Bill that seeks to lift green card cap amended to protect US 06-21

Eliminating the country quota from the most sought-after Green Cards will end the current discrimination in the US labour market, but would allow countries like India and China to dominate the path to American citizenship, according to the latest Congressional report.
Having a Green Card allows a person to live and work permanently in the United States.
Indian-Americans, most of whom are highly skilled and come to the US mainly on the H-1B work visas, are the worst sufferers of the current immigration system which imposes a seven per cent per country quota on allotment of Green Cards or the Legal Permanent Residency (LPR).

The bipartisan Congressional Research Service (CRS), an independent research wing of Congress, said if the per-country cap for employment-based immigrants was removed, many expects that Indian and Chinese nationals would dominate the flow of new employment-based LPRs for as many years as needed to clear out the accumulated queue of prospective immigrants from those countries.

This queue would include those with approved employment-based immigrant petitions waiting to file either a visa  .. 

The CRS regularly prepares reports on various issues for the lawmakers to take informed decisions.

A copy of the report 'Permanent Employment-Based Immigration and the Per-country Ceiling' dated December 21 was made available to PTI, ahead of the new Congress beginning January 3, wherein several lawmakers are planning to introduce a legislation to eliminate per-country quota for issuing Green Cards to foreign nationals. 

As of April 2018, a total of 306,601 Indian nationals – mostly IT professionals – were waiting in line for Green Cards, according to the USCIS figures. 

Indians constitute 78 per cent of the 395,025 foreign nationals waiting for Green Cards in just one category of employment-based LPR applications.

Due to the cap, the current wait period for the majority of Indians to get a Green Card is nine and half years, the CRS said, adding this could increase or decrease further depending on the number of new applications every year. India is followed by China with 67,031 in line for Green Cards.

Lawmakers favouring eliminating the per-country cap contend that such circumstances effectively encourage employers to sponsor prospective employment-based immigrants primarily from India.

Proponents argue that removing the per-country ceiling from employment-based immigrants would "level the playing field" by making immigrants from all countries more equally attractive to employers, the CRS said. 

According to the CRS, eliminating the per-country ceiling would reduce certain queues of prospective immigrants more quickly, and remove the perceived employer incentive to choose nationals from these countries over other countries. 

"Shorter wait times for LPR status might actually incentivise greater numbers of nationals from India, China and the Philippines to seek employment-based LPR status. If that were to occur, the reduction in the number of approved petitions pending might be short-lived.

"A handful of countries could conceivably dominate employment-based immigration, possibly benefitting certain industries that employ foreign workers from those countries, at the expense of foreign workers from other countries and other industries that might employ them," the CRS said. 

Because the Immigration and Nationality Act (INA) grants LPRs the ability to sponsor family members through its family-sponsorship provisions, removing the per-country ceiling would alter, to an unknown extent, the country-of-origin composition of subsequent family-based immigrants acquiring LPR status each year, it said. 

Changes in the country's demographic profile tilted towards people from one part of the world, was one of the prime reasons for the current per country quota. This, on the other hand, restricts the flow of the best talented foreign workers.

The INA allocates 140,000 visas annually for all five employment-based LPR categories, roughly 12 per cent of the 1.1 million LPRs admitted in fiscal 2017. It further limits each immigrant-sending country to an annual maximum of seven per cent of all employment-based LPR admissions, known as the per-country ceiling, or "cap". 

Two popular employment-based pools of foreign nationals, who have been approved as employment-based immigrants but must wait for statutorily limited visa numbers, totalled in excess of 900,000 as of mid-2018. Most originate from India, followed by China and the Philippines, the CRS said.

Some employers maintain that they continue to need skilled foreign workers to remain internationally competitive and to keep their firms in the US, it said. 

Proponents of increasing employment-based immigration levels argue it is vital for economic growth. Opponents cite the lack of compelling evidence of labour shortages and argue that the presence of foreign workers can negatively impact wages and working conditions in the US, the CRS said.

"Some argue that eliminating the per-country ceiling would increase the flow of high-skilled immigrants from countries such as India and China, who are often employed in the US technology sector, without increasing the total annual admission of employment-based LPRs," it added.

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AI analyzes language to predict schizophrenia 06-21

AI analyzes language to predict schizophrenia.....

A machine learning method found out a hidden clue in people’s language that can predict psychosis episodes. 

A machine learning method uncovered a hidden clue in people’s language predictive of the later manifestation of psychosis: the frequent use of words associated with sound. A paper published by the journal npj Schizophrenia released the findings by scientists from Emory University and Harvard University.

Hidden details

The researchers developed a new machine-learning methodology to more precisely quantify the semantic richness of people’s conversational language (a known indicator for psychosis). Their results indicated that automated analysis of the two language variables (more frequent use of words associated with sound and speaking with low semantic density, or vagueness) can predict if an at-risk person will later develop psychosis with an impressive 93 percent accuracy.

Trained clinicians had not noticed how individuals at risk for psychosis use more words associated with sound than the average population, though abnormal auditory perception is a pre-clinical symptom.

“Voices: Living with Schizophrenia” by WebMD, YouTube.

Machine learning can spot patterns in people’s use of language that even doctors who have undergone training to diagnose and treat those at risk of psychosis may not notice. “Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes,” says first study author Neguine Rezaii, a fellow in the Department of Neurology at Harvard Medical School. That being said, it is possible to use machine learning to find subtle patterns hiding in people’s language. “It’s like a microscope for warning signs of psychosis,” she adds. Rezaii started working on the study while she was a resident in the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine.

“Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes,” Neguine Rezaii, fellow in the Department of Neurology at Harvard Medical School.

Behind the data

Researchers first used machine learning to establish “norms” for conversational language. They fed a computer software program the online

conversations of 30,000 users of Reddit, a popular social media platform where people have informal discussions about a wide array of sujects. The software program, known as Word2Vec, utilizes an algorithm to change individual words to vectors, assigning each one a location in a semantic space based on its meaning. Such with similar meanings are positioned closer together than those with different meanings.

They also developed a computer program to perform “vector unpacking,” or analysis of the semantic density of word usage. Previous work has measured semantic coherence between sentences. Vector unpacking enabled the researchers to quantify how much information was packed into each sentence. After generating a baseline of “normal” data, the researchers applied the same techniques to diagnostic interviews of 40 participants that had been conducted by trained clinicians, as part of the multi-site North American Prodrome Longitudinal Study (NAPLS), funded by the National Institutes of Health. 

Vector unpacking enabled the researchers to quantify how much information was packed into each sentence.

The automated analyses of the participant samples were then compared to the normal baseline sample and the longitudinal data on whether the participants converted to psychosis.

"This research is interesting not just for its potential to reveal more about mental illness, but for understanding how the mind works” concludes senior author Phillip Wolff, a professor of psychology at Emory.

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An unexpected connection between insulin receptor and gene expression opens new doors 04-10

 The discovery of insulin in the 1920s marked the breakthrough in the almost 3,500-year-long mystery of diabetes, a disease first described in ancient Egyptian papyruses.

Until its discovery, physicians struggled to explain how symptoms such as sugary urine, constant thirst and frequent urination could lead to ailments ranging from blindness and nerve damage to coma and death.

Over the past century, scientists have detailed the hormone’s central role as a regulator of blood sugar, mapped its cell-signaling pathways and established its involvement in diabetes and a staggering array of other chronic conditions, including neurodegeneration, cardiovascular disease and cancer.

Still, many aspects of insulin signaling remain unclear, particularly its long-term effects on cells, and there are currently no effective cures for the hundreds of millions of people around the world living with diabetes.

Now, researchers from Harvard Medical School have made key new insights into the molecular behavior of insulin. Reporting online in Cell on April 4, they describe an unexpected mechanism by which insulin triggers changes to the expression of thousands of genes throughout the genome.

Their analyses show that the insulin receptor—a protein complex at the cell surface—physically relocates to the cell nucleus after it detects and binds insulin. Once there, it helps initiate the expression of genes involved in insulin-related functions and diseases. This process was impaired in mice with insulin resistance.

The results outline a set of potential therapeutic targets for insulin-related diseases and establish a wide range of future avenues of research on insulin signaling, including potential clues toward the underlying biological mechanisms that differentiate type 1 and type 2 diabetes.

“Our findings open the door for a new field of study on the insulin receptor, a remarkable protein complex expressed in almost all cells and implicated in major chronic diseases that affect hundreds of millions of people,” said senior study author John Flanagan, professor of cell biology at HMS.

“Understanding the fundamental mechanisms of how cells work can help us design new drugs or improve existing ones, and the insulin receptor certainly has potential for tremendous returns on investment,” Flanagan added.

Produced by specialized cells in the pancreas, the hormone insulin serves as the main signal to cells to absorb glucose from the bloodstream and begin the production and metabolism of carbohydrates, fats and proteins. This process is essential for normal cell function, growth and nutrient storage.

Dysfunctions in insulin signaling give rise to a number of serious chronic diseases. In type 1 diabetes, pancreatic cells fail to produce enough insulin, and in type 2 diabetes—the far more common form of the condition—cells become resistant to insulin. Without proper insulin signaling, glucose accumulates in the blood where it damages tissues and organs. Insulin resistance has also been implicated in neurodegenerative diseases such as Alzheimer’s and Parkinson’s, and excessive insulin signaling contributes to a variety of cancers.

Strange bedfellows

Flanagan and colleagues were broadly interested in studying how cell surface receptors communicate with the interior of a cell and performed screens to identify proteins associated with the insulin receptor.

Their experiments suggested that one of the most prominent such proteins is RNA polymerase, an enzyme responsible for transcribing DNA into RNA—the first step in gene expression.

This was unexpected, said Flanagan, because RNA polymerase functions inside the nucleus of a cell—far away from surface of the cell where the insulin receptor is located. Additional analyses revealed an unexpected explanation.

The team found that after the insulin receptor binds insulin, it moves from the cell surface to the nucleus via a yet unidentified mechanism. Once there, it binds to RNA polymerase on chromatin—the protein-DNA complex that cells use to store their genomes.

A genome-wide search revealed around 4,000 genomic regions where the insulin receptor bound with a degree of specificity that essentially makes random chance impossible. The striking majority of these sites were at promoters—sequences of DNA that initiate the expression of genes.

A high proportion of targeted genes were involved in insulin-related functions, particularly the synthesis and storage of lipids and proteins. Certain subsets of genes appeared to be unique to different tissue types. The analyses also identified numerous disease-related genes, including ones linked with diabetes, cancer and neurodegeneration.

Lipid paradox

Counterintuitively, the researchers found the insulin receptor does not specifically target genes involved in carbohydrate metabolism—one of the primary functions of insulin signaling.

This was an intriguing result for many reasons, Flanagan said, particularly because of the observed differences between the two major forms of diabetes. Both types involve problems with carbohydrate synthesis and storage. However, if left untreated, patients with type 1 diabetes lose weight, while type 2 diabetes is associated with obesity.

“The excessive lipid storage seen in type 2 diabetes compared with type 1 is a bit of a paradox because disrupted insulin signaling should cause issues with both lipid synthesis and storage in either condition,” he said.

“The finding that genes downstream of the pathway we identified are involved in lipid metabolism but not carbohydrate metabolism potentially gives us a window into that differential effect between carbohydrate and lipid,” Flanagan said. “But we won’t know until we perform further experiments.” 

New paths

The researchers made a number of other insights on how the insulin receptor regulates genes.

They identified several additional proteins that play a role in this process. One of particular interest was HCF-1 (host cell factor-1), which is expressed in all cells and is involved in regulating cell cycle and growth. It appears to play a critical role in recruiting the insulin receptor and other proteins to the location of a promoter to initiate gene activation.

The team also studied the effects of insulin resistance on this pathway. Giving mice glucose to trigger a rise in blood insulin led to an increase in insulin receptor-chromatin binding. Mice with insulin-resistance, however, showed 30-fold reduction in receptor-chromatin binding, an observation that suggests a high degree of sensitivity to insulin resistance.

While the insulin receptor has been studied intensely for decades, these findings represent a new pathway for insulin signaling function and shed light on potential mechanisms for the long-term effects of insulin in the body.

Intriguingly, as far back as the 1970s, scientists had clues that the insulin receptor and other members of the same class of cell surface receptors, known as receptor tyrosine kinases, can be found within the cell nucleus. These observations remained poorly understood, and the process behind them never fully described.

The identification of this pathway opens new avenues of investigation into the insulin receptor and other receptor tyrosine kinases, which function as key “on” or “off” switches for a wide range of important cellular processes.

“We were surprised to find such strong evidence that the entire insulin receptor complex moves to the nucleus, and we were initially very skeptical,” Flanagan said. “We still don’t know how exactly this happens, but we’ve pinned down the details of much of this process on a genome-wide scale.”

“A better understanding will help us improve our knowledge of the biology of insulin signaling in health and in disease, as well as other receptor tyrosine kinases, which are attractive targets for drug therapies due to their involvement in such a broad range of diseases,” Flanagan added.

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Guiding Students to Apply What They Learn 03 - 24

 My school has been encouraging the use of project-based learning (PBL) for many years, but the math department—which I’m part of—was very slow to adopt it. I bought into myths about PBL—that it takes too long, that it’s hard to assess, etc.

I wasn’t opposed to all innovations: I flipped my classroom. I allowed my students to move at their own pace. I was the first in my school to adopt a competency-based approach in my class.

But while my students were mastering individual mathematical skills, they were missing the big picture. My assessments were well aligned to the practice work and the standards, but they rarely asked students to transfer and apply their knowledge. These assessments gave me clear information about a standard on its own but not about my students’ ability to problem-solve and think critically.

Why I Chose PBL

I needed an authentic assessment that would ask students to use their skills outside the context of my class, and it seemed that PBL would help my students transfer their knowledge. And I thought that PBL could challenge my students in a way that I had never challenged them before by requiring creativity, grit, and real problem-solving.

I knew that some of my students were also taking psychology and that statistics has countless applications in that field, so I approached the psychology teacher. We began by comparing our standards to see where we might collaborate. We found that one of the Common Core math standards, “Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each,” was similar to one of the psychology standards.

Next we broke down the standard to come up with a task. The students would need to design both a survey and an observational study that would answer research questions they would design themselves. They would need to explain how they used randomization to select samples.

We also needed to ensure that the task would be authentic. The psychology teacher had a connection at the local elementary school, so we decided that our students would design their research questions around things that could be observed there.

So we had our authentic task: “What would you like to learn about the state of elementary education today? What can you learn about the patterns and behaviors of elementary school students through observation and data analysis?”

Students spent two weeks planning for a day of observations at the elementary school, and they wrote survey questions. They had to gather analyzable data on variables of their own choosing, and then they had to report objective findings.

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