What Is The Difference Between Woven And Knit Fabrics?

There are a few key differences between woven and knit fabrics. For one, woven fabrics are made on a loom, while knit fabrics are made on needles. This means that woven fabrics have more of a “grid-like” structure, while knit fabrics have more of a “net-like” structure.

This also means that woven fabrics are usually stronger than knit fabrics. Additionally, woven fabrics can be made from any type of fiber, while knit fabrics are usually only made from natural fibers (such as cotton or wool). Finally, because of their different structures, woven and knit fabrics behave differently when they get wet – woven fabric will become stiffer and less elastic, while knit fabric will become softer and more elastic.

Knitted Vs. Woven Fabrics – What's The Difference???

Woven and knit fabrics are both popular choices for clothing, but they have some key differences. Woven fabric is made by interlocking threads at right angles, while knit fabric is created by looping together yarn. This results in a denser, heavier fabric for wovens and a more elastic, stretchy fabric for knits.

Wovens are typically better suited for structured garments like pants and skirts, while knits are great for casual pieces like t-shirts and sweaters. However, there are many hybrid fabrics that combine the two construction methods to get the best of both worlds!

Difference between Woven And Knitted Fabric Pdf

Woven and knitted fabrics are both popular choices for clothing, home décor, and other textile products. But what’s the difference between them? Woven fabrics are made on a loom, where threads are woven together in a crosswise pattern to create a fabric.

Knitted fabrics, on the other hand, are created by looping yarn together in a series of connected stitches. Both woven and knitted fabrics have their own unique benefits. Woven fabrics are typically more durable and less likely to stretch out of shape than knitted fabrics.

They also tend to be less expensive to produce. Knitted fabrics, on the other hand, often have more give and can be more comfortable to wear. They can also be easily shaped into fitted garments like sweaters and hats.

READ MORE:  What Is A Row In Knitting?

So which type of fabric is right for your project? It depends on your needs! If you’re looking for something strong and sturdy, go with a woven fabric.

If you need something that’s more flexible or comfortable, go with a knit.

What Is The Difference Between Woven And Knit Fabrics?

Credit: www.dutchlabelshop.com

How Can You Tell the Difference between a Knit And a Woven Fabric?

There are a few key things that you can look for when trying to determine if a fabric is knit or woven. First, you can look at the edges of the fabric. If the fabric is woven, the edges will be smooth and even.

If the fabric is knit, you may be able to see small loops along the edge. Another way to tell the difference between a knit and a woven fabric is by looking at the back side of the fabric. If you are looking at a woven fabric, the back side will look similar to the front side.

However, if you are looking at a knit fabric, you may be able to see where the stitches have been made. Finally, you can try to gently stretch the fabric. If it is a woven fabric, it should not stretch very much.

However, if it is a knit fabric, it should stretch quite easily.

Which is Better Knit Or Woven?

There are a few things to consider when deciding between knit and woven fabrics. The first is the intended use of the fabric. If you need a durable fabric that can withstand a lot of wear and tear, then woven is the better option.

Woven fabrics are also better for projects that require precise shaping, like tailored clothing. On the other hand, knit fabrics are more flexible and have more give, making them ideal for garments that need to stretch or drape well. They’re also generally more comfortable to wear since they don’t bunch up or feel as stiff as woven fabrics can.

Another consideration is how the fabric will look after it’s been made into a garment. Woven fabrics tend to hold their shape better than knits, so they often look neater and more polished. Knits can sometimes look slouchy or stretched out, especially if they’re made from heavier yarns.

And finally, knit fabrics can be easier to work with since there’s no right side or wrong side – you can just start knitting! Woven fabrics have a right side and a wrong side (the wrong side is usually rougher), so you need to be careful when cutting and sewing them together.

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So which is better?

It really depends on your needs!

Is Cotton a Knit Or Woven Fabric?

Cotton is often classified as a natural fiber, meaning it comes from plants or animals. In the case of cotton, the plant is actually a small shrub called Gossypium. There are many different species of Gossypium, but the one that is used to make most commercial cotton is Gossypium hirsutum.

This plant is native to Central America, Mexico, and the Caribbean islands. The fibers of the cotton plant are actually hollow in the center and grow in balls around the seeds of the plant. Cotton can be both knit and woven, although it is most commonly found in woven fabrics such as denim or chambray.

When knit into a fabric, cotton has a soft hand and good drape qualities making it ideal for t-shirts or summery dresses. Woven fabrics made from cotton are strong and durable which makes them perfect for items like jeans or bedding. Although there are many synthetic fibers on the market today, natural fibers like cotton still have an important place in fashion and textile production.

What is the Difference between Knit And Woven Cotton?

The plant is a shrub native to tropical and subtropical regions around the world, including parts of Africa, South America, and India. The greatest diversity of wild cotton species is found in Mexico, followed by Australia and Africa. Cotton was independently domesticated in antiquity in Peru (cotton Gossypium barbadense), Mexico (Gossypium hirsutum), and India (Gossypium arboreum).

It has been cultivated since antiquity and there are many references to it throughout history; however, its use only began to become common during Industrialization following improvements in spinning technology during 1790s Britain. Worldwide production of cotton lint grew from about 2 million metric tons 1980-81 to about 26 million tons 2010-11.

READ MORE:  What Is Skpo In Knitting?
Knit vs Woven

Cotton can be woven or knit into fabric. Both methods have their own benefits depending on what you need the final product for. Woven fabrics are made on a loom where lengthwise yarns (the warp) are interlaced with crosswise yarns (the weft) at right angles forming a cloth whereasknit fabrics are made by looping together lengthwise yarns with knitting needles creating interconnected rows resembling small loops aka stitches.

. Woven materials tend to be stronger than knitted fabrics because they do not have as much stretch making them ideal for things like upholstery or denim jeans that need to keep their shape well over time but may not be as comfortable because they aren’t as flexible..

On the other hand knitted fabrics are often more elastic allowing them to easily stretch which makes them great for things like activewear or underwear that need give when moving around but may wear out faster since their threads aren’t interlocked as tightly.. In terms of durability both types can hold up well if cared for properly but generally speaking woven materials will last longer than knit ones simply because there are fewer weak points along its surface area where tearing could occurr.

There are two main types of fabrics: woven and knit. Woven fabrics are made by interlocking threads at right angles, while knit fabrics are made by looping threads together in a continuous row. Woven fabrics are stronger and more durable than knit fabrics, making them ideal for items that will see a lot of wear, such as upholstery or clothing worn in rough environments.

They can also be more difficult to sew, as the fabric can unravel if not handled carefully. Knit fabrics are more flexible and have a softer feel than wovens. They’re often used for garments that need to stretch, such as leggings or sweaters.

Knits can also be easier to sew than wovens, as the fabric is less likely to unravel.

I’m Jane and I’m the editor of janesknittingkits.com! I am a long-time craft and clothing design fan who has been writing about these interests for years.


Humidity in Museum Spaces – What to Consider

Systems engineers must understand that HVAC design is secondary to building envelope performance on historical structures.

The ability of a facility to maintain a stable environment protects the permanent items on display. (ASHRAE provides a thorough summary of the environmental conditions of these facilities in Chapter 23 of its HVAC Applications Handbook.) There are countless examples where these environmental conditions were established with the goal to preserve the items within the building, only to cause costly damage to the structure housing the collections.  It is important to realize that these structures are often historically significant buildings and are irreplaceable.

This analysis is limited to the ability to provide a barrier to heat and moisture between the controlled environment and the outdoor conditions.

Building environmental consultants are regularly approached by facility owners who want a system to provide temperature and humidity control for these critical environments within an existing historical or otherwise older structure. In most of these cases, developing a mechanical system capable of maintaining the desired space conditions is relatively straight forward. However, as a building systems engineer, it is crucial to understand that the HVAC design is secondary to the building envelope performance when determining the feasibility of providing critical environment systems.

When dealing with historically significant buildings, building systems engineers are often asked to provide an HVAC system capable of delivering a museum quality environment in the building, only to help the owner discover that the costs associated with addressing the building envelope issues – not the mechanical systems – makes the project impractical.

Figure 1: Thermal Imaging

Whether a facility is in a cold, northern climate or the hot and humid south, when controlling an indoor environment to maintain a constant temperature and humidity year-round, the building envelope will surely experience significant differences in the indoor and outdoor conditions in at least one, if not several, of those seasons.

When there are significant differences between the temperature and more importantly humidity within a structure and the environment outside, the motive forces trying to push moisture through that envelope is substantial.  Relative air pressures, vapor diffusion and liquid transport through capillary action all work to push moisture through the building structure, creating transient conditions within the envelope. To maintain these indoor environmental conditions, the structure must function as a barrier to these forces or at the very least accommodate these transient conditions without having detrimental effect on the structure itself.

Figure 2: Wall Cavity Structural Elements

When vapor is forced through a wall or other building component that sees a temperature gradient from the internal to external faces, conditions are created in which moisture begins to form in this assembly.  When these building assemblies were originally built without consideration for moisture, or sometimes even thermal control, the consequences can be catastrophic. These conditions permit condensation (and potentially mold growth) in these cavities, freezing and subsequent spalling of masonry elements, or even simply surface condensation on the inside walls behind the same objects intended on being preserved.

Take the example of a simple plaster wall and air cavity constructed in front of a structural masonry.  This assembly may have worked adequately for the entire life of the building, but once this space is conditioned to maintain a 50 percent r.h. throughout the year, building moisture that reaches that cavity will result in condensation during cold winter conditions. This is a recipe for negative consequences.  As a result, these conditions must be prevented by either managing cavity temperatures (through location of insulation), moisture removal or prevention of transfer of water vapor using vapor barriers or retarders.  In most of these applications, this requires a significant re-build of the exterior envelope at a cost that can easily exceed the HVAC system installation by 3, 5 even 10 times, making an otherwise reasonable project cost prohibitive.

Please understand, this article is focusing on building structures with respect to their participation in delivering critical temperature and humidity control desired by these types of facilities.  These structures must perform many diverse functions, but when focusing on environmental control, this analysis is limited to the ability to provide a barrier to heat and moisture between the controlled environment and outdoor conditions.

Understanding the thermal and hygro-thermal performance limitations of a historical building begins with having a firm understanding of the building construction.  High mass walls constructed with little or no insulation or vapor barrier present a challenge for a typical museum environment.  In addition, it is critical to understand how the interface happens at transitions between construction types (walls to roof, walls to windows, etc.).

The process should start with researching the original building drawings, but the plans may not always have enough information to determine the exterior wall cross section.  It also may be true that the group operating the facility has information from past projects at the facility.

Figure 3: Thermal Bridging

If building construction information cannot be readily obtained, additional information can be collected by observing the actual performance of the building.  Thermal cameras can be used to identify leaks around the building envelope, thermal bridging and other inconsistencies that may exist in the building construction. Infrared thermometers can be used to measure the indoor and exterior wall surfaces to determine the wall thermal resistance.

Once the building information is gathered and cross sections are drawn for the thermal boundaries of the facility, they can be used to make educated decisions regarding the operation of the facility.  The engineering analysis begins by estimating the temperature gradient through the known building construction.  For cold climates with a humidified building the engineer needs to evaluate how far moisture will penetrate the wall construction and if freezing of the moisture will occur.  Condensation in the wall may occur if the vapor barrier has been located where it allows the dew point of the indoor environmental air to intercept the temperature gradient of the thermal barrier.  This condition can lead to spalling of exterior masonry and brick due to the freezing and expansion of moisture during cold weather.  In hot and humid climates, the engineer must focus on vapor drive moving from outside to inside the building.  For many hours during the summer, the indoor space temperature will be below the outside dew point.  While this condition exists, it is possible for moisture to condense inside the wall cross section for extended periods of time.  This condensation could lead to mold growth in the wall cross section, or even worse, behind artwork hanging on the walls.

It is not uncommon to adopt separate summer and winter operating conditions with a gradual reset period between seasons.

If a problem is identified with the performance of a building the engineer must work with the project team to either change the indoor design criteria or modify the building envelope.  It is not uncommon to adopt separate summer and winter operating conditions with a gradual reset period between seasons.  A decision may also be made to augment the existing wall construction to accommodate the required indoor design conditions.  For historical buildings, the ideal design approach would be one that minimizes the architectural impact on the building and does not destroy the original construction.

It is the responsibility of the engineer to initiate these discussions during the early stages of the design process.  Proper alignment of owner expectations with the physical capabilities of the building construction make for a successful project.

Keith Esarey, P.E.

Steve Vehige, P.E.

Published December 22, 2017 in HPAC Engineering

Keith Esarey, PE, is a Principal, and Steve Vehige, PE, is a Mechanical Engineering Fellow specializing in critical temperature and humidity system design. Both are with McClure Engineering, a 65-year-old mechanical and electrical consulting firm based in St. Louis.


Unpacific meanings in Urdu

Unpacific meanings in Urdu is غیر معقول Unpacific in Urdu. More meanings of unpacific, it's definitions, example sentences, related words, idioms and quotations.

غیر معقول

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What are the meanings of Unpacific in Urdu?

Meanings of the word Unpacific in Urdu is غیر معقول - ghaer maquul. To understand how would you translate the word Unpacific in Urdu, you can take help from words closely related to Unpacific or it’s Urdu translations. Some of these words can also be considered Unpacific synonyms. In case you want even more details, you can also consider checking out all of the definitions of the word Unpacific. If there is a match we also include idioms & quotations that either use this word or its translations in them or use any of the related words in English or Urdu translations. These idioms or quotations can also be taken as a literary example of how to use Unpacific in a sentence. If you have trouble reading in Urdu we have also provided these meanings in Roman Urdu.

Frequently Asked Questions (FAQ)

What do you mean by unpacific?

Meaning of unpacific is غیر معقول - ghaer maquul

Whats the definition of unpacific?

Definition of the unpacific are

What is the synonym of unpacific?

Synonym of word unpacific are indefeasible, impunctual, absurd, iridal, uncreative, inequilobate, unadvisable, exsanguineous, chauvinism, unremunerative


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How to Add Super Admin to WordPress Multi-site Network

The simplest way to add a user as super admin is to have another super admin grant super admin privileges from WordPress dashboard. However, there may be times when this is not possible or is not convenient and you still need to add a super admin to your multi-site

Another easy way to assign an existing user with super admin privileges is to add the following function to your functions.php file.

grant_super_admin( $user_id );

You’ll need to replace the $user_id parameter with the user ID of the specific user you’d like to assign these privileges to. If you don’t want to have to dig up the user ID but know the email address of the user, you could use these two lines of code and replace ‘’ with the email address of the user.

$new_super_admin = get_user_by_email( '' );
grant_super_admin( $new_super_admin->ID );

Once this function has been added, check to confirm that the user now has super admin privileges. If it was successful, remove the piece of code as it is no longer needed.


Use Case of Digital Twin in Healthcare Industry

Use Cases of Digital Twin In The Healthcare Industry

Jonathan Raabe (Technology Advisor)

November 22, 2022

Digital twin technology has been actively used in sectors like energy & manufacturing sectors for asset utilization, operations, and performance. Similar to these sectors, digital twin technology holds great potential to revolutionize the healthcare sector to improve efficiency, productivity, and profitability.

hospital digital twin

Worldwide adoption of hospital digital twins opens up endless possibilities for the healthcare sector. Proponents believe that healthcare digital twins are ideal for creating an interconnected real-time information-sharing ecosystem.

With a single click, important information such as patient health records, insurance policies, medication records, and others can be easily accessed. We have discussed the scope and digital twin healthcare use cases in depth further in our post, so keep reading.

Are you planning to leverage next-gen technologies and launch your own hospital digital twin? Then, connect with our experts at RisingMax Inc., a top digital twin development company. Our blockchain development team will create a customized software solution based on your business needs. Schedule a FREE consultation call with our experts and discuss your hospital digital twin requirements TODAY.

Before we delve deep into the topic, let’s start from the basics first…

What Is A Hospital Digital Twin?

A hospital digital twin is a virtual representation of a real-world hospital, but that’s an oversimplified definition of a digital twin. The virtual representation of real-world objects like hospitals requires the integration of next-gen technologies such as artificial intelligence, VR, AR, cloud storage, and blockchain. Hospital digital twins are well-capable of accurately mimicking real-world objects and assist in streamlining operations and enhancing efficiency.

Real-world data of a hospital is collected and added to the hospital digital twin to analyze overall system efficiency and possible scenarios. Integrated technologies empower hospitals to streamline operations with real-time information sharing. Virtual representation of hospitals in the metaverse assists in expanding the digital footprint and opens up endless possibilities.

Digital twins empower healthcare sectors to share real-time information, and risk-based assessments, enhanced efficiency, and improve operations, and profitability. Let’s look at the benefits of the digital twin in the healthcare industry.

Facts And Statistics Related To Digital Twin

  • The global digital twin market size is projected to grow from $6.75 billion in 2021. (Fortune Business Insight)
  • The global digital twin market size is expected to reach 96.49 billion US dollars by 2029, with a CAGR of 40.6% (2022-29). (Fortune Business Insight)
  • Business sectors were most likely to adopt the digital twin: Manufacturing, healthcare, automotive, aviation, energy, utilities, logistics, and retail. (Statista)
  • Digital twin technology in the manufacturing sector is forecast to reach over six billion U.S. dollars by 2025. (Statista)

The growing popularity and promising future of digital twins are attracting various industries and healthcare is one among them. Growing digital twin healthcare use cases is one of the biggest reasons why proponents are so confident about this futuristic technology. Let’s delve

Benefits Of Digital Twin In The Healthcare Industry.

Personalized Healthcare Services

Healthcare digital twins are effective in creating an ecosystem where patients’ health conditions can be easily monitored using fitness bands or watches, mobile apps, and other IoT devices. The valuable data collected can be analyzed effectively to offer personalized healthcare services, thus enhancing the effectiveness of the entire healthcare system.

Customize Drug Treatment

The digital twin can enhance treatment accuracy by effectively monitoring patients’ past health records, health conditions, and medicine dosages. Based on the patient’s current health conditions and customized drug treatments, different dosages can be prescribed for faster recovery and better results.

Enhance Healthcare Facilities

Investing in hospital digital twin enables the healthcare sector to improve and enhance their healthcare facilities. From creating a virtual process model to scheduling surgeries and effectively monitoring healthcare facilities, healthcare digital twins have become an effective tool.

Speed Up Critical Treatments

Hospital digital twins enable healthcare institutes to streamline their workflow and speed up critical treatments or surgeries. Healthcare facilities can reduce the time taken to treat critical patients suffering from Heart stroke, fatal injuries, and others. With easy monitoring of healthcare processes, healthcare facilities can offer unmatched services.

Streamline Supply Chain

Shortage of drugs, medical equipment, PPE kits, or any other essential equipment has the potential to cripple the entire healthcare system. With healthcare digital twins the entire supply chain can be monitored effectively. Integration of smart contracts enables healthcare facilities to place automated orders and maintain fixed inventory levels.

Fast Drug Delivery

Hospital digital twins can become an effective tool in the healthcare industry to improve drug delivery. Patients can use digital twin technology to take doctor’s consultations and order prescribed drugs in real time. Automating the process enables healthcare facilities to make drug delivery processes fast and efficient.

Different Types of Digital Twins Solutions We Hold Expertise In

Businesses in different verticals are investing in digital twin development as it enables them to expand their digital footprint and explore new horizons. As a leading digital twin development company, we are assisting all types of clients in launching sophisticated digital twins based on their business needs. Different types of digital twins solutions we offer;

Parts Twinning

Hire our development team to launch a part-twinning digital twin to create a virtual replica of the human body or part. Conduct experiments, perform virtual surgeries, understand working, analyze response to drugs, and find effective dosage. Part twinning assists in creating virtual scenarios and analyzing real-life behavior for effective treatment results.

Product Twinning

Leverage our expertise in digital twin technology to launch a product twinning platform. Product twining enables organizations to understand how the product works, optimize the product, create a virtual environment for conducting R&D, and reduce development costs on new products.

System Twinning

Investing in system twinning allows organizations to streamline their workflow, eliminate obsolete processes, and integrate next-gen technologies. With system digital twin, organizations can analyze the efficiency of communication and traffic control systems in a virtual environment.

Process Twinning

Reach out to our experts at RisingMax Inc. to launch a process twinning platform that assists in optimizing the entire process from raw material to finished goods. Our team will create a digital twin for your process that enables the integration of disruptive technologies and optimizing operations.

Based on the client’s business requirements, our team can create a digital twin platform that assists business owners in monitoring key metrics and managing the business efficiently. Investing in the digital twin empowers businesses to enhance their efficiency, productivity, and profitability.

Digital Twin Healthcare Use Cases

Effective Resource Management

Hospital digital twins can be a practical tool in effective resource management, from the allocation of beds to drug supplies and human resources. Healthcare facilities can leverage these next-gen tools to better manage operations and personalized services to patients.

Easy Surgical Appointments

Getting timely surgical appointments is a challenge that can be easily solved by employing a healthcare digital twin. Patients can leverage these next-gen technologies to make easy surgical appointments. Healthcare facilities can analyze this information to schedule surgical appointments and efficiently manage surgical staff without any hassle.

Laboratory Digital Twin

A lot of research, testing and development goes into finding a drug’s right dosage and effectiveness. Investing in laboratory digital twins assists in speeding up the modern drug development process. Effective data analysis and integrated automation software assist in reducing the overall cost and sharing discoveries across labs.

Streamlining Workflow

With digital twin technology, healthcare facilities can create an implementation roadmap to streamline workflow. Different scenarios can be created and analyzed to create a roadmap on how to plan things effectively and streamline workflow.

Virtual Staffing Model

Investing in digital twins enables healthcare facilities to manage and hire medical staff effectively. Analyzing a big chunk of data assists in predicting the demand and hiring new resources or relocating existing ones for efficient management and serving patients better.

Real-Time Information Sharing

Integrating digital twins in the healthcare sector assists in creating a decentralized ecosystem where doctors, patients, and healthcare workers can access information in real-time. This will empower the healthcare sector to save valuable time and speed up treatment.

hospital digital twin

How Will The Integration of Next-Gen Technologies Evolve The Healthcare Industry?

Here in this section, we will focus on some of the leading factors supporting that the integration of hospital digital twins can be revolutionary for the healthcare sector. Let’s have a look at how the integration of next-gen technologies will evolve the healthcare sector in upcoming years;

Real-Time Data Sharing

One of the major benefits of the digital twin in the healthcare sector is real-time data sharing and analysis. As a large chunk of data is being shared and analyzed in real-time, presenting users with trustworthy information will be a huge challenge. The ongoing advancement in AI will enable hospital digital twins to effectively diagnose and share relevant data.

Enhance Security

With increasing digitalization, the major challenge for healthcare facilities is to ensure a safe and secure environment for data sharing. Blockchain technology will play a pioneering role in ensuring a secure environment for real-time data sharing and storage of patient data.

Digitizing Healthcare

Technologies are playing a major role in making healthcare facilities more accessible, even for common users. The wide adoption of next-gen healthcare solutions such as digital twins will enable healthcare facilities of all sizes to integrate these technologies and expand their digital footprint.

Effective Hospital Management

Investing in the hospital digital twin enables organizations to effectively manage their operations. From effectively managing staff schedules, scheduling surgeries, checking shortages of medical supplies, and managing bed shortages, healthcare facilities can enjoy various management benefits.

Digital Replica of The Human Body

Creating a digital replica of the human body or patient will open up new unexplored opportunities for healthcare facilities. These digital replicas can be employed to check patients’ responses to treatment, predict treatment responses, and find exact dosages and undiscovered illnesses.

Efficient Data Storage

Effective storage and analysis of massive amounts of data is always a major challenge for healthcare facilities. With IoT devices such as smartwatches sharing real-time data, swift data analysis and response can save a life. Integrating blockchain technology for secure data storage and AI for efficient data analysis can do wonders for the healthcare sector.

Improving User-Interface

A major challenge for the healthcare sector is to make health services more accessible and digital platforms user-friendly. Integration of next-gen technologies will empower healthcare facilities to provide a friendly interface, i.e., easy to use. With an enhanced user interface, even non-tech-savvy patients can access healthcare services without any hassle.

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Integrating the digital twin into the healthcare sector will undoubtedly enhance its efficiency, productivity, and profitability. Streamlined operation and effective resource management enable healthcare facilities to offer enhanced and personalized care to patients.

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How to become a Self-taught Chef Without Culinary School11 min read

Is it possible that one can become a self-taught chef without going to culinary school? Yes, it is possible.

You can learn to cook on your own as a self-taught chef, but it will be a more difficult path to becoming a chef.

A low-level entry-level position in a restaurant will be necessary for you to learn the ropes. Eventually, you will be given the opportunity to advance and demonstrate your abilities.

The culinary student, on the other hand, will receive a diploma, which is always a good thing. This article will give a detailed explanation of how to be a self-taught chef without passing through culinary school.

How to become a Self-taught Chef:

Hard work/Strength:

In order to succeed as a chef, one must put in long and exhausting hours. There is no need to worry about whether or not you can make it through a full day of work in high temperatures with only a few breaks and minimal pay initially.

Chefs, in general, need a lot of stamina, however, this varies from restaurant to restaurant and professional position to role.

Having both physical and emotional stamina is crucial. Having to put in long hours at a restaurant is no fun at all. 10 to 17 hours is the normal length of a shift.

Passion for Cooking:

Cooking, planning to cook, and presenting the food you’ve made are going to take up more of your time than other activities in your life.

Additionally, you’ll find that if you’re forced to undertake things that are difficult but linked to cooking, you’ll do them rather than avoid doing them.

It is assumed that you have a love for the profession if you wish to become a self-taught chef. You have adequate enthusiasm if you enjoy cooking shows and have thought about it for a long time.

Before applying for a job as a self-taught chef, you must determine your enthusiasm levels. However, if you can, obtain a job and see if it’s for you.

Practice the process of cooking:

There is no substitute for practice. Even though it doesn’t guarantee you’ll become a chef, it does greatly boost your chances.

It’s impossible for even the most experienced chefs to know everything because food and restaurant equipment is continually evolving and new learning curves wait around every turn.

More so, before you can start studying the basics, you’ll need some essential supplies and equipment. To get started at home, you will need a good chef’s knife, quality cookware, a diverse equipment set, and a good cutting board.

How do I become a chef with no experience?

Choose Your Perfect Position:

There are different positions you can take up as a chef, what matters is the part you play the best role in. In the case of a perfect position, it comes from practice and trials till you know the ideal position that works best for you.

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Apply for a Job:

The job application process will help you learn the requirements needed in becoming a chef. While on the job, you will get to learn and meet people who could mentor you till you become good at the job.

Sit down and learn:

Unlike other professions, the job of a chef is delicate such that a mistake in a pinch of salt can ruin a whole meal. With this being known, it is advisable to stay humble and watch the professional do the job while you ask a couple of questions.

Practice, Practice, Practice:

Practice is the only way to get better. It does increase your chances of becoming a chef, but it does not ensure it.

Even the most seasoned chefs can’t know everything because food and restaurant equipment are always improving and new learning curves await around every corner.

Get Your Dream Chef Job:

This is the final step where you can do and apply for your dream job or work in your dream restaurant. What really matters the most is that you’ve got all the skills you need to excel on the job even without supervision.

Read more: Requirements of Culinary School (You need to see these)

Essential Skills to Cook Like a Chef:

Choosing Kitchen Tools:

To be a self-taught chef, first, make sure you have the appropriate tools to cook like a chef at home. This doesn’t mean spending hundreds of dollars on kitchen equipment at a high-end store.

Get the right supplies from any restaurant supply or discount store instead of wasting your time and money on unnecessary purchases. In addition to the knife, cutting boards, spoons, and cups, you’ll need a variety of other kitchen tools.

Sharpen Your Knives:

Sharp knives not only keep you safer in the kitchen but also help you cook better. Sharp knives cut food more effortlessly and with less pressure, reducing the risk of accidents.

Proper silverware care is an important part of learning to cook like a chef. More so, watch chef training videos to learn how to safely clean and sharpen silverware.

Make a Recipe Book:

Planning meals ahead of time is a good way to learn how to cook like a chef. It’s common practice in cooking schools for pupils to plan their meals for the day.

Put this into action in your own kitchen. Using a notebook to plan your meals saves time and generates a personal cookbook you can refer to.

10 Most Popular Self-Taught Chefs You Need to Be Familiar With:

Ina Garten:

Ina Garten is an American author, anchor of the Food Network’s Barefoot Contessa, and former White House Office of Management and Budget staffer.

Ina Garten spent years developing nuclear energy policies for the White House. Garten was attracted by an ad for a modest food store in New York. So, she flew to New York to see it and fell in love with the moment.

The store was immediately sold to her after she and her husband made an offer. She christened the store Barefoot Contessa, a moniker she still uses today.

Garten had a dream and a passion for food, and she was determined to make it work. Over the last two decades, Barefoot Contessa has doubled in size, with over 20 chefs and bakers.

She sold it to the chef and management since she wanted to try something new. She has now written twelve cookbooks, recorded a long-running show for Food Network, and written food essays for various periodicals.

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Tom Colicchio:

Crafter Hospitality’s owner and chef, Tom Colicchio, is a self-taught chef from New Jersey. He is enthusiastic about food issues, which led to the creation of the film A Place at the Table.

The origins and answers to America’s hunger epidemic are examined. His contributions are valued, particularly his support for affordable, accessible, and nutritious food.

To help alleviate this issue, his restaurants donate money to the Children of Bellevue, Wholesome Wave, and City Harvest, which he helped build.

Jamie Oliver:

Jamie Oliver, the celebrity chef who advocates healthier eating habits for school-aged children, is a self-taught chef who has worked in a number of London eateries.

He began working at River Café after graduation, which was included in a documentary. Oliver went on to star in his own show, The Naked Chef, which followed the preparation of meals from beginning to end.

Apart from his Feed Me Better campaign, Oliver has published a number of books and appeared in a number of TV shows.

The program attempts to improve children’s school lunch options. As a result, he’s built a website that lists alternate components for healthier kid’s food.

Charlie Trotter:

Charlie Trotter grew up in a restaurant family but never studied culinary arts in college.

He discovered his love for cooking and the restaurant industry in college. Helping a roommate with a recipe lead him to become one of America’s most successful self-taught chefs.

From his father, Trotter learned everything he knows about food and restaurants. At 28, he opened his own restaurant in Chicago.

His eatery became a global sensation. His patrons adored his innovative Asian cuisine. As his restaurant flourished, it garnered many awards and accolades.

To-go shops in Chicago and Las Vegas followed. He also had a food show on PBS and wrote cookbooks.

Read more: 10 Best Friendsgiving Food Ideas

Tunde Wey:

Tunde Wey is a self-taught chef who teaches his fans and viewers how to prepare, present and sell food and recipes. His works have centered on American culinary culture and how people have adapted to different meals.

Wey’s parents intended him to attend medical school and become a doctor, but that was not his calling. His passion for food began when he assisted a roommate in opening a restaurant.

He co-founded Revolver in Detroit in a matter of months. He left the restaurant after a few years and began organizing pop-up cooking events across the country, specializing in Nigerian cuisine.

He enjoys inventing new cuisines for people to sample, as well as changing the flavor of old ones.

Kia Damon:

Kia Damon is a self-taught chef who is extremely proud of her accomplishments and an inspiration to others. The Supper Club from Nowhere is her brainchild, and it’s a web series all about supper recipes.

When Damon started cooking for pop-up dinners, demos, and private events, he rapidly gained the moniker “Kia Cooks.

She went to New York at the age of 24 and was swiftly promoted to the post of Culinary Director for Cherry Bombe Magazine after impressing the magazine’s editors.

The New York Times recently named her as one of the “16 black chefs revolutionizing food in America”.

Rachael Ray:

Rachael Domenica Ray is a well-known celebrity self-taught chef in the United States. She grew up in a family that worked in the food and restaurant industry, but she taught herself the majority of what she knows about cooking.

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Her “30 Minute Meals” television classes, for which she had worked for numerous companies, were her big break. Ray went on to host other Food Network shows and write several cookbooks as a result of his success.

Rachael Ray has her own magazine, Every Day with Rachael Ray, as well as a talk show that draws cooks from all over the world.

Makini Howell:

Makini Howell is a vegan who encourages others to experiment with vegan cooking. Makini Howell is the owner and operator of Seattle’s Makini Howell Plum Vegan Restaurants and Plum Bistro.

Howell’s has hosted such celebrities as Queen Latifah, Serena Williams, Stevie Wonder, and others. She also works as a consultant for major corporations on culinary and food-related topics.

Howell has created a vegan paradise in the form of a cookbook. She is proud to encourage her family to eat wonderful plant-based meals at home.

Gordon Ramsay:

Gordon Ramsay developed a passion for food while competing as a professional athlete.

He retired from soccer to become a well-known chef in the United Kingdom. As a result, he began to teach himself the techniques and strategies that he still uses today.

Marco Pierre White, Albert Roux, Joel Robuchon, and Guy Savoy were among the chefs who began coaching him.

Ramsay was hired at Aubergine, a two-star London restaurant when he felt ready to enter the food profession. He opened Restaurant Gordon Ramsay after several years at Aubergine.

The restaurant was well-received, receiving three stars. He was inspired to open other eateries as a result of this. Ramsay is best known for hosting the reality television shows Hell’s Kitchen and MasterChef.

Thomas Keller:

Thomas Keller has been cooking since he was a child. He is a self-taught chef that has worked for a number of famous establishments as a result of his transfer. He opened Rakel in New York City after he returned to America.

Keller then relocated to Los Angeles, where he worked as the Executive Chef of the Checkers Hotel. He is regarded as one of the best chefs in California and the United States.

Keller’s five books have had an indelible impact on the hospitality industry, and he will continue to do so for years to come.

What’s the difference between a cook and a chef?

There are two types of chefs: those working in hotels and restaurants, and those who are self-taught and self-taught in their own homes.

However, chefs are distinct from other types of cooks in that they have mastered their craft and have received formal training.

A chef, on the other hand, is someone who has received specialized training in the art of food preparation and preparation procedures, as well as the ability to develop recipes from scratch using only the freshest ingredients.

In the kitchen, a cook is someone who follows a set of instructions to prepare food.

It’s difficult, but not impossible, to land a job as a chef with no previous experience.

To begin, send out as many applications as possible and get yourself ready to accept a position as a prep cook or a dishwasher. Additionally, if you’re willing to put in the time and effort, you may become an intern at a restaurant.


4 Reasons Why People Prefer Wearing Vintage Moschino Belts

It appears that the block letter belt buckle has become something of a phenomenon among fashion bloggers, having made its way into the closets of some of our favorite internet trendsetters. Curiously, a Moschino logo represents something that is very different from what a Chanel or Gucci logo does. Franco Moschino’s company had been announced as extremely stylish since the beginning, but he was also a huge advocate of public consciousness and actively played against the fashion industry in the 1980s and 1990s. You can easily buy Moschino belts online, and here are the reasons why vintage Moschino belts are still a trend.

1. Easy to pair

In today’s society, donning a Moschino belt is not just recommended but is also expected. Moschino belts have now become a popular fashion accessory. Believe us when we say that if you are sporting a long top, it would be a fantastic idea to add a belt to your outfit in order to take your style to the next level. You have a few different options to select from when it comes to the designs. All you need to do is plan your outfit, whether a dress, pant top, or other combinations, and easily accessorize them by adding a Moschino belt.

2. Makes you stand out

The majority of individuals follow the herd mentality to blend in easily, especially in regard to fashion and trends. If following the latest trends makes you happy, then keep doing what you’ve always done, and you never know, who might appreciate and follow. When you wear a Moschino belt, you will attract the attention around you as the design stands out. And what better way than adding a vintage belt to your collection of accessories to make a confident fashion statement, while looking like a boss?

3. Enhances personality

Belts from Moschino should not be confused as just a belt to tighten your jeans hook or hold your pants in one place. Their belts are an embodiment of the Italian brand’s fun and rebellious personality, as evidenced by their bright colors and metallic finishings. Following that, there is a sequence of designs that have peace logos, hearts, and other small creative aspects on them. On the front side of the Moschino belts, the name of the brand is always highlighted. And it is believed that ladies who wear these one-of-a-kind Moschino belts are brimming with creativity and modernity and possess great self-confidence.

4. Showcases vintage vibes

For many individuals, the fact that an item has a long and interesting past is precisely what gives it its allure and makes it more desirable. People have a fascination with the idea that an item has been worn, used, and loved by others in the past.

All the belt designs by Moschino have something vintage about them, whether it be the entire design, the way the logo is placed, or the colors used. Each belt has its own perfect fit as some are better suited for an evening out. However, Moschino belts can make even the simplest of outfits look like they belong on the red carpet!

The audience has been won over by the exceptionally eye-catching belts that Moschino creates, and the brand deserves credit for this. Certain belts have left their imprint on the biggest names of fashion history as essential accessories. Thus, these belts definitely should be at the top of anyone’s shopping list for their wardrobe if they want to remain at the forefront of fashion, trends, and strong personalities. Choose from the wide collection and never second guess your outfit after adding this accessory, you are only setting up higher fashion expectations from yourself and doing it to make you happy.


Randy Smerik

Randy Smerik is an experienced serial entrepreneur, with over 30 years’ experience in the high technology fields of database systems, analytics, big data, mobile, social, networking, SaaS, security, and semi-conductors as well as businesses in the spirits and restaurant markets.

Randy’s background and experience includes: Founder/CEO of five high tech companies in the fields of database, security, networks, and social media. In addition to technology, Randy owns and runs the award winning San Diego-based restaurant, Solare Ristorante (a Michelin Guide Bib Gourmand restaurant), and is on the Board of Directors for Fortaleza Tequila (Jalisco, Mexico).

A frequent industry speaker in the areas of venture capital, entrepreneurship, agave, and hospitality, Randy is an active angel investor and advisor to a variety of startups, accelerators, and investment organizations.

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PLoS ONEplosplosone1932-6203 Public Library of Science San Francisco, USA PONE-D-12-1663110.1371/journal.pone.0043287 Research Article Biology Computational biology Genomics Genome analysis tools Genetic networks Metabolic networks Regulatory networks Signaling networks Systems biology Genetics Gene networks Genomics Genome analysis tools Genetic networks Systems biology Genetics and Genomics Computational Biology CytoKavosh: A Cytoscape Plug-In for Finding Network Motifs in Large Biological NetworksCytoscape Plug-In for Finding Network Motifs Masoudi-Nejad Ali * Ansariola Mitra Razaghi Moghadam Kashani Zahra Salehzadeh-Yazdi Ali Khakabimamaghani Sahand Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran Dalby Andrew Rowland Editor University of Westminster, United Kingdom * E-mail:

The authors have developed CytoKavosh: a Cytoscape Plug-in for Finding Network Motifs in Large Biological Networks. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Conceived and designed the experiments: AMN MA. Performed the experiments: MA ASY. Analyzed the data: MA SK ZRMK. Contributed reagents/materials/analysis tools: MA SK. Wrote the paper: MA AMN. Checked and validated the results: SK ASY.

2012 29 8 2012 78e43287 28 5 2012 18 7 2012 Masoudi-Nejad et al This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavosh's C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool.

The authors have no support or funding to report.

The network concept is widely used to analyze and predict the dynamics of a complex system [1]. Biological processes are often represented in the form of networks such as: gene regulation, signal transduction, protein interaction and metabolic networks. The study of biological networks, their modeling, analysis and visualization, is an important task in the field of life science. Computational tools help biologists through this difficult study. An understanding of these networks is essential to make the biological sense of much of the complex data that is now being generated [2]. When we talk about the networks, the complex system is perceived as a set of interacting elements (nodes, vertices), which are bound together by links (contacts, edges, interactions). In usual networks (graphs), links represent the interactions between element pairs [3]. Graphs are used for representing and modeling the networks to analyze them in a scientific and formal manner.

The main attributes of the biological networks are their complexity and vast amount of data, so extracting the meaningful data from them needs powerful and accurate methods. Motifs are the building blocks of complex networks, providing a bridge between local vertex-related properties and global functional properties of networks. Motif analysis in the network is notably important because they may reflect functional properties [1].

A motif is a small connected graph expressed as G. The size of a motif is represented by the number of vertices (nodes) [4]. Different methods and stand-alone tools have been developed for analyzing the network motifs: Mfinder [5], MAVisto [6], NeMoFinder [7], Grochow-Kellis [8], Color-coding approach [9], G-Tries [10], FANMOD [11], Kavosh [12] and MODA [13]. Mfinder implements two kinds of motif-finding algorithms: a full enumeration and a sampling method. The sampling protocol is the faster one that assigns probability values to the motifs to be identified, and infers frequencies from these values. It is also a tool without the option of a visual presentation and the results are only provided in the format of a text. For achieving best analysis results from network analysis tools, undoubtedly, visual capability of the analysis tools is an important factor. MAVisto is a visual tool, but in comparison with other programs, it needs more time and in the matter of time it has limitation in motif size. NeMoFinder utilizes frequent size-n trees to partition the input network into a collection of size-n graphs, afterward finds frequent size-n sub-graphs through expansion of frequent trees edge-by-edge until getting a complete size-n graph Kn. The algorithm finds network motifs from undirected network and is not limited to extracting just induced sub-graphs. Furthermore, NeMoFinder is an exact enumeration algorithm and is not based on a sampling method. Grochow and Kellis algorithm is based on a motif-centric approach, which means that the frequency of a given sub-graph, called query graph, is exhaustively determined through searching for all possible mappings from the query graph into the larger network. Ribeiro proposed a novel data structure for storing a collection of sub-graphs, called G-trie. This data structure that is conceptually akin to prefix tree, store sub-graphs due to their structures and find occurrences of each sub-graphs in a larger graph. One of the noticeable aspects of this data structure is that coming to the network motif discovery, the sub-graphs in the main network are needed to be evaluated. So, there is no need to find the ones in random network which are not in the main network. FANMOD algorithm is one of the best among others, in regard to the computational time. But it can handle sub-graphs, consisting of a maximum of eight vertices. MODA and Kavosh are two non-visual algorithms having good performances. The Kavosh plug-in is the best tool, according to the time and memory usage, among all and has no limitation in the size of motifs studied by this program [12].

Here, we introduce CytoKavosh as the first network motif finder plug-in for Cytoscape, which uses all Kavosh features and strengthen the studies of finding network motifs based on Milo [1]. CytoKavosh finds network motifs with less memory and CPU time in comparison with other existing tools (for more information see reference [12]) and has implemented in a known graphical environment that is familiar among the analysts of biological-networks. The results of CytoKavosh can be used in-line with other analysis tools in the integrated software to achieve the desired goals.

CytoKavosh is implemented in Java that uses Cytoscape API. It uses Kavosh algorithm, which makes it faster than similar programs, based on other algorithms. CytoKavosh is suitable to detect network motif in both the directed and undirected networks. The main idea of the enumeration is based on Kavosh [12] and is similar to the FANMOD and MODA algorithms, which first find all k-sized sub-graphs that a particular vertex participated in, then remove that vertex, and subsequently repeat this process for the remaining nodes [12]. By using this idea, we gradually reduce the size of input network that leads us to better performance..For applying the idea of removing totally studied vertices, and also for simplifying the implementation, each vertex in the input network is given an integer label as its identification. We use these labels to apply necessary restrictions. Building an implicit tree according to some restrictions, that will be discussed later in details, causes the improvement in both time and memory usage. The tree structure with its restrictions ensures that each individual sub-graph is enumerated only once that leads us to an efficient solution.

For counting the sub-graphs of size k that include a particular vertex, implicit trees with maximum depth of k, rooted at this vertex and based on neighborhood relationship are implicitly built. Descendents of each vertex include both incoming and outgoing adjacent nodes. To descend the tree, a descendent is chosen at each level with the restriction that a particular descendent can be included only if it has not been included at any upper level. After having descended to the lowest possible level, the tree is again ascended and the process is repeated with the stipulation that vertices are visited in earlier descending paths that is now considered as the unvisited vertices. A final restriction in building the trees is that all descendents within a particular tree must have numerical labels larger than the label of the root of a tree. This restriction simulates the process of removing the vertices which are formerly considered.

The protocol for extracting sub-graphs makes use of the composition operation of an integer. For the extraction of sub-graphs of size k, all possible compositions of the integer k-1 must be considered. This composition is a pattern for choosing vertices at each level of the implicit tree. Clearly choosing the root as one of the sub-graph's vertices is unavoidable, so in order to extract a k-size sub-graph, k-1 vertices are remained. The compositions of k-1 consist of all possible manners of expressing k-1 as the sum of positive integers. Summations in which the order of the summands differs are considered as distinct. A composition can be expressed as k2, k3, …, km where k2+k3+…+km = k-1. To count sub-graphs based on the composition, kinodes are selected from the i-th level of the tree that represents the nodes of the sub-graphs (i = 2, 3, …, m). The k-1 selected nodes, along with the node at the root, define a sub-graph of size k within the network.

To clarify the algorithm, it is necessary to mention that for a particular level i, it is possible to have ki<ni, where ni is the number of nodes present at the level i. At level i, C(ni, ki) (C(n, k) is the number of different combinations of k through n elements) different selections of nodes needed to be considered. Here, by using the “revolving door order” algorithm [14], all combinations containing ki from ni nodes are selected. The “revolving door ordering” algorithm is considered as well-known and efficient algorithm for generating combinations [15]. This step is described by an example on a given graph shown in Figure 1. For this graph, all 4-size sub-graphs containing the vertex 1, are going to be found. This is illustrated in Figure 2. For more explanation on this part you can go to [16].The enumeration step is the power of this algorithm, which makes it different from other algorithms. The data structures used in the implementation of this step and the algorithmic idea makes it less time consuming. Also, having no limitation in the implementation of this step due to the size of motif is its advantage comparing to some algorithms like FANMOD or mfinder.

10.1371/journal.pone.0043287.g001 An instance of a network. 10.1371/journal.pone.0043287.g002 The implicit built trees rooted at vertex 1 of size 4 for network in <xref ref-type="fig" rid="pone-0043287-g001"><bold>Figure 1</bold></xref>.

(a) Trees built according to (1,1,1) pattern. According to this pattern, after selecting vertex 1 in root, one of its neighbors must be selected, so the second selected vertex is vertex 2. Continuing the selecting process, one of the neighbors of the vertex 2 (vertex 6) and after that vertex 4 is selected. All chosen vertices are shown by specified circles in these figures. (b) Trees built according to (1,2) pattern. (c) Trees built according to (2,1) pattern. (d) Tree built according to (3) pattern.

After discovering a sub-graph, involved as a match in the target network in order to be able to evaluate the size of each class according to the target network, Kavosh employs the Nauty algorithm [16] in the same way as the FANMOD does.

Generating random networks in CytoKavosh and Kavosh is similar to Milo's random model-switching operations. Generating random networks is an essential step in any motif discovery algorithm, as the concept of network motif is meaningful in comparing the frequencies of each sub-graph in the given network with which we except in random ones. By using switching operations, more restricted random networks are generated. The operation is applied on the edges of the input network repeatedly, until the network is well randomized. This switching operation is applied on the randomly chosen nodes of the network. By applying this switching operation repeatedly on the input network, an ensemble of random networks is generated.

CytoKavosh improves the CPU time and memory usage in comparison with other algorithms. It can also be employed for finding motifs of the sizes greater than eight, while most of the other algorithms have restriction on motifs with the size greater than eight. Besides, comparing with other algorithms, CytoKavosh has better performance for large networks.

The time complexity of the algorithm is O(G(s)×(s2+N(s))), where G(s) is the number of sub-graphs of size s in the network, N(s) = s! is the complexity of Nauty in the worst case, and s2is the time required in an embedded part of the classification (isomorphism testing) step of the algorithm (see ‘Results’ section). But the point is that the mentioned upper bound only occurs very seldom, if not in practice ‘Nauty’ will encounter the worst case only when the sub-graph is regular, which is a rare occasion in the biological networks with a scale-free nature. Thus, practically, the algorithm runs several orders of magnitude faster. The most important factor influencing and limiting the speed of CytoKavosh is the number of sub-graphs of size s of the network, G(s), which obviously might differ between two distinct networks of even the same size. So, the running time of the algorithm depends on the network for which the algorithm is being executed.

The upper bound of the memory required by CytoKavosh can be measured by formula O(2s∧2+s×MD2+s2), where MD is the maximum degree of nodes in the given network. In practice, the size of the balanced binary tree used for isomorphism testing will never reach 2s∧2 (the first term in the formula). Indeed, the size of this binary tree depends on I(s), the number of non-isomorphic classes of sub-graphs with size s found in the network, which indicates the number of leaves of the mentioned balanced binary tree. As our experiments indicate, I(s) is usually very less than its maximum possible amount, let's call it Im(s), which is yet very smaller than 2s∧2. For example, when s = 4, then Im(4) = 199<<216, but in our experiments the greatest I(4) observed in a network is 108. So the memory usage of CytoKavosh is mainly determined by I(s) in the given network.

With respect to above, as in other algorithms, it is the nature of a given network which determines the required processing and storage capacities for finding motifs of a specific size in that network. But what makes Kavosh different is the enumeration method it utilizes, which costs less when finding each sub-graph in the network. In other words, G(s) is multiplied by a smaller amount in Kavosh than other similar algorithms. Moreover, exploiting Nauty algorithm (as a fast algorithm for isomorphism testing) is another point of strength.

CytoKavosh uses Java for executing native Kavosh program, written in C++. CytoKavosh can also group the isomorphic sub-graphs by computing the canonical labeling. Native C++ code uses Nauty API for computing canonical labeling that makes the native code very fast. So, we decided to import these native C++ codes to make this plug-in as fast as the original program, written in C++. Table 1 demonstrates the running time of the execution of plug-in for some given sample networks. The transcriptional network of E. coli with 672 nodes and 1276 edges, a social network with 67 nodes and 182 edges, and an electronic network with 97 nodes and 189 edges are examined networks. As mentioned in the previous part, CytoKavosh can detect and compute motifs in any given networks with any given size; this characteristic makes this plug-in the best choice for finding motifs among other existing tools.

10.1371/journal.pone.0043287.t001 Execution results of some sample networks, related to 4 to 7 motif sizes with 100 random network sizes.
Network 4-size motif 5-size motif 6-size motif 7-size motif
E. coli 2 16 149 1407
Electronic 0.45 2.37 11.8 73.11
Social 0.3 1.89 11.49 70

Rows indicate the running time (seconds) of the studied tool for each motif size.

Furthermore, processing time is more important than memory in Kavosh and can limit the size of discoverable motifs. It is difficult to specify the exact relationship between the size of motif and the processing time, but roughly speaking based on our experiments, in Kavosh it is closer to an exponential relationship than other algorithms. This is illustrated in figure 3 for networks of table 1.

10.1371/journal.pone.0043287.g003 Exponential growth of processing time by motif size.

Illustrated results are for (a) transcriptional network of E. coli, (b) Electronic network, and (c) Social network.

The first step of running the plug-in is the loading of network into Cytoscape among loading dialogs. The input graph (directed or undirected) is build from loaded network. By choosing “CytoKavosh” sub menu from “Plug-ins” menu and by starting this plug-in, the program will start working. The next step, following loading of the network, is specifying the input parameters required by the program. These parameters are located in the separate control tab that is named as “CytoKavosh”. Figure 4shows the control panel including input parameters. Plugin can operate on any network format, supported by Cytoscape engine.

10.1371/journal.pone.0043287.g004 Control Panel of CytoKavosh, including the CytoKavosh control tab for getting input parameters.

The right side of the figure shows the ‘results’ table panel after running the CytoKavosh for given input parameters. A table for each run of plug-in appears in the separate tab in ‘result’ panel. These tabs keep the results until finishing the plug-in. For larger sizes of the motifs, the number of detected motifs increases exponentially. So, the ‘results’ table can be explored page by page. The below panel shows the graphical representation of selected motif in the table.

The size of motif to be discovered would be given by the user. This size for current version of plug-in can vary from 3 to 9 but the main algorithm supports any given number for motif size.

The significance of a sub-graph is evaluated by some measures such as its frequency, Z-Score and P-Value which are later described in details. Accuracy of these measures increases if the program generates more random networks. Results show that 100 random-networks is a good count to begin. When the size of motif increases, the total number of detected motifs increases exponentially. So, filtering the results on result attributes comes into necessary. For achieving this goal, there are two parameters for filtering results in the control tab.

There are two minimum thresholds for frequency and Z-Score, which can be given by the users for filtering results, according to their purpose. The calculated significance of each sub-graph in the network (like Z-score and P-value with respect to the generated random networks) can be calculated from frequency concept. CytoKavosh computes and analyses the network for finding motifs, in following steps:

Sub-graph detection

CytoKavosh traverses the input network and finds all sub-graphs of a given size, which exist in the input network. Each sub-graph should be tested for its isomorphism class.

Sub-graph isomorphism testing

During step 1, a binary tree is constructed. This tree holds non-isomorphic sub-graphs in different paths of the tree. Traversing this tree gives the adjacency matrix of each motif, and each path from root to a leave is related to one isomorphic class.

Evaluation of a sub-graph

The number that is stored in the leaves of tree represents the number of matches of each sub-graph in the input network. As mentioned before there are three measures for evaluating an isomorphic class of sub-graphs. Each are described in details in the following.

Frequency for each sub-graph is calculated from the bellow-stated formula:

Statistical measures such as Z-Score and P-Value are very important factors for comparing sub-graphs. CytoKavosh gives Z-Score by generating random networks. One of the flexibility of this too is that the number of random networks is determined by user and it varies from 1 to 100. The Z-Score for a discovered sub-graph GP is calculated by the following formulas:

Where NP is the number which GP occurred in the input network, is the mean number which GP occurred in random networks and σ is standard deviation. The larger the Z-Score, the more the significance of the sub-graph.

The P-Value measure indicates the number of random networks in which a sub-graph GP occurred more often than in the input network, divided by the total number of random networks. The smaller P-Value, the more significant is the sub-graph.

Figure 4 shows the input parameters and output table for E. coli network.

For each execution of the program for given input parameters, there is a separate tab in the ‘Results’ panel, containing a table that sorts and classifies the detected motifs. Each row represents the attributes of a sub-graph (directed or undirected, depends on the input network) that is expressed as the motif. This sub-graph is a unique topological sub-graph in the whole given network.

Attributes of a motif in the resulting table are frequency, Z-Score and the number of occurrences of that motif in the network. Sorting the attributes assists users to export knowledge from the results with graphical facilities deployed in the program. Showing the graphical representation of sub-graph, related to selected motif, is one of the graphical facilities. Figure 5 shows the graphical representation of selected motif in the bottom of Results' table.

10.1371/journal.pone.0043287.g005 A sample of CytoKavosh ‘results’ table in which the motif size is 5 and the number of generated random networks is 100.

All 590 motifs are listed in this picture in separate pages. Each page shows only 10 motifs. By clicking on each row (each motif), a graphical image of that motif appears in below panel. For exporting this view, we can right click on the row and export the motif in a separate network. For each running of CytoKavosh, there will open a new tab in ‘result’ panel, with the number shown in <> is the number of found motifs. In this sample, there are 590 motifs of size 5 for the transcriptional network of E. coli. Sub-graph count presents the number of sub-graphs of this motif type.

User can select desired motif in the Results' table and view the occurrences of all of its sub-graphs in the main loaded network-graph. CytoKavosh highlights all sub-graphs of a selected motif in main graphical view of loaded network. Highlighting the sub-graphs in the main network helps the analysts to identify where each of these sub-graphs are occurred mostly in order to recognize the structural behavior of each part of the network. Figure 5 represents the ‘Results’ panel concluding from the running of CytoKavosh to find motifs with size 5 in the given network. All highlighted nodes are represented for selected motif in the ‘Results’ table and the sub-graph image of that motif are shown in the below-stated panel.

With right clicking on a motif in the list, user can export the image of that motif in a separate network view in the Cytoscape. As the size of motif increases, the number of finding motifs, especially in large networks, increases too. Figure 6 represents the sub-graph of exported motif in a separate network view. User can save this network or use it in the Cytoscape like other networks.

10.1371/journal.pone.0043287.g006 A sample of exported motif in a new network view.

For better understanding the advantages of CytoKavosh plug-in, we chose FANMOD software to compare and detect the motifs. Because the FANMOD is the most efficient tool according to time among other existing tools like MAVisto, Mfinder and MODA. So, the comparisons with other tools are just limited to FANMOD.

The main advantage of CytoKavosh plug-in over FANMOD is that CytoKavosh is based on Kavosh algorithm that operates over any given size of motif (more than 8) but FANMOD cannot detect up to 8 vertices of motifs. Table 2 shows the running time of two tools for E. coli network. It can be seen that the time usage of CytoKavosh is less than that in FANMOD in the equal size of motif. We limit the comparing part to one network, because there is well comparison in the main algorithm paper [16]. For more information about time and memory performance going to [16] is recommended.

10.1371/journal.pone.0043287.t002 Comparison of the running times of two tools on the E. coli network.
3 4 5 6 7 8 9
CytoKavosh 0.3 1.84 14.51 141.98 1374 13173 121110
FANMOD 0.81 2.53 15.71 132.24 1205.97 92566.1 -

Rows indicate the running time (seconds) of the studied tool for each motif size. 10 random networks are generated for this comparison.

Both tools can operate on directed and undirected graphs and both of them allow users to filter their results. But, another advantage of CytoKavosh is the graphical options to display the results. FANMOD can export the table of result for detected motifs in a dummy file, which does not allow us to carry out any modification or additional operation on it. On the other hand, CytoKavosh makes it possible for users to get an overview of occurrences of motifs within the whole network by highlighting them in the main network view.

There is not any other Cytoscape plug-in, specific for finding network motifs, based on the original term introduced by Milo [1]. CytoKavosh is the first plug-in, introduced by our research group, which can be further improved to cover other options to make it the best motif-analyzing tool.

In Molecular Biology, there is a need to study interactions between biological elements like DNA, RNA, proteins or genes. To model these interactions, we adopt graph theory concepts: the biological elements that interact with others are represented by vertices and their interactions are represented by edges or arcs, depending on the type of interactions. We, hence, obtain a large graph called biological network. To extract pertinent knowledge from a biological network, we adopt several techniques, like network clustering [17], network modularity [17] and network motifs extraction [1]. Biological network motifs are of notable importance largely because they may reflect the properties relating to the biological functions. They play an important role in the analysis of biological networks since they are seen as the building blocks of these networks and shape their behaviors.

CytoKavosh is a flexible and extendable tool, used to analyze motifs in the biological networks. We believe that embedding the CytoKavosh in Cytoscape will further contribute to the establishment of Cytoscape as an integrated suite of tools for the analysis of biological networks. The high performance of CytoKavosh is achieved by dynamically linking the highly optimized C++ functions of the Kavosh program to the Cytoscape Java program, which makes CytoKavosh extremely suitable for the analysis of large biological networks. As Cytoscape continues to evolve, CytoKavosh evolves alongside it. The original algorithm is highly efficient and allows further extension. In particular, future versions can extend CytoKavosh towards the evaluation of motifs with larger sizes and also the parallelization of the algorithm for analyzing huge networks. Details of the main part of CytoKavosh algorithm are explained in Appendix 1.

The tutorial, full package and associated examples are available at the following website:

We would like to appreciate Dr. Ameneh Javid for editing the English language of the manuscript.

References MiloR, Shen-OrrS, ItzkovitzS, KashtanN, ChklovskiiD, et al. (2002) Network motifs: simple building blocks of complex networks. Science 298: 824827. Bachmaier C, Brandes U, Schreiber F (2012) Biological Networks in R, Handbook of Graph Drawing and Visualization: Chapman & Hall/CRC Press. BodeC, KovacsIA, SzalayMS, PalotaiR, KorcsmarosT, et al. (2007) Network analysis of protein dynamics. FEBS Lett 581: 27762782. DobrinR, BegQK, BarabasiAL, OltvaiZN (2004) Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics 5: 10. KashtanN, ItzkovitzS, MiloR, AlonU (2004) Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20: 17461758. SchreiberF, SchwobbermeyerH (2005) MAVisto: a tool for the exploration of network motifs. Bioinformatics 21: 35723574. Chenj, HsuW, LeeML, NgSK (2006) NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs. 12th ACM SIGKDD international conference on Knowledge discovery and data mining 106115. JoshuaA, GrochowMK (2007) Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking. Terence P Speed HH Oakland, CA, USA 92106. AlonN, DaoP, HajirasoulihaI, HormozdiariF, SahinalpSC (2008) Biomolecular network motif counting and discovery by color coding. Bioinformatics 24: i241249. Fernando Silva PR (2010) G-Tries: an efficient data structure for discovering network motifs. 25th ACM Symposium on Applied Computing - Bioinformatics and Computational Systems Biology Track. Sierre, Switzerland: ACM. WernickeS, RascheF (2006) FANMOD: a tool for fast network motif detection. Bioinformatics 22: 11521153. KashaniZR, AhrabianH, ElahiE, Nowzari-DaliniA, AnsariES, et al. (2009) Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics 10: 318. OmidiS, SchreiberF, Masoudi-NejadA (2009) MODA: an efficient algorithm for network motif discovery in biological networks. Genes Genet Syst 84: 385395. Kreher DL, Douglas RS (1998) Combinatorial Algorithms: Generation, Enumeration, and Search: CRC Press. 344 p. Kreher D, Stinson D (1998) Combinatorial algorithms: Generation, Enumeration and Search:Florida: CRC Press LTC. McKayBD (1981) Practical Graph Isomorphism. CongressusNumerantium 4587. Junker BH, Schreiber (2008) Analysis of Biological Networks: Wiley-Interscience. 368 p.


Uploaded by Patricia Nova

15. Monocots vs. Dicots

Comparing Monocots and Dicots
Both monocot and dicot seeds develop in similar ways and have the same parts. There are a few
minor differences: monocots start out with one seed leaf, while dicots have two. The technical word
for seed leaf is cotyledon: you can find it on the coloring sheet; it is the first leaf to emerge from a
developing seed. Color all the cotyledons (A) on the seeds dark green. As a seed, both
monocots and dicots are covered by a seed coat. Color the seed coat (B) yellow.
The seed consists of the outside seed coat and a large area called the endosperm which functions
as a source of reserve materials and food for the developing embryo. As germination occurs, the
endosperm will be broken down and used by the plant. Color the endosperm blue (C).
Germination occurs when the seed begins to sprout, usually in the spring and under appropriate
conditions the radicle, the part of the seed that will become the root, begins to elongate and grow
downward. Color the root brown (D). Meanwhile, the coleoptile begins to grow upward. The
coleoptile is a sheath that encloses the shoot of the embryo. The primary function of the coleoptile is
to provide protection to the developing shoot as it is passing through the soil. Color the coleoptile
orange (E). Extending out from the coleoptile is the shoot. Color the shoot purple (F).
Eventually adult leaves grow on the plant. Color these leaves light green. (G)
Adult Monocots and Dicots
Angiosperms are divided into two classes, the monocots and the dicots. The majority of flowering
plants are dicots. Dicots include maples, oaks, and magnolias. Monocots are grasses, wheat, corn,
and rice. Most of our food supply comes from monocots. The diagram compares the differences
between the two.
First of all look at the roots. The root of a monocot is called a fibrous root and the root of a dicot is a
taproot. Notice how taproots have one main part - called the primary root. In a taproot the primary
root grows very large and small roots spread out from it. Fibrous roots, on the other hand, do not
have very large primary roots, and many small roots develop and remain near the surface. Color the
taproot dark brown and the fibrous root light brown.
Monocots and dicots also differ in their leaf structure. Adult monocots usually have parallel venation,
whereas dicots have net-like venation. For monocots and dicots, color the leaves green and
outline the veins in a darker green. The flowers of monocots and dicots differ in the number of
petals they have. Monocots tend to have flower parts that occur in 3's ( 3, 6, 9, 12…). Dicot flowers
usually have 4 to 5 petals. Color the monocot flower purple, and the dicot flower pink (make
sure all petals are colored). Stems hold the flowers up and attach the leaves, color the stems blue.
Monocot and dicots also differ in the way their vascular systems are arranged. In monocots, the
vascular bundles are scattered throughout the stem. In dicots, the vascular bundles are arranged in
a ring. Color the vascular bundles in both types of plants purple (V), color the stems blue.
1. Give two examples of plants that are monocots.
2. Give two examples of plants that are dicots.
3. What is a cotyledon?
4. What is the radicle?
5. What is the coleoptile?
6. What is the function of the endosperm?
7. Fill out the table below.
Number of Seed
Type of Leaf
Number of Flower
Type of
8. An unknown plant is brought to you and your job is to determine whether it is a monocot or a dicot.
You observe that the plant has 6 petals and its leaves have parallel veins. Is it a monocot or a dicot?


Speed Reads

A Changing of the Guard

Millennial candidates represent political shift in 1 Ohio district

With the midterm elections just one day away, one House race in battleground Ohio will see a generational shift no matter the outcome, with The Washington Post highlighting the pair of millennial women fighting to win the state's 13th Congressional District.

The race's Democratic nominee, Emilia Sykes, is a 36-year-old who became the first African-American under 30 to serve in the Ohio state legislature when she was elected in 2014. She would later go on to become the minority leader of the Ohio House when the state's Democrats elected her to helm their party. She announced her congressional run in January 2022.

Sykes' Republican challenger is Madison Gesiotto Gilbert, a 30-year-old attorney who previously worked for the presidential campaign of Mitt Romney. She is also a previous Miss Ohio USA winner, and first met former President Donald Trump as a pageant contestant, the Post reported. Gesiotto Gilbert would later become an ardent supporter of Trump, and served on his 2016 campaign and presidential inaugural committee.

The race is projected to be close, with FiveThirtyEight reporting that Gesiotto Gilbert holds a 52.2 percent forecasted vote share to Sykes' 47.8 percent vote share. However, two out of the three pollsters cited by FiveThirtyEight in the race had Skyes as the likely winner on Tuesday.

No matter who wins, the outcome will represent a significant generational change, as the two are running to replace Rep. Tim Ryan (D-Ohio), who himself is running to be the state's next senator. The election constitutes a noted shift in younger candidates running for elected office across the U.S.


Marvel Studios: Assembling A Universe Review

The special does a fine job highlighting what the studio has done right, but skips over the various missteps they’ve had along the way. This does air on ABC (Disney’s TV studio), after all, so there’s no doubt that its bias is heavily skewed. For example, The Incredible Hulk is mentioned a couple of times, but never really gets the same focus as say, Iron Man or Captain America. This makes sense considering the trouble that the studio had with Edward Norton (who doesn’t get a second of screen time here), but it still would have been nice to see them look at their mistakes as well as their successes. Furthermore, the special also only features one quote from Hulk’s director, Louis Leterrier, cementing the fact that The Incredible Hulk is perhaps the studio’s most forgotten film.

Interestingly enough, Iron Man 2 is given a bit of spotlight for setting the stage for future outings, but Iron Man 3 is only mentioned very briefly and then left alone. They manage to talk about the film’s impressive box office haul, but considering how it contributes very little to the overall continuity of the MCU, it didn’t surprise me that they decided not to talk about very much. The same goes for Thor and its sequel, Thor: The Dark World, which are both only mentioned and then brushed aside for more Avengers talk.

Much of the special is devoted to Iron Man, Captain America, The Avengers, Agents of S.H.I.E.L.D., and, oddly enough, the Marvel One-Shots. The Winter Soldier in particular gets a nice chunk of screen time and at times the special really just seems like a glorified advertisement for the movie. We do learn that Marvel is very serious about exploring S.H.I.E.L.D. though, one of the franchise’s biggest chess pieces that’s been in play since the beginning.

A highlight here is the interview with Samuel L. Jackson, who shares a couple interesting insights regarding his first time seeing Nick Fury and how he remembers the incredible secrecy surrounding his cameo in Iron Man. Apparently, the studio didn’t even let him walk to the set by himself.