of a multi-hour behavioral evaluation by a team of clinicians, usually in a specialized diagnostic clinic or developmental medicine center and only after referral from the child's general pediatrician. During the diagnostic encounter, trained specialists will administer a battery of behavioral instruments and rating scales, which are standardized and can aid clinicians in reaching a best-estimate diagnosis. One of the most commonly utilized behavioral instruments is the Autism Diagnostic Observation Schedule (ADOS) [4], which is considered a gold-standard diagnostic tool. The ADOS is an observation-based clinical assessment that is broken into four modules based on age and language level: Module 1 is intended for young children with no or single-word speech, module 2 is intended for individuals with phrase speech, module 3 is intended for verbally fluent children, and module 4 is intended for verbally fluent adolescents and adults. The ADOS administrator will participate in a number of standardized activities with the child and subsequently answer a set of 28–30 questions about the child's behavior during the activities. Each answer is coded on a scale from 0 to 3, with higher codes indicating more severe impairments in each measured behavior. Domain subscores and a total score are calculated by converting codes of 3 to 2 and totaling the codes from the appropriate subset of questions, and from these scores, a final classification of autism, autism spectrum, or not met is reached. In the original ADOS-G [5], subscores for communication, social interaction, and restricted, repetitive behavior (RRB) domains were calculated, but only the social and communication domains were used to determine the final classification. In the revised ADOS-2 [6] scoring algorithm, communication and social interaction were combined into a single social affect domain. The ADOS-2 also calculates a RRB domain, and both the social affect and RRB domains are used in determining the final classification. In addition to domain/total scores and a classification, the ADOS-2 also provides an algorithm for calculating a comparison score, which ranges from 1 to 10 and is meant to capture autism severity after calibration for age and language level. The total time for administration and scoring of the ADOS is approximately 60 min.
Due to the rigorous and time-consuming nature of diagnostic examinations for autism, many diagnostic centers have expanding waiting lists for appointments as the increasing demand exceeds their capacity to see patients. This bottleneck translates to delays in diagnosis of 13 months and longer for minority or lower socio-economic status groups. These delays can also delay insurance coverage and access to behavioral therapies [7, 8]. These issues indicate that there is a need for short, easily accessible, and accurate risk assessments for ASD both to provide feedback to parents and to provide clinics better abilities to triage and manage their patients. In our previous work, we applied machine learning approaches to identify minimal sets of behavioral features from commonly used behavioral instruments that yielded high accuracy in distinguishing children with ASD from those without ASD [9–12], as well as from children with other developmental delays [13]. In the present study, we focus on expanding the generalizabilty of those approaches, with specific focus on observation-based data from modules 2 and 3.
While identifying a minimally viable set of features for behavioral detection of varying forms of autism is an important step forward in an effort to construct a clinical process that reduces time to diagnosis, the models derived from our earlier work [12] did not account for instances when core features of the model could not be answered. As a consequence, these models may suffer from a lack of generalizability, such as in instances when the answer to one or more questions cannot be given, a phenomenon we might expect to be common in clinical practice. The present study attempts to address this limitation by focusing on creating more robust models that explicitly account for missing features. Specifically, we focus on data-driven identification of a small set of behavioral features on which several types of classifiers yield high accuracy to characterize an underlying structure in the data that is important under a variety of optimization criteria—that is, the same features used within and across models—and that confers an inherent robustness to the task of classification. The work here uses three guiding principals for the use of machine learning in building a process for faster autism detection, namely:
To evaluate model accuracy, we computed the area under the receiver operating characteristic (ROC) curve. This metric deals well with label imbalance and remains agnostic to thresholding choices made for classification (i.e., tradeoff between false positive and false negative rates). Our objective was to reach accuracies close to those of the full ADOS test and when available to the best-estimate clinical diagnosis.
A desired behavior for the selected models was simplicity or parsimony. A model with less parameters and more regularization (high penalization, small tree depth) will have better generalization, more stability to noisy data, and less over-fitting.
Finally, a crucial part of our model selection was the potential for clinical application, and our ability to interpret exactly how the model's features influence the detection of ASD. Interpretable models should be preferred. Linear models for regression, logistic regression, LDA, and linear SVM provide an intuitive interpretation on how much (value) and how (sign) each feature is used in the classification. Simple decision trees explicitly show how features are used together to decide if a patient has high risk for autism. With that knowledge, the selected model should result in a better understanding of the characteristics of ASD within each subject.
Using these guiding principles, we tested our approach on an aggregated collection of databases for ADOS modules 2 and 3. To derive sound estimates of the accuracies that could be reached on unseen data and avoid overfitting due to testing too many models, we selected only one or two models per module based on the three above criteria—accuracy, simplicity, and interpretability—and tested them on 20% of the data. We show the outcomes in light of the above criteria and describe the potential contribution of chosen models to the clinical detection of autism as well as their limitations. Finally, we discuss how the derivation of a quantitative phenotype for autism via the use of smaller sets of features in an interpretable machine learning model could help to accelerate the diagnostic process and therefore help ameliorate bottlenecks in access to care.
Data sample and preprocessing
We aggregated item-level Autism Diagnostic Observation Schedule (ADOS) module 2 and module 3 score sheets from four autism spectrum disorder (ASD) data repositories: the Boston Autism Consortium (AC), Autism Genetic Resource Exchange (AGRE), Simons Simplex Collection (SSC), and the Simons Variation in Individuals Project (SVIP). The module 2 data set consisted of 1389 subjects (1319 ASD, 70 non-ASD), and the module 3 data set consisted of 3143 subjects (2870 ASD, 273 non-ASD). Subjects were classified as ASD or non-ASD based on best-estimate clinical diagnosis where available, and for the small subset where clinical diagnosis was not available (n=75 module 2, n=125 module 3), the ADOS algorithm outcome was used to define the class. For module 3, the controls had a mean age of 109 months (std dev = 35 months) and children with ASD had a mean age of 115 months (std dev = 38 months; average 116 months (std dev = 38 months) for classic autism and 114 (std dev = 38 months) for autism spectrum). For module 2, controls had a mean age of 60 months (std dev = 28) and children with ASD had a mean age of 83 months (std dev = 38; 85 (std dev = 38) for classic autism and 74 (std dev = 36) for autism spectrum). For module 3, our non-ASD control set consisted of 137 males and 136 females and our ASD cohort consists of 2420 males and 450 females. For module 2, our non-ASD control set consisted of 41 males and 29 females, and our ASD set consists of 1060 males and 259 females.
Before beginning our machine learning pipeline, we performed preprocessing steps on our dataset. Both modules 2 and 3 contained missing value codes (e.g., answer codes "8," "9," "N/A"). Module 2 feature A3 (speech abnormality) was missing 2.3% of the time, with all other features missing answers in less than 1% of the subjects. Module 3 had more missing values, with 1.8% of feature A3 (echolalia) missing, 78.6% of feature B3 (vocalized nonverbal communication) missing, and 8.2% of D3 (self-injury) missing. We elected to incorporate these missing values as features for model development. First, for each item in modules 2 and 3, we created a binary "not answered" feature that captured whether or not the administrator answered the question, irrespective of the code provided. We grouped answers that indicated "N/A" (e.g., an 8 or 9) together with answers that were omitted and left blank. We then coded this binary feature (1 if X is missing and 0 if X is present) as X-missing, so, for instance, if feature A3 was coded as missing, A3-missing took the value 1. Although this doubled our initial feature set, it allowed us to interpret how the inability to answer a question, or inability to assess a certain behavior, related to diagnosis in both modules. In addition to the primary items from the instruments and the missing features, we included age and gender as features, resulting in a 58-item feature set for both module 2 and module 3. Next, we performed a normalization step to guarantee that our feature data were all on a uniform scale. Since all of the original ADOS items range from 0 to 3, we transformed the rest of the features to be on the same scale. Gender and other binary indicators were represented by 0's and 3's, and age was rescaled to fit in the 0–3 range.
We used the best-estimate clinical diagnosis or ADOS classification when diagnosis was not available as the prediction class for our machine learning experiment. When using regression, to increase the granularity of our prediction class, we split our subjects into three diagnostic groups at training time: autism, spectrum, and non-ASD. The autism class included diagnoses of autism, autistic disorder, and classic autism, while the spectrum class included diagnoses of autism spectrum disorder/ASD, Asperger's, high-functioning autism, and pervasive developmental disorder-not otherwise defined (PDD-NOS). In our machine learning analyses, these group labels would then be converted into integers (non-ASD (0), spectrum (1), and autism (2)) which captured the increasing severity of the classes. This assignment applies only to the linear regression tasks but is reasonable in order to test whether increased ordinal structure on a regression task leads to different performance on a two-class (0 vs 1 and 2) classification.
We tested the performance of 17 unique machine learning classifiers on both our module 2 and module 3 feature sets to accurately predict the diagnosis of ASD or non-ASD, using a small stable subset of features yielding comparable accuracy to the complete feature set. Training and testing of our models was performed in Python using the package scikit-learn [14].
We chose to test models from five distinct classifier families: linear regressions (thresholded for classification), nearest neighbor models, general linear models (classifiers, as opposed to regressors), support vector machines, and tree-based methods. Table 1 contains a summary of the different models we tested. Some classifier families provide a built-in sparsifying parameter—for example, Lasso penalizes the weights using the L 1 norm. By increasing this penalty term, one can force the model to use fewer features to generate predictions [15]. However, some of our models, namely, most of the kernels in support vector machines (SVMs) and tree-based classifiers, do not have an easily tunable sparsifying parameter. For these classifiers, we added a feature selection technique to the training phase. Three techniques for scoring each feature individually were used, detailed in Table 2: ANOVA, nonzero coefficients of a Lasso trained on the data or more important features of a full decision tree trained on the data (referred to as tree in subsequent sections). The number of features selected was tuned using a parameter as detailed in Table 2. This parameter was then treated as the sparsifying coefficient for the sparsified model (feature selection followed by classification performed on selected features). We annotated this sparsified model with the feature selection method ("ANOVA-," "Lasso-," or "Tree-"; so, for instance, a SVM trained on a subset of features found to be important by application of Lasso was written "Lasso-SVM") as a prefix. For comparison, we also used the base model without any additional feature selection, denoted with the prefix "NS-." In this case, and for the second phase, grid search was used to optimize non-sparsifying regularization parameters (e.g., L 2 regularization coefficient) only.
Summary of tested classifiers
Classification family
Models used
Built-in sparsifying coefficient, other penalization
Under-sampling used
Penalized linear regression
L 1 penalization
∙ Very interpretable
∙ Simple model
∙ Linear like ADOS
Elastic net
∙ Can use gradation in label
Relaxed Lasso
(ASD vs spectrum)
Nearest neighbors
Nearest shrunken centroids
∙ Can identify subgroups within classes,
which is likely for our sample
General linear models for classification
LDA (L 1)
Logistic regression (L 1, L 2)
∙ Interpretable
∙ Based on linear assumptions
Linear kernel (L 1)
∙ Can capture more complex shapes in data when using nonlinear kernels
Polynomial kernel
Regularization parameter
Radial kernel
Exponential kernel
Tree-based classifiers
Tree depth
∙ Performs well on categorical data
∙ Better captures feature interactions
Gradient boosting
∙ Tree is interpretable
AdaBoost
∙ Boosting techniques often gives higher accuracy than simpler models
We trained and tested 17 unique machine learning classifiers on both our module 2 and module 3 training data sets. Linear regressions models were trained to differentiate autism, spectrum, and non-ASD (3 prediction classes) but tested to detect only ASD from non-ASD
Summary of feature selection techniques used for classifiers without sparsity enforcing parameters
Feature score
Sparsifying coefficient
ANOVA
The k most discriminative features
∙ Simple test
when doing the ANOVA test
∙ Fast
∙ A priori information on what features
would not be useful in classification using
only the variance for each features
Nonzero coefficients of the Lasso
L 1 coefficient
∙ Linear model
trained on the data for a given L 1 coef
∙ Features used by a more parsimonious model
The k most important features when building
∙ Good with categorical data as it can use
a full decision tree on the data
multiple cuts per feature, unlike linear models
The third column gives the parameter that will be used by the full model as the sparsifying coefficient for the grid search
Feature reduction
The first phase of our machine learning pipeline consisted of identifying a reduced feature set that was subsequently used to build our final models. First, we randomly split our preprocessed data into distinct training (80%) and testing (20%) sets, with the same proportion of ASD to non-ASD subjects in each set. Preserving a portion of our data strictly for testing enabled us to choose our final model based on how well it could generalize to unseen data, preventing the selection of a model overfit to our training set.
To identify an optimal subset of features for each of our models, we performed a stratified 10-fold cross-validation (feature selection CV) with a nested grid search on each fold, using only our training data set. We corrected imbalance in class size by setting class weights inversely proportional to the class sizes. When classifiers/classifier implementations did not allow for different class weights, we used undersampling in each fold of the feature selection CV, resulting in a 1:1 ratio of ASD to control data in each fold. The grid search technique searches for the set of parameters that optimizes the performance of the model, traditionally measured by classification accuracy. For our purposes, we altered the traditional grid search method in two ways. First, due to our class imbalance, we utilized the area under the receiver operating characteristic (AUCROC) and area under the precision-recall curve (AUCPR) as our performance metrics instead of basic accuracy. Second, we added a penalization term to the grid search objective function to enforce sparsity in the model. To do this, we found the set of parameters θ that maximize the penalized objective of the equation below where AUC denotes the area under curve of either ROC or PRC.
$$\begin{aligned} \hat \theta &= \text{argmax}_{\theta} \left(M_{{sparsity}}(\theta)\right) \\ &= \text{argmax}_{\theta} \left(\! {AUC}(\theta) - \mu \frac{\text{Number of features used}(\theta)}{\text{total number of features}}\right) \end{aligned} $$
By penalizing the use of more features, we force some bias in the selected algorithm but decrease the variance by decreasing the model complexity. This approach can be seen as similar to Lasso but for the L 0 norm (how many features are in our model) and will be called L 0 regularization in the following sections. This grid search metric improves the selection of simpler, sparser models that highlight important features and limit overfitting. After testing different values on a first cross-validation, μ = 0.1 Gr was found to be a good coefficient for the regularization as it yielded a small number of features while remaining within 1% accuracy of the non-regularized version.
In addition to the above, and to further enforce sparsity, we used the one standard error rule. We chose the most parsimonious model whose error is no more than one standard error above the error of the best model [16]. In our case, we defined parsimony as the amount of regularization, i.e., L 1 or L 2 penalization, small tree depth, and/or small numbers of trees. We computed the average objective function on all folds, selected the classifier with the maximum value, computed its standard error on all folds, and selected the most parsimonious (regularized) parameters within one standard error of that highest objective. This corresponds to the maximization problem over parsimony in the equation below, where we define \(\hat \theta \) to be the optimal set of parameters without the one standard error rule and SE to be the standard error.
$$\begin{aligned} \hat \theta_{1se} &= \text{argmax}_{\theta} \,\,\texttt{parsimony}(\theta) \\ & \text{s.t.}\,\, M_{{sparsity}}(\theta)\geq M_{{sparsity}}(\hat \theta) - SE\left(M_{{sparsity}}(\hat \theta)\right) \end{aligned} $$
Once this grid search was performed, we constructed a heatmap to determine how often each of our features were selected among our models. This heatmap is a table where each cell corresponds to a feature and a classifier (see Figs. 1 and 6). Using this heatmap, we compared the feature sets among the best performing models to arrive at a reduced feature set that is stable both within and between classifiers. It can also be used to test our assumption that the same subset of features will be selected by multiple classifiers to reach optimal accuracy. In the experiments, we computed two heatmaps, using the one standard error rule and L 0 regularization for the first, and only the one standard error rule for the second. We then chose sets of 5 and 10 most used features with the L 0 penalization heatmap. When two features were used equally, we broke the ties by choosing the most frequently utilized in the non-regularized heatmap. We labeled these feature sets as reduced-5 and reduced-10, respectively.
Heatmap of features used on the different folds for module 3. The darker the color of the cell, the more the feature was used in the different folds of the feature selection cross-validation (CV). Classifiers were sorted along the y-axis such that those with the highest AUCROC function were at the top. Color intensity of each cell denotes how often that feature was selected in all folds of the feature selection CV for that model. The top figure (a) used L 0 regularization, and the bottom one (b) did not. Both used the one standard error rule
Model training and testing
The second phase of our machine learning pipeline consisted of training each of our models using the reduced feature set identified in phase 1. To train our models, we used another stratified 10-fold cross-validation approach with nested grid search (model tuning CV) in the same way as the feature selection CV, but in this phase, the objective function was set to find the values for model hyper-parameters (such as L 2 norm or tree depth) that maximize only the AUC ROC without any additional sparsity-enforcing parameter.
For each model, we selected the hyper-parameters that gave the best overall score over all folds, and, using these, we estimated which classifier should perform well by averaging the corresponding AUC ROC for each fold of our model tuning CV. In addition to this performance estimation, we took into account two other objectives when choosing our final model: simplicity and interpretability. For our purposes, simplicity is important because simpler and more parsimonious models generally decrease the chances of overfitting on the training data. Finally, we wanted to choose an interpretable model to gain more insight on how to reliably detect ASD. In practice, in this second phase, we selected all the models with high accuracy (close to ADOS accuracy) and selected the best trade-off between accuracy, simplicity, and interpretability from the remaining models. As an additional metric to distinguish models, we use the area under the PR curves (both AUCPRcontrol, counting controls as the positive class, and AUCPRASD, counting the ASD cohort as positive class).
The third and last phase of our pipeline was testing our final selected model from phase 2 on our dedicated testing set (20% of our original data) to see how well this model could separate ASD from non-ASD in completely unseen data. We computed the area under the three curves described previously to estimate how our selected model would perform on unseen data.
ADOS module 3 (children with medium to low levels of autism symptom severity)
Reduced feature set
Using module 3, we obtained two heatmaps using the one standard error rule with and without L 0 regularization that can be found in Fig. 1. The full grid search results can be found in Table 3. Taken together, these heatmaps suggested that the reduced feature set was stable, as the same features were highlighted by both.
Grid search results of the classifiers for module 3, with and without L 0 penalization
Classifier
Linear reg
L1 logreg
L 0 penalized ROC AUC
Associated real ROC AUC
Features used with L 0
Not penalized ROC AUC
Features used without L 0
pSVM
rSVM
eSVM
L1 lSVM
Grad Boost
Rand Forest
pSVM, rSVM, eSVM, and lSVM correspond to different kernels for SVM (polynomial, radial, exponential, and linear) and logreg to logistic regression. Italicized data points highlight the worst performing models (too many features used and/or poor performance)
The L 0 regularization heatmap highlighted A2-, A4-, A8-, and B3-missing as the top features, and when considering the non-regularized heatmap, B2 was also frequently chosen. We denoted this collection of behavioral elements as our reduced-5 feature set. For the reduced-10 feature set, we added B7, B8 by the L 0 regularized heatmap and D4, gender and D3-missing from the heatmap without regularization. Finally, B9 was also frequently used in the first heatmap and A7, B1, B6, and D2 on the second one. The categories of the chosen feature are summarized in Table 4.
Summarized description of the features chosen by the feature reduction process for module 3
Language (intonation, volume, rhythm)
Language (words/phrases)
Language (conversation)
B3-missing
Language: examiner could not test nonverbal communication linked to language production
Social interaction (facial expressions)
Social interaction (social overtures)
Social interaction (social overtures, attention)
Behavior (repetitive behavior)
D3-missing
Behavior: examiner could not test self injurious behavior
Gender of the patient
Selected classifier
Reduced-10 feature set
Four classifiers performed better on the reduced-10 feature than the others: SVM, boosting on decision trees (with both AdaBoost and Gradient Boosting methods) and logistic regression with AUC ROC of all three around 0.93. However, when considering our three criteria for choosing the best model, logistic regression and linear SVM were simpler and more interpretable than the the boosting methods for similar performance. Although logistic regression tends to be more interpretable, we provide results for both classifiers. Moreover, it supported the ADOS rating criterion, suggesting that the class distinction had an underlying linear structure. By looking at the probability plots for these three well-performing classifiers (see Fig. 2), we can see that the AdaBoost probability plot was unstable and SVM and gradient boosting yielded a higher rate of misclassification among controls than the logistic regression model. In the performed grid search, the optimal regularization coefficient (inverse of the penalization coefficient) was C = 1 and L 2 penalization performed slightly better than L 1 penalization.
Histogram of predicted probabilities of the two discarded models on the cross-validation set. The x-axis denotes the predicted probabilities and the y-axis the number of subjects. The top figure (a) corresponds to AdaBoost and the bottom figure (b) to Gradient Boosting. AdaBoost probability plot is unstable, and gradient boosting has a high rate of misclassification among the control subjects
Lastly, we computed the performance of our final logistic regression and SVM models on the test set, composed of 20% of our original data. Logistic regression, on the reduced-10 feature set, achieved AUC ROC = 0.95 and a balanced accuracy (Bal Acc ) of 0.90, while SVM performed lower with AUC ROC = 0.95 and Bal Acc = 0.80. Other statistics can be found in Table 5. AUC for precision-recall indicated that it is harder to detect controls than it is to detect children with ASD, an expected phenomenon given the compositional biases of our dataset. The full PRC curve on Fig. 3 indicated that for a recall above 0.8, we could not achieve a reasonable precision. Figure 4 shows that most of the controls were well classified and most of the ASD cases were identified as well. The cutoff area inducing reasonable accuracy is quite large (between 0.2 and 0.4), suggesting that we have a stable prediction. Classic autism subjects, i.e., the higher severity group, were more difficult to classify than the spectrum. This is counter-intuitive—one would expect them to be more distinguishable. We defer to the "Discussion" section reasoning as to why this might be the case.
Precision-recall curve of controls for logistic regression with L 2 penalization on the test set. Above 0.8 of recall, precision decreases drastically. Precision-recall curve of ASD is the whole square (area under the curve is ∼ 1)
Histogram of predicted probabilities for logistic regression with L 2 penalization (a) and linear SVM with L 1 penalization (b) on the test set. The x-axis denotes the predicted probabilities for logistic regression and the decision function for SVM and the y-axis represents the number of subjects. Each subplot correspond to a different label
Summary of accuracies for modules 2 and 3 with best classifier, best parameters, and different feature sets
Number of features
Best classifier
L2 LR
L1 Lin SVM
Optimal parameters
C = 0.5
S = 0.8
C = 0.05
Area under ROC
Recall/sensitivity
Balanced accuracy
F1 score
LR denotes logistic regression, L1 Lin SVM denotes L 1 penalized linear SVM, and S denotes the LDA shrinkage parameter
Given the imbalance in the gender ratio found in the non-ASD and ASD data, we elected to perform the same test on classifiers trained on the top 9 features, excluding gender as a feature. Results were essentially unchanged; see Table 6 for details. Further, see Table 7 for correlations between age, gender, and chosen features. All correlations with age and gender are relatively low and therefore not likely to be impacting the classification.
Summary of accuracies for module 3 with best classifiers and parameters for our 10-feature set, this time trained without gender
LR denotes logistic regression, and L1 Lin SVM denotes L 1-penalized linear SVM
Correlations between used features and age and gender for module 3
Age_months
B3_miss
D3_miss
− 0.096
Reduced-5 feature set
Using only the reduced-5 feature set, the grid search results suggested that AdaBoost, logistic regression, and gradient boosting performed best, with AUC ROC close to 0.92. Following the same logic used for the reduced-10 feature set, we chose logistic regression with L 2 penalization for our final model. Its optimal sparsity coefficient was C = 10, confirming the intuition that less regularization was necessary with fewer features.
The final performance estimate yielded AUC ROC =0.93 and Bal Acc =0.88. Other statistics can be found in Table 5. Figure 5 shows that even though the AUC ROC did not decrease much, achieving good precision and high recall was not possible.
Precision-recall curve of controls for logistic regression with L 2 penalization on the test set with only five features. Above 0.4 of recall, we reach 0.8 of precision. After 0.7, the precision decreases drastically
Module 2 (children with phrase speech)
Our ability to build a stable classifier was limited in part by the small size of the dataset available for module 2. We derived two heatmaps for module 2 using the one standard error rule, both with or without L 0 regularization (Fig. 6). The full grid search results can be found in Table 8.
Heatmap of features used on the different folds for module 2. The darker the color of the cell, the more the feature was used in the different folds of the feature selection CV. Classifiers were sorted along the y-axis such that those with the highest AUCROC function appear at the top. Color intensity of each cell denotes how often that feature was selected in all folds of the feature selection CV for that model. The top figure (a) shows results with L 0 regularization, and the bottom figure (b) shows results without regularization. In both analyses, we used the one standard error rule
The L 0 regularized heatmap shows A3, A5, B1, B2, and B10 to be the most highly selected features, which we denote as the module 2 reduced-5 feature set. The non-regularized heatmap confirmed these features to be top-ranked. Although age was selected quite often, we decided not to add it to the larger feature set because of the arbitrary age difference between the two classes in our dataset. No other feature was selected consistently, suggesting that five of the 29 total features captured within module 2 may be sufficient to identify autism from non-autism subjects. The categories and descriptions of chosen features are summarized in Table 9.
Language (echolalia)
Social interaction (amount of maladjusted eye contact)
Social interaction (social responses)
The best performing classifiers on the reduced-5 feature set for module 2 were SVM, LDA, and logistic regression with AUC ROC almost reaching 0.88. Each of the three models were simple and interpretable. Although polynomial kernel performed slightly better, for parsimony reasons, an SVM with a linear kernel and L 1 penalization proved to be optimal. For all classifiers, \({AUC}_{{PR_{controls}}}~=~0.5\) and \({AUC}_{{PR_{ASD}}}~>~0.99\). Considering their similar performance and interpretability, we elected to test all of these models on the test set.
On the full training set (all subjects excluding the 20% held out from the test set), the optimal logistic regression model found in the grid search used L 2 penalization and C=0.05, the optimal LDA model used shrinkage = 0.8, and the optimal SVM C=0.5. On the final test set, the LDA model exhibited AUC ROC =0.93, Bal Acc =0.74, and the SVM 0.93 and 0.80 and the logistic regression model 0.92 and 0.78, respectively. All statistics can be found in Table 5. Figure 7 shows the distribution of predicted probabilities from LDA, SVM, and logistic regression models. The small number of controls in the data set decreased the accuracy of the curve. We found high recall on detecting ASD using SVM and higher specificity using logistic regression. However, the ability of these classifiers to generalize to new data may be limited.
Histogram of predicted probabilities for LDA (a), logistic regression with L 2 penalization (b), and linear SVM with L 1 penalization (c) on the test set. The x-axis denotes the predicted probabilities (or decision function for SVM), and the y-axis shows the number of subjects. Each subplot corresponds to a different label
As for module 3 (and perhaps more so in the case of gender for module 2), there is a concern that imbalance between classes with age and gender limits these results. We computed the correlation matrix between age, gender, and chosen features; please see Table 10.
In this study, we aimed to identify a core subset of behavioral features from the gold-standard ADOS examination that can reliably discriminate between ASD and non-ASD cases. By considering the implication of missing answers as well as employing robust sparsity-enforcing feature selection methods during the first phase of our machine learning pipeline, we arrived at a novel subset of features from modules 2 and 3 that consistently optimized performance both within and across classifiers. Once the reduced feature sets were identified for each module, we fit each of our models to the reduced feature data set without any sparsifying coefficients and evaluated their performance on our dedicated test set. When choosing our final model for each module, we considered simplicity and interpretability in addition to overall classification performance. We found that logistic regression with L 2 regularization performed best on the module 3 (AUC ROC = 0.93) and, depending on the task, logistic regression or SVM performed best on the module 2 (AUC ROC = 0.92 and 0.93). The full results are reported in Table 5. These results not only indicate that there is a stable subset of questions that contains the necessary information to distinguish ASD from non-ASD cases, but also points to an underlying linearity in the ADOS, since most top-performing models are linear. Table 11 shows the correlation between these chosen features and the others for module 3. Except for E1, E2, D2, and D3, most features are highly correlated with at least one chosen feature. This suggests that the feature set we selected is composed of the least number of features containing a majority of the useful information for the classification task, but that some features could be replaced by one or more of those not chosen. This in turn may provide additional flexibility for screening, for example, in instances where a particular behavior is not exhibited by the child during a video clip or short observation session.
Correlations between used features and not used non-indicator features for module 3
This shows the high correlations between features and so, the need for a robust feature selection technique and classification
We hypothesized that including a binary encoding of whether the data is missing, instead of coding the missing values as a mean of the data or as other values in the same features as existing data, would be useful in this classification task. That a data point is missing might be informative (e.g., it might, in some circumstances, indicate some behavioral trait that led to the item not to be filled out). Given the use of fairly simple (e.g., linear) classifiers, the coding of missing data within a feature that otherwise represents the severity of some trait is undesirable. We found that, in the case of module 3, missing features ranked among the most informative of the top 10 features for classification.
Although somewhat counterintuitive, we found ASD classes no more difficult to classify than classic autism cases and, in some cases, error rates were higher for classic autism cases. This could be due to the particular feature set we extracted. It is possible that the difficulty to label cases is not correlated to severity but type of autism. Our models were selected for performance on the task of separating ASD and classic autism from typical development without distinguishing the former two. While it might be the case that it is easier to distinguish classic autism from controls than it is to distinguish ASD from controls, models that perform as such on both tasks simultaneously may require more complexity and, with that, likely larger amounts of data to distinguish them. Supporting this, we found that a regression task optimized to assign 0 to neurotypical, 1 to ASD (not classic autism), and 2 to classic autism sacrificed accuracy as a two-class (0 vs 1 or 2) thresholding.
The differences in performance for modules 2 and 3 are perhaps due to the numbers of controls, with module 3 having four times more controls than module 2. The low area under the PR curve (when controls are considered positive) seems to support this hypothesis. Good scores for other metrics could also be due to significant age difference between ASD and non-ASD in module 2. The use of reduced feature set of sizes 5 and 10 yielded similar performance. The model with 5 features was simpler and therefore limits overfitting, but a true test of generalization power requires further data collection efforts.
Despite strong evidence for the significant role genetics play in autism risk, there are still no molecular methods for diagnosing ASD. Until reliable biomarkers for autism are identified, behavioral evaluation will necessarily remain the standard for autism diagnosis. However, as the incidence of ASD continues to increase, more and more strain will be placed on diagnostic centers, which often do not have the resources to meet the demand for evaluation of children at risk. This can translate to long wait times for appointments and missed windows of opportunity for beneficial early interventions.
The ADOS [5] has long been found useful, both as a way to gather qualitative observations with which clinicians can make an informed diagnosis and as a standalone scoring mechanism. However, ADOS can present clinical challenges; the full ADOS measure can be time-consuming to apply, and as found in [17], its standard scoring mechanisms can have low specificity, in particular in cohorts with several developmental issues and with cases that are on the spectrum but do not qualify for a classical autism diagnosis.
As a step towards reducing waiting lists at diagnostic clinics, strides have been made to develop mobile screening systems for risk of ASD and other related disorders [9–13]. If such a mobile system could accurately detect ASD from children at risk for developmental delays in general, it would provide utility both for triaging patients in need of more formal clinical evaluations as well as for providing feedback to parents during the often long and arduous process of diagnosis. The classification experiments performed here supports the claim that accurate ASD detection can be performed using the responses to a small set of behavioral features. Previously, we have shown that the behavioral features captured in the ADOS evaluation can be measured in short, unstructured home videos [18]. Considering this result, the classification system described here has potential for utility in shorter format approaches potentially including video-based home screening using mobile devices.
Conclusions & limitations
This study was limited by the contents of available data sets. The phenotype data used here were obtained from publicly available autism research data repositories, which have relatively few ADOS examinations for non-ASD control subjects. However, it is important to note that the control subjects who did receive an ADOS examination were initially suspected of having autism and later failed to meet the cutoffs for formal diagnosis. Therefore, although the number of controls available for model training was minimal, the controls used may represent challenging "edge" cases that help the classifier create a robust boundary between actual ASD cases and cases that exhibit some ASD-like characteristics but who may have another underlying condition. Considering that ASD-specific screening using ADOS is most often performed for children that are suspected of having ASD, the high accuracy of our classifier on this control set is a good representation of its performance in the actual population that requires ASD screening. Of course, more training data points would improve the overall accuracy of the classification system. We plan to conduct future studies to tune the classifiers as more control data become available.
The data for modules 2 and 3 contained balance concerns. Namely, in module 2, age and gender were not well balanced between ASD and non-ASD, and in the module 3 data, gender was not well balanced. Gender appeared as the lowest of the top 10 ranked features from the module 3 analysis, suggesting a limited role in classification. In our module 2 analysis, neither gender nor age appeared in our top 5 ranked feature set. While the low ranking of these imbalanced features provides some confidence that they do not negatively impact classification, the possibility remains that the classifier could be capturing correlations with these features, and thus could be performing in a way that would not generalize to the full population. To understand this potential limitation better, we retrained with age and gender features removed and achieved comparable results. We also computed correlation matrices (Tables 7 and 10) of selected features and age and gender and found correlation of age and gender with selected features to be negligible. While these results suggest that the imbalance within these features did not have a biasing effect, data collected with better balance will be an important next step to determine the generalization of our classifiers.
ADIR:
Autism diagnostic interview revised
We gratefully acknowledge the participants who provided the data used in our study and the members of the Wall Lab for the support and edits throughout the study.
Support was provided from awards to DPW from the National Institutes of Health grants 1R21HD091500 and 1R01EB025025, and from the Bio-X, Beckman, and Coulter Centers at Stanford and from the Hartwell Foundation.
All data sets used herein are available as described in the manuscript.
DPW, SL, and NH designed the study; SL, MD, NH, and DPW conducted the analyses and interpretation of the results. SL, NH, and DPW wrote the manuscript. All authors edited and approved the final version.
Dennis P. Wall is a founder of Cognoa.com, a company focused on building digital solutions for child health.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA, USA
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
Christensen DL, Bilder DA, Zahorodny W, Pettygrove S, Durkin MS, Fitzgerald RT, Rice C, Kurzius-Spencer M, Baio J, Yeargin-Allsopp M. Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network. J Dev Behav Pediatr. 2016; 37:1–8.View ArticlePubMedGoogle Scholar
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Wall DP, Dally R, Luyster R, Jung JY, DeLuca TF. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PloS ONE. 2012; 7(8):43855.View ArticleGoogle Scholar
Wall D, Kosmicki J, Deluca T, Harstad E, Fusaro V. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry. 2012; 2(4):100.View ArticleGoogle Scholar
Duda M, Kosmicki J, Wall D. Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Transl Psychiatry. 2014; 4(8):424.View ArticleGoogle Scholar
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Duda M, Ma R, Haber N, Wall D. Use of machine learning for behavioral distinction of autism and adhd. Transl Psychiatry. 2016; 6(2):732.View ArticleGoogle Scholar
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text:
Q: How to delete firebase child from background service when a deadline is due? In my app, when the user is posting a request he also needs to set the deadline.  So when the deadline is due, I want the app to delete that request automatically.  I haven't tried anything yet coz I cant find anything similar to what i need.
I'll at least show the code when the user posts a request
post = (Button) myView.findViewById(R.id.post);
        post.setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View view) {

                if (ContextCompat.checkSelfPermission(getActivity(), android.Manifest.permission.ACCESS_COARSE_LOCATION) != PackageManager.PERMISSION_GRANTED) {
                    if (ActivityCompat.shouldShowRequestPermissionRationale(getActivity(),android.Manifest.permission.ACCESS_COARSE_LOCATION)) {
                        ActivityCompat.requestPermissions(getActivity(), new String[]{android.Manifest.permission.ACCESS_COARSE_LOCATION}, MY_PERMISSION_REQUEST_LOCATION);
                    }
                    else {
                        ActivityCompat.requestPermissions(getActivity(), new String[]{android.Manifest.permission.ACCESS_COARSE_LOCATION}, MY_PERMISSION_REQUEST_LOCATION);
                    }
                }
                else {
                    LocationManager locationManager = (LocationManager) getActivity().getSystemService(Context.LOCATION_SERVICE);
                    Location location = locationManager.getLastKnownLocation(LocationManager.NETWORK_PROVIDER);
                    try {
                        uloc = hereLocation(location.getLatitude(), location.getLongitude());
                    }
                    catch (Exception e) {
                        e.printStackTrace();
                        //Toast.makeText(getActivity(), "NOT FOUND", Toast.LENGTH_SHORT).show();
                    }

                }
                dead = deadline.getText().toString();
                r = reqdesc.getText().toString();

                if(reqdesc.getText().toString().trim().length()==0){
                    Toast.makeText(getActivity(), "PLEASE ENTER YOUR REQUEST", Toast.LENGTH_LONG).show();
                }
                else if(click==0 || dl==null){
                    Toast.makeText(getActivity(), "PLEASE SET THE DEADLINE FOR YOUR REQUEST", Toast.LENGTH_LONG).show();
                }
                else {
                    String date = java.text.DateFormat.getDateTimeInstance().format(Calendar.getInstance().getTime());
                    writeNewRequest(r, dead, uemail, uid, uname, uprofile, uloc, date);
                    String reqKey = p.getKey().toString();
                    writeDuplicate(reqKey, r, dead, uemail, uid, uname, uprofile, uloc, date);
                    Intent i = new Intent(getActivity(), RequestConfirm.class);
                    i.putExtra("key", reqKey);
                    startActivity(i);
                    //showNotification();
                }
            }
        });

A: Databases in general are unable to implement such time-based functionality on their own.
You have two possible courses of action:

*

*Run a maintenance app on a server, which loads the list with request's deadlines and deletes requests when their deadline is due. You run this 24/7 on 1sec time loop for example, but you don't check the deadlines in the db each second - you maintain a list by reacting to inserts, deletes and updates (you can just leave a connection to firebase open and observe a ref and it will tell you when it changes).

*Make additional check / modify your queries to ignore items that are past the deadline. Run a housekeeping routine once a day for example, that deletes these items.


I love a good fashion and beauty collaboration and @HarpersBazaarUS did not disappoint! They teamed up with @LOrealParisUSA a beauty powerhouse to give their Most Vocal readers a unique sampling opportunity.
I applied to receive a full size FREE sample of @LOrealParisUSA Voluminous Lash Paradise Mascara and was selected for sampling. At a minimum, my makeup regime consists of mascara and lipstick, with these two products I feel confident to face anything that comes my way in my day-to-day life. I was super excited to be able to try a @LOrealParisUSA product that will give me the length and volume I want. When you read product claims of 98% and 91% of users seeing results your mind can only imagine the possibilities!!!
The L'Oreal's Voluminous Lash Paradise product is amazing!!! My lashes looked fuller and fuller which opens up my eyes and gives my face an instant pick me up. There is no clumping when this mascara dries and two coats is all I needed for my desired effect. If you like a natural looking eye with a little enhancement, this is the product you want to use. I was asked to use this product for 5-7 days, here are my favorite looks using L'Oreal's Voluminous Lash Paradise Mascara.
What do you think about my lashes? Leave your comments below.


DuoDERM® Extra Thin dressing is a hydrocolloid dressing indicated for the management of lightly exuding wounds. It combines a unique hydrocolloid formulation that distinguishes it from other hydrocolloid dressings and a vapour permeable outer film to provide an occlusive moist environment.
DuoDERM®Extra Thin dressings protect the wound, support debridement, are simple to use, and offer continuous care throughout all the healing phases.
DuoDERM® is easy to mould and can be cut to shape to allow easy treatment of awkward areas.
DuoDERM® dressings are designed to reduce the risk of further skin breakdown due to friction by preventing contact with clothes/bed linen.
Pack Size: Pack of 5.


Art Classes at Got Art Gallery on Third (Up to 50% Off) Four Options Available.
Promotional value expires 120 days after purchase. Amount paid never expires. Valid for both on location and off location classes. Not valid toward or at private parties. Registration required. Subject to availability. Limit 3 per person, may buy 1 additional as gift(s). May be repurchased every 180 days. Valid only for option purchased. Merchant's standard cancellation policy applies (any fees not to exceed voucher price). Merchant is solely responsible to purchasers for the care and quality of the advertised goods and services.
The nonprofit organization of Summit Art, Inc. and Teresa Hogan Keene manage Got Art Gallery on Third, an independent nonprofit gallery that works closely with local artists and is dedicated to enriching the cultural landscape of citizens in Lee's Summit and the Eastern Jackson County area. Gallery director and mixed-media artist Teresa Hogan Keene covers exposed brick walls with rotating exhibitions that showcase artists skilled in photography, painting, and mixed-media creations.
At the back of the gallery, a classroom hosts adult students learning to paint in BYOB sessions, where they can sip libations such as wine or flavored watercolors, as well as classes aimed at teaching drawing and acrylics to children and teens.


This is the P2418HZ 24 inch monitor from Dell. A desktop display specifically designed to make video conferencing simple and more seamless. Dell has included a 2MP Full HD infrared sensor camera in the top bezel and has also built in to the unit 2x 5.0W speakers as well as an Omni-directional microphone.
This 24 inch Dell monitor uses an IPS panel (In-plane switching) across the 16:9 aspect ratio 1920 x 1080 Full HD display. It features a 1000:1 contrast ratio as well as the 250cd/m2 brightness and a fairly quick 6ms grey to grey response time.
This conferencing monitor is certified for Microsoft® Skype® for Business and Windows Hello. This allows you to login using the facial recognition functionality with Windows Hello.
As with all P-series monitors this 24 inch monitor has a fully flexible stand. It allows for height-adjustability of up to 125mm as well as tilt from -5° to 21°. It can swivel 45° to 45° left and right and it can also pivot through 90° clockwise from landscape to portrait mode. The stand also has built in cable-management.
If you would like to read an independent review of the Dell P2418HZ 24 inch Monitor then please click here.


Protect your complexion against harmful environmental stressors with the Sunforgettable Total Protection Face Shield SPF50 (PA+++) from health-forward, mineral cosmetics label, Colorescience; a hydrating, antioxidant-packed liquid sunscreen with a weightless texture that blends invisibly into all skin tones.
Recommended by the Skin Cancer Foundation, the water and sweat resistant formula is powered by the brand's exclusive, barrier-forming EnviroScreen™ Technology, which combines Lipochroman®, Infraguard and mineral oxides to counteract aggressors including UVA/UVB, pollution, blue light and infrared radiation. Offering unmatched broad spectrum protection, the multi-action formula is enriched with Phytomoist for intense hydration and calming agents that soothe redness and irritation.
Suitable for all skin types. Dermatologist-recommended.
Active Ingredients: Zinc Oxide 12%.
Inactive Ingredients: Water, Butyloctyl Salicylate, C12-15 Alkyl Benzoate, Isododecane, Lauryl PEG-8 Dimethicone, Dimethicone/Vinyl Dimethicone Crosspolymer, Propanediol, Dimethicone, Niacinamide, Caprylyl Methicone, Dimethiconol/Propylsilsesquioxane/Silicate Crosspolymer, Trilaureth-4 Phosphate, Allantoin, Tremella Fuciformis Sporocarp Extract, Bisabolol, Disodium Lauriminodipropionate Tocopheryl Phosphates, Caesalpinia Spinosa Fruit Pod Extract, Helianthus Annuus (Sunflower) Sprout Extract, Glycerin, Tocopherol, Lauryl PEG-10 Tris(trimethylsiloxy)silylethyl Dimethicone, Maltodextrin, Sodium Chloride, Ethylhexylglycerin, Isoceteth-10, Silica, Betaine, Capryl Glycol, Dimethylmethoxy Chromanol, Hexylene Glycol, Tetrasodium Glutamate Diacetate, PEG-10, Dehydroacetic Acid, Phenoxyethanol, Potassium Sorbate, Benzoic Acid, Sodium Benzoate, Iron Oxides (CI 77491, CI 77492, CI 77499).
This sun cream is very fluid and greasy. My face looks like full of oil after using it.


Is there anything more confusing than skincare? Aren't you tired of all the product hype? Don't you wish you knew what really works – and what are simply "lotions and potions"?
The fact is, your skincare products should be tailored to the specific needs of your skin – and those needs change with your age, habits, pregnancies and major changes in your life. No one product will serve you all your life; at the same time, science has recently made some remarkable advancements in skincare.
To stay on top of what's new and what's right for you, we offer custom skincare management, using products hand-selected by Dr. Jacobson for their effectiveness and aesthetics. Prescription strength and/or clinically effective OTC choices can be recommended.
Whether you need a regiment assembled from scratch, or simply some fine-tuning to your current plan, we can help.


text:
Gates in Iraq amid deadly attacks
US defence secretary underlines difficult choices as bombs kill scores of people.
Gates held a meeting over dinner on Sunday with Iraqi cabinet ministers and and US officials in Baghdad
Initial reports said that at least 10 people were wounded and were being ferried to hospitals.
Car-bomb and gun attacks against US allies, police and civilians in northern Iraq, meanwhiled, killed up to 53 people during the defence secretary's trip.
The violent events also coincided with the release of a seized diary and documents by the US military that it says show al-Qaeda in Iraq cracking under a Sunni revolt against its tactics.
Suicide attack
The heaviest loss of life on Sunday occurred at a checkpoint at the entrance of a bridge in the district of Yathrib, on the outskirts of Balad, 80km north of Baghdad.
Fear breeds poverty in Iraq's Sadr City
A suicide truck bomber attacked Iraqi police and awakening group members manning the checkpoint, killing 34 people and wounding 37 others, police said.
Security forces fired at the bomber but could not prevent him from detonating the explosives.
A nearby car market and other shops were destroyed.
The US said in a statement that the toll in Balad was 23 killed and 25 injured.
They would not say whether the blast was by a suicide bomber, but stated that it was a car bomb at an Iraqi checkpoint near a market in Balad.
US and Iraqi forces secured the area.
Villages stormed
Elsewhere, two villages near the country's border with Syria were stormed by fighters allegedly linked to al-Qaeda in Iraq.
The group was repelled by awakening council members and Iraqi soldiers, but 22 people were killed in the clash.
What are the reasons for the upsurge of violence in Iraq?
This number included 10 assailants, six awakening council members, and four women and children, according to Sheik Fawaz al Jarba, the head of the Mosul anti-al Qaeda group.
Another attack occurred near Sinjar, about 100km west of Mosul, killing five members of a local awakening council.
The attackers struck a compound in the north of the country, where the awakening council members lived.
The US military said five other awakening council members were injured, while 10 of the assailants were killed.
Civilians killed
Separately, a group of civilians were attacked in the province of Ninawa on Sunday, the military said.
Two men and one child were killed in the attack and two other men, two women and two infants were injured.
In yet another incident near Mosul, explosives on a tanker truck were detonated, killing four civilians.
The blast occurred in the southern outskirts of the city, near an army checkpoint.


text:
El Bogotá F.C. Femenino es un club de fútbol femenino vinculado al Bogotá F.C., cuyo primer equipo militará en la segunda edición de la Liga Profesional Femenina de fútbol de Colombia en 2018, la máxima competición de fútbol femenino en Colombia.

Historia

El equipo no fue presentado oficialmente aunque realizó una convocatoria para sus integrantes en la ciudad de Bogotá.
El club accedió a la disputa del torneo profesional colombiano haciendo uso de su derecho a participar merced a contar con reconocimiento deportivo de Coldeportes y la Dimayor.

Para la primera participación en la Liga Femenina, conformó el grupo A con Equidad, Santa Fe, Fortaleza, Deportivo Pasto y Patriotas. El equipo debutó profesionalmente el 11 de febrero de 2018, empatando 0-0 a Fortaleza.

Datos del club

 Temporadas en 1ª: 1
 Mejor posición en 1ª: 5a
 Primer partido oficial: Bogotá FC 0-0 Fortaleza.
 Mayor goleada en contra en un partido de Liga Femenina: Bogotá FC 0-10 Santa Fe, 17 de febrero de 2018

Jugadoras

Plantel actual

|-
| colspan=8 bgcolor=#000ADC style="color:Red;" align=center| Porteras

|-
| colspan=8 bgcolor=#000ADC style="color:Red;" align=center| Defensa

|-
| colspan=8 bgcolor=#000ADC style="color:Red;" align=center| Mediocampo

|-
| colspan=8 bgcolor=#000ADC style="color:Red;" align=center| Delantera

Palmarés

Torneos nacionales oficiales

Véase también
 Bogotá F.C.
 Liga Profesional Femenina de fútbol de Colombia

Referencias

Equipos de fútbol femenino de Colombia


The Minister of Agriculture and Rural Developement, Chief Audu Ogbeh on Thursday met with Rice Farmers Association of Nigeria (RIFAN) to explore possibility of adopting flood resistant rice variety.
The Minister also discussed with the association on new structure that will ensure drastic reduction in the cost of local rice.
The meeting which held at the minister's conference room at the Federal Ministry of Agriculture had in attendance RIFAN state chairmen from the 36 states as well as rice millers.
Audu restated commitment to supporting the rural farmers, reduce hunger and promote massive food production and exports.
Speaking on the rice, he said: "We have been discussing the modalities to put in place to make sure that local rice sells much cheaper and that the prices can fall to the levels of foreign rice and eventually, below the levels of foreign rice. Every country in the world supports agriculture whether they call it subsidy or support, it is exactly the same thing.
"We are happy that the CBN has agreed to bring down interest rates on agric and manufacturing to one digit. We applaud that decision but at the level of FMARD and BOA, we have set up a fund which we intend to manage to support both the farmers, millers and marketers to bring down the price of rice because we are concerned about the cost of rice for the Nigerian family.
"We want our people to feed well and feed cheaply but these things have to be managed carefully because we are dealing with public funds. We are also going to meet in about two weeks and organise rice fair beginning in Lagos, Abuja, Enugu, PH and one or two other cities to showcase the achievements of Nigerian in rice production.


text:
Q: I want a paragraph to get the line through style whenever a checkbox is checked and vise versa It doesn't work as expected
I managed to make it work in this way :

Hello World

This is the hello world note!

                <input onclick="setLineThrough()" type="checkbox" name="done" class="cbDone">

let checkBoxes = document.getElementsByClassName("cbDone");
const setLineThrough = () => {
    for (let i = 0; i < checkBoxes.length; i++) {
        if (checkBoxes[i].checked) {
            checkBoxes[i].parentElement.style.textDecorationLine = "line-through";
        } else {
            checkBoxes[i].parentElement.style.textDecorationLine = "none";
        }
    }
};

It wasn't exactly what I wanted
But then the line through also goes through my icon and to the checkbox so I decided to get them out of the
 and made some changes and I was expecting it to work since they were so similar but it just adds the line through but it does not remove the line through when the checkbox is unchecked!

Hello World

This is the hello world note!

            <input onclick="setLineThrough()" type="checkbox" name="done" class="cbDone">

let checkBoxes = document.getElementsByClassName("cbDone");
let notePara = document.getElementsByClassName("note");
const setLineThrough = () => {
    for (let i = 0; i < checkBoxes.length; i++) {
        if (checkBoxes[i].checked) {
            notePara[i].style.textDecorationLine = "line-through";
        } else {
            notePara[i].parentElement.style.textDecorationLine = "none";
        }
    }
};

What is wrong with my second iteration? I just can't find it.
Also take note that there are some css in my code but I don't think it is needed here.

A: You just need to delete one instance of .parentElement from your JavaScript.
Your JavaScript will then look like this:
let checkBoxes = document.getElementsByClassName("cbDone");
let notePara = document.getElementsByClassName("note");
const setLineThrough = () => {
    for (let i = 0; i < checkBoxes.length; i++) {
        if (checkBoxes[i].checked) {
            notePara[i].style.textDecorationLine = "line-through";
        } else {
            notePara[i].style.textDecorationLine = "none";
        }
    }
};


Work has started on a £800,000 scheme to demolish and rebuild Kerrera's new breakwater.
The breakwater was less than two years old when it was struck by a boat in December causing significant damage.
The Scottish Government had invested £2.05 million into a project to extend the two slipways at the ferry crossing between Gallanach and Kerrera, completed on time and within budget in August 2016.
A new 40-metre breakwater was also constructed on the Kerrera side to allow lifeboats to moor for medical evacuation and provide a more reliable and resilient ferry service for its islanders in poor weather, making cancellations and delays less likely.
After the collision, a spokesperson for Caledonian Maritime Assets Ltd (CMAL), which owns CalMac's ferries and ports, told The Oban Times that 10 metres of the breakwater structure had been damaged, displacing a number of the pre-cast concrete deck sections and steel sheet piles.
CMAL conducted a public procurement exercise to appoint a contractor to demolish and rebuild the damaged section of the breakwater.
A project budget of £800,000 was allocated for the rebuilding works, which would be fully recovered through CMAL's infrastructure insurance arrangements.
A notice on the Gallanach slipway reassures passengers the work, due to be completed in December, will have no impact on ferry timetables.


Last Monday, Siham from Frenchbydesign and I spent the day walking around the streets of San Francisco.
I saw many pretty ceramic shops and coffee places in Valencia and I loved discovering Paxton Gate, which is quite an interesting, curious shop considering they specialise in entomology and taxidermy and plants. So yes you get welcomed by skulls and dead animals but then as you walk in, you discover this gorgeous urban jungle garden at the back of the shop filled with cacti, succulents and other beautiful plants.
I thought it was quite funny the contrast between all things dead and these beautiful, green plants celebrating life… Isn't this beautiful!? It makes me crave for succulents all around the house….


text:
Q: How to get data difference between two columns My start and finish columns are in the format
'Yyyy-mm-dd HH:mm:ss.ms'
I want the difference between the two in HH:mm:ss.ms format.
How do I go about this?
My query looks like this:
Select *,convert(time,   Dateadd(s,
    Datediff(s,
      A.Finish,A.Start),
       Cast('1900-01-01 00:00:00.000000' as    datetime2)))as dif from(select *,dateadd(s,convert(int,left(start,10)),'1970-01-01') as Start,convert(int,left(finish,10)),'1970-01-01') as Finish from tableB)A order by dif asc

I've converted unix time stamps to standard format in inner query.
When I run this the start date and start time appear as '2019-12-11 15:45:20.000' and '2019-12-12 15:45:17.000' but my dif appears as '00:00:03'

A: Try to use TIMESTAMPDIFF() instead of datediff as you have time and second in your query.
https://docs.singlestore.com/managed-service/en/reference/sql-reference/date-and-time-functions/timestampdiff.html
You can check the syntax and example in the above link.