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Leash Biosciences Announces $9.3 Million Seed Financing to Pioneer AI-Driven Medicinal Chemistry – GlobeNewswire

April 8, 2024 | Author: admin

Financing will support development of a foundational machine learning model of medicinal chemistry that can accurately predict small molecule drug candidates for any protein

Releases unprecedented dataset publicly to address critical challenges in drug discovery with machine learning

SALT LAKE CITY, April 05, 2024 (GLOBE NEWSWIRE) -- Leash Biosciences, an artificial intelligence and machine learning (AI/ML)-native biotechnology company unleashing machine learning to solve medicinal chemistry, today announced the completion of a $9.3 million seed financing round to advance its mission of revolutionizing medicinal chemistry through modern computational methods and massive biological data collection. The oversubscribed round was led by Springtide Ventures with participation from MetaPlanet, Top Harvest Capital, Mitsui Global Investment, MFV Partners, Recursion CEO and co-founder Chris Gibson, and Recursion co-founder Blake Borgeson.

Leash aims to develop a foundational and generalizable machine learning model of medicinal chemistry that can accurately predict small molecule drug candidates for any protein in silico, and more broadly, interactions between any protein and any chemical. To achieve this, Leash is producing bespoke, expansive datasets of protein targets binding to chemicals. To date, the Company has physically generated over 17 billion high-quality protein-chemical interaction measurements. In its new Salt Lake City headquarters, Leash plans to screen 500+ protein targets against many millions of machine learning-designed, proprietary chemicals by 2025.

ML improvements in chess, Go, image recognition, language translation, text generation, and protein folding all were driven by the collection and curation of massive datasets. We believe a similar strategy will revolutionize how we approach medicinal chemistry," said Ian Quigley, CEO of Leash Biosciences. "We are thrilled to have the support of this group of top-tier investors who share our vision for transforming drug discovery through an ML-first approach."

To advance its machine learning engine, Leash will use the funding to scale its data collection and computational capabilities. The Companys ML engine will also support advancing multiple internal therapeutics programs toward in vivo studies.

"Leash's platform stands apart with its combined excellence in machine learning, experimental biology, and medicinal chemistry," said Claire Smith, Lead Investor at Springtide Ventures. "We are excited to back this exceptional team as they leverage cutting-edge tech to tackle the toughest drug discovery challenges."

Alexey Morgunov of MetaPlanet added, "Leash sits at the forefront of innovating the next paradigm of AI-driven, scalable, and rapid drug design. We are honored to partner with them as thought leaders in this space."

The Leash team is comprised of TechBio veterans with expertise spanning AI/ML, biology, and chemistry. Five of the company's six employees are former Recursion employees with experience building and scaling transformational drug discovery platforms. The team also brings experience from Eikon Therapeutics, Myriad Genetics, insitro Biosciences, LinkedIn, Stripe, and other leading technology and biotechnology players.

In parallel, Leash announced the launch of its inaugural machine learning Kaggle competition, the Big Encoded Library for Chemical Assessment (BELKA). Leveraging a dataset of unprecedented scale, BELKA sets out to address one of the most critical challenges in drug discovery: predicting the likelihood of chemical materials binding to pharmaceutically-relevant targets. The competition will be hosted on the Kaggle platform, the worlds largest data science community.

"By providing participants with access to such a comprehensive dataset, we are empowering the global scientific community to develop innovative solutions that could revolutionize the way we identify potential drug candidates, said Ian Quigley, Leash Bio CEO.

About the Kaggle Competition: Predict New Medicines with BELKA (Big Encoded Library for Chemical Assessment)

BELKA aims to contribute to groundbreaking advancements in predictive modeling for pharmaceutical research by harnessing the capabilities of artificial intelligence and machine learning. Participants will be tasked with analyzing a vast dataset comprised of 133 million physically-measured activities for each of three key protein targets.

Leash rigorously produced a dataset that exceeds all existing small molecule binding datasets combined. With 133 million molecules screened against each protein and evaluated with deep sequencing coverage and many replicates, participants will have access to an unparalleled wealth of data in scale and depth. Importantly, this competition dataset is larger than the worlds largest existing drug-target dataset (PubChem), providing a unique opportunity for groundbreaking insights and discoveries. It represents a small fraction of Leashs screening data.

Committed to transparency and collaboration in scientific research, Leash plans to publicly release the full dataset of all conditions and replicates aggregated for the contest dataset, some 3.6 billion physically-measured interactions, at the conclusion of the competition. This resulting collection, expected to be released in the summer of 2024, will be approximately 10 times larger than the largest publicly available dataset to date and 1,000 times larger than higher-quality, curated public datasets, providing researchers worldwide with an invaluable resource for future drug discovery efforts.

The BELKA competition is open for registration and concludes on July 8, 2024. For more information, including participation criteria and registration, visit the competition page on Kaggle.

About Leash Leash Biosciences is a biotechnology company unleashing machine learning to solve medicinal chemistry, with headquarters in Salt Lake City, UT. Powered by a team of experts, Leash aims to expand the boundaries of whats possible in drug discovery. Through the combination of leading-edge machine learning and large-scale chemical and biological datasets, Leash aims to rapidly design novel small molecule therapeutics. For more information, visit https://www.leash.bio and follow on LinkedIn.

Leash Contact Info Becca Levin, PhD Head of Business Development & Strategy becca@leash.bio

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AI Training Could be a Lifeline to Tackling the Skills Gap – Technology Magazine

April 8, 2024 | Author: admin

AI may be developing at a fast pace, but global workforces are currently not skilled enough to handle such rapid changes.

The technology holds great potential to transform the global business landscape, boosting productivity and improving workplace efficiency. However, whilst companies are keen to invest, they do not have the skills required to handle AI, so it is not utilised to its full potential.

Research conducted throughout 2023 found that many employees feared being replaced by AI, contributing to workplace anxieties. However, HSBC found at the start of 2024 that most businesses are considering how AI could advance employee skillsets, with 83% surveyed planning to re-train their workforces to better utilise the rapidly developing technology.

As AI continues to play myriad roles moving forward, one thing is absolutely certain - it will impact nearly every job.

Big tech companies are already starting to enact transformational changes in favour of boosting AI. SAP, for instance, recently announced plans to focus on upskilling workers and driving growth in AI business areas.

In a rapidly changing digital landscape that sees businesses continue to be impacted by threat actors exploiting business infrastructure, the need for employee upskilling is crucial. As a result of AI technology developing at such a fast pace, there are ever-increasing talent shortages within the technology sector that need addressing.

IBMs Global AI Adoption Index found that a lack of relevant skills was the top barrier to AI adoption among UK enterprises. Jon Lester, VP of HR Technology, Data and AI at IBM explains that this is why upskilling for AI is arguably the most critical development area for the workforce moving forward.

Employees who are seen as domain experts will still be highly sought-after as they are the ones who will help to develop and train AI, he says. We have seen some of our support desk employees who have the highest customer satisfaction when answering phone calls or responding to emails, reskill to become conversational specialists who design chatbot interactions that provide a great end-user experience.

Those same people are now learning to become prompt engineers who are training large language models to generate responses to questions. AI is measurably moving domain experts to higher value work.

In recent months, the business landscape has changed beyond measure, with AI-related skills no longer considered desirable - but necessary, across the majority of job sectors. AI has ultimately opened up new opportunities and challenges by development teams, as businesses must retain their employees to satisfy AI demands.

Research conducted by ServiceNow found that the majority of office workers already use generative AI (Gen AI) for tasks like drafting content (69%), transcribing meeting notes (66%) and reviewing documents (65%). However, ServiceNow also found that almost half of workers still dont understand how AI can best support them in their role, suggesting that employers are still not making the most of the technology.

In the future I predict that we will see all jobs be categorised into two buckets: sunrise jobs and sunset jobs, the companys Area VP of Solution Consulting, Simon Morris, says. Sunset jobs are at risk of being significantly disrupted by AI and may eventually be replaced. Sunrise jobs will also change as they are enhanced and supported by AI.

In the future your job will involve training, supervising and correcting the algorithm rather than completing the task yourself.

As Akhil Seth, Head of Open Talent at UST, explains, AI is no longer simply a piece of innovative technology. In the maturity curve, it is now an implementation technology, he says. Companies should be looking to acquire a workforce that knows how to implement AI based solutions.

Having a workforce that is well acquainted with deep machine learning infrastructure skills as well as the critical thinking required to apply the technology to business initiatives will be critical in maintaining a competitive edge.

In line with such rapid AI advancements, business leaders are now seeking to develop new strategies that adopt an AI-as-copilot approach. Put simply, this means having AI work alongside human employees.

IBMs Jon Lester identifies three key emerging areas within AI that are already driving new skillsets, citing code generation, customer engagement and the concept of a hybrid workforce.

These emerging areas for AI are driving speed of task completion, improving decision-making for managers and leaders, and enabling significant productivity gains for employees, he says. What educators and professionals need to think about is how to ready the organisation for this seismic change.

Simon Morris, meanwhile, envisages a future where it will be normal for AI to suggest ways to develop employees or even assign workers to projects, tying employee learning closely to workforce planning, all while also ensuring they feel more fulfilled in their jobs. This will in turn not only result in talent retention, but also more satisfying customer experiences, creating a positive ripple effect on a business bottom line.

What is becoming clearer is that the human element to AI will remain integral to business developments, with companies working to address ethical challenges. With this in mind, ensuring that workforces can fully harness AI will be necessary, particularly when it comes to sectors like cybersecurity.

Increasing numbers of business leaders around the world have called for AI risk training in a new age of digital threats, in addition to ensuring that employees can handle the technology when things go wrong.

So, how can companies best upskill their workforces in the age of AI?

Jon Lester says that understanding the difference between traditional and generative AI will be key for businesses, so that employees can develop skills from a clear benchmark.

The pace of change that AI is creating means that the AI skills you learn today may have a half-life of less than three years and so a mindset of continuous development will be required to keep skills up to date, he says.

Ultimately, the onus is on the employer to ensure that their workforces are up-to-date with existing AI strategies and are adequately trained to propel the company forward to enact real technological business transformation.

This can be in the form of regular training courses and workshops with external organisations that can provide industry-leading expertise. It is also important that employees are given the opportunity to gain practical experiences and implement real-world AI projects, Morris comments.

As Seth concludes, organisations need to establish ongoing training programmes for employees. Workshops and online courses allow workers to acquire new skills and update existing ones. Another priority should be around customised training, ensuring that the courses are tailored to the companys specific needs, given that the applications of AI can vary widely from industry to industry.

Companies need to get the workforce comfortable with playing with AI tools and nurture a sense of curiosity around them. Companies that enable workforces to play with large language models (LLMs) will undoubtedly stumble upon market-defining use cases for the technology.

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Machine learning-based survival prediction nomogram for postoperative parotid mucoepidermoid carcinoma | Scientific … – Nature.com

April 8, 2024 | Author: admin

Screening and characteristics of the patients

This study examined 882 patients with stage IIVA P-MEC, who met the inclusionexclusion criteria, from the SEER database between 2004 and 2015. Figure1 illustrates the patient selection process, while Table 1 summarizes patients demographic and clinicopathological characteristics. The lymph node ratio (LNR) cut-off was determined using X-tile analysis, with a resultant cut-off of 1.15%. The median (95% CI) follow-up time was 99 (92105) months, and the median (IQR) age at diagnosis was 52 (3766) years. A majority of the patients were white (661, 74.9%), with most tumors being grade II (396, 44.9%), stage I (353, 40%), T1-stage (381, 43.2%), N0-stage (685, 77.7%), and LNR0 (686, 77.8%) according to the AJCC 6th stage. All variables, except for chemotherapy (94.2% vs 5.8%), had proportions exceeding 10%. The study encompassed 12 variables, including age, gender, grade, stage, tumor (T) stage, node (N) stage, radiation, chemotherapy, laterality, marriage, and LNR. Nine factorsage, gender, grade, stage, T stage, N stage, radiation, chemotherapy, and LNRwere selected based on univariate Cox regression. Multivariate Cox regression revealed that four factors (age, grade, T stage, and chemotherapy) were independent risk factors, each with P-values less than 0.05. In the multivariate analysis, individuals aged 6070years (HR=5.936, 95% CI=3.01611.681, P<0.001), those over 70years old (HR=11.962, 95% CI=6.30322.703, P<0.001), Grade III (HR=2.324, 95% CI=1.2354.375, P=0.009), Grade IV (HR=3.148, 95% CI=1.7105.795, P<0.001), T2 (HR=3.162, 95% CI=1.0599.440, P=0.039), T3 (HR=4.300, 95% CI=1.50112.316, P=0.007), T4 (HR=4.414, 95% CI=1.43913.535, P=0.009), and chemotherapy (HR=1.721, 95% CI=1.0962.703, P=0.018) emerged as independent risk factors for overall survival (OS). Nevertheless, radiation(HR=0.750, 95% CI=0.5251.072, P=0.114), LNR (HR=0.868, 95% CI=0.1146.602, P=0.891), and other variables demonstrated no prognostic value (Table 2).

Figure2A displays the relationship between the LASSO coefficients and the regularization parameter, lambda (), and demonstrates the variable selection process and the effect of on the coefficients. The lambda.min value, which represents the lambda value corresponding to the minimum likelihood deviation or the highest C-index, was utilized for selecting tuning parameters in LASSO regression. Another vertical line was lambda.1se, which corresponds to the most regularized model within one standard error of the minimum (Fig.2B). The .min (=0.0050724) was chosen for the best predictive performance. A ten-fold cross-validation was employed. Ten variables were chosen through the LASSO regression algorithm, including age, gender, grade, T stage, N stage, radiation, chemotherapy, laterality, marriage, and LNR. Employing the adjusted R-squared maximum of the BSR, we selected eight variables: age, grade, stage, T stage, N stage, radiation, chemotherapy, and marriage(Fig.3). In the RF model and XGBoost, we independently extracted the top 10 variables, excluding laterality, radiation (RF), and LNR (XGBoost) (Fig.4). We assessed the key performance of machine learning and traditional statistics using AUC and AIC. Multivariate Cox stepwise backward regression reconfirmation identified LASSO, BSR, and XGBoost as the best of the five screening methods based on both AUC (AUC=88.4) and AIC (AIC=2118.9) criteria (Table 3).

Predictor Screening: the least absolute shrinkage and selection operator (LASSO) regression and fivefold cross-validation.

Predictor Screening: A SHAP plot and a feature importance plot are visualizations used to interpret XGBoost model results.

Predictor Screening: (A) Random Forest importance plot; (B) Best Subset Regression (BSR), it selected the best subset of predictor variables to accurately model a response variable.

Consequently, we constructed a nomogram with seven variables from the three algorithms (LASSO, BSR, and XGBoost), including age, grade, tumor stage, node stage, chemotherapy, radiation, and marriage. We developed an OS-nomogram capable of predicting a patients 3-, 5-, and 10-year OS rates using these variables (Fig.5). By converting clinical, pathological, and therapeutic factors into points, the nomogram accurately predicted OS. The total risk point score, calculated by summing all points, significantly correlated with 3-, 5-, and 10-year OS. We utilized a 5-year ROC curve to determine the optimum risk score cut-off point. KaplanMeier curves revealed that low-risk group patients (risk score<80.29) had better survival prognosis compared to high-risk group patients (risk score80.29, log-rank test, P<0.001) (Fig. S1).

A survival nomogram for predicting overall survival (OS) for patients with P-MEC. (1) When using the nomogram, seven predictors were quantified as point based on patient-specific factors and then the sum of the point corresponded to the total point below, which corresponded to the 3, 5, 10year OS ; (2) The optimal cut-off total point was 80.29 (the median of patients point), which divided the patients into high-risk group and low-risk group.

We evaluated the predictive ability of our nomogram by constructing time-dependent receiver operating characteristic (ROC) curves at 3, 5, and 10years. The ROC curves demonstrated excellent discriminative capacity of our model, with areas under the curves (AUCs) of 86.9 (95% CI=83.390.6), 88.4 (95% CI=83.591.4), and 87.7 (95% CI=84.191.3) (Fig.6). This indicates that our model has high accuracy in predicting overall survival in parotid MEC patients.

(AC) The calibration curves. The calibration curves of the nomogram predicting (A) 3-years, (B) 5-years, and (C) 10-years OS. (DF) Time dependent ROC curve. (D) ROC curves for 3-year, (E) 5-year, and (F) 10-year overall survival rates. (GI) Decision curve analysis (DCA) plot. (G) DCA plot for 3-year, (H) 5-year, and (I) 10-year overall survival rates.

We also performed 1000 bootstrap resampling analyses on the dataset and generate calibration plots for the prediction model. The calibration plots showed that the curves closely aligned with the 45-degree line, indicating a well-calibrated model in practical use (Fig.6). Furthermore, the 1000 bootstrap resamplings indicated good concordance between actual and predicted values in both the training and validation datasets, as evidenced by C-index (3-year, 0.8499, 0.7750.914; 5-year 0.8557, 0.7930.911; 10-year, 0.8375, 0.7720.897) and AUC (3-year, 0.8670, 95 CI%=0.7870.935; 5-year, 0.8879, 95 CI%=0.820.945; 10-year, 0.8767, 95 CI%=0.7920.947). (Fig.7). These results further support the reliability and accuracy of our prediction model.

This figure presents a bootstrap analysis of a dataset, displaying the 3-year and 5-year AUC and C-index values. The analysis was performed using 1000 bootstrap replicates. The figure demonstrates the accuracy and predictive power of the model for the specified time intervals.

To determine the clinical utility of our prediction model, we utilized the decision curve analysis (DCA) plot. The DCA plot illustrates the net benefit of the prediction model across a spectrum of threshold probabilities. Our model demonstrates clinical utility, as evidenced by its net benefit curve lies above both two lines across the range of threshold probabilities (Fig.6). This suggests that our prediction model is more effective than TNM stage or grade and can aid in making clinical decisions for P-MEC patients.

In summary, our nomogram exhibited excellent predictive ability and calibration, as well as clinical utility, indicating its potential usefulness in clinical practice.

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Unleashing AI’s Potential: A Dive Into AI Types for Enhanced Multifamily Operati – myRealPage | Real Estate Marketing Blog

April 8, 2024 | Author: admin

Differentiating machine learning, deep learning and AI

To navigate AI effectively, it's crucial to understand the hierarchy of machine learning, deep learning and AI. Machine learning algorithms, operating without explicit programming, detect patterns and make data-driven decisions. Deep learning, a subset of machine learning, uses sophisticated artificial neural networks to extract complex features from raw data, enabling advanced pattern recognition. AI encompasses both deep learning and machine learning, delving into tasks like natural language processing and autonomous decision-making, mimicking human-like intelligence.

Generative AI creates new data samples resembling its training data, expanding possibilities. In multifamily, Generative AI acts as a copilot for prospects, residents and staff, aiding tasks from information retrieval to operational assistance. By understanding not just what is requested but also why, Generative AI unlocks efficiency and creativity in multifamily operations.

Classic AI excels in identifying patterns within data to fulfill specific tasks, lacking generative capabilities. In multifamily, classic AI is crucial for maintenance issue detection, repair prediction and prospect engagement optimization. Leveraging historical data and classification algorithms, Classic AI enhances decision making and operational efficiency.

As AI evolves, integrating it into multifamily operations promises transformative outcomes. Embracing AI as an innovative tool enables multifamily professionals to navigate this dynamic landscape confidently, ensuring prosperity and competitiveness. Embrace AI to unlock the full potential of multifamily operations in the digital age.

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The future of AI: Machine learning working together with scientific expertise – pharmaphorum

April 8, 2024 | Author: admin

In a new episode of the pharmaphorum podcast recorded live on site at WIRED Health in London in March, web editor Nicole Raleigh spoke with Elise de Reus, co-founder of Cradle, the generative AI platform that helps scientists to design and program proteins.

What if, instead of trial-and-error iteration, there was move to machine learning and a decoding language? It is a huge paradigm shift in the field of protein engineering.

A biologist by training, de Reus has always been fascinated by what microorganisms and biology can do in large human applications. Looking to overcome the high failure rate in the lab, Cradle was founded, together with Harmen van Rossum, to move away from the trial-and-error approach, and is spearheaded by a team that includes Googles Stef van Grieken, Daniel Danciu, and Eli Bixby.

When it comes to challenges faced by scientists developing bio-based products today, GenAI can permit better protein design from the get-go, says de Reus, speeding up development over fourfold. These advances represent significant real-world gains when it comes to R&D of biological products, a process which has historically been extremely costly, also. Enzymes and proteins can offer sustainable solutions for planetary and one health, as well.

And when it comes to a future vision, Cradle wants this technology, state of the art machine learning, to be available to all whether big pharma or start-up and that these tiny but powerful machines be the best they can be in the process: machine learning working together with scientific expertise, not replacing scientists, but simply trying to give them better tools.

You can listen to episode 125a of thepharmaphorum podcastin the player below, download the episode to your computer, or find it - and subscribe to the rest of the series - iniTunes,Spotify,acast,Stitcher,andPodbean.

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Binance trading volumes hit yearly high at $1.12T in March – Cointelegraph

April 8, 2024 | Author: admin

Spot trading volume on the Binance exchange hit its highest level since May 2021 afte seven consecutive months of ascent, according to a new report from CCData.

According to an April 5 report by cryptocurrency analytics platform CCData, Binances spot trading volume increased by 121% to $1.12 trillion in March

The report said the combined market share of the exchange also increased by 1.04% to 44.1% in March.

CCData highlights Binances recovery after settling its case with the United States Department of Justice and paying a $4.3 billion settlement fine. This is evidenced in its derivatives trading volumes, which have risen by 89.7% to $2.91 trillion, also achieving their highest levels since May 2021.

CCData analysts also noted that Binance made the largest gain in spot markets, increasing its market dominance by 2.3% compared with February. The exchange also saw the biggest gains year-to-date, now accounting for 38.0% of the spot trading volumes on centralized exchanges (CEXs).

In January, analytics firm Kaiko reported that Binance experienced an increase in trading volume, with its market share climbing 50% within just two months of its settlement with the United States Department of .

In spite of the regulatory challenges, the exchange claimed to have seen amore than 40 million increase in the number of users in 2023. Binance highlighted that this was nearly a 30% increase compared to the previous year and attributed the growth to its key services.

Related: Binance exec's legal case in Nigeria adjourned until April 19

Meanwhile, the combined spot and derivatives trading volume on CEXs also rose 92.9% to a new all-time high of $9.12 trillion in March, as traders flocked to the markets while Bitcoin also reached new all-time highs, CCData reported.

Trading volume in crypto derivatives CEXs also rose 86.5% to a record high of $6.18 trillion, which is triple the total market capitalization of all cryptocurrencies.

The spike in spot trading and derivatives trading activity also coincides with the growing excitement around the success of spot Bitcoin ETFs and the BTC supply halving, which is expected later in April.

This development highlights how much the public still trusts centralized exchanges despite recent failures such as FTX.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

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Binance to cease support for Bitcoin Ordinals by April 18 – Crypto Briefing

April 8, 2024 | Author: admin

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In an effort to streamline its product offerings, Binance has announced that it will cease support for trading and depositing Bitcoin (BTC) nonfungible tokens (NFTs), also known as Bitcoin Ordinals, on its NFT marketplace starting April 18, 2024.

In a blog post dated April 4, Binance stated that the decision to wind down support for Bitcoin NFTs is part of its ongoing efforts to streamline its product strategy for offerings on the Binance NFT marketplace. Users are advised to withdraw their Bitcoin NFTs from the marketplace via the Bitcoin network before May 18, 2024, at 00:00 (UTC).

Starting from April 18, 2024, at 06:00 (UTC), users will no longer be able to buy, deposit, bid on, or list NFTs on the Binance NFT Marketplace via the Bitcoin network. All impacted listing orders will be automatically canceled at the specified time.

Please note that Binance NFT Marketplace will not support any further airdrops, benefits, or utilities associated with Bitcoin NFTs after 2024-04-10, the blog post stated.

The announcement also addressed Runestone NFT users who meet the conditions for the Runestone airdrop. Binance NFT had distributed these NFTs to eligible users accounts before April 4, 2024, at 10:00 (UTC). Users are advised to withdraw these NFTs by April 10, 2024, at 10:00 (UTC) to ensure they still have the opportunity to receive any associated tokens, utilities, and benefits after that date. Binance will not be responsible for any losses incurred if users fail to withdraw their NFTs before the stated time frame, the exchange said.

Bitcoin Ordinals, which allow for the inscription of digital content like art, text, music, or video directly onto the Bitcoin blockchain, have gained popularity since their introduction in late 2022. The protocol, created by Casey Rodarmor, enables unique digital arts to be directly embedded into Bitcoin transactions, similar to Ethereums NFTs.

Binances decision to discontinue support for Bitcoin NFTs comes as a surprise to the community, as the exchange had only added support for these tokens in May 2023, promising more opportunities for collectors.

The high volume of NFT transactions has occasionally clogged the Bitcoin network, increasing fees and slowing processing times as more transactions are validated on-chain. Recent data from Dune Analytics, the network has over 64 million inscriptions to date and has generated over $423 million in transaction fees.

As Binance phases out support for Bitcoin NFTs, users are encouraged to take the necessary steps to withdraw their assets from the marketplace within the specified timeframes to avoid any potential losses.

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Binance: Navigating the Landscape of the World’s Leading Cryptocurrency Exchange – FinanceFeeds

April 8, 2024 | Author: admin

Binance has emerged as a powerhouse in the cryptocurrency world, offering a diverse array of trading options and innovative financial services to users globally.

In the rapidly expanding universe of cryptocurrency, Binance stands out as a colossus, dominating the landscape as the worlds leading cryptocurrency exchange by trading volume. Since its inception in 2017, Binance has evolved from a simple trading platform to a comprehensive ecosystem that offers a wide range of services, including spot and futures trading, an initial coin offering (ICO) platform, a native blockchain known as Binance Chain, and even its own cryptocurrency, Binance Coin (BNB). This remarkable growth has not only solidified Binances position at the pinnacle of the crypto exchange world but also highlighted its pivotal role in shaping the future of digital finance.

Binances rise to prominence is largely attributable to its user-centric approach, offering an intuitive user interface, a wide array of cryptocurrencies for trading, and competitive fees. The platform caters to both novice and experienced traders by providing a multitude of trading options, from basic buy and sell orders to complex derivatives trading. This inclusivity has attracted a vast user base, further enhancing the liquidity and trading volume on the exchange.

A key factor in Binances success is its relentless pursuit of innovation and expansion. The launch of Binance Chain and the subsequent introduction of Binance Smart Chain (BSC) are testaments to its commitment to fostering blockchain technology and DeFi (Decentralized Finance) ecosystems. BSC, in particular, has gained significant traction for its smart contract functionality and compatibility with Ethereums ecosystem, offering a high-speed, low-cost alternative for dApp developers and users.

Binance Coin (BNB), initially launched as a utility token for discounted trading fees, has evolved into a multifaceted digital asset used for a variety of purposes within the Binance ecosystem and beyond. Its use cases have expanded to include payment for transaction fees on Binance Chain, participation in token sales on Binances Launchpad, and even as a medium of exchange in the wider crypto market. The increasing utility and demand for BNB have driven its price up, making it one of the top cryptocurrencies by market capitalization.

Moreover, Binance has not limited its ambitions to the digital realm. Recognizing the importance of regulatory compliance and the need to bridge the gap between traditional and digital finance, Binance has sought to establish physical presence and obtain operating licenses in several countries. This strategic move not only demonstrates Binances commitment to adhering to global financial regulations but also its vision of making cryptocurrencies accessible and acceptable to a mainstream audience.

Despite its success, Binance has faced its share of challenges, including regulatory scrutiny in various jurisdictions. Nonetheless, its proactive approach to compliance and dialogue with regulators signifies its dedication to sustainable growth and the long-term viability of the cryptocurrency market.

In conclusion, Binances meteoric rise and ongoing evolution reflect the dynamic nature of the cryptocurrency sector. By continuously adapting to market demands, embracing technological advancements, and fostering a global community, Binance has not only secured its status as a leading cryptocurrency exchange but also positioned itself as a key player in the broader narrative of digital finance. As the crypto landscape continues to evolve, Binances journey offers valuable insights into the challenges and opportunities that lie ahead in the quest to mainstream digital currencies.

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What Is Saga Crypto? Top Exchange Binance Reveal 51st Launchpool Project – Over $2Bn Staked – 99Bitcoins

April 8, 2024 | Author: admin

In the latest Binance Launchpool listing, a new token called Saga (SAGA) has listed marking the 51st Binance Launchpool project. In this article, deep-dive and find out What is SAGA crypto? and explore SAGA coin possibilities.

Binance, undoubtedly the largest exchange by client count and trading volume, has revealed the listing of Saga as the 51st Launchpool project.

In an announcement on April 4, the exchange saidusers could begin staking BNB and FDUSD stablecoin via dedicated pools. By staking, they farm SAGA tokens from April 5 to 9.

So far, the launchpool has been a tremendous success with over $2Bn in assets staked in dedicated SAGA pools.

SAGA coin serves as a governance token for the Saga ecosystem, through a novel governance staking mechanism, holders are able to stake and receive rewards.

In the ongoing Launchpool, 45 million SAGA has been allocated for farming. Of note, 36 million SAGA will be distributed from the BNB pool.

At the same time, 9 million will be released to users from the FUSD pool.

(Launchpool)

An individual can receive a maximum of 37,500 SAGA in the BNB pool and 9,375 SAGA in the FDUSD pool so far, roughly 300,000 users are actively staking their BNB and FUSD tokens.

In total, there will be a total supply of 1 billion SAGA and Binance plans to list SAGA on April 9, at approximately 14:00 UTC in a major event.

The token will launch with five trading pairs: SAGA/BTC, SAGA/USDT, SAGA/BNB, SAGA/FDUSD, and SAGA/TRY.

The initial supply will be 90 million SAGA, or 9% of the total supply on listing.

As the name suggests, a launch pool is a token launch pad in Binances case. However, this is explicitly designed so that projects benefit from the exchanges immense retail investor base.

Indeed, many projects have benefited from supercharged listings via Binance Launchpool including SUI, SEI, PENDLE, BEAMX, and ENA.

There are multiple benefits to this:

Saga is a mainnet platform created explicitly for developers similar to Ethereum or Solana.

The goal is to empower these crucial contributors, allowing them to build infinitely scalable applications using Chainlets. SAGA will serve as a medium of exchange. Developers compensate Validators who maintain Chainlets using SAGA.

Of note, Saga coin plans to remove the high upfront costs and complexities of blockchains like Ethereum, making it easier for developers to focus on creating innovative applications.

Ahead of the official exchange listing, Saga has partnered with key players such as Polygon, Avalanche, Celestia, Marble, and Com2uS.

While Saga coin initially targets the gaming and entertainment industries, it plans to serve decentralized finance (DeFi) by enabling entirely new classes of applications.

EXPLORE: Liquid Bootstrapping Pools: Heres Why LBPs Are Best Way to WAGMI Via Presales

Disclaimer: Crypto is a high-risk asset class. This article is provided for informational purposes and does not constitute investment advice. You could lose all of your capital.

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What Is Saga Crypto? Top Exchange Binance Reveal 51st Launchpool Project - Over $2Bn Staked - 99Bitcoins

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Binance Executive Held in Nigeria Makes First Court Appearance – PYMNTS.com

April 8, 2024 | Author: admin

Binances head of financial crime compliance,Tigran Gambaryan, appeared in court in Nigeria for the first time Thursday (April 4).

Gambaryan, who is a U.S. citizen, was served with charges and did not take a plea, ReutersreportedThursday.

Gambaryan, another Binance executive and the cryptocurrency exchange itself have been charged with four counts of tax evasion, as well as money laundering, according to the report.

The other executive Nadeem Anjarwalla, a British-Kenyan who is a Binance regional manager for Africa escaped custody and fled Nigeria in March after being detained along with Gambaryan on Feb. 26, the report said.

Following his court appearance Thursday, Gambaryan will be formally arraigned for the money laundering charges Monday (April 8) and for the tax evasion charges April 19, per the report.

NigeriasEconomic and Financial Crimes Commission(EFCC) has argued that Gambaryan can face the charges on Binances behalf, the report said.

His lawyer, Chukwuka Ikuazom, has argued that Gambaryan was not a director, partner or company secretary at Binance; had no written instructions from the company to face the charges on its behalf; and cannot take a plea until the company itself has been served, according to the report.

Binance said Wednesday (April 3) that Gambaryan was not responsible, as he had no decision-making power in the company, per the report.

The two Binance executives weredetainedby Nigeria in February as the country increased pressure on the cryptocurrency sector. They were picked up by national security officers after arriving in Nigeria, with authorities saying that the company was operating illegally in the African nation.

It was reported March 11 that Nigeria blames Binance for driving down the value of itscurrency. Before their detention, Gambaryan and Anjarwalla headed to Nigeria to help solve the problem.

Following their detention, Anjarwallafled the countryin March.

On March 29, it was reported that the two executives sued the Nigerian government. They filed their suit against the countrys national security advisor, Nuhu Ribadu, and the EFCC for violating their fundamentalhuman rights.

In their court filings, Gambaryan and Anjarwalla pleaded with the Federal High Court to order the agencies to release them, return their passports and apologize publicly.

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