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COVID Testing

How BU scientists have prepared to meet the “audacious goal” of testing 6,000 people per day

Across BU, coronavirus sampling sites will be open daily from 8 am to 10 pm and be able to collect about 100 tests per hour.

With less than two weeks until Boston University’s Charles River Campus begins welcoming undergraduate and graduate students back to school, the University has laid the groundwork for how its elaborate coronavirus testing program will work.

A few key details:

Individuals will perform their own nostril swabs at designated sampling sites on campus.

Sampling sites will be open daily, altogether collecting up to 500 samples per hour.

It will take a person about three to six minutes to check in, do the nasal swab, and complete the sample collection.

Sampling will require a person to rub a swab around the inside edges of their nostrils for several seconds.

Observers at the sampling sites will be stationed behind plexiglass barriers; each station will be separated from adjacent stations by six feet of space and plexiglass barriers.

Students, faculty, and staff will be notified of upcoming testing appointments online.

Students, staff, and faculty will learn their results within 24 hours.

That plan—the development and build-out of a dedicated coronavirus testing lab, the BU Clinical Testing Lab, operated by, and located at, BU—has been spearheaded by Catherine Klapperich, director of BU’s Precision Diagnostics Center. Since May, when it became clear that the coronavirus pandemic would linger throughout the summer and into the fall semester, Klapperich and collaborators from across BU have been working day and night to scale up a clinically validated testing process capable of surveilling BU’s entire on-campus community for the presence of SARS-CoV-2, the virus responsible for COVID-19 infections.

Upon checking in and verifying identity, students, faculty, and staff will be handed a self-swabbing kit.

Assembled over the course of two weeks, the BU Clinical Testing Lab is located inside the Kilachand Center for Integrated Life Sciences & Engineering.

After receiving a self-swabbing testing kit, each person will proceed over to a sampling station, where an observer will provide instructions on how to swab the nostrils.

Testing sites across BU will be open daily. The testing station on the Medical Campus, currently open and piloting BU’s coronavirus testing program, is at 72 East Concord Street. Charles River Campus testing stations will be at the Kilachand Center for Integrated Life Sciences & Engineering, the 808 Gallery, the Agganis Arena lobby, and for those who report feeling symptoms of the coronavirus, 925 Commonwealth Avenue Rear, in the back of Agganis Arena. The sites, which are scheduled to begin operations on August 15, will be open for collection from 7 am to 8 pm or 8 am to 9 pm depending on the site, and will collect an average of about 500 tests per hour across all sites.

BU’s testing methodology—supported through intensive modeling efforts led by Eric Kolaczyk, director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering and a College of Arts & Sciences professor of mathematics and statistics—will make sure individuals are screened at frequent enough intervals to prevent infected people from going undetected. Kolaczyk and collaborators developed a simulation of how people interact with each other at BU to figure out which testing strategies would work best to contain any outbreaks.

“The structure of the model captures the way students, faculty, and staff interact through residences and classes,” he says. 

Student-facing faculty and staff employees in Categories 1 and 2 of BU’s designated coronavirus testing tiers will be tested once per week. People in denser social situations—undergraduate students living in dorms—will be tested twice per week. 

In BU’s Clinical Testing Lab, laboratory technicians will load test tubes, delivered from testing sites by medical couriers, into the eight robots that will prep the samples and perform coronavirus screening.

Like all coronavirus tests, the RT-PCR test does have the potential to miss detecting a person with coronavirus if the virus wasn’t present in the nostrils, or if an adequate sample wasn’t collected. In addition to screening for coronavirus, each RT-PCR test will also screen for ribonuclease P, or RNase P, an enzyme found inside human cells. Its presence indicates that the nasal swab successfully captured enough human cells for a valid test.

In each sample, the robots look for the presence of RNase P and the SARS-CoV-2 virus. Fluorescent dyes are added to the sample, which bind to two different fragments of the virus. Another dye binds to RNase P. The dyes give off glowing colors if the virus or RNase P is present, allowing a computer program to return a positive, negative, or inconclusive test result based on what colors appear in a sample.

“At the end of the sample run, the computer tells you how fast the colors changed in a sample, and it uses mathematical logic to decide if that means positive, negative, or inconclusive,” Klapperich says.

An analysis that detects the presence of RNase P, but not coronavirus, indicates a negative result, meaning enough cells were collected for valid screening and those cells were not infected with SARS-CoV-2, Klapperich says. 

If the analysis does not detect RNase P, but does detect SARS-CoV-2, it is considered a positive result, because it indicates the virus may have been present inside a person’s nose even if the swab did not capture enough human cells. Klapperich says a test that fails to detect either would indicate an inconclusive result—meaning not enough human cells were captured to be sure that a person isn’t infected. Inconclusive results will require repeat testing to confirm results. 

Once tested, students, staff, and faculty can expect to find out their results within 24 hours, via an online portal, or in the case of a positive result, by phone. Kolaczyk says that rapid testing turnaround time is critical. “It’s fantastic that people will get notification of their results within 24 hours—that’s a lot faster than the week turnaround time that many professional companies are currently promising,” he says. 

After swabbing the nostrils, the swab will be placed inside of a test tube filled with saline, and handed through a window to the sampling observer, who will place it into safe storage. Four times per day, medical couriers will bring completed kits to the BU Clinical Testing Lab for processing and testing.

People who test positive will be retested in a separate location from general testing, and receive care if they are beginning to experience symptoms. People who get inconclusive test results will return to one of the general testing sites to complete another nasal swab.

Combined with contact tracing, Kolaczyk says, the testing process will create synergy in managing the spread of the coronavirus. “Contact tracing is going to be very important in the case of households. If you can trace an infection outward from a roommate, to a household, to a dormitory, to a classroom structure, you can intervene,” he says.

The testing data will be collected in a digital dashboard, which will be made available to the BU community and be scrutinized by the BU administration in search of any indicators of community spread. 

“If there are infections on a dorm or a floor? Everyone will be tested or quarantined,” Klapperich says. The BU Clinical Testing Lab has left a buffer of bandwidth so that coronavirus screening efforts can rapidly home in on specific campus dorms and buildings that may contain infections. “We can surge into those environments to test everyone,” she says.

The BU Clinical Testing Lab is the only one of its kind in Boston currently. Northeastern University will send samples from their community to their own Life Sciences Testing Center and a third-party testing facility. Harvard University, Massachusetts Institute of Technology, and Tufts University are outsourcing all of their testing to the Broad Institute.

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Google Is Not Building A Coronavirus Testing Website

President Donald Trump erroneously stated in an emergency address that Google is building a coronavirus testing website.

Trump said 1,700 Google engineers are working on a coronavirus screening site that could be launched as early as this Sunday.

On the contrary – not only is the site not ready to launch but it’s not even being developed by Google. The site is being developed by a company called Verily, which is owned by Google’s parent company Alphabet.

Google and Verily are entirely different companies, which Google itself clarified in a tweet:

“We are developing a tool to help triage individuals for Covid-19 testing. Verily is in the early stages of development, and planning to roll testing out in the Bay Area, with the hope of expanding more broadly over time.

“We appreciate the support of government officials and industry partners and thank the Google engineers who have volunteered to be part of this effort.”

Verily provided its own statement to TechCrunch:

“Verily is developing a tool to help triage individuals for COVID-19 testing. We are in the early stages of development, and planning to roll testing out in the Bay Area, with the hope of expanding more broadly over time,” the company said in its statement. “We appreciate the support of government officials and industry partners and thank the Google engineers who have volunteered to be part of this effort.”

That’s a fairly important piece of information to leave out in a statement that was broadcast live to the entire United States.

Just for comparison’s sake, read Trump’s statement below:

“I want to thank Google. Google is helping to develop a website. It’s going to be very quickly done, unlike websites of the past, to determine whether a test is warranted and to facilitate testing at a nearby convenient location… Google has 1,700 engineers working on this right now. They have made tremendous progress.”

Based on what we’ve learned since Trump’s statement, here are the key takeaways from all of this:

Verily is developing a coronavirus screening website, not Google.

Google and Verily are both owned by Alphabet, but are otherwise separate companies.

People will be able to visit the website and enter their symptoms to discover if they’re consistent with symptoms of coronavirus.

The website will help direct individuals to the nearest facility where they can get properly tested.

Results of in-person tests can later be retrieved on the website.

At this time we still don’t know when the site will launch, or any other pertinent information.

10 Best Data Science Tools And Technologies

The article below is an extensive guide to the 10 best data science tools and technologies

Whether you refer to it as business decision-making, planning, or forecasting for the future, data science has become increasingly important in almost every sector of the modern economy. Everything falls under the innovation and patterns that we’re going ahead with. In the world of digital technology in 2023, we have a lot of data and are using a variety of tools and methods to make it useful for a variety of purposes. On the off chance that you’d discuss any famous innovation, it would be “Data Science” as it were.

To do certain things, you need to know how to use a variety of tools and any of the programming languages in data science. Even if you’re willing to dig a little deeper, there are approximately 5,24,000 jobs worldwide and more than 38,000 in India right now. Based on these figures, it is necessary to stay up to date on the most data science tools and data science technologies because there is a growing demand for data scientists in almost every industry.

10 Best Data Science Tools and Technologies: 1. Python:

In recent years, Python has been by far the most widely used programming language among data scientists. In the Kaggle overview, 86.7% of information researchers said that they use Python, which was over two times the second most famous reaction. Since Python is relatively straightforward to learn, it is simple for people with no prior experience with programming to read and write Python code. A significant number of the most famous information science devices are either written in Python or exceptionally viable with Python.

2. TensorFlow:

TensorFlow is an open-source machine learning application development library developed by Google. Giving clients a huge range of assets and instruments, TensorFlow is notable for empowering AI designers to construct enormous and exceptionally complex brain organizations. Additionally, TensorFlow’s software libraries include a large number of pre-written models to assist with specific tasks and are highly compatible with Python.

3. Apache Hadoop:

Apache Hadoop is an open-source framework for processing and storing enormous amounts of data that is extremely popular for “big data” repositories. Big data tasks are distributed across computing clusters in the way that Hadoop works. This is crucial because it makes it possible for a company’s big data systems to function in a way that is both scalable and economical.

4. R:

The R programming language is generally utilized for information science, all the more explicitly for measurable demonstrating and investigation. Besides Python, it’s presumably the main language to be aware of for anybody working in information examination. R and Python are used by data scientists for a lot of the same things, but there are a few key differences. R places a greater emphasis on the statistical aspects of data science than Python does.

5. Tableau:

Perhaps of the most generally utilized datum perception apparatuses among information researchers, Salesforce’s Scene can investigate a lot of both organized and unstructured information. It can then take the information it breaks down and convert it into various accommodating perceptions including intuitive diagrams, outlines, and guides. Tableau’s ability to connect to a wide range of data sources is what makes it so useful.

6. SAS Viya:

SAS Viya was designed specifically for data analysis, making it one of the most complete platforms available for data management and analysis. It is one of the most well-known factual examination apparatuses among huge organizations and associations, because of its incredible dependability, security, and capacity to work with enormous informational indexes. In addition, SAS integrates with numerous well-known programming languages and tools to provide data scientists with extensive libraries and tools for data modeling.

7. Excel: 8. SQL:

While unstructured information stores get a ton of press, information researchers accomplish a lot of work with organized information that dwells in conventional data sets. Additionally, they frequently rely on SQL (Structural Query Language) when attempting to access that data.

A large number of them are questioning information from SQL-based data sets like MySQL, PostgreSQL, SQL Server, and SQLite, yet you can likewise utilize SQL with huge information instruments like Flash and Hadoop.

9. DataRobot:

DataRobot utilizes man-made brainpower and AI to help inform clients with an information display. It truly has something for everyone and aims to democratize the data modeling procedure. Business analysts with little programming experience can build sophisticated predictive models thanks to the platform’s ease of use and lack of requirements for programming or machine learning.

10. Trifacta/Alteryx:

Learnbay: Most Acknowledged Data Science Institute Offering Comprehensive Data Science Courses

Data has become an important part of everybody’s life. Without data there is nothing. Data mining for digging insights has marked the demand for gaining knowledge of using data for business strategies. Data science is not limited to only consumer goods or tech or healthcare. There is a high demand to optimize business processes using data science from banking and transport to manufacturing. Therefore, the field of data science is growing with increasing demand.

Developing Analytical and Technical Skills

The institute aims at securing working professionals' careers by assisting them in developing analytical and technical skills. This will enable them to make a transition into high-growth analytical job roles by leveraging their own domain knowledge and work experience at an affordable cost.  

Data Science Courses

Presently, Learnbay is offering six different data science courses as follows: • Business Analytics and Data Analytics Programs for working professionals with 1+ years of experience in any domain. Course duration: 5 months with 200+ hours of classes. Project: 1 Capstone project and more than 7 real-time projects. Course Fee: 50,000 INR • Data Science and AI Certification for working professionals with 1+ years working experience in any domain Course duration: 7 months with 200+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 59,000 INR • AI and ML Certification to Become AI Expert in Product Based MNCs for working professionals with 4+ years of working experience in the technical domain Course duration: 9 months with 260+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 75,000 INR • Data Science and AI Certification for Managers and Leaders with 8 to 15 years of working experience in any domain Course duration: 11 months with 300+ hours of classes. Project: 3 Capstone projects and more than 15 real-time projects. Course Fee: 75,000 INR Course duration: 9 months with 300+ hours of classes. Project: 2 Capstone projects and more than 12 real-time projects. Course Fee: 95,000 INR. • Industrial Training in AI and Data Science for Fresh Graduates Course duration: 4 months with 200+ hours of classes. Project: 1 Capstone project and more than 7 real-time projects. Besides, there is a 6-month internship program. Course Fee: 39,999 INR.  

Key Features of Learnbay Courses

• 1to1 learning supports via complete live interactive classes, additional discussion sessions, etc. • 24/7 instant tech support. • Regularly updated learning modules. • Flexible installment options for course fees. • Lifetime free access to the premium learning materials and recorded videos of the attended classes. • Hands-on live industrial project-based learning.  

About the Initiator

Krishna Kumar, the Founder of Learnbay, has observed the data-related job market for different industries as well as the data science training platforms very closely. He founded Learnbay in 2023. Although he started his journey with Learnbay as a founder, he worked for the institute from the very grassroots. To understand students' expectations, he took classes, conducted career counseling sessions, and provided personalized doubt clearance assistance by himself. During that time, he worked with the motto of staying connected with his students directly and revealed many of the hidden facts of the data science training business/ teaching platforms. He found that most of his students having doubts concerning the efficacy of data science courses available in the market from the perspective of learning support- even after paying a fair amount of course fees. They came to the institute with the hope of a complete industry-grade data science learning experience with dedicated learning support at affordable prices. He started focusing on the efficacy of learning assistance and placement support of his institute's courses from that time. Within one year, the institute got many impressive responses from the students. Even though he has plenty of expert faculties (trainers, counselors, project organizers, etc) today, still at some level, he maintains direct interaction with each of the Learnbay students. Based on their feedback, he keeps updating, altering, and upgrading the institute's learning modules, teaching approaches, and learning supports.  

Personalized Data Science Career Counselling

The edge of the institute’s Analytics and Data Science Program over other institutes in the industry is owing to the following factors: • Instead of a generalized course, the institute offers different courses according to Student’stheir personal career needs. • It offers personalized data science career counseling to help a student in investing in the course that best his present working experience and future growth. • Its placement assistance helps in securing a student’s first data science job. • It offers the flexibility of attending multiple sessions of the same modules instructed by different instructors for better understanding.  

Internships and Placements Training on Analytical Tools Notable Awards and Achievements

This year (2023), Learnbay is stepping into the successful journey of a total of 5 years. Within these five years, it has grown a lot and currently holds an excellent reliability percentage from data science aspirants/training seekers. In the last five years, the institute has earned highly positive responses and feedback from students, professionals, and new data science aspirants. Leranbay has already achieved industrial collaboration from the IT giant, IBM. It got placed in the top seven data science institutes listed by chúng tôi The institute ranked 3 for Bangalore and 1 for Chennai locations. Even being a Bangalore-based organization, it got massive recognition across the different metro cities of India, like Hyderabad, Kolkata, Mumbai, Delhi, Mumbai, etc. The course review of Learnbay is 4.8 on Google.  

Foreign University Certification, a Key Challenge

Initially, the students were showing more interest infor foreign university certification tag, even if they had to pay three times more than the actual fees. But as mentioned earlier, the key mission of the institute is to offer the appropriate learning guide to the career transformation seeker and not to confuse or divert the students with decorative and eye-catching staff. In the real-world data science job market, what matters only is the hands-on experience and project work. Recruiters are not even interested in the student certification tag. So, the institute keeps enriching the course efficacy from the hands-on learning perspective and project works without focusing on the certification tag. Its efforts got rewarded with the IBM collaboration. From last year, the scenario has entirely changed. The continuous success of the students and plenty of available data science job market analytical insight now support its training approach. Although plenty of competitors started providing such university tags, still it's not effective for its colossal student base.  

The Future of Big Data Analytics

Difference Between Coding In Data Science And Machine Learning

Any computer-related job requires the use of coding. Machine learning and artificial intelligence are both aspects of computer science, and anyone who works with them should know how to program. If you’re just a regular user, you generally won’t need programming abilities. If all you want to do is use other people’s libraries, you don’t need to be a serious coder. You only need some semantic and syntactic understanding in this scenario, which is more than adequate.  

Coding in Data Science

Low-level and high-level coding languages are the two categories of coding languages. Low-level languages are the most intelligible and less complex languages used by computers to execute various functions. A machine language is essentially binary read and executed by a computer, whereas assembly language tackles direct hardware control and performance difficulties. The assembly language is converted into machine code using assembler software. When compared to their high-level equivalents, low-level coding languages are quicker and use less memory. The second category of programming languages abstracts details and programming ideas more effectively. These high-level languages can generate code that is unaffected by the type of computer. Furthermore, they are portable, more human-like in appearance, and extremely valuable for problem-solving instructions. However, many data scientists choose to use high-level coding languages to deal with their data. Those interested in entering the subject might consider focusing on a data science language as a starting point.  

Coding in Machine Learning

Machine learning is applied through coding, and coders who know how to write that code will have a better understanding of how the algorithms function and will be able to more effectively monitor and improve them. C++, Java, and Python are the most common programming languages mentioned, although they may get much more detailed. When it comes to machine learning, languages like Lisp, R Programming, and Prolog become essential. Having said that, prior knowledge of other languages such as HTML and JavaScript is not required. Instead, begin with more applicable languages like Python, which are regarded as reasonably straightforward to learn due to characteristics such as the usage of English terms instead of punctuation.  

Best Programming Languages

Python Python is currently the most used data science coding language on the planet. This flexible and general-purpose language is naturally object-oriented. It also supports a variety of programming paradigms, including functional, structured, and procedural programming.   JavaScript Hundreds of Java libraries exist now, addressing any problem that a programmer may encounter. When it comes to generating dashboards and displaying data, there are a few languages that stand out. This flexible language can handle numerous jobs at the same time. Everything from electronics to desktop and online programs may be embedded with it. Java is used by popular processing systems like Hadoop. It’s also one of those data science languages that can be scaled up rapidly and easily for massive applications.   Scala This attractive and sophisticated programming language was born only a few years ago, in 2003. Scala was created in order to solve problems with Java. It has a wide range of applications, from web development to machine learning. It’s also a scalable and efficient language for dealing with large amounts of data. Scala enables object-oriented and functional programming, and also concurrent and synchronized processing, in today’s businesses.   R R is a statistical computer language developed by statisticians for statisticians. The open-source language and tools are frequently used for statistical computing and visualization. It does, however, have a lot of applications in data science, and R includes a number of useful data science libraries. R may be used to explore data collections and do ad hoc analysis. The loops, on the other hand, contain over 1000 iterations, making it more difficult to master than Python.   SQL SQL, or Structured Query Language, has become a prominent computer language for data management throughout the years. Although SQL tables and queries are not primarily utilized for data science activities, they can assist data scientists when interacting with database systems. For storing, manipulating, and recovering relational database management systems databases, this domain-specific language is particularly useful.   Julia Julia is a data science coding language designed specifically for high-performance numerical methods and computational research. It has the ability to swiftly apply mathematical principles such as linear algebra. It’s also a fantastic language for working with matrices. Julia’s API may be incorporated in applications that can be used for various back-end and front-end developments.  


In the present era, there are over 250 programming languages. Python emerges as a clear leader in this huge sector, with over 70,000 libraries and around 8.2 million users globally. Python supports TensorFlow, SQL, and a variety of additional data science and machine learning frameworks. Rudimentary familiarity with Python can also help you discover computing frameworks like Apache Spark, which is recognized for its data engineering and huge data analytic applications.

The Dangers Of Rogue Household Robots

While the machine uprising may not be upon us just yet, a group of University of Washington researchers has conducted a study on the various threats to security and privacy that household robots currently on the market could introduce to our homes. While their findings found little to fear in the way of an I, Robot-esque revolt, it turns out common household robots can open a home to various security and privacy threats, mostly via web-enabled features that are supposed to make the robots more useful. To put it briefly: if you can access your camera-equipped household robot remotely via the Internet, so can someone else.

The U of Washington team conducted their study on three of the more common household robots available today, with the goal of looking toward a future when more widely-available, more sophisticated robots take residence in our homes. While it all sounds a bit sci-fi, think about the many ways you can already remotely interact with objects inside your home. Both TiVo and Slingbox let you control your television’s DVR from afar, and many homes allow residents to control the lighting, air conditioning, and even door locks without being physically present. Implementation of the smart grid will only increase this remote control capacity. While the idea that some malicious hacker would break into your DVR to cancel your weekly recording of Gossip Girl sounds far-fetched, factor in a mobile robot equipped with camera, microphone, and “hands,” and the scenario becomes a bit more frightening.

Using WowWee’s RoboSapienV2 and Rovio as well as the Erektor Spykee, the U of W team tested for vulnerabilities that could make the robots a threat to privacy or security. Unfortunately, they found several. To pare it down, it is relatively easy for a hacker to remotely identify a robot within a home simply by scanning the digital air for packets of information robots periodically leak as part of their communication with wireless networks. Once located, it is child’s play for a hacker to hijack the username and password used to access the robot. From there a third-party could actively take control of the ‘bot’s mobility, or just passively monitor the video and audio feeds being transmitted.

Due to current technological limitations, the physical damage these particular robots could do is minimal, though they provide a conduit for a third-party to have eyes and ears inside of one’s home. A person with ill intentions could turn a tool you use to keep an eye on the kids when they’re home alone to a tool for finding out when you’re not there. Or where the spare key is. Or whatever, there’s numberless scenarios that could play out, some benign and some creepy. The point is, the more we integrate our lives with technology, the more we open ourselves to technological vulnerabilities.

As the U of W report shows, as our technology becomes more sophisticated, so do the threats. Their recommendations: think about what vulnerabilities a household robot could bring into your home, and if you still must have the technology, do absolutely everything you can to secure it.

In the meantime, don’t turn your back on your Roomba.

University of Washington

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