Image: The Respiratory Epithelium (Photo courtesy of Wikimedia Commons)

In the study, the scientists identified ciliated cells as the major target of SARS-CoV-2 infection. The bronchial epithelium acts as a protective barrier against allergens and pathogens. Cilia removes mucus and other particles from the respiratory tract. Their findings offer insight into how the virus causes disease. The scientists infected HBECs in an air-liquid interface with SARS-CoV-2. Over a period of three days, they used single-cell RNA sequencing to identify signatures of infection dynamics such as the number of infected cells across cell types, and whether SARS-CoV-2 activated an immune response in infected cells.

The scientists utilized advanced algorithms to develop working hypotheses and used electron microscopy to learn about the structural basis of the virus and target cells. These observations provide insights about host-virus interaction to measure SARS-CoV-2 cell tropism, or the ability of the virus to infect different cell types, as identified by the algorithms. After three days, thousands of cultured cells became infected. The scientists analyzed data from the infected cells along with neighboring bystander cells. They observed ciliated cells were 83% of the infected cells. These cells were the first and primary source of infection throughout the study. The virus also targeted other epithelial cell types including basal and club cells. The goblet, neuroendocrine, tuft cells, and ionocytes were less likely to become infected.

The gene signatures revealed an innate immune response associated with a protein called Interleukin 6 (IL-6). The analysis also showed a shift in the polyadenylated viral transcripts. Lastly, the (uninfected) bystander cells also showed an immune response, likely due to signals from the infected cells. Pulling from tens of thousands of genes, the algorithms locate the genetic differences between infected and non-infected cells. In the next phase of this study, the scientists will examine the severity of SARS-CoV-2 compared to other types of coronaviruses, and conduct tests in animal models.

Machine learning allows us to generate hypotheses. Its a different way of doing science. We go in with as few hypotheses as possible. Measure everything we can measure, and the algorithms present the hypothesis to us, said senior author David van Dijk, PhD, an assistant professor of medicine in the Section of Cardiovascular Medicine and Computer Science.

Related Links:Yale School of Medicine

Follow this link:

Yale Researchers Use Single-Cell Analysis and Machine Learning to Identify Major COVID-19 Target - HospiMedica

Global Machine Learning in Medical ImagingMarket is comprehensively prepared with main focus on the competitive landscape, geographical growth, segmentation, and market dynamics, including drivers, restraints, and opportunities. This report provides a detailed and analytical look at the various companies that are working to achieve a high market share in the Global Machine Learning in Medical ImagingMarket. Data is provided for the top and fastest growing segments.

Machine Learning in Medical Imaging Market competition by top manufacturers as follow: , Zebra, Arterys, Aidoc, MaxQ AI, Google, Tencent, Alibaba,

Get a Sample PDF copy of the report @ https://reportsinsights.com/sample/13318

The global Machine Learning in Medical Imaging market has been segmented on the basis of technology, product type, application, distribution channel, end-user, and industry vertical, along with the geography, delivering valuable insights.

The Type Coverage in the Market are: , Supervised Learning, Unsupervised Learning, Reinforced Leaning

Market Segment by Applications, covers: , Breast, Lung, Neurology, Cardiovascular, Liver, Others

Market segment by Regions/Countries, this report covers North America Europe China Rest of Asia Pacific Central & South America Middle East & Africa

What does the report offer?

To get this report at a profitable rate.: https://reportsinsights.com/discount/13318

Furthermore, it offers valuable insights into the businesses for boosting the performance of the companies. Different sales and marketing approach have been mentioned to get a clear idea about how to achieve the outcomes in the industries.

The major geographical regions which include, North America, Asia Pacific, Europe, the Middle East & Africa and Latin America are studied. Top manufacturers from all these regions are studied to help give a better picture of the market investment. Production, price, capacity, revenue and many such important data is been discussed with precise data.

Most important data include the key recommendations and predictions by our analysts, intended to steer a strategic business decision. The company profiles section of this research service is a compilation of the growth strategies, financial status, product portfolio, and recent developments of key market participants. The report provides detailed industry analysis of the Global Machine Learning in Medical ImagingMarket with the help of proven research methodologies such as Porters five forces. The forces analyzed are bargaining power of the buyers, bargaining power of suppliers, threat of new entrants, threat of substitutes, and the degree of competition.

Access full Report Description, TOC, Table of Figure, Chart, etc.@ https://reportsinsights.com/industry-forecast/Machine-Learning-in-Medical-Imaging-Market-13318

About US:

Reports Insights is the leading research industry that offers contextual and data-centric research services to its customers across the globe. The firm assists its clients to strategize business policies and accomplish sustainable growth in their respective market domain. The industry provides consulting services, syndicated research reports, and customized research reports.

Contact US:

:(US) +1-214-272-0234

:(APAC) +91-7972263819

Email:info@reportsinsights.com

Sales:sales@reportsinsights.com

More:

Astonishing growth in Machine Learning in Medical Imaging Market | Competitive Analysis, Industry Dynamics, Growth Factors and Opportunities - Daily...

Machine Learning in Retail Market report is to provide accurate and strategic analysis of the Profile Projectors industry. The report closely examines each segment and its sub-segment futures before looking at the 360-degree view of the market mentioned above. Market forecasts will provide deep insight into industry parameters by accessing growth, consumption, upcoming market trends and various price fluctuations.

Machine Learning in Retail Market competition by top manufacturers as follow: , IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze

Get a Sample PDF copy of the report @ https://reportsinsights.com/sample/13166

Global Machine Learning in Retail Market research reports growth rates and market value based on market dynamics, growth factors. Complete knowledge is based on the latest innovations in the industry, opportunities and trends. In addition to SWOT analysis by key suppliers, the report contains a comprehensive market analysis and major players landscape. The Type Coverage in the Market are: , Cloud Based, On-Premises

Market Segment by Applications, covers: , Online, Offline

Market segment by Regions/Countries, this report covers North America Europe China Rest of Asia Pacific Central & South America Middle East & Africa

To get this report at a profitable rate.: https://reportsinsights.com/discount/13166

Important Features of the report:

Reasons for buying this report:

Access full Report Description, TOC, Table of Figure, Chart, etc.@ https://reportsinsights.com/industry-forecast/Machine-Learning-in-Retail-Market-13166 About US:

Reports Insights is the leading research industry that offers contextual and data-centric research services to its customers across the globe. The firm assists its clients to strategize business policies and accomplish sustainable growth in their respective market domain. The industry provides consulting services, syndicated research reports, and customized research reports.

Contact US:

:(US) +1-214-272-0234

:(APAC) +91-7972263819

Email:info@reportsinsights.com

Sales:sales@reportsinsights.com

More:

Covid-19 Positive Impact on Machine Learning in Retail Market 2020-2025 Country Level Analysis, Current Trade Size And Future Prospective - Daily...

Last week, OpenAI published a paper detailing GPT-3, a machine learning model that achieves strong results on a number of natural language benchmarks. At 175 billion parameters, where a parameter affects datas prominence in an overall prediction, its the largest of its kind. And with a memory size exceeding 350GB, its one of the priciest, costing an estimated $12 million to train.

A system with over 350GB of memory and $12 million in compute credits isnt hard to swing for OpenAI, a well-capitalized company that teamed up with Microsoft to develop an AI supercomputer. But its potentially beyond the reach of AI startups like Agolo, which in some cases lack the capital required. Fortunately for them, experts believe that while GPT-3 and similarly large systems are impressive with respect to their performance, they dont move the ball forward on the research side of the equation. Rather, theyre prestige projects that simply demonstrate the scalability of existing techniques.

I think the best analogy is with some oil-rich country being able to build a very tall skyscraper, Guy Van den Broeck, an assistant professor of computer science at UCLA, told VentureBeat via email. Sure, a lot of money and engineering effort goes into building these things. And you do get the state of the art in building tall buildings. But there is no scientific advancement per se. Nobody worries about the U.S. is losing its competitiveness in building large buildings because someone else is willing to throw more money at the problem. Im sure academics and other companies will be happy to use these large language models in downstream tasks, but I dont think they fundamentally change progress in AI.

Indeed, Denny Britz, a former resident on the Google Brain team, believes companies and institutions without the compute to match OpenAI, DeepMind, and other well-funded labs are well-suited to other, potentially more important research tasks like investigating correlations between model sizes and precision. In fact, he argues that these labs lack of resources might be a good thing because it forces them to think deeply about why something works and come up with alternative techniques.

There will be some research that only [tech giants can do], but just like in physics [where] not everyone has their own particle accelerator, there is still plenty of other interesting work, Britz said. I dont think it necessarily creates any imbalance. It doesnt take opportunities away from the small labs. It just adds a different research angle that wouldnt have happened otherwise. Limitations spur creativity.

OpenAI is a counterpoint. It has long asserted that immense computational horsepower in conjunction with reinforcement learning is a necessary step on the road to AGI, or AI that can learn any task a human can. But luminaries like Milafounder Yoshua Bengio and Facebook VP and chief AI scientist Yann LeCunargue that AGI is impossible to create, which is why theyre advocating for techniques like self-supervised learning and neurobiology-inspired approaches that leverage high-level semantic language variables. Theres also evidence that efficiency improvements might offset the mounting compute requirements; OpenAIs own surveys suggestthat since 2012, the amount of compute needed to train an AI model to the same performance on classifying images in a popular benchmark (ImageNet) has been decreasing by a factor of two every 16 months.

The GPT-3 paper, too, hints at the limitations of merely throwing more compute at problems in AI. While GPT-3 completes tasks from generating sentences to translating between languages with ease, it fails to perform much better than chance on a test adversarial natural language inference that tasks it with discovering relationships between sentences. A more fundamental [shortcoming] of the general approach described in this paper scaling up any model is that it may eventually run into (or could already be running into) the limits of the [technique], the authors concede.

State-of-the-art (SOTA) results in various subfields are becoming increasingly compute-intensive, which is not great for researchers who are not working for one of the big labs, Britz continued. SOTA-chasing is bad practice because there are too many confounding variables, SOTA usually doesnt mean anything, and the goal of science should be to accumulate knowledge as opposed to results in specific toy benchmarks. There have been some initiatives to improve things, but looking for SOTA is a quick and easy way to review and evaluate papers. Things like these are embedded in culture and take time to change.

That isnt to suggest pioneering new techniques is easy. A 2019 meta-analysis of information retrieval algorithms used in search engines concluded the high-water mark was actually set in 2009. Another study in 2019 reproduced seven neural network recommendation systems and found that six failed to outperform much simpler, non-AI algorithms developed years before, even when the earlier techniques were fine-tuned. Yet another paper found evidence that dozens of loss functions the parts of algorithms that mathematically specify their objective had not improved in terms of accuracy since 2006. And a study presented in March at the 2020 Machine Learning and Systems conference found that over 80 pruning algorithms in the academic literature showed no evidence of performance improvements over a 10-year period.

But Mike Cook, an AI researcher and game designer at Queen Mary University of London, points out that discovering new solutions is only a part of the scientific process. Its also about sussing out where in society research might fit, which small labs might be better able determine because theyre unencumbered by the obligations to which privately backed labs, corporations, and governments are beholden. We dont know if large models and computation will always be needed to achieve state-of-the-art results in AI, Cook said. [In any case, we] should be trying to ensure our research is cheap, efficient, and easily distributed. We are responsible for who we empower, even if were just making fun music or text generators.

See more here:

OpenAIs massive GPT-3 model is impressive, but size isnt everything - VentureBeat

IEDs and so-called butterfly landminescould be detected over wide areas using drones and advanced machine learning, according to research from Binghamton University, State University at New York.

The team had previously developed a method that allowed for the accurate detection of butterfly landmines using low-cost commercial drones equipped with infrared cameras.

EPSRC-funded project takes dual approach to clearing landmines

Their new research focuses on automated detection of landmines using convolutional neural networks (CNN), which they say is the standard machine learning method for object detection and classification in the field of remote sensing. This method is a game-changer in the field, said Alek Nikulin, assistant professor of energy geophysics at Binghamton University.

All our previous efforts relied on human-eye scanning of the dataset, Nikulin said in a statement.Rapid drone-assisted mapping and automated detection of scatterable mine fields would assist in addressing the deadly legacy of widespread use of small scatterable landmines in recent armed conflicts and allow to develop a functional framework to effectively address their possible future use.

There are at least 100 million military munitions and explosives of concern devices in the world, of various size, shape and composition. Furthermore,an estimated twenty landmines are placed for every landmine removed in conflict regions

Millions of these are surface plastic landmines with low-pressure triggers, such as the mass-produced Soviet PFM-1 butterfly landmine. Nicknamed for their small size and butterfly-like shape, these mines are extremely difficult to locate and clear due to their small size, low trigger mass and a design that mostly excluded metal components, making them virtually invisible to metal detectors.

The design of the mine combined with a low triggering weight have earned it notoriety as the toy mine, due to a high casualty rate among small children who find these devices while playing and who are the primary victims of the PFM-1 in post-conflict nations, like Afghanistan.

The researchers believe that these detection and mapping techniques are generalisable and transferable to other munitions and explosives. They could be adapted to detect and map disturbed soil for improvised explosive devices (IEDs).

The use of Convolutional Neural Network-based approaches to automate the detection and mapping of landmines is important for several reasons, the researchers said in a paper published inRemote Sensing. One, it is much faster than manually counting landmines from an orthoimage (i.e. an aerial image that has been geometrically corrected). Two, it is quantitative and reproducible, unlike subjective human error-prone ocular detection. And three, CNN-based methods are easily generalisable to detect and map any objects with distinct sizes and shapes from any remotely sensed raster images.

More here:

Butterfly landmines mapped by drones and machine learning - The Engineer

The psychedelic experience can be rough on a person's ego. Those who experiment with magic mushrooms and LSD often describe a dissolution of the self, otherwise known as ego-death, ego-loss, or ego-disintegration.

For some, the experience is life-changing; for others, it's downright terrifying. Yet despite anecdote after anecdote of good trips and bad trips, no one really knows what these drugs actually do to our perception of self.

The human brain's cortex is where the roots of self awareness are thought to lie, and growing evidence has shown the neurotransmitter, glutamate, is elevated in this region when someone is tripping.

But up until now we've only had observational evidence. Now, for the first time, researchers have looked directly into how taking psilocybin affects glutamate activity in the brain. And the evidence suggests thatour tripping experience, whether good or bad, might be linked to glutamate.

In a double-blind, placebo-controlled experiment, neuroscientists carefully analysed what happens to glutamate levels and a person's ego when taking psilocybin, the active ingredient in magic mushrooms.

Using magnetic resonance imaging (MRI) to monitor the brains of 60 healthy volunteers, the team found significant changes in activity in both the cortex and the hippocampus in those taking psilocybin.

Glutamate is the most common neurotransmitter in the brain, and it's known to be critical for fast signalling and information, especially in the cortex and hippocampus, the latter of which is thought to play a role in self esteem.

It also looks like psychedelics have a way of tapping into this system.

Interestingly enough, in the new clinical study, these two regions of the brain had quite different glutamate responses to psilocybin. While the authors found higher levels of glutamate in the prefrontal cortex during a trip, they actually found lower levels of glutamate in the hippocampus.

What's more, this may have something to do with whether a person has a good experience with their ego or a bad one.

"Analyses indicated that region-dependent alterations in glutamate were also correlated with different dimensions of ego dissolution," the authors write.

"Whereas changes in [cortical] glutamate were found to be the strongest predictor of negatively experienced ego dissolution, changes in hippocampal glutamate were found to be the strongest predictor of positively experienced ego dissolution."

Practically, we still don't really understand how this activity in the brain is linked to our ego, or even if it is. Still, it's been suggested that psychedelics decouple regions of the brain, so factual or autobiographical information is momentarily separated from a sense of personal identity.

"Our data add to this hypothesis, suggesting that modulations of hippocampal glutamate in particular may be a key mediator in the decoupling underlying feelings of (positive) ego dissolution," the authors suggest.

After decades of limited research, drugs like psilocybin, LSD and DMT are now finally being considered for their therapeutic benefits.

Understanding how these drugs work on a neurochemical basis could allow scientists to develop better treatments for those with mental health issues, such as depression and anxiety.

Although if we're going to be using these substances to treat mental health issues like anxiety, depression and addiction, we're going to need to also understand the way the drugs mess with our ego - hopefully without the bad trip to go along with it.

The study was published in Neuropsychopharmacology.

Go here to read the rest:
First-of-Its-Kind Study Hints at How Psilocybin Works in The Brain to Dissolve Ego - ScienceAlert

BOSTON (CBS) There will be no olive branch between Barstool Sports founder Dave Portnoy and NFL commissioner Roger Goodell.

Portnoy and Barstool have a now infamous rivalry with Goodell, stemming back to Deflategate and the suspension of Tom Brady.

When Goodell announced during the NFL Draft that the person who won an auction to benefit coronavirus charities would have the chance to watch Monday Night Football at his home, Portnoy went to work and won with a bid surpassing $250,000.

But Friday night, Portnoy posted on Twitter that the NFL alerted him he would not be allowed into Goodells home because he did not pass a background check due to a series of run-ins with the league. Among other things, Portnoy was arrested during a Free Brady protest at league headquarters, snuck into Super Bowl media night, and was later dragged out of the game by security.

We knew it was going to happen, Portnoy posted on TwitterFriday night.

Portnoy said the NFL told him his credit card will not be charged and the league will donate the amount to the charities.

You did the research on me? You dont have to do the research on me. You have a file on me the size of Niagara, said Portnoy. People are like Oh. Roger Goodell will play ball. Hell make himself look good. I told everybody, Roger Goodell has no self-awareness. No sense of humor. Doesnt know how to deal with a brain like this.

I won it fair and square and they say no. Why? Because theyre afraid of me. Theyre afraid of the brain. And theyre not going to have some fun with it.

View original post here:
Barstools Dave Portnoy Wont Be Allowed To Watch Monday Night Football At Roger Goodells House - CBS Boston

This column is part of our ongoing opinion commentary on faith, called Living Our Faith. Find this weeks reader question and get weekly roundups of the project in your email inbox by signing up for the Living Our Faith newsletter.

In John Donnes Ninth Holy Sonnet, there is a terrible moment when the poems speaker blasphemes, suggesting that if God can do anything, then he can certainly forgive any sin (including telling ones creator how to run the universe). The speaker catches himself, and to the relief of faithful readers, utters an abashed rhetorical question: But who am I that dare dispute with thee/O God?

A moments failing is resolved in penitence, certainty is restored, all is well. Except that Donne does not provide instructions insisting the question is rhetorical. If it is not, then the speaker, after his freedom of conscience leads him into defiance, is left with doubt: God, why did you give us the freedom to cross lines you said never to cross?

Doubt pervades Donnes religious art, and the art has survived and is still debated precisely because it evokes, but does not pretend to resolve, this fundamental and sometimes agonizing human experience. Donnes readers have a choice a blue pill or red pill moment in which an unchallenging faith may be restored in a moment of foreclosed options, or faith becomes infinitely more complex and daunting. This choice is a struggle, an open mind confronted with a world that is more complex than can be immediately understood.

I mention Donne because, several years ago, I had a student who, when we discussed the poem mentioned above, told me (in tears) that her minister had proclaimed doubt to be a sin. I discussed the matter one day with a Baptist youth minister who worked with some of my other students. He found it appalling that a fellow cleric had so casually demanded impressionable followers to sever themselves from an essential aspect of their humanity in the name of faith. I will never forget his description of this advice to switch off doubt: A spiritual lobotomy performed without anesthesia.

I have always considered that my primary purpose is to present puzzles made out of words John Donnes are sublime and to ask young people to take the supreme risk of casting aside the comfortable lie that a poem, or the world, comes with instructions for easy understanding. In the best of times, some students struggle with competing standards set by those whom they trust.

This year was the worst of times. Our school closed its doors to students in early March, in response to a pandemic that first seemed likely to be transient and even something of an adventure. Most students were engaged, and many were eager to discuss demanding ethical issues directly connected to the new normal.

But April was crueler, and brought a closure more ominous than the physical shuttering of the school. Isolation, loss of routine and fear of economic uncertainty wrought graver changes, drawing far too many of my students away from any academic engagement and towards despair.

What seemed to challenge my students most viscerally was a faith-shattering confrontation with a world of uncertainty. The pandemic was (and is) relentlessly elusive its purpose and its containment beyond any adult assurances designed to comfort or to engender faith in the capacity of religion or science (or politics) to make the natural world cooperate in preserving the trust and innocence of young people.

In the end, my students have had to confront the same terrible dilemma that tore apart the girl who was ordered never to doubt: There are times, like some poems, too fraught with unknowns to be covered up by any demand simply to maintain faith. As Donne demonstrates, faith is ultimately not a simple thing at all. It demands not infinite awareness, but self-awareness, the serenity to accept the things that cannot be changed or even understood.

Some students accepted the profound limits of adult understanding (my own included): They have grown in wisdom and humility. But others withdrew from all attempts to engage with schoolwork or even to be contacted. I am more afraid of what these withdrawn ones have lost than I am of anything else I do not know about the world right now.

David Newman is a high school English teacher in Odessa. He wrote this column for The Dallas Morning News.

Continued here:
Many teens are wrestling with doubts about God amid the pandemic; I'm worried about those who refuse to engage - The Dallas Morning News

On Tuesday, Major League Baseball, following weeks of a will they, wont they that would make Greys Anatomy proud, finally offered up an economic proposal to the Players Association. This marked the culmination of a two-week period where owners, after internally approving a 50-50 revenue split with the players in March, stopped leaking plans/information to the public long enough to send a copy to MLBPA. For now, owners have moved off their 50-50 revenue sharing idea, as well as their proposal for deferrals. Their initial proposal to MLBPA turned out to be one of their newest ideas: a sliding scale salary where higher paid players face higher cuts than lower paid players.

According to Jeff Passan, the sliding scale works like this:

Thats right, folks; the owners produced a plan where higher paid players accept losses just so that the lower paid players do not have to. The owners, worth billions of dollars, created a proposal wherein the richer players pay more into the system than lower paid players. In arguing why the players (not billionaires) should take more of a financial hit, the owners (are billionaires) proposed a system of progressive taxation. If this seems like a cosmic lack of self-awareness, well, thats because it is.

But self-awareness would lead to learning a lesson, something MLB owners have never had a desire to do. Instead, they want to double-down on becoming some version of an evil cartoon caricature where the schemes are always transparent, the goals are always obvious, and the worst-case is always thwarted. If you do not believe me, lets break down the actual financials of this proposal.

I used two data sources (Cots contracts and Spotrac player salaries) to run the numbers through the proposed sliding scale. Both data sources contain projected 2020 salaries, although Cots does not contain any player making below $600,000. Regardless, think of the numbers coming from Spotrac as a closer representation of the truth, while the numbers coming from Cots should serve as a minimum. All told, the numbers themselves are almost unbelievable:

Major League Baseball proposed a system which lowered full player salaries from $4.04 billion to $0.97 billion a truly staggering cut. The sliding scale proposal also represented a substantial cut from prorated salaries, which would go from $2.05 billion to $0.97 billion. Both Cots and Spotrac data describe a scenario where the owners aimed to institute a 50+ percent cut from the prorated salaries agreed to in March:

If it were not so nefarious and blatantly immoral, having this proposal be the culmination of a month-worth of saber rattling might almost be comical. Here we are, amid one of the worst time periods many of us have faced in our lifetimes, and the owners have refused to break character, even if for a minute. Commitment to the bit is one thing, but committing to be a cartoonish caricature of rampant, unchecked capitalism is another.

Consider for a moment the figures MLB presented to MLBPA (released through the Associated Press), where MLB projected to see significant, deep revenue losses. In those figures, the players were projected to receive the full prorated salary, over an 82-game season. You know, the salary both sides agreed to in March. When we aggregate salary cuts (using Spotrac data) and line them up against what each MLB team projected to lose, in terms of EBITDA, one thing is clear the cuts do not actually change much:

The final column here is the scenario where players get paid nothing for an 82-game season. This scenario will not happen, but it serves to reinforce the point. Using MLBs own numbers and taking them at face value (you should not, but bear with me), each team is destined for significant financial losses in an 82-game season. No matter what the additional salary cuts could be 50, 75, or 100 percent no amount is going to help teams turn positive profits in 2020. Put another way, there is nothing the players can give that would make a dent.

Yet, in this case, an analysis of EBITDA losses only works if you do something ill-advised take the owners at face value. As I alluded to before, there is no reason to do that. Excluded from the numbers MLB presented to MLBPA are the many sources of revenue that baseball owners have used baseball funds to cultivate for decades. These investment vehicles help temper financial losses in a half-season, particularly a half-season without fans. They are also entirely inaccessible to the MLBPA.

In truth, this was never a serious offer. It was never an offer that MLB expected MLBPA to accept. It was not even one of those really bad initial offers you get from someone who just googled how to bargain and followed step 1. This offer was part union busting tactic, part PR move a ridiculous concoction where the subtext is truly all that matters.

The part union breaking tactic reveals itself when you consider the spread in salary among MLBPA. At the top, a small group of players make upwards of $25 million. At the bottom, most of the pre-arbitration eligible players make MLB minimum. The idea behind the offer was to force the richer, smaller group of players into a bind where preservation of the deal already in place creates tension within the union itself. The goal was to make the richer players look like they are defending their own salaries at the expense of the players who make much less. In this part, it is the owners who are willing to take losses, but it is the richest players who want no losses.

The part PR move works in the same manner. Players at the top-end of the scale who call the proposal terrible are to be painted as greedy and acting in a self-preserving manner. The fans are now provided with a target for all that disappointment and frustration about a season that never happened. In this part, it is the owners who are willing to take losses, but it is those greedy players (particularly the rich ones) who want no losses.

The goal of this offer was to paint the MLBPA as a roadblock to another season of baseball. To offer up MLBs most well-known and widely loved players as greedy and selfish, unable to play a childs game at a smaller salary. The goal of this proposal was to launch a bad-faith attack on the very reason MLB exists in the first place the players.

Quite obviously, this proposal will fail. And it should. If MLB is going to keep all the profits when things are going great, they should own the losses when things are bad. It is this sticking point that the owners have refused to acknowledge, and it is exactly this sticking point that will ensure MLB wont return in 2020. When considering the proposed additional pay cuts, one thing is clear: the players stand to lose much more than the owners stand to gain.

Shawn Brody is a graduate student and contributor for Beyond the Box Score. You can find him on Twitter @ShawnBrody, where he likes to yell about New York Mets.

View original post here:
MLBs proposal is actually a $1 billion pay cut from prorated salaries - Beyond the Box Score

World Meditation Day 2020: Paloma Gangopadhyay, Celebrity Yoga Instructor and Meditation Expert, has shared beginner's guide to meditation. Read on to know more.

Stress and anxiety are very common these days. Many of us fight with these mental health monsters almost daily. And now, stress has gone beyond roof as we cope up with COVID-19 pandemic and lockdown. For the unversed, one of the best ways to deal with them is meditation. As per a study, just a single session of mindful meditation can reverse the physical and psychological toll of stress on the body. Not just stress and anxiety, it can also help to reduce the chances of other health complications including cardiovascular diseases which are related to chronic stress. Speaking of the other Science-backed health benefits, better emotional health, better self-awareness, better sleep, helps to control pain, decreases blood pressure among others.

In an exclusive interview with Pinkvilla, Paloma Gangopadhyay, Celebrity Yoga Instructor and Meditation Expert, opened up on the same. She said, "Immerse in your deepest realization of the self. You realise your true purpose of being (existence) when you meditate. Meditation brings out a new perspective in your thoughts. You can think clearly and in the right direction. It brings you clarity of thought and effective action. We look for peace when we have it in us and meditation is that journey to the self that brings back the peace in us and wisdom of understanding. Meditation is your key to understand your true self and the universe around you. It increases your power of concentration and the ability to control your emotions and it can be very useful for temper management and control your mood swings."

If you want to meditate and have no idea about it fret not. Paloma also shared beginner's guide for the Pinkvilla readers.

Beginner's Guide to Meditation:

Meditation is an introspective process. It is an ancient science and philosophy behind self-realization and God-realisation. It is a technique to self-heal and connect the body with the mind and spirit, thus channelizing positivity throughout your being. But the technique requires understanding:

1. Choose a quiet corner of your room, sit down quietly, arms by your side, or on the knees as you are comfortable. If you have a weak spine you can rest against the wall. (Posture preferable is lotus posture or vajrasana).

2. Look intensely in front of you, keep your spine straight, breathing normal and slowly close your eyelids and transport your mind to a distant land, a distant thought or memory in your mind which makes you happy and smile, then slowly channelize your emotions towards that very moment or thought.

3. Take a long inhale and a long exhale. Slowly your mind calms. Various other thoughts may keep coming in your mind. At that moment, just let go. Allow your emotions and feelings to flow. Maintain your stillness, maintain the calm.

4. Keep your shoulder relaxed. Concentrate on your breathing. Inhale and exhale using your nostril but the throat is the passageway of the breathing. Inhaling long, as much air you can take in, and exhale, thus, increasing the lung capacity.

5. Thoughts would keep pouring, just keep breathing and stay still and calm. Keep your eyes closed. Belly rise and fall. Let go of yourself of all the emotions, i.e. happiness, anger, frustration, sorrow, despair, hope.

6. Let the tears roll down. Remember it's your healing and it might bring tears. Thats normal.

x Your comment has been submitted to the moderation queue

Continued here:
World Meditation Day 2020: Check out the step by step beginner's guide to meditation - PINKVILLA