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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. – DocWire…

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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

PLoS Med. 2020 Jun;17(6):e1003149

Authors: Atabaki-Pasdar N, Ohlsson M, Viuela A, Frau F, Pomares-Millan H, Haid M, Jones AG, Thomas EL, Koivula RW, Kurbasic A, Mutie PM, Fitipaldi H, Fernandez J, Dawed AY, Giordano GN, Forgie IM, McDonald TJ, Rutters F, Cederberg H, Chabanova E, Dale M, Masi F, Thomas CE, Allin KH, Hansen TH, Heggie A, Hong MG, Elders PJM, Kennedy G, Kokkola T, Pedersen HK, Mahajan A, McEvoy D, Pattou F, Raverdy V, Hussler RS, Sharma S, Thomsen HS, Vangipurapu J, Vestergaard H, t Hart LM, Adamski J, Musholt PB, Brage S, Brunak S, Dermitzakis E, Frost G, Hansen T, Laakso M, Pedersen O, Ridderstrle M, Ruetten H, Hattersley AT, Walker M, Beulens JWJ, Mari A, Schwenk JM, Gupta R, McCarthy MI, Pearson ER, Bell JD, Pavo I, Franks PW

AbstractBACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or 5%) rather than a continuous one.CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

PMID: 32559194 [PubMed as supplied by publisher]

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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. - DocWire...

Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill…

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The global Machine Learning in Finance Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning in Finance Market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning in Finance Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning in Finance Market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning in Finance Market.

Leading players of the global Machine Learning in Finance Market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Machine Learning in Finance Market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the global Machine Learning in Finance Market. It also provides useful recommendations for new as well as established players of the global Machine Learning in Finance Market.

Final Machine Learning in Finance Report will add the analysis of the impact of COVID-19 on this Market.

Machine Learning in Finance Market competition by top manufacturers/Key player Profiled:

Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

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With the slowdown in world economic growth, the Machine Learning in Finance industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Machine Learning in Finance market size to maintain the average annual growth rate of 15 from XXX million $ in 2014 to XXX million $ in 2019, This Report analysts believe that in the next few years, Machine Learning in Finance market size will be further expanded, we expect that by 2024, The market size of the Machine Learning in Finance will reach XXX million $.

Segmentation by Product:

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

Segmentation by Application:

BanksSecurities Company

Competitive Analysis:

Global Machine Learning in Finance Market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Machine Learning in Finance Market for Global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Scope of the Report:The all-encompassing research weighs up on various aspects including but not limited to important industry definition, product applications, and product types. The pro-active approach towards analysis of investment feasibility, significant return on investment, supply chain management, import and export status, consumption volume and end-use offers more value to the overall statistics on the Machine Learning in Finance Market. All factors that help business owners identify the next leg for growth are presented through self-explanatory resources such as charts, tables, and graphic images.

Key Questions Answered:

Our industry professionals are working reluctantly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions. We acknowledge everyone who is doing their part in this financial and healthcare crisis.

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Table of Contents

Report Overview:It includes major players of the global Machine Learning in Finance Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global Machine Learning in Finance Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global Machine Learning in Finance Market are discussed.

Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application:Besides an overview of the global Machine Learning in Finance Market by application, it gives a study on the consumption in the global Machine Learning in Finance Market by application.

Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles:Almost all leading players of the global Machine Learning in Finance Market are profiled in this section. The analysts have provided information about their recent developments in the global Machine Learning in Finance Market, products, revenue, production, business, and company.

Market Forecast by Production:The production and production value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the global Machine Learning in Finance Market.

Key Findings: This section gives a quick look at important findings of the research study.

About Us:Report Hive Research delivers strategic market research reports, statistical surveys, industry analysis and forecast data on products and services, markets and companies. Our clientele ranges mix of global business leaders, government organizations, SMEs, individuals and Start-ups, top management consulting firms, universities, etc. Our library of 700,000 + reports targets high growth emerging markets in the USA, Europe Middle East, Africa, Asia Pacific covering industries like IT, Telecom, Semiconductor, Chemical, Healthcare, Pharmaceutical, Energy and Power, Manufacturing, Automotive and Transportation, Food and Beverages, etc. This large collection of insightful reports assists clients to stay ahead of time and competition. We help in business decision-making on aspects such as market entry strategies, market sizing, market share analysis, sales and revenue, technology trends, competitive analysis, product portfolio, and application analysis, etc.

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Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill...

This Startup Is Trying to Foster an AI Art Scene in Korea – Adweek

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A South Korean startup is holding a competition to fill one of the worlds first galleries for machine learning-generated art in a bid to foster a nascent artificial intelligence creativity scene in the country.

The company, Pulse9, which makes AI-powered graphics tools, is soliciting art pieces that make use of machine learning tech in some waywhether to produce an image out of whole cloth or restyle or supplement an artists workthrough the end of September.

The project is a notable addition to a burgeoning global community of technologists, new media artists and other creatives who are exploring the bounds of machine creativity through art, spurred by recent research advances that have made AI-generated content more realistic and elaborate than ever.

The medium had perhaps its biggest mainstream breakthrough in 2018, when Christies Auction House sold its first piece of AI-generated art for nearly half a million dollarsa classical style painting of a fictional character named Edmond de Belamy. That was also the moment that inspired the team at Pulse 9, which had just launched an AI tool to help draw and color a Korean style of digital comic called webtoons earlier that year.

We asked ourselves, Could we also sell paintings? and we started looking for art platform companies to work with, Pulse 9 spokesperson Yeongeun Park said.

The company teamed with an art platform called Art Together on a series of crowdfunded AI pieces that proved to be more popular than they had expectedone hit its goal a full week ahead of scheduleand the team began considering parlaying it into a bigger project.

With great attention from the public and the good funding results, we gained confidence in pioneering the Korean AI art market, Park said. So, we eventually decided to open our own AI art gallery.

The company acknowledges that questions of authorship and originality still hang over the concept of AI art but stresses that the gallery is about collaboration between humans and technology rather than AI simply replacing artists. Even pieces generated entirely by machines require a host of human touches, whether its curating a collection of visuals for training or adjusting training regimens to achieve a desired results.

The theme of this competition is Can AI art enhance human artistic creativity?' Park said. We hope that this competition will also be an opportunity to discover creative, competent and new artists who would like to engage AI tools as a new artistic medium in their artwork.

The goal is to establish AIA Gallery as a well-recognized institution in the art world and educate people on the potential for AI-powered creativity. The organizers hope the process will also inspire other efforts and create an AI creativity hub in the country.

Groups or communities of AI artists have formed and are gradually growing, especially overseas, Park said. In the case of Korea, the AI Art market has not been well-recognized yet, but weve been continuing to play our role with our own initiative.

The AIA Gallery recently partnered with one of the leading startups in the new space, Playform, which is led by Rutgers University Art and AI Lab director Ahmed Elgammal (after learning about the company from an Adweek article).

Progress in generative AI creativity isnt confined to the art world, either. Agencies have started to experiment with various AI-generated graphics in campaigns, and brands have filed a slew of patent applications around the central technology powering the revolutiona neural net structure called a generative adversarial network.

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This Startup Is Trying to Foster an AI Art Scene in Korea - Adweek

5 Reasons Artificial Intelligence Will Improve Greenhouse Production – Greenhouse Grower

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Artificial intelligence (AI) involves using computers to do things that traditionally require human intelligence. This means creating algorithms to classify, analyze, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time.

Thats the definition of AI, at least. But what does it actually mean for greenhouse growers?

According to Gursel Karacor, Senior Data Scientist at Grodan, a supplier of sustainable stone wool growing media solutions for the horticulture market, greenhouses will, to a large extent, be autonomous in the near future.

My mission is the realization of autonomous greenhouses through the use of all this data with state-of-the-art machine learning methodologies, Karacor says. I want to realize this goal step-by-step in five years.

Click here to learn more about why AI will change the way you work, for the better.

Gursel Karacor is a Senior Data Scientist with Grodan. See all author stories here.

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5 Reasons Artificial Intelligence Will Improve Greenhouse Production - Greenhouse Grower

Crypto Exchanges And Bitcoin Are Poised For Massive Growth By 2030 – Forbes

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U.S. crypto exchanges made an estimated $1 billion in trading fee revenue in 2019 and are poised for ... [+] further growth as retail participation increases.

Crypto is a disruptive technology designed to be an alternative to the fiat monetary system and fundamentally challenge countless industries. Over the next decade, the space will compete with incumbent financial services and banking institutions and crypto exchanges are poised to capture the growth.

Historically, exchanges have served as the primary access point introducing users to crypto and enabling them to engage with a variety of crypto assets. Crypto started as a retail phenomenon marking the first time retail investors were able to access a new asset class prior to institutional investors. Thus, retail focused crypto exchanges positioned themselves to serve retail demand. In just 8 years, Coinbase propelled crypto to the mainstream serving over 30 million users and other exchanges followed suit as crypto entered the public consciousness.

The internet is often touted as the closest analogy to the emergence of crypto and blockchain technology. The internet was a fundamentally disruptive and paradigm shifting technology, forever changing the way users interact, communicate, and conduct commerce. Crypto very well may exhibit similar societal change, and thus its growth trend may mimic that of the internet. User adoption of the internet achieved hockey stick growth, and it reached 10% of American households in 1995, five years after the first web browser was launched. User adoption reached 50% in the U.S. by the year 2000.

Reports vary and user adoption of crypto in the U.S. is currently reported to be approximately 5%. Although Bitcoin is 11 years old and has come a long way, it has yet to see hockey stick growth in terms of user adoption. Bitcoin is currently working through issues of scalability, privacy, and ease of use, which are all things the internet had to overcome in order to reach maturity. Assuming Bitcoins growth story follows that of the internet, Bitcoin is positioned to achieve user adoption between 20-50% by the year 2030.

To project future exchange growth in the U.S., I assumed 5% user adoption of crypto in the US currently and calculated revenue growth if user adoption reaches 10% (conservative case), 20% (base case), and 50% (optimistic case) in the year 2029.

Using Messaris Real 10 exchange volume data set, the aggregate exchange volume of US trading activity in 2019 was over $227 billion. Growing from $1.3 billion in estimated exchange volume in 2015, this increase represents a compounded annual growth rate (CAGR) of 15.7% per year.

Estimated BTC/USD exchange volume for the years 2015-2019.

Further, assuming the average trading fee was 0.42% (using Krakens fee schedule, 16 basis points for maker and 26 basis points for taker), aggregate exchange revenue from trading fees was approximately $956 million in 2019.

Estimated BTC/USD trading fee exchange revenue for the years 2015-2019.

Now, taking the assumption that crypto adoption is currently 5% in the U.S., we can estimate the future projected exchange revenue across the three scenarios of adoption in 2029 (10%, 20%, and 50% adoption). When doing so, the resulting exchange revenues in 2029 for each scenario are $1.9 billion in the conservative case, $3.8 billion in the base case, and $9.6 billion in the optimistic case. Assuming linear growth, the exchange revenues per year are shown below.

Projected BTC/USD trading fee exchange revenue for the years 2020-2029 estimated for three adoption ... [+] scenarios.

Since the launch of the first web browser in 1990, the internet took just seven years to reach 20% user adoption in the U.S. The exchange revenue base case explored above assumes the same user penetration of 20% would be reached 19 years after the launch of the first mainstream Bitcoin exchange, Mt. Gox. Considering the explosive growth of crypto networks and the acceleration of technology writ large, this assumption may serve as a lower bound of user adoption.

Furthermore, this analysis only includes Bitcoin spot trading revenue and does not factor in other sources of exchange revenue such as trading fees from other cryptoassets, derivatives/futures trading, staking, asset withdrawal/deposit fees, net interest margin, asset lending, etc.

Although the 50% user adoption optimistic scenario may seem far-fetched, there are indicators pointing to the possibility. Compared to the institutional crypto market, retail users have been much quicker to adopt crypto and more eager to gain exposure. Bitcoin reached its all-time high of ~$20,000 in December 2017 with virtually zero institutional participation, as retail investors sought to front run the first institutional-grade cash-settled Bitcoin futures markets (CME & CBOE).

As of mid-2019, Binance Research estimated just 7% of crypto assets are held by institutional investors. Furthermore, Bitwises financial advisor survey estimated 6% of financial advisors were allocating crypto to their clients portfolios, which is expected to double to 13% in 2020. Despite retail participants acting as the primary driver of the crypto markets, only 5% of the total U.S. population own or use crypto currently. Although institutionalization of the space is happening, there is still ample growth potential amongst retail, which has served as the core user base to date.

Over the next ten years, we may see the most growth in the demographic of people currently between the ages 18-39 and living in cities/suburbs (excluding rural areas). This cohort is familiar with digital technologies and virtual goods. According to this report by Schwab the Grayscale Bitcoin Trust is already the fifth largest holding amongst Millennials, greater than Disney, Netflix NFLX , and Microsoft MSFT . By 2030, millennials will inherit $68 trillion from the baby boomer generation. With bond yields at historic lows and asset prices at historic highs, young adults are looking for new ways to generate yield and store their wealth.

To date, retail-focused crypto exchanges have fueled the growth of the crypto market to reach its current market size of $270 billion. Although institutional investors are poised to enter the market, retail investors and users will continue to serve as its foundation. As new use cases and killer apps emerge, retail users will flood the market and exchanges are poised to capture this growth.

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Crypto Exchanges And Bitcoin Are Poised For Massive Growth By 2030 - Forbes

As Markets End The Week In Red – Data Analysis Explains Why Bitcoin Is Tethered To Equities – Forbes

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American President with a face mask against CoV infection. 100 dollar banknote. Coronavirus in ... [+] United States. Concept quarantine and recession. Global economy hit by corona virus outbreak and pandemic

Equity and bitcoin markets upward trajectories have stalled recently, which has been tied to uncertainty surrounding the economic ramifications of a second wave of Covid-19. The aforementioned uncertainty has bitcoin and S&P 500 price walking in lock step once again. The heightened correlation calls into question whether bitcoin is a risk or store of value asset; especially since it has historically been uncorrelated to equity markets.

The anonymous bitcoin analyst, PlanB, has risen to notoriety by showing a strong linear price relationship between bitcoin and stock to flow ratio (S2F), including aggressive price forecasts.

Recently, PlanB began tweeting about a meaningful statistical relationship between bitcoin and S&P 500 beyond correlation, called cointegration, as a way to explain the recent price dynamic.

By definition, correlation measures whether two assets move in tandem by some magnitude - either positively or negatively. Cointegration measures the long-term price spread between two assets, i.e. both assets eventually revert to their historical spread despite periodic widening.

https://www.diversifyportfolio.com/blog/2017/05/09/stock-correlation-vs-cointegration/

Our data analysis suggests that bitcoin and S&P 500 are historically cointegrated. This implies that bitcoin is a risk asset that benefits from the same macro and monetary factors that drive equities, within its historical spread.

However, shortening the testing dataset to more recent years, analysis shows no cointegration and weak statistical relationship between the two assets. One possible explanation for this is that bitcoin has historically behaved as a risk asset, but in recent years, it has begun to transition to a store of value asset. Further validation for this hypothesis is the analysis between gold (GLD) and S&P 500 showing no cointegration and weak relationship.

Interestingly, the preliminary data results suggest bitcoin is currently transitioning from a risk asset to a store of value asset. Our hypothesis will only be validated over the coming years if cointegration and statistical relationship break down, thus more closely resembling gold to S&P 500.

Additionally, bitcoin may find itself in a win-win scenario whereby ever-increasing actions by the Federal Reserve to buoy the equity market will benefit bitcoin as well. For example, if equities increase in value, albeit for the wrong reasons, i.e. increased inflation expectations from Fed money printing, bitcoin will follow suit given cointegration.

Furthermore, if and when the Feds monetary experiment unravels, or equities experience a Japanese-style lost decade, bitcoin may have already transitioned fully to a store of value asset, thus broken its tether to equity markets.

Disclosure: the author owns bitcoin and ethereum.

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As Markets End The Week In Red - Data Analysis Explains Why Bitcoin Is Tethered To Equities - Forbes

Market Wrap: Bitcoin Spot Volumes Are Weak While Options and DeFi Strengthen – CoinDesk – CoinDesk

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Bitcoin spot volume may have been low this week, but the real action in crypto has been in the options market and decentralized finance.

Bitcoin (BTC) was trading around $9,274 as of 20:00 UTC (4 p.m. ET), slipping 1% over the previous 24 hours.

At 00:00 UTC on Friday (8:00 p.m. Thursday ET), bitcoin was changing hands around $9,368 on spot exchanges such as Coinbase. It slogged around a tight range between $9,280 and $9,428 during the preceding 19 hours. Its price is now below its 10-day and 50-day moving averages a bearish signal for market technicians who study charts.

Since the halving mid-May, bitcoin has gone nowhere, basically stuck in a range of $8,500 to $10,200, said David Lifchitz, chief investment officer for quantitative trading firm ExoAlpha.

Trading has dipped on spot exchanges like Coinbase, with its three-month average daily volume at $171 million. Over the past week, its seven-day average has been $82 million, more than 50% lower.

Next week, on June 26, approximately $1 billion in bitcoin options will expire, and traders expect price movements that could be violent as a result. Price action is like a spring, said Lifchitz. The longer it remains stuck in a narrow range, the more any breakout on the upside or the downside will be violent, just like a spring expands the more violently the more it is compressed.

The majority of bitcoin options expiring are bullish bets on the price going up, wrote Singapore-based quantitative trading firm QCP Capital in an investor note Friday. The end-June open interest is concentrated in calls with strikes around $10,000-$15,000, and likely a function of institutional interest as a good portion of the calls were executed on CME.

This may suggest the smart money is betting on a better bitcoin price. CME is a venue professional commodities traders use for different futures and options strategies. The growing bitcoin options open interest there, including a record $372 million in open interest June 10, shows increased crypto interest by sophisticated investors.

Weve now had a long period of sideways consolidation since the beginning of May, out of which will come a sharp move higher or lower, said Rupert Douglas, head of institutional sales for London-based brokerage Koine. As long as the market can hold above $9,000, I still favor the upside, which could see bitcoin testing above $12,000.

Compound token creating opportunities for some traders

Excitement around COMP, the governance token of the Ethereum-based Compound lending network, has certainly given some traders new ideas on how to profit from the growing interest in decentralized finance, or DeFi. Ether (ETH), the second-largest cryptocurrency by market capitalization powering the Ethereum network, was trading around $228 and slipped 1% in 24 hours as of 20:00 UTC (4:00 p.m. ET).

One quantitative firm has seen traders use Ethereum-based stablecoin arbitrage as part of a strategy to make gains on COMPs growth. We saw traders using USDC to borrow USDT and other stablecoins on Compound to earn COMP, then use Curve to swap the USDT back to USDC and repeat the process, said Peter Chen, a trader at Hong Kong-based OneBit Quant.

Curve is a decentralized exchange, or DEX, that launched earlier this year. Many well-capitalized traders say DEXes are slow and have low liquidity, making it difficult to execute large trades. However, the growth of stablecoin-heavy Curve and other DEXes as an alternative to the centralized spot and derivative crypto exchanges may allow many traders, over the long-term, to develop exciting new DeFi-based strategies.

Curve is dominating the DEX market Friday, with its $24.7 million volume in the past 24 hours outpacing second-place Uniswap, at $16.2 million in volume, according to aggregator Dune Analytics.

Other markets

Digital assets on CoinDesks big board are almost all in the red Friday. Significant losers include dash (DASH) in the red 2.2%, zcash (ZEC) dipping 2.1% and monero (XMR) slipping 2%. The lone cryptocurrency winner on the day is ethereum classic (ETC) up 3.4%. All price changes were as of 20:00 UTC (4:00 p.m. ET).

In commodities, oil jumped 1.6% Friday, with a barrel of crude priced at $39.58 at press time.

Gold is up 1.2%, trading around $1,742 for the day.

The Nikkei 225 of publicly traded companies in Japan ended trading up 0.55% Friday and in the green 0.78% for the week as the government lifted travel restrictions.

The U.S. S&P 500 index gained 0.56%, up 2% for the week, as a roller-coaster ride Friday was fueled by concerns of the coronavirus continuing to wreak havoc on the economy.

U.S. Treasury bonds all slipped Friday. Yields, which move in the opposite direction as price, were down most on the two-year bond, in the red 15%.

The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.

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Market Wrap: Bitcoin Spot Volumes Are Weak While Options and DeFi Strengthen - CoinDesk - CoinDesk

Another Bitcoin Scam Hits Canada – Cointelegraph

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An alleged Bitcoin (BTC) scam is now reportedly targeting residents of Winnipeg, Canada. A local grocery store owner warned that many of his customers were victims of the scammers.

According to Global News, Husni Zeid placed a large sign on the Bitcoin machine he has in his store, asking people to exercise caution with regard to phone scams that ask for fake Bitcoin investments.

Zeid told the local media outlets:

"A lot of people are getting phone calls saying that they have to transfer the money to Bitcoin regarding CRA; we've had Manitoba Hydro as well."

He stressed that the scams have happened repeatedly multiple times a week, and states that they receive complaints from victims continuously.

Aura Morissette, an employee of the grocery store, spoke about a woman who was profoundly affected by the scam:

"Yesterday (a) mom was in here and she said she gave all her savings to them and she was just crying. It was heartbreaking that she fell for it; it was sad, and all she kept saying was 'I have kids.' (It) was awful."

The employee also said that when someone is buying bitcoin at their machine while on the phone, they often warn people that a scam may be underway.

The reports from Winnipeg share similarities with another scam we reported on June 19. In that instance, the Royal Canadian Mounted Police, or RCMP, allege that fraudsters impersonated the local authorities in order to extort Bitcoin from their victims.

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Another Bitcoin Scam Hits Canada - Cointelegraph

Maxthon releases version 6 beta of its Bitcoin SV-based browser – ZDNet

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The way we use the internet is evolving and there are new and easier ways to be able to take advantage of this 'new' internet

San Francisco-based software firm Maxthon has released the beta version 6 of its browser, moving toward new internet features that are available using the Bitcoin SV digital currency. Its browsers are installed across platforms for over 670 million users.

Maxthon beta version 6 promises to be a full-featured browser combining features from Maxthon 5 and adding features from Google's open-source Chromium project. It is downloadable now-- however, it is not yet available for the Windows platform from this link (well, not my version of Windows 10 on my PC).

Maxthon intends this browser to be the first step for users to access the Metanet -- the 'new internet.' The Metanet promises to give users ownership of their data and content instead of it going to online service providers.

Users will be able to monetize their data, content, online activity, and identity, and eliminate intrusive advertising, currently pervasive across the internet.

It also promises to inhibit bots and trolling and encourage higher quality content, enabling users to create a global identity for themselves that can be used across websites and applications.

This seems like a big ask. But since the Genesis upgrade enabled transactions to be stored in blocks up to 2GBin size, higher scalability will ensure that higher volumes of data can be processed, including micropayments to power internet activities.

The Maxthon 6 beta includes a blockchain identity manager called VBox. This will enable users to create their own singular identity on the blockchain, which can be used to log into all of their applications, make payments, and save their data and content onto the blockchain.

You will not need to create a new identity for each online application or use your Google or Facebook login to access other sites.

The new Metanet will enable users to be paid for the online interactions and data they previously provided for free to social platforms.

The browser will include two new protocols: TX (used to view data associated with blockchain transaction) and NB (to visit websites that have been built on the Bitcoin SV blockchain).

Jeff Chen, CEO of Maxthon, commented:

"I believe the future requires re-inventing the Internet in a manner that lets user keep ownership and control of -- and actually monetize -- their own data, content, online activity and identity. The Maxthon 6 browser delivers the first steps towards this powerful new future, which is only possible on the Bitcoin SV blockchain."

Using blockchain services is not free. Users will pay a tiny amount -- a micropayment -- to use the Metanet. These BSV micropayments are usually a fraction of a cent per post, like, comment across items on the blockchain.

In this way, you can pay to reward good content, receive payment for your content, and force trolls to pay for spamming you. You can also use a variety of apps to store your content or spend your digital currency in physical stores.

Previously the realm of hard-core developers and serious investors, these new Metanet applications are making it easier for ordinary users to use blockchain and bitcoin for their daily internet activities.

The ability to minimize intrusive ads, restrict trolls and spammers, and get paid for your own content, what's not to like? In 10 yeas, you will wonder why you waited so long.

Disclaimer: I have never bought or sold any types of Bitcoin, nor processed any blockchain transactions.

Superhero offers instant payment, turning web addresses into P2P wallets

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Tech Firms in Developing Economies to Receive Funding in Bitcoin (BTC) and Other Digital Currencies from UNICEFs Crypto Fund – Crowdfund Insider

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Eight tech firms in developing and emerging world economies will reportedly receive investments from the United Nations Childrens Fund (UNICEF), in order to solve local and global challenges. The fund now includes cryptocurrencies.

In October 2019, UNICEF created a crypto-asset fund in order to receive, hold and distribute donations in Bitcoin (BTC) and Ether (ETH).

As noted in a release published by UNICEF on June 19, 2020:

The Crypto Fund will invest 125 ETH in the eight companies from seven countries to develop prototypes, pilot, or scale their technologies over six months: Afinidata, Avyantra, Cireha, Ideasis, OS City, StaTwig, Somleng and Utopic.

Chris Fabian, Senior Adviser, co-Lead, UNICEF Ventures, stated that were increasingly seeing the digital world come at us more quickly than we could have imagined. UNICEF should responsibly use the latest technologies to help children throughout the world, Fabian said.

He added:

The transfer of these funds to eight companies in seven countries around the world took less than 20 minutes and cost us less than $20. Almost instant global movement of value, fees of less than 0.00009% of the total amount transferred, and real-time transparency for our donors and supporters are the types of tools we are excited about.

As mentioned in the announcement, all investees previously received up to $100,000 from UNICEFs Innovation Fund. Theyre now receiving cryptocurrency in order to help them with further developing their open-source and digital public goods.

The release noted that several investees are working to address the socio-economic challenges created due to COVID-19. UNICEF is mainly concerned with offering assistance to children and youth across the globe.

UNICEF confirmed that its working cooperatively with national governments and local partners to send vital messages on COVID-19, monitor the effectiveness of rice delivery to underserved or vulnerable members of the community, improve childrens literacy via remote learning programs, and treat pandemic and isolation-related anxieties and problems.

The list of firms (details found here) chosen to receive funding were selected from nearly 40 startups that graduated from the UNICEF Innovation Fund.

The eight companies that will receive financial support had to pass technical evaluations, quality assessments of their open-source software, and had to show that their projects would have a meaningful impact on society.

Investees will also get mentorship, product, and technical help, the release confirmed.

See the original post:
Tech Firms in Developing Economies to Receive Funding in Bitcoin (BTC) and Other Digital Currencies from UNICEFs Crypto Fund - Crowdfund Insider


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