FOUR TIPS TO MAKE THE MOST OF VIRTUAL CONFERENCES

 

Virtual conferences are the new normal – checking in, watching keynote speakers, interacting with attendees, and bumping into friends. They are easy but require an entirely new playbook – for how you interact with people, access meaningful content, and maximize what you get out of the experience.

Virtual events require more preparation and action from attendees. For more than 7 months due to this COVID-19 pandemic, Employees from the various field need to focus on how to communicate with their colleagues. For this type of thing, virtual meetings emerge as a new way to communicate, share ideas, and conduct a lot of conferences, webinars, etc. A few tips for the above-said process are:

Use the tools to your advantage

Virtual conference organizers have brainstormed to create tech tools for networking and interactivity, so use them. During keynote speeches, chat boxes allow participants to share insights or provide queries for Q & A sessions. ‘Breakout’ areas offer chatrooms for specific interests or places for one-on-one dialogues. Some are also experimenting with virtual reality so that attendees can feel like they are present in person.

Prepare beforehand

In-person interactions tend to be a lot looser: Running into someone you know, having a drink at a restaurant, making last-minute dinner arrangements. Online, you have to be more deliberate and strategic about who you want to meet.

That means sourcing over attendee lists, researching which exhibitors or sponsors to contact, and connecting with speakers and presenters. By doing your homework and publicizing the takeaways on social media channels, you are setting the table for relationships that could endure beyond the event itself.

Minimize your distractions

Conferencing from your own house has many positives: No travel, no health risks, lower costs. When you are at a conference in-person, it allows you to get away from everything and really focus. At home, it’s more challenging to put away all the distractions. So turn off your mobile and don’t try to multitask all the time.

Interruptions are inevitable – pets, kids, work e-mails – but if you get pulled away, an online event allows you to go back and access that content later.

Focus on the benefits

Almost 70 percent of such meetings cost less than before, often significantly. Think of it as an opportunity to do things you couldn’t do before, which produces TV-quality virtual events. These events used to take place behind closed doors, with high prices, in faraway places. Now, dozens of fascinating people around the world are available to talk about these issues. You can tour the world and listen to interesting people all day long.

Source: TOI

Immune Boosters for People to Avoid Covid-19 & Future Diseases

 

• COVID-19 is the leading challenge across the globe.
• Scientists and Researchers are attempting to create a specific vaccine for this virus.
• High possibility that other antimicrobial-resistant infections will prevail in society.
• Nutritional status is very important to maintain a strong immune system against the virus.
• COVID-19 attacks people with low immune systems and people especially people of under and overages.
• The immune system is built on beneficial live bacteria that live in the gut which protect the human body from various diseases.
• When the immune system response is low, weak, or damaged, it becomes an open invitation for infections such as coronavirus or other diseases like diabetes, heart disease, or cancer.
• Current pandemic of COVID-19 where no effective preventive and curative medicine is available – a healthy immune system is one of the most important weapons.
• Balanced nutrition which can help in maintaining immunity is essential for the prevention and management of viral infections.
• Activation of the host’s immune response and nutrition could be a means of achieving it.
• “Food as medicine” has been a powerful approach to health and healing from ancient times.
• A functioning immune system is crucial in the fight against COVID-19. To maintain it, the body needs sufficient vitamins and other nutrients. But that is exactly what many people lack.
• Your immune system is your vaccine today.
• A healthy gut will strengthen your immune system.
• Good nutrition will play a vital role in providing all the key nutrients for immune health support.

·        What is a Balanced diet?

Ø  Carbohydrates

Ø  Fats

Ø  Proteins

Ø  Vitamins

Ø  Minerals

Ø  Fiber (roughage)

Ø  Water (fluids)

·       Nutrition is a critical determinant of the outcome of host-microbe interactions through modulation of the immune response.

·       Immune Boosters

Ø  Vitamin C

Ø  Vitamin A

Ø  Vitamin D – Sunshine vitamin

Ø  Vitamin E

Ø  Zinc

Ø  Iron

·       When should we hand wash in COVID-19 Pandemic?

Ø  After blowing your nose, coughing or sneezing

Ø  After visiting a public space, including public transportation, markets

Ø  Surfaces outside home

Ø  Before, during and after caring for a sick person

Ø  Before and after eating

·       Managing stress

Ø  Regular Exercise – Aerobic exercise indoor

§  Walking

§  Climbing stairs

§  Playing with children

§  Stretching exercise

Ø  Yoga – exercise form that helps both the body and mind

Ø  Step away from media if you are overwhelmed

Ø  Take a break

§  Play with puzzles

§  Read a book that you wish

§  Reorganize your home

§  Enjoy and clean

Ø  Be connected with your family members, friends, and relatives to reduce stress and depression due to unwanted fear and isolation.

Ø  Get enough sleep: optimal sleep

§  Sleeping 7-8 hours for adults and up to 10 hours for children and teenagers are less likely to be at risk of viral infections.

Ø  Do not overeat or eat an unhealthy diet

Ø  Discover ways to express your talent

§  Writing articles in journals, blogging, painting, cooking.   

Ø  Avoid paranoia

Ø  Distract yourself from negative emotions

§  Watch TV shows, Gardening, Quiz, watching cartoons with children listen to music.

Ø  Feeling angry or irritated – Calm yourself count back from 10 to 1

Ø  Practice mind exercise for concentration

·       Taking care of our gut health and especially nurturing a highly diverse microbiota through a dietary intake rich in a wide range of plant sources, fruits, vegetables, grains, and nuts will favor a robust immune system and indirectly increase our ability to fight viral attacks in every organ including the lungs.

·       For a viral disease like COVID-19, where no pharmacological strategies for prevention or treatment are presently available and where the exact time of the ending of the alarming situation is unknown, nutritional strategies for enhancing immunity is something to be explored.

Main Statistical Concepts that helps the Data Scientist in 2020

All Must know Statistical concepts for the Data Science journey

Statistics is a powerful tool while performing the art of Machine Learning and Data Science. A basic visualization such as a box plot gives you some more useful information, and with the help of statistics, we can get more info and insights into data in a targeted way.

By using the statistical concepts, we get more useful insights that let us know the distribution of data and structure where we can apply more Machine Learning techniques to acquire more knowledge of data. Without further ado, let’s dive into it.

Probability Distribution

Fig. 1 

• A Uniform distribution has a single value that occurs in a particular range while anything outside the range is just 0. We can assume it as a representation of categorical variables either 0 or 1. The categorical variable might have multiple values but we can visualize the same as a piecewise function of multiple uniform distributions.
• Ø  A Normal Distribution is also known as Gaussian Distribution that is defined by its mean and standard deviation. The mean shifts distribution spatially where standard deviation controls the spread. We know the average value of our dataset and the spread of the data with Gaussian distribution.
• Ø  A Poisson Distribution is the same as Normal but with the addition of skewness. It has a relatively uniform spread in all directions just like normal at the time of low-value skewness. The spread of data will be different in different directions when the skewness value is high.

There are many distributions you can know which help to interpret our categorical data with Uniform distribution. Many algorithms perform well with Gaussian. In Poisson, we have to take extra care and choose a robust algorithm.

Probability distributions helped calculate confidence intervals for parameters and critical regions for the hypothesis tests. For univariate data, it is useful to determine a reasonable distributional model for the data. Statistical intervals and hypothesis tests depended on specific distributional assumptions.

Over and Under Sampling

Classification problems use these techniques. Sometimes, our classification dataset is biased towards one side. For example, we have 1000 samples for class 1, but only 200 for class 2. We have ML techniques to model the data and make predictions. In this case, we have two preprocessing options that help in the training of our ML models.

Under-sampling means we select only some data from the majority class, as the same number of the minority classes. Now we have a balance on the probability distribution of the classes. The dataset is level out by choosing fewer samples.

Oversampling means we multiply the minority class such that it has the same count as the majority class. Now we have leveled out our dataset and the distribution of minorities without additional data.

Fig. 2

In the above-mentioned example, we can solve the issue in two ways. By using undersampling we select only 200 records for both the class 1 and 2. Another method is using oversampling or upsampling we replicate 200 examples to 800 such that both classes have 100 examples each where the model works better.

Accuracy

True positive: whenever the predicted and the original value is the same then it is true or counted.

True negative: doesn’t recognize or identify when the condition is not true.

False-positive: whenever the condition is not present it is false or detected.

False-negative: doesn’t identify the condition when it is not present.

Sensitivity: also termed as recall; measures the proportion of actual positive cases that got predicted as positive (True positive). sensitivity = TP/(TP+FN).

Specificity: measures the proportion of actual negative cases that got predicted negative (True negative). specificity = TN/(TN+FP).

Precision: measures the proportion of both True and False positive that got predicted correctly. precision = TP/(TP+FP).

Accuracy helps to evaluate the performance of models, and in some cases, it is not an efficient metric. Precision tells how precise/accurate our model is out of that predicted positive. When the costs of false-positive are high, Precision is a good measure to determine. In email spam detection, a false positive means that a non-spam email (actual negative) had shown as spam (predicted spam). The email user might lose important emails if the Precision is not high for the spam detection model. When there are high costs associated with false-negative, recall is the best metric to choose the model. For example, in fraud detection. If a fraudulent transaction (Actual Positive) resulted as non-fraudulent (Predicted negative), the consequence would be worse for the bank.

Hypothesis Testing and Statistical Significance

Fig. 3

Null Hypothesis: the hypothesis that there is no difference between the specified population.

Alternative Hypothesis: The hypothesis that states something is happening to the sample observations due to an external cause.

P-value: it is the probability of obtaining the expected result of a test, assuming that the null hypothesis is true. A smaller p-value means there is stronger evidence in favor of the alternative hypothesis.

Alpha: the probability of rejecting the null hypothesis when it is true, this is also known as Type 1 error.

Beta: known as Type 2 error, failed to reject the false null hypothesis.

Out of all the statistical concepts, this is my favorite one. Hypothesis testing is an essential step in statistics. It helps to evaluate two mutually exclusive statements about a population to determine which argument is best supported by sample data. Statistical significance is a measure of the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the correct answer. A p-value of 5% or lower is considered statistically significant. Statistical hypothesis testing helps to determine whether the result of the data set is statistically significant.

Dimensionality Reduction

It is the process of reducing the dimensions of our dataset. The purpose of this is to solve problems that arise in the case of datasets with high dimensions. In other words, it has many features. When a more dependent variable exists in a dataset, then more samples need to have every combination of features that increase the complexity of our model. Dimensionality reduction can include less data equivalent to many features that help in faster computing, fewer redundancies, and more accurate models.

Fig. 4 

From the above representation, we consider our dataset as a cubical structure that has three dimensions and 1000 points or values. With today’s computational power and techniques, 1000 records are easy to process, but on a large scale, we may run into problems. However, when we look at our data in a 2-Dimensional view that is one side of the cube, we can conclude that it’s easy to separate all the colors from this view. The projection of 3D data onto a 2D plane is possible by Dimensionality reduction. It effectively reduces the number of values we need to compute onto a 100. When it comes to vast data the reduction would be a more computational saving that leads to good results.

Future pruning is another way we can perform dimensionality reduction. In this, we remove features that are not important to our analysis. For example, after exploring our dataset we may find out of 9 independent variables, 6 of them have a high correlation with output, but the other 3 have a very low correlation. Then removing those three features doesn’t impact our results.

PCA is one of the most common statistical concepts used for Dimensionality reduction that creates vector representation of features that impact output that is a correlation.

Conclusion

Many other statistical concepts needed to be known for those who are interested in the journey towards Data Science based on the scenario. These are like building blocks of Data science. Statistics help to solve complex problems in the real world so that Data scientists and researchers can look for meaningful trends and modifications in data. In simple words, used to derive useful insights from data by performing mathematical computations on it. Hence, my advice is to spend enough time to learn skills that would be helpful in your journey.

Source:

Heart Care & COVID-19 Immune System

 ^{CARDIAC CARE}

With each passing year, the risks posed by cardiovascular diseases (CVD) seem to grow in magnitude. On World Heart Day (29th September), researchers need to gain more knowledge by attending several webinar series to throw light on different topics pertaining to the heart and related ailments. The prediction and prevention of CVD is a must, since a lot of people suffering more when compared to the last 5 or 10 years.

Many countries were not ready with the requisite health infrastructure when Covid suddenly hit them. Similarly, the sheer number of people who are likely to end up with heart diseases is disquieting. But the good news is that 70 percent of CVDs can be tackled through lifestyle choice; almost 28 percent of risk can be reduced by simply not smoking and around 17 percent with exercises.

Many patients who come with CVD are as young as 26. They have just finished college and are in the early years of their career. The most common factors leading them to CVD are stress, smoking, and many other lifestyle factors. Among non-communicable diseases, CVD is the leading cause of death. It is important to take care of what you eat. Avoid eating too many carbs, exercise for 20-30 minutes five days a week, and get good sleep. For detecting erratic heart rhythms, the concept of electrophysiology is used. It is a widely known fact that genetically, Indians are prone to heart ailments. Some other factors need to be changed in our day-to-day life, such as calorie intake. We love excess oil, salt, and sugar but these are very detrimental for our heart. The idea is not how long you live but how well you live. Sit down and eat. Do not use gadgets and television while eating and watch your portion size. Diabetics do not often experience symptoms pointing to hypertension or heart ailments and for that, we need to go for regular check-ups.

Today AI can predict heart ailments and monitor the risk factors. When people come with symptoms of giddiness and palpitations, a small patch is stuck to their body and they are sent home. They go about their routine activities even as the machine continuously records their heart rhythm.

In the current scenario on daily life in this pandemic time, we now know that if you people wear a mask, you may not get infected. Similarly, you should not neglect symptoms. Heart diseases have seemingly reduced during Covid. It could be because of ample rest, proper eating, and exercise.

Healthcare has seen sweeping changes over the past decade thanks to the innovation and research happening in that space. In the next three years, telemonitoring is going to create an explosive revolution.

Robotics and minimally invasive technologies have changed the way surgeries are conducted today. We have got into the big league with robotics. The advantages have magnified by 10 times and the system is foolproof. Everyone in the operating room can see what is happening and there is hardly any blood loss or pain. Trauma is thus avoided. Splitting the chest open was always a concern and there are patients who will still need open-heart surgeries. But with these advancements, we have been able to reduce the hospital stay after the surgery for the elderly and for younger patients who have to get back to work.

COVID-19 IMMUNE SYSTEM

Immunity against COVID-19 may persist for at least five months after being infected with the SARS-CoV-2 virus, according to a study led by an Indian-origin researcher in the US.

The researchers from the University of Arizona studied the production of antibodies from a sample of nearly 6,000 people infected with the novel coronavirus.

"We clearly see high-quality antibodies still being produced five to seven months after SARS-CoV-2 infection," said Deepta Bhattacharya, associate professor at the University of Arizona.

"Many concerns have been expressed about immunity against COVID-19 not lasting. We used this study to investigate that question and found immunity is stable for at least five months," who led the study, published today in the journal Immunity, alongside Professor Janko Nikolich-Zugich from UArizona.

When a virus first infects cells, the immune system deploys short-lived plasma cells that produce antibodies to immediately fight the virus, the researchers explained.

Those antibodies appear in blood tests within 14 days of infection, they said.

The second stage of the immune response is the creation of long-lived plasma cells, which produce high-quality antibodies that provide lasting immunity, according to the researchers.

Deepta Bhattacharya and Janko Nikolich-Zugich tracked antibody levels over several months in people who tested positive for SARS-CoV-2 antibodies.

They found SARS-CoV-2 antibodies are present in blood tests at viable levels for at least five to seven months, although they believe immunity lasts much longer.

"Whether antibodies provide lasting protection against SARS-CoV-2 has been one of the most difficult questions to answer," said UArizona Health Sciences Senior Vice President Michael D Dake.

"This research not only has given us the ability to accurately test for antibodies against COVID-19, but also has armed us with the knowledge that lasting immunity is a reality," Mr. Dake said.

Earlier studies extrapolated antibody production from initial infections and suggested antibody levels drop quickly after infection, providing only short-term immunity.

Mr. Bhattacharya believes those conclusions focused on short-lived plasma cells and failed to take into account long-lived plasma cells and the high-affinity antibodies they produce.

"The latest time-points we tracked in infected individuals were past seven months, so that is the longest period of time we can confirm immunity lasts," Mr Bhattacharya said.

He said people who were infected with the first SARS coronavirus, which is the most similar virus to SARS-CoV-2, are still seeing immunity 17 years after infection.

"If SARS-CoV-2 is anything like the first one, we expect antibodies to last at least two years, and it would be unlikely for anything much shorter," the scientist added.