Trending December 2023 # Tracking A Viral War In Real Time # Suggested January 2024 # Top 16 Popular

You are reading the article Tracking A Viral War In Real Time updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Tracking A Viral War In Real Time

But at the microscopic level, the battle is complicated. A number of different immune cell types are involved in the campaign served with the purpose of killing the opponent without doing too much damage to the surrounding cells. Researchers have been examining this process for decades and have gained some understanding of the art of this war.

Most research has been accomplished in the laboratory through indirect analysis of molecules such as cytokines, reactive oxygen species, and antibodies. But visually, there has been a gap in knowledge. Though we may know how the immune system responds over the course of an infection, finding a way to see the fight in real time has been difficult at best.

Now that elusive goal may be possible. A group of American researchers have found a method to observe the viral war in the mouse. Their efforts have provided a glimpse of the strategy the immune system uses to fight an infection as well as offered perspective on why it takes so long to rid ourselves of one particular viral invader.

Influenza virus Source: Wikipedia

The area of the mouse chosen for visualization was the trachea. This was found to be accessible through non-fatal surgery and provided the right translucent properties necessary for real-time imaging. In addition, opening up this area allowed for the use of a ventilator to maintain oxygen levels in the animal.

The virus chosen was influenzavirus. Although this virus primarily infects the lung, the trachea is also at risk for invasion. Influenza also is one of the most common viral pathogens of humans suggesting the technique could have value in understanding how humans deal with infection.

The cell is known as a CD8+ T-cell – or CTL – and it is responsible for producing toxins and other molecules capable of killing infected cells. These cells are known to migrate throughout the body and tend to gather when a virus is detected. But what happens during the fight has been a bit of a mystery.

When the team ran the experiments, they were greeted with no surprises. As expected, when the virus was introduced into the trachea, infection initiated. To get to the real heart of the battle, the team waited for five days while the infection took hold. At this point, the CTLs were migrating toward the attack area meaning the fight was about to get interesting.

By Day 7, the movement of CTLs slowed significantly. The cells had reached their destination and were beginning the assault. The engagement in battle was apparent on Day 8. The cells moved in short bursts, going from cell to cell finding the infected victims and killing them off. The troops were moving in a coordinated fashion making sure to leave no stone unturned. The virus was most certainly doomed in this area.

As the cells gained ground, movement in the local region increased. On Day 9, the cells were highly motile but remained in the confined area. This suggested the campaign had moved from the direct battle to surveillance. This continued for several days as the cells maintained their hold on the area. The only difference, however, was the state of attack. The cells began to calm in their activity. By Day 14, they were arrested. They had done their job and were ready for some rest.

With the campaign visualized, the team wished to learn one more aspect to the wartime strategy. They lowered the dose of the virus and wanted to see if there would be less infiltration of immune cells. Their theory was partly correct. In two of the three tests, there were fewer immune cells. But this wasn’t absolute, suggesting the immune response may be affected but not driven by the virus.

For the authors, this study represented the first step in understanding how the immune system reacts to a virus. For the most part, the CTLs acted as expected. They swarmed the area, fought off the attack, and then maintained their position long after the threat had dissipated. The results gained from years of molecular study had been given credence through this visual means.

Of course, CTLs are only one part of the immune response. There are many other cell types involved in fighting off a virus and their contribution does play a role in battle. For the authors, this information is just waiting to be found in future studies. As these roles become visually clear, we will have a much better understanding of how our immunity responds to a viral threat. We may also be able to use this information to develop better treatments to help fight off this all too common infection.

You're reading Tracking A Viral War In Real Time

Is Trypophobia A Real Phobia?

Scroll through the gallery below to see some photos that may trigger your latent trypophobia.

My editor tasked me with investigating what causes this bizarre and irrational fear, which I had never heard of before.

It turns out that I’m not alone. I contacted roughly 10 psychologists for this story, and of those who got back to me, none had heard of it. The evolutionary psychologists I emailed were unwilling to speculate on the potential biological underpinnings for a fear of small, clustered holes. Trypophobia is not an official phobia recognized in scientific literature. For many (though perhaps not all) who have it, it’s probably not even a real phobia, which the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders says must interfere “significantly with the person’s normal routine.” Having just looked at a bunch of holey pictures and videos, I’m severely grossed out, but I can still write this story.

Although this may be of no comfort to those who suffer from it, trypophobia is simply one of an infinite number of fears that people experience, some more idiosyncratic than others. The online Phobia List, run by an amateur etymologist, contains the names of hundreds of fears, from the well-known (fear of heights: acrophobia) to the fringe (fear of the great mole rat: zemmiphobia). Trypophobia hasn’t made the list yet.

According to Martin Antony, a psychologist at Ryerson University in Toronto, past-president of the Canadian Psychological Association and author of The Anti-Anxiety Workbook, with the exception of a few terms (agoraphobia, claustrophobia and arachnophobia among them), professionals who study and treat phobias tend not to use all the Latin and Greek names that get tossed around on message boards and in the press.

Antony wasn’t surprised to hear that some people have an intense aversion to clustered holes because “people can be afraid of absolutely anything.” The factors that contribute to fears and phobias include traumatic experiences (getting bitten by a dog leading to a fear of dogs, for example), observational learning (watching others be afraid of heights), information and instruction (learning to fear being alone in the dark after watching too many horror movies), and various biological factors (like an inherited predisposition to anxiety). “Although the studies on causes of fears have all focused on more common ones, such as spiders and snakes, there is no reason to think that different factors would be responsible for more unusual fears, Antony says.

One trypophobic reported on Facebook that her fear stems back to childhood, when she had a Renaissance Faire dagger with a handle covered in little holes. Another member wrote: “I was stung by a bee in high school on my outer thigh. I had an allergic reaction, and my skin started to swell. The swelling was so bad, I could see each individual pore on my leg and I freaked out. Since then, I have not been able to look at clusters of holes without getting the heebie-jeebies.” Just. Gross.

Fear and disgust often go hand in hand, Antony says. “Evolutionarily speaking, almost all of the things that arouse a strong disgust-reaction–spiders, mice, blood, vomit–are things that could have been triggers for fear of illness.” Perhaps the same could be true for little holes, especially in natural objects where they seem particularly out of place. I suspect that we’re disgusted by pockmarked objects because they don’t look quite “right”; these perceived deformities signal danger, which we manifest as revulsion. But then again, a fear of asymmetry (another form of things looking not quite right) in some people with obsessive-compulsive disorder is not associated with disgust, Antony says. Perhaps holes, particularly in organic objects, subconsciously remind us of the symptoms of contagious illnesses that affect the skin, such as the rash or blisters associated with measles and chicken pox, respectively. All of this, of course, is speculation, and just goes to show how little we know about trypophobia.

Masai Andrews hopes that will change. Andrews, who runs chúng tôi founded the Facebook group page in 2009 when he was a sociology minor at SUNY-Albany. “I started the website and Facebook page because I suspected this was a very common phobia and I wanted a place where people could compile information,” Andrews says. “It is my hope that one day the academic and scientific communities will, at the very least, acknowledge the aversion to holes and certain patterns.”

When that happens, a Wikipedia page dedicated to the fear should follow. Surprisingly, one doesn’t exist today. “I can barely keep a page up on the subject without it getting taken down,” Andrews says. In March 2009 the powers that be at Wikipedia determined trypophobia to be a “likely hoax and borderline patent nonsense.” The deletion page also says that Wikipedia is “not for things made up one day.” As for who actually made the word up, that distinction probably belongs to a blogger in Ireland named Louise, Andrews says. According to an archived Geocities page, Louise settled on “trypophobia” (Greek for “boring holes” + “fear”) after corresponding with a representative at the Oxford English Dictionary. Louise, Andrews and trypophobia Facebook group members have petitioned the dictionary to include the word. The term will need to be used for years and have multiple petitions and scholarly references before the dictionary accepts it, Andrews says. I, for one, would prefer to forget about it forever.

Want to find out if you’re trypophobic? Take this quick visual test. But beware: You may be skipping lunch today.

Holes in Wood net_efekt

Boring Sponge Holes in Shell Wikimedia Commons

Pollen Micrograph Wikimedia Commons

Fungal Infection in Bone Marrow Euthman

Air Bubbles in Chocolate Ezhar/Ingmar

White Strawberry Qisur

Surinam Toad Wikimedia Commons

Dishcloth HoskingIndustries

Sponge Wikimedia Commons

Honeycomb BotheredByBees

Grate Ted Percival

Holes leonard_ripper

Swiss Cheese Hellebardius

Cheese Grater Muffet

Pancake Bubbles athrasher

Colander CraigMoulding

A Complete Tutorial On Time Series Modeling In R

23 minutes


Rating: 5 out of 5.


‘Time’ is the most important factor which ensures success in a business. It’s difficult to keep up with the pace of time.  But, technology has developed some powerful methods using which we can ‘see things’ ahead of time. Don’t worry, I am not talking about Time Machine. Let’s be realistic here!

I’m talking about the methods of prediction & forecasting. One such method, which deals with time based data is Time Series Modeling. As the name suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making.

Time series models are very useful models when you have serially correlated data. Most of business houses work on time series data to analyze sales number for the next year, website traffic, competition position and much more. However, it is also one of the areas, which many analysts do not understand.

So, if you aren’t sure about complete process of time series modeling, this guide would introduce you to various levels of time series modeling and its related techniques.

What Is Time Series Modeling?

Let’s begin from basics.  This includes stationary series, random walks , Rho Coefficient, Dickey Fuller Test of Stationarity. If these terms are already scaring you, don’t worry – they will become clear in a bit and I bet you will start enjoying the subject as I explain it.

Stationary Series

There are three basic criterion for a series to be classified as stationary series:

1. The mean of the series should not be a function of time rather should be a constant. The image below has the left hand graph satisfying the condition whereas the graph in red has a time dependent mean.

2. The variance of the series should not a be a function of time. This property is known as homoscedasticity. Following graph depicts what is and what is not a stationary series. (Notice the varying spread of distribution in the right hand graph)

3. The covariance of the i th term and the (i + m) th term should not be a function of time. In the following graph, you will notice the spread becomes closer as the time increases. Hence, the covariance is not constant with time for the ‘red series’.

Why do I care about ‘stationarity’ of a time series?

The reason I took up this section first was that until unless your time series is stationary, you cannot build a time series model. In cases where the stationary criterion are violated, the first requisite becomes to stationarize the time series and then try stochastic models to predict this time series. There are multiple ways of bringing this stationarity. Some of them are Detrending, Differencing etc.

Random Walk

This is the most basic concept of the time series. You might know the concept well. But, I found many people in the industry who interprets random walk as a stationary process. In this section with the help of some mathematics, I will make this concept crystal clear for ever. Let’s take an example.

Example: Imagine a girl moving randomly on a giant chess board. In this case, next position of the girl is only dependent on the last position.

Now imagine, you are sitting in another room and are not able to see the girl. You want to predict the position of the girl with time. How accurate will you be? Of course you will become more and more inaccurate as the position of the girl changes. At t=0 you exactly know where the girl is. Next time, she can only move to 8 squares and hence your probability dips to 1/8 instead of 1 and it keeps on going down. Now let’s try to formulate this series :

X(t) = X(t-1) + Er(t)

where Er(t) is the error at time point t. This is the randomness the girl brings at every point in time.

Now, if we recursively fit in all the Xs, we will finally end up to the following equation :

X(t) = X(0) + Sum(Er(1),Er(2),Er(3).....Er(t))

Now, lets try validating our assumptions of stationary series on this random walk formulation:

1. Is the Mean constant?

E[X(t)] = E[X(0)] + Sum(E[Er(1)],E[Er(2)],E[Er(3)].....E[Er(t)])

We know that Expectation of any Error will be zero as it is random.

Hence we get E[X(t)] = E[X(0)] = Constant.

2. Is the Variance constant?

Var[X(t)] = Var[X(0)] + Sum(Var[Er(1)],Var[Er(2)],Var[Er(3)].....Var[Er(t)]) Var[X(t)] = t * Var(Error) = Time dependent.

Hence, we infer that the random walk is not a stationary process as it has a time variant variance. Also, if we check the covariance, we see that too is dependent on time.

Let’s spice up things a bit,

We already know that a random walk is a non-stationary process. Let us introduce a new coefficient in the equation to see if we can make the formulation stationary.

Introduced coefficient: Rho

X(t) = Rho * X(t-1) + Er(t)

Now, we will vary the value of Rho to see if we can make the series stationary. Here we will interpret the scatter visually and not do any test to check stationarity.

Let’s start with a perfectly stationary series with Rho = 0 . Here is the plot for the time series :

Increase the value of Rho to 0.5 gives us following graph:

You might notice that our cycles have become broader but essentially there does not seem to be a serious violation of stationary assumptions. Let’s now take a more extreme case of Rho = 0.9

We still see that the X returns back from extreme values to zero after some intervals. This series also is not violating non-stationarity significantly. Now, let’s take a look at the random walk with rho = 1.

This obviously is an violation to stationary conditions. What makes rho = 1 a special case which comes out badly in stationary test? We will find the mathematical reason to this.

Let’s take expectation on each side of the equation  “X(t) = Rho * X(t-1) + Er(t)”

E[X(t)] = Rho *E[ X(t-1)]

This equation is very insightful. The next X (or at time point t) is being pulled down to Rho * Last value of X.

For instance, if X(t – 1 ) = 1, E[X(t)] = 0.5 ( for Rho = 0.5) . Now, if X moves to any direction from zero, it is pulled back to zero in next step. The only component which can drive it even further is the error term. Error term is equally probable to go in either direction. What happens when the Rho becomes 1? No force can pull the X down in the next step.

Dickey Fuller Test of Stationarity

What you just learnt in the last section is formally known as Dickey Fuller test. Here is a small tweak which is made for our equation to convert it to a Dickey Fuller test:

X(t) = Rho * X(t-1) + Er(t)

We have to test if Rho – 1 is significantly different than zero or not. If the null hypothesis gets rejected, we’ll get a stationary time series.

Stationary testing and converting a series into a stationary series are the most critical processes in a time series modelling. You need to memorize each and every detail of this concept to move on to the next step of time series modelling.

Let’s now consider an example to show you what a time series looks like.

Exploration of Time Series Data in R

Here we’ll learn to handle time series data on R. Our scope will be restricted to data exploring in a time series type of data set and not go to building time series models.

I have used an inbuilt data set of R called AirPassengers. The dataset consists of monthly totals of international airline passengers, 1949 to 1960.

Loading the Data Set

Following is the code which will help you load the data set and spill out a few top level metrics.

[1] “ts”

#This tells you that the data series is in a time series format [1] 1949 1 #This is the start of the time series

[1] 1960 12

#This is the end of the time series

[1] 12

#The cycle of this time series is 12months in a year Min. 1st Qu. Median Mean 3rd Qu. Max. 104.0 180.0 265.5 280.3 360.5 622.0 Detailed Metrics #The number of passengers are distributed across the spectrum #This will plot the time series # This will fit in a line

Here are a few more operations you can do:

#This will print the cycle across years. #This will aggregate the cycles and display a year on year trend #Box plot across months will give us a sense on seasonal effect Important Inferences

The year on year trend clearly shows that the #passengers have been increasing without fail.

The variance and the mean value in July and August is much higher than rest of the months.

Even though the mean value of each month is quite different their variance is small. Hence, we have strong seasonal effect with a cycle of 12 months or less.

Exploring data becomes most important in a time series model – without this exploration, you will not know whether a series is stationary or not. As in this case we already know many details about the kind of model we are looking out for.

Let’s now take up a few time series models and their characteristics. We will also take this problem forward and make a few predictions.

Introduction to ARMA Time Series Modeling

ARMA models are commonly used in time series modeling. In ARMA model, AR stands for auto-regression and MA stands for moving average. If these words sound intimidating to you, worry not – I’ll simplify these concepts in next few minutes for you!

We will now develop a knack for these terms and understand the characteristics associated with these models. But before we start, you should remember, AR or MA are not applicable on non-stationary series.

In case you get a non stationary series, you first need to stationarize the series (by taking difference / transformation) and then choose from the available time series models.

First, I’ll explain each of these two models (AR & MA) individually. Next, we will look at the characteristics of these models.

Auto-Regressive Time Series Model

Let’s understanding AR models using the case below:

The current GDP of a country say x(t) is dependent on the last year’s GDP i.e. x(t – 1). The hypothesis being that the total cost of production of products & services in a country in a fiscal year (known as GDP) is dependent on the set up of manufacturing plants / services in the previous year and the newly set up industries / plants / services in the current year. But the primary component of the GDP is the former one.

Hence, we can formally write the equation of GDP as:

x(t) = alpha *  x(t – 1) + error (t)

This equation is known as AR(1) formulation. The numeral one (1) denotes that the next instance is solely dependent on the previous instance.  The alpha is a coefficient which we seek so as to minimize the error function. Notice that x(t- 1) is indeed linked to x(t-2) in the same fashion. Hence, any shock to x(t) will gradually fade off in future.

For instance, let’s say x(t) is the number of juice bottles sold in a city on a particular day. During winters, very few vendors purchased juice bottles. Suddenly, on a particular day, the temperature rose and the demand of juice bottles soared to 1000. However, after a few days, the climate became cold again. But, knowing that the people got used to drinking juice during the hot days, there were 50% of the people still drinking juice during the cold days. In following days, the proportion went down to 25% (50% of 50%) and then gradually to a small number after significant number of days. The following graph explains the inertia property of AR series:

Moving Average Time Series Model

Let’s take another case to understand Moving average time series model.

A manufacturer produces a certain type of bag, which was readily available in the market. Being a competitive market, the sale of the bag stood at zero for many days. So, one day he did some experiment with the design and produced a different type of bag. This type of bag was not available anywhere in the market. Thus, he was able to sell the entire stock of 1000 bags (lets call this as x(t) ). The demand got so high that the bag ran out of stock. As a result, some 100 odd customers couldn’t purchase this bag. Lets call this gap as the error at that time point. With time, the bag had lost its woo factor. But still few customers were left who went empty handed the previous day. Following is a simple formulation to depict the scenario :

x(t) = beta *  error(t-1) + error (t)

If we try plotting this graph, it will look something like this:

Did you notice the difference between MA and AR model? In MA model, noise / shock quickly vanishes with time. The AR model has a much lasting effect of the shock.

Difference Between AR and MA Models Exploiting ACF and PACF Plots

Once we have got the stationary time series, we must answer two primary questions:

The trick to solve these questions is available in the previous section. Didn’t you notice?

The first question can be answered using Total Correlation Chart (also known as Auto – correlation Function / ACF). ACF is a plot of total correlation between different lag functions. For instance, in GDP problem, the GDP at time point t is x(t). We are interested in the correlation of x(t) with x(t-1) , x(t-2) and so on. Now let’s reflect on what we have learnt above.

In a moving average series of lag n, we will not get any correlation between x(t) and x(t – n -1) . Hence, the total correlation chart cuts off at nth lag. So it becomes simple to find the lag for a MA series. For an AR series this correlation will gradually go down without any cut off value. So what do we do if it is an AR series?

Here is the second trick. If we find out the partial correlation of each lag, it will cut off after the degree of AR series. For instance,if we have a AR(1) series,  if we exclude the effect of 1st lag (x (t-1) ), our 2nd lag (x (t-2) ) is independent of x(t). Hence, the partial correlation function (PACF) will drop sharply after the 1st lag. Following are the examples which will clarify any doubts you have on this concept :

The blue line above shows significantly different values than zero. Clearly, the graph above has a cut off on PACF curve after 2nd lag which means this is mostly an AR(2) process.

Clearly, the graph above has a cut off on ACF curve after 2nd lag which means this is mostly a MA(2) process.

Till now, we have covered on how to identify the type of stationary series using ACF & PACF plots. Now, I’ll introduce you to a comprehensive framework to build a time series model.  In addition, we’ll also discuss about the practical applications of time series modelling.

Framework and Application of ARIMA Time Series Modeling

A quick revision, Till here we’ve learnt basics of time series modeling, time series in R and ARMA modeling. Now is the time to join these pieces and make an interesting story.

Overview of the Framework

This framework(shown below) specifies the step by step approach on ‘How to do a Time Series Analysis‘:

As you would be aware, the first three steps have already been discussed above. Nevertheless, the same has been delineated briefly below:

Step 1: Visualize the Time Series

It is essential to analyze the trends prior to building any kind of time series model. The details we are interested in pertains to any kind of trend, seasonality or random behaviour in the series. We have covered this part in the second part of this series.

Step 2: Stationarize the Series

Once we know the patterns, trends, cycles and seasonality , we can check if the series is stationary or not. Dickey – Fuller is one of the popular test to check the same. We have covered this test in the first part of this article series. This doesn’t ends here! What if the series is found to be non-stationary?

There are three commonly used technique to make a time series stationary:

1.  Detrending : Here, we simply remove the trend component from the time series. For instance, the equation of my time series is:

x(t) = (mean + trend * t) + error

We’ll simply remove the part in the parentheses and build model for the rest.

2. Differencing : This is the commonly used technique to remove non-stationarity. Here we try to model the differences of the terms and not the actual term. For instance,

x(t) – x(t-1) = ARMA (p ,  q)

This differencing is called as the Integration part in AR(I)MA. Now, we have three parameters

p : AR

d : I

q : MA

3. Seasonality: Seasonality can easily be incorporated in the ARIMA model directly. More on this has been discussed in the applications part below.

Step 3: Find Optimal Parameters

The parameters p,d,q can be found using  ACF and PACF plots. An addition to this approach is can be, if both ACF and PACF decreases gradually, it indicates that we need to make the time series stationary and introduce a value to “d”.

Step 4: Build ARIMA Model

With the parameters in hand, we can now try to build ARIMA model. The value found in the previous section might be an approximate estimate and we need to explore more (p,d,q) combinations. The one with the lowest BIC and AIC should be our choice. We can also try some models with a seasonal component. Just in case, we notice any seasonality in ACF/PACF plots.

Step 5: Make Predictions

Once we have the final ARIMA model, we are now ready to make predictions on the future time points. We can also visualize the trends to cross validate if the model works fine.

Applications of Time Series Model

Now, we’ll use the same example that we have used above. Then, using time series, we’ll make future predictions. We recommend you to check out the example before proceeding further.

Where did we start?

Following is the plot of the number of passengers with years. Try and make observations on this plot before moving further in the article.

Here are my observations:

1. There is a trend component which grows the passenger year by year.

2. There looks to be a seasonal component which has a cycle less than 12 months.

3. The variance in the data keeps on increasing with time.

We know that we need to address two issues before we test stationary series. One, we need to remove unequal variances. We do this using log of the series. Two, we need to address the trend component. We do this by taking difference of the series. Now, let’s test the resultant series.

adf.test(diff(log(AirPassengers)), alternative="stationary", k=0)

Augmented Dickey-Fuller Test

data: diff(log(AirPassengers)) Dickey-Fuller = -9.6003, Lag order = 0, p-value = 0.01 alternative hypothesis: stationary

We see that the series is stationary enough to do any kind of time series modelling.

Next step is to find the right parameters to be used in the ARIMA model. We already know that the ‘d’ component is 1 as we need 1 difference to make the series stationary. We do this using the Correlation plots. Following are the ACF plots for the series:

#ACF Plots


What do you see in the chart shown above?

Clearly, the decay of ACF chart is very slow, which means that the population is not stationary. We have already discussed above that we now intend to regress on the difference of logs rather than log directly. Let’s see how ACF and PACF curve come out after regressing on the difference.

acf(diff(log(AirPassengers))) pacf(diff(log(AirPassengers)))

Clearly, ACF plot cuts off after the first lag. Hence, we understood that value of p should be 0 as the ACF is the curve getting a cut off. While value of q should be 1 or 2. After a few iterations, we found that (0,1,1) as (p,d,q) comes out to be the combination with least AIC and BIC.

Let’s fit an ARIMA model and predict the future 10 years. Also, we will try fitting in a seasonal component in the ARIMA formulation. Then, we will visualize the prediction along with the training data. You can use the following code to do the same :

(fit <- arima(log(AirPassengers), c(0, 1, 1),seasonal = list(order = c(0, 1, 1), period = 12))) pred <- predict(fit, n.ahead = 10*12) ts.plot(AirPassengers,2.718^pred$pred, log = "y", lty = c(1,3)) Practice Projects

Now, its time to take the plunge and actually play with some other real datasets. So are you ready to take on the challenge? Test the techniques discussed in this post and accelerate your learning in Time Series Analysis with the following Practice Problems:


With this, we come to this end of tutorial on Time Series Modelling. I hope this will help you to improve your knowledge to work on time based data. To reap maximum benefits out of this tutorial, I’d suggest you to practice these R codes side by side and check your progress.

Frequently Asked Questions Related

Black Widows Battle Their Even Deadlier Cousins In A Brutal Spider War

It can be pretty tough to be a bug, especially for spiders that have a not so great reputation. While black widow spiders (Latrodectus hesperus) and their venomous bites are a common fear for humans, spiders who call parts of the southern United States home have quite a bit to fear themselves. Brown widow spiders (Latrodectus geometricus) don’t seem to get along with their cousins from the same genus, and over the past few decades scientists have observed brown widow spiders increasingly displacing black widows. 

But like any family drama, it’s more complicated than fighting over food or habitat. A new study published March 13 in the journal Annals of the Entomological Society of America suggests that brown widow spiders seek out and kill nearby black widows for reasons that scientists are still trying to really understand.

[Related: Jumping spiders might be able to sleep—perchance to dream.]

“We have established brown widow behavior as being highly aggressive towards the southern black widows, yet much more tolerant of other spiders within the same family,” Louis Coticchio, a former zookeeper who specialized in venomous animals a University of South Florida undergraduate student who led the study, said in a statement. 

Scientists believe that brown widow spiders were native to Africa before being introduced to every continent except Antarctica. The black widows native to North America have two closely related species, the western black widow (Latrodectus hesperus) and the southern black widow (Latrodectus mactans).

While comparing growth rates and fertility, they saw that sub-adult brown widow females were 9.5 percent larger than black widows, and adult female brown widows reached reproductive maturity 16 percent earlier and were about twice as fertile as black widows. Brown widow males are smaller than their black widow counterparts, but become fertile in a shorter time.

[Related: These male spiders fling into the air to escape post-coital cannibalism.]

The team designed experiments that paired brown widow spiders in a contained habitat with related cobweb spider chúng tôi found that the brown widows were 6.6 times more likely to kill their roommates if they were southern black widows versus any other related species. 

While living with the red house spider, sub-adult brown widow females simply cohabitated with other females in 50 percent of pairings. The red house spiders killed and consumed the brown widows in 40 percent of the observed pairings. 

Brown widows peacefully cohabitated with triangulate cobweb spiders (Steatoda triangulosa) in 80 percent of pairings ,and they were killed in just 10 percent of the observed pairings. 

However, when sub-adult brown and black widow females were put together, the brown widows killed and ate the black widows in 80 percent of pairings. Adult black widows were killed in 40 percent of trials, but they defensively killed brown widows in 30 percent of trials, and simply cohabitated during the remaining 30 percent.

“We didn’t expect to find such a dramatic and consistent difference in the personalities of the brown widow and the black widow,” said co-author and biologist Deby Cassill, in a statement. “Brown widows are boldly aggressive and will immediately investigate a neighbor and attack if there is no resistance from the neighbor. For two bold spiders, the initial attack is often resolved by both individuals going to separate corners and eventually being OK with having a nearby neighbor. The black widows are extremely shy, counterattacking only to defend themselves against an aggressive spider.”

The team is still uncovering what is driving this aggression towards a closely related species, noting that invasive species can typically outcompete native species through fertility, growth, dispersal, or defenses against predators. 

“One question I would love to answer is how brown widows interact with other species of spiders, more specifically black widows in Africa, where brown widows are believed to have originated,” Coticchio said. “I would love to see if their behavior and displacement of black widows is something that they have adapted here in North America, or if this behavior is something they exhibit naturally even in areas where they have coevolved with black widows for much longer periods of time.”  

Latepoint Booking Conversion Tracking In Google Ads

Feeling stuck with your booking tracking? No idea where to grab these dynamic values for your conversion? Let’s dive into your tracking case together. 

You run your WordPress webpage and use the LatePoint plugin to manage your appointments. However, you would love to go further and track bookings as conversions together with the prices. 

🚨 Note: Check out our five strategies to analyze your Google Ads data in our handy guide on optimizing Google Ads campaigns.

Here’s a quick roadmap of our tracking setup:

Set up a conversion in the Google Ads account

The first step is to set up your Ads conversion in your Google Ads account. Go to your Google Ads account and navigate to Tools & Settings → Conversions.

Select New conversion action to set up a new conversion. 

Select the first option, which is Website conversions.

Do not feel confused when the system asks you to provide the URL of your webpage. This is a Google Ads update with new steps added to the configuration. Do not hesitate to enter your URL.

Wait for your webpage to be scanned and scroll down to find the option – Add a conversion manually.

Provide all the settings for your conversion action. Do not forget to choose Use different values, as we want to track your product prices dynamically.

Your conversion is set up, and now it is time to choose your implementation option. 

In this guide, we are covering implementation via GTM. Select Use Google Tag Manager, and you would see a conversion ID and label. 

Great! Ads configuration is done, and we can move to the LatePoint settings. 

Add a data layer to LatePoint

Login to your WordPress admin dashboard and navigate to LatePoint on the left side.

Now for the most exciting part. You should copy the given snippet and paste it into the Conversion Tracking field. 

What is this snippet about? We are adding a data layer that contains information about your bookings, such as appointment ID and total price. Since you have multiple prices, you cannot simply track them statically. 

We created this data layer for you, to ensure that the correct variables are provided.

🚨 Note: We are showing how to set up Ads conversion tracking, but you can use this code for any kind of tracking. This would give you dynamic values that you can use in your configuration.

window.dataLayer.push({ event: ‘latepoint_confirmation’, appointment_id: {booking_id}, service_id: {service_id}, agent_id: {agent_id}, customer_id: {customer_id}, total_price: {total_price} });

Create data layer variables

Adding this snippet is not enough, as we also have to do some additional setup via GTM.

Select Data Layer Variable.

Let’s create the first variable for your price. Provide your variable name and save it.

The second variable to be created for your Ads conversion is the appointment ID.

Set up your tag and trigger

Everything is ready for your Ads tag. Copy and paste your conversion ID and label. Add the dynamic variables we have configured earlier.

Set up a trigger. Since we are pushing an event in the data layer, the trigger type should be Custom Event with the latepoint_confirmation as an event name.

Test your implementation

Now for the most exciting part of the whole process: TESTING! Preview your GTM container and make a test booking to see if your tag is firing.

Testing a tag itself is not enough, as we should also check if our variables are working correctly. You should go to Variables and check if the correct values are displayed there. 

If you have reached this point, it is time to pat yourself on the back: You did an excellent job! One small remark: Do not forget to publish your GTM container.

FAQ Why should I track LatePoint bookings as conversions in Google Ads? Can I use LatePoint Booking Conversion Tracking for other tracking purposes?

Yes, the code provided in the blog post can be used for tracking purposes other than Ads conversions. The data layer snippet captures dynamic values related to bookings, such as appointment ID and total price, which can be utilized for any type of tracking configuration.

Do I need to have a Google Tag Manager (GTM) account to implement LatePoint Booking Conversion Tracking?

Yes, LatePoint Booking Conversion Tracking is implemented using Google Tag Manager (GTM). You will need to have a GTM account and access to your WordPress admin dashboard to complete the setup process.


We have set up an Ads conversion for LatePoint bookings via GTM. Key takeaways from this lesson:

1) The snippet we have provided is the key component here. You can use it not only for Ad tracking but also for your own tracking purposes if you want to grab those values related to your bookings.

2) You should know now how to set up Google Ads conversion tracking and verify if it is working.

3) Tracking is fun!

Another thing that will help you optimize the conversion tracking of your campaigns would be setting up Google Ads enhanced conversions with GTM.

Best Sleep Tracking Apps For Iphone In 2023

How often do you pay attention to your sleeping pattern? Not very often, I guess. Getting quality sleep is essential for your productivity. Anything short of this can reduce your quality of life. One way to alleviate your sleeping habit is to track it. This helps you decipher what you need to improve to strike a balance. Fortunately, you can achieve this through a sleep tracking app for iPhone.

However, while the iPhone’s Health app already features a tracker, third-party apps induce a bit of dedication. And they’re even more accessible and less complex than the built-in Health app. So, keep reading to explore the best sleep tracking apps.

1. Sleep Cycle: Sleep Tracker – Editor’s choice

How would you feel about knowing what you do while sleeping? Sleep Cycle brings the best features to satisfy your curiosity. And whether you’ve been dealing with insomnia or find it hard to follow a sleep routine, you can use its sleeping aid to hypnotize yourself.

Further, its spectacular soundtracking feature uses a microphone component to record whatever you say in your sleep, including the sound around you. The dashboard also lets you set up a sleeping window, helping you stick to a sleeping routine rather than the random timeless seeping pattern.

Sleep Cycle relies partly on users’ data to improve its accuracy, making it a health data collector. However, you can disallow it from collecting your health information. You can also connect Sleep Cycle with your Apple watch to make it handier.

Many users also find its stats section helpful for monitoring how their sleep improves over time. However, you need to subscribe to a premium plan to use most features.


Set your sleeping window

Monitor what you do while asleep

Features expert-level hypnotic soundtracks

Use the app on your Apple Watch


You have to pay to use the app

Price: Free (Subscription starts at $4.99)


2. ShutEye: Sleep Tracker – Check what your symptoms mean

Ranging from sleep stories to guided meditations and more, ShutEye gives you the best of them. The app features every sleep-tracking functionality typical of a sleep tracker. You can record the sounds you make while sleeping.

However, you can choose which of these you want it to record while you’re asleep. Additionally, it lets you set a sleeping window.

ShutEye stands out as it uses patterns mined from your sleeping habit to identify health risks. And if you want to know what your symptoms mean, the app features succinct health checklists for more insights. Although the app comes free, you might want to subscribe to enjoy its primary features.


Record only what you want to hear while sleeping

Hypnotic sleep stories and guided meditations

Define sleeping window


Primary features exclusive to the premium plan

Price: Free (Subscription starts at $3.99)


3. SleepWatch: Top Rated Tracker – Best hypnotic natural sounds

SleepWatch features AI-powered components to help improve your sleeping habit. If you love to hear the sound of rain dropping on your rooftop or the rolling sound of fan blades puts you to sleep, I bet SleepWatch is what you want to try.

The app has many features, but above others, it boasts of its many hypnotic sounds and efficient sleep-recording functionality, which you’ll have to set up when choosing a sleeping goal. You can assess stats like sleep disruption, snoring, sleep rhythm, sleep efficiency, and fatigue on its detailed dashboard.

If you allow it, its AI algorithm works by tapping health information contained in the Health app on your iPhone. You only need to give the app access to your health data by simply following the on-screen instructions when setting it up. Hence, you might not need to supply additional health details explicitly when using the app.

Additionally, it allows you to set a sleeping goal, and it tracks this over time to give you insightful reports on your progress.

However, you have to turn on the notification for the app to get this stat. An upvote I have for the app is its disclaimer policy, which says you shouldn’t rely on it or replace it with proper medical intervention. The app is free, but the premium plan comes with additional benefits.


Intuitive and detailed dashboard

Syncs with the Health app on iPhone

Set a sleeping goal and assess sleeping habits over time

Comprehensive setup instructions

Adaptive hypnotic sounds


You can’t set a waking alarm

Price: Free (Subscription starts at $3.99)


4. Pillow: Auto Sleep Tracker – Title your sleep schedules

Pillow is a detailed app that lets you set up your sleep schedule and give them appropriate titles. It’s not different from the typical sleep trackers I’ve mentioned earlier.

Like SleepWatch, you can permit your health data on the Health app and record sounds while you sleep. So it uses that to determine some factors related to your sleeping health.

It also uses patterns in your motion and fitness activities to assess your sleeping habits over time. You’ll get alerts on sleep reports after you wake up each day. Its sleeping aid section is also worth mentioning. This section provides hypnoses to help put you to sleep.

But while some features are free, most come with a premium subscription. It also features a Snooze lab; you’ll get insightful coaching tips that can help you sleep better from here. Ultimately, Pillow syncs your data and reports across other devices like Apple Watch and iPad once you register on the app.

You can check our full review of Pillow to learn more.


Ability to set sleep schedules

Syncs with iPhone’s Health app

Get wake up alerts based on your sleep schedules

Comprehensive sleep reports


Advanced features like comprehensive stats are limited to the premium plan

Price: Free (Subscription starts at $6.99)


5. Sleepzy: Sleep Cycle Tracker – Sleep tracker with health articles

Sleepzy adapts to your natural sleeping time by waking you up at your light sleep hour. This one also records whatever is going on while you sleep and lets you set a sleeping plan with relaxing sounds to put you to sleep. And it doesn’t leave out the typical comprehensive stats either.

Nonetheless, its unique attribute is it features various articles centered around getting healthy sleeping habits. The app also features what it calls bedtime rituals. These give you tips and recommend pre-bedtime exercises and hypnoses that help you sleep better.

However, one problem with the app’s free version is its buggy ad pop-ups. You can remove these when you subscribe to a paid plan.


Read various tips and articles on healthy sleep

Get sleeping stats over time

Bedtime rituals to help you get better sleep

Record sounds and snores while sleeping


Price: Free (Subscription starts at $3.99)


6. SleepScore: Go beyond Tracking – Room check features

SleepScore is the best app to use if you want to know what might be preventing you from sleeping well. It features everything you need to assess your sleeping pattern and improve your sleep. But it doesn’t leave out unique details that make it stand out among others like it.

A notable feature of SleepScore is its bedroom check. This allows the app to assess the conditions in your room, including the noise level, light level, and the surrounding temperature. It then uses what it curates to recommend actions that might help you sleep better. You can also record snoring sounds and get sleep stats as you go.

Also, one of its features, called Sleep Screener, determines if you need an expert’s intervention based on your sleep reports. All these come with the premium plan, though.

However, getting quick tips that can help you sleep better from its sleep library and tips section comes at no price. You can log your daily lifestyle impact assessment when you sync the app with your iPhone’s Health app.


The room checker tells what might be preventing you from sleeping well

Helps determine when you need an expert’s intervention

Get tips to sleep healthy from its list of articles and tips

Record snoring sounds while asleep

Comprehensive sleeping stats based on many conditions


The premium plan can be expensive

Price: Free (Subscription starts at $9.99)


7. Sleep++ – Simplest interface

Want a sleep tracker that you can pair with your Apple Watch? Sleep++ might be the app to try. This one has a simple interface to assess your sleep pattern based on data pulled from the Health app and your sleeping pattern. It pulls metrics, including blood oxygen level and respiratory rate, to provide comprehensive stats.

The app isn’t as functional as the others. But you can use it as a diary to record your sleeping habit over time and get insights into how it affects your health.


Get daily sleeping stats

Syncs perfectly with the Health app

Simple interface

Most features are free


Only works well with Apple Watch

Doesn’t record sounds during sleep

Price: Free (Subscription starts at $1.99)


8. SnoreLab: Record Your Snoring – Best for people with sleeping disorders

SnoreLab not only tracks your sleeping pattern but also uses its assessment algorithm to give you helpful tips to improve wherever needed. Like other apps, this one also syncs with the Health app to draw valuable insights. Additionally, there’s an alert to wake you based on your chosen sleep cycle.

The app records your snoring sounds while sleeping and helps you curate monthly stats. Unfortunately, you might not get a full-night recording unless you upgrade to its premium plan. There’s also a section to check the factors that impact your sleep.

I find this helpful since you’ll know what habits to avoid in the future to improve your sleep. While you might not depend heavily on its suggested remedies, a few trials may help.


Assess your sleeping stats based on sleeping patterns

Determine the factors that might be responsible for your sleeping disorder

Suggests remedies

Alerts you based on your chosen sleep cycle


Full-night recording only available with the premium plan

Price: Free (Subscription starts at $3.99)


9. Sleep Tracker: Sleep Recorder – Rate your sleeping

Whether it’s a mild sleeping disorder or chronic insomnia, I can bet that combining Sleep Tracker with other remedies can help alleviate your symptoms.

Notably, I love the wake-up button on this one, as you have to long-press it for about three seconds to stop the sleep tracker. This feature is helpful for those who usually wake before their chosen sleep cycle expires. Moreover, it ensures that you can keep your finger on the phone long enough to be sure you’re really awake.

The app uses your sleeping time and activities it notices while you sleep to draw insights into your sleeping pattern. However, you have to give the app access to your iPhone’s mic to record activities. You can also set up notifications to get appropriate wake alerts.

I find its array of natural hypnoses valuable for falling asleep swiftly. You can even mix hypnotic sounds if you like. For example, combining the sound of heavy rain with thunderstorms might do the trick.

Like other apps reviewed earlier, this one also syncs with the Health app and provides timely sleeping stats. But the unique attribute of this feature is that it rates your sleeping pattern based on a percentage. So you’ll know if you’re at the cross-road to sleeping disorder or not.

While you can use the Sleep Tracker for free, a few more features come with a premium plan!


Syncs well with the health app

Natural hypnoses

Record activities during sleep

Mix hypnoses for better result

Rate your sleeping pattern


The premium plan is expensive

Price: Free (Subscription starts at $19.99)


That’s it!

A healthy sleep comes with a tinge of self-fulfillment and improves your confidence. You might think you get quality sleep right now. But even if you feel your sleeping pattern is top-notch. How do you track it?

You might not realize how bad you’ve been steaming your sleep life until you track it. Each of these above-listed sleep tracking apps for iPhone has unique attributes. And I’m sure you’ll find the one that works best for you.

Other related articles that you’d find helpful:

Author Profile


Idowu is an avid tech writer and a software surfer who loves covering knowledge gaps in consumer software, including anything related to iPhones. Well, when he’s not reading and learning new things, you’ll find Idowu losing gallantly on a solid chessboard or virtually on Lichess.

Update the detailed information about Tracking A Viral War In Real Time on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!