The parable of those who take to them other than God for guardians (to entrust their affairs to) is like a spider: it has made for itself a house, and surely the frailest of houses is the spider’s house. If only they knew this! (Qur’an, 29:41)

A prehistoric Greek fairytale says a young girl named Arachne was a superb spinner and knitted the most gorgeous cloth. She dared the goddess Athena to a competition. When Athena saw Arachne’s stunning work, she ripped the cloth and hit the young girl. Disgraced, Arachne committed suicide by hanging herself. Athena regretted and transformed Arachne into a spider, so that she could whirl repeatedly and endlessly. Arachnida is the scientific name for spiders. It comes from the young girl in the famous Greek fairytale.

Although usually feared and disliked by people, spiders in fact make life easy for us by feeding on mosquitoes, flies, and locusts, thus saving our crops and eliminate the need for man-made insecticides which pose environmental problems. Besides, spiders are much less dangerous than people think they are; most spiders are keen to avoid interaction with people and will bite only when wounded or scared. Even poisonous spiders are rarely as dangerous as popular myths would have us believe: though black widows are poisonous, and their bites painful, they rarely kill people. If handled properly and quickly the adverse consequences of a black widow’s bite typically diminish in a few hours, and, after a couple of days’ rest or cessation of activities, the victim will fully recuperate [1].

There are countless features of spiders. But their silk is exceptionally unique and this article covers its various aspects.

Spider silk

Biomaterials, having developed over millions of years, frequently surpass man-made substances in their properties. Spider silk is an exceptionally stringy biomaterial which is made almost completely of substantial proteins. Silk fibers have stretchy powers similar to steel and some silks are practically as elastic as rubber on a weight-to-weight basis. In uniting these two properties, silks disclose a hardiness that is two to three times that of artificial fibers like Nylon or Kevlar. In addition, spider silk is also antimicrobial, hypoallergenic, and completely biodegradable [2].

The power of spider silk, so fragile in manifestation, is astonishingly great. A filament can be outstretched as much as one half its normal length before breaking, and has a tensile strength exceeded only by fused quartz fibers. Fine fibers are sturdier than others, the power to some degree depending on the velocity with which they are pulled out of the spider’s body. The higher the speed, the superior the strength.

Most of the silken fibers are not single fibers but are made up of two or more strings. A thread may be as fine as a millionth of an inch in width but, frequently, it is ten or twenty times as dense, and the assemblage of these threads unsurprisingly creates larger threads of a diversity of thicknesses. Furthermore, some threads are gluey whereas others are not.

Scientific research demonstrates that a single thread of spider silk, thick as a pencil, could stop a 747 Jumbo Jet in flight, and that on an equivalent footing, the spider’s silk is stronger than steel, per unit weight. It has been shown that the dragline silk of the golden orb spider is one of the planet’s hardest threads.

Spiders employ silk for webs, but also for trap lines, draglines, ballooning lines, for egg pouches and nursery nets, for compartments in which to sleep through winter or to copulate, and for entrapping and wrapping their victims. Silk for all these objectives is not accomplished with one kind of gland; there are at least seven distinct kinds. A few distinctive spiders have as many as six kinds and probably have more than six hundred independent glands; others have fewer than this [1].

Mechanism behind the formation of spider silk

A batch of scientists headed by researchers from the RIKEN Center for Sustainable Resource Science (CSRS) have scrutinized spider silk and discovered that a formerly undiscovered organizational constituent is critical to how the proteins form into the beta-sheet conformation that gives the silk its extraordinary power [3]. If humans can cultivate equivalents to spider silk, they could be applied in industrial and medical applications. It is well-known that the beta-sheets in spider silk are significant to its strength, but how the sheets are created is scantily comprehended, making it difficult to produce synthetic variations. It is hard to comprehend the process: the silk is originally produced as soluble proteins, which very swiftly crystalize into a solid form.

To explain this, the CSRS scientists obtained silk proteins using genetically altered bacteria that can generate silk from a golden orb-web spider (Nephila clavipes) and then executed multifaceted examinations of the soluble proteins. They discovered that the reiterating area is comprised of two designs – unsystematic spirals and a design called polyproline type II helix. Their investigations confirmed that the polyproline type II helix is critical for the creation of the stiff construction, which can then be rapidly converted into beta-sheets, letting the silk be swiftly intertwined. Fascinatingly, it was discovered that pH – which is supposed to be significant for the molecular exchanges of the N- and C- terminus areas – does not play a significant role of the foldup of the recurring areas, and that it is rather the elimination of water and mechanistic forces through the silk gland.

According to Keiji Numata, who is a project leader of JST ImPACT and led the research group, “Spider silk is a wonderful material, as it is extremely tough but does not contain harmful substances and is readily biodegradable, so it does not exert any harmful load on the environment” [4]. Numata hopes that this discovery may lead to the production of artificial silk that will prove useful for society.

Analysis of silk

The silk itself is a material identified as a “scleroprotein.” When created in the glands it is a fluid; only when dragged outside the body does it solidify into thread. Once it was believed that contact with air produced the toughening, but it currently looks that the drawing-out activity alone is accountable for the change.

To carry out the exertion done by the glands, a spider is armed with spinnerets, usually six in number. These are as accommodating as fingers; they can be prolonged, compacted, and overall be applied like human hands. In the “spinning field,” where the spinnerets are congregated, single threads are joined into numerous compound threads, and some of the dehydrated threads may be covered with a gluey substance. Thus, a completed thread may be thin or thick, dry or sticky. It may also have the look of a bead-trimmed necklace. For the last kind, the spider spins rather unhurriedly and, drawing out the gluey thread, lets it go with a jolt. The liquid thus is organized in beads spread out lengthwise across the completed line.

The strand known as the dragline may be understood as a spider’s “life line” because it performs as a lifeguard in all kinds of situations. The dragline goes along with the spider, no matter where or how far it journeys, winding out from spinnerets at the back of the body. It forms a portion of the building of webs, it grips its tiny builder firmly in problematic places, and it helps in absconding from adversaries. When a spider is inactive in a web, the dragline enables a rapid descent and escape. It allows energetic chasing spiders to jump from buildings, cliffs, or any tall position with absolute security. [1]  

Benefits of spider silk to us

The silk of the silkworm could be very profitable and marketable. There are, however, challenges. One is the changing thickness of a spider’s strand; the other is that it doesn’t well endure the interweaving process. Housing and feeding large numbers of silkworms is not difficult. But housing and feeding large numbers of spiders? There are enormous difficulties.

Native inhabitants of New Guinea have used spider silk in a variety of conditions. They make fishing nets, traps, and such objects as bags, headdresses that will keep away rain, and caps. These are not formed from single threads but from tangled, warped threads. The aboriginals of North Queensland, Australia, look to spiders for their angling supplies.

Spider silk has been valuable to the manufacturers of such complex instruments as astronomical telescopes, guns, and engineers’ levels. The threads, being exceedingly fine but nonetheless robust, are outstanding for sighting marks. Throughout the Second World War, there was a significant demand for spider thread for surveying and laboratory instruments. Black widow spiders were utilized for the manufacture of this silk.

One drawback to the use of spider silk in industry is that it might slump in a moist environment. To overcome this problem, strands of platinum or etching on glass plates take its place in such instruments as periscopes and bombsights. [1]

Spider’s silk also might have healing properties. Due to its antibacterial properties and because the silk is abundant in vitamin K, it may be efficient at clotting blood. Because of the problems in obtaining and handling extensive amounts of spider silk, the largest known piece of cloth made of spider silk is an 11 by 4-foot (3.4 by 1.2 m) fabric made in Madagascar in 2009. Eighty-two persons labored for a period of four years to gather over one million golden orb spiders and extract silk from them. [5]  

Applications of spider silk

As mentioned, human beings have been using spider silk for thousands of years.

The manufacture of contemporary synthetic super-fibers such as Kevlar (bulletproof material) includes petrochemicals, which adds to pollution. Kevlar is also strained from concentrated sulphuric acid. In comparison, the manufacture of spider silk is totally ecologically sustainable. It is created by spiders at ambient temperature and pressure and is strained from water. Furthermore, silk is totally biodegradable. If the manufacture of spider silk ever becomes industrially practical, it could be a substitute for Kevlar and be used to create a varied extent of articles such as: bulletproof vests, wear-resistant lightweight clothing, ropes, nets, seat belts, parachutes, rust-free boards on motor vehicles or boats, biodegradable bottles, bandages, surgical thread, artificial tendons or ligaments, and backings for weak blood vessels. [6]

Synthetic spider silk [5]

Duplicating the multifaceted settings needed to make threads that are similar to spider silk has been difficult to both research and manufacture. Through genetic engineering, Escherichia coli bacteria, yeasts, plants, silkworms, and animals have been utilized to produce spider silk proteins. Yet, these synthetic threads have diverse, simpler features than those of a spider. Manmade spider silks have lesser and unsophisticated proteins than natural dragline silk, and have subsequently half the diameter, strength, and flexibility.

One tactic is to remove the spider silk gene and utilize additional life forms to generate the spider silk. Canadian biotechnology company Nexia effectively produced spider silk protein in transgenic goats that passed the gene for it; the milk made by the goats comprised noteworthy amounts of the protein: 1-2 grams of silk proteins per liter of milk. To make spider silk, Nexia utilized damp whirling and pressed the silk protein across minor extrusion cavities in order to mimic the performance of the spinneret, but this process was not adequate to duplicate the sturdier characteristics of innate spider silk.

In March 2010, investigators from the Korea Advanced Institute of Science and Technology was able to produce spider silk by means of the bacteria E. coli, altered with definite genes of the spider Nephila clavipes. This tactic removes the necessity of milking spiders.

It should be noted that the manufacture of spider silk is not easy and there are intrinsic difficulties. First of all, spiders cannot be cultivated like silkworms since they are flesh-eaters and will merely eat each other if in proximity to each other. The silk produced is very slight, so 400 spiders would be required to make only one square yard of cloth. The other problem is, silk also toughens when subjected to air, which makes working with it problematic.

A different tactic is to study how spiders whirl silk and then replicate this process to make artificial spider silk. The silk itself would also have to be synthetically produced. Chemical production of spider silk is not feasible at present due to the absence of information about the makeup of silk. Randolph V. Lewis, Professor of Molecular Biology at the University of Wyoming in Laramie, has introduced silk genes into Escherichia coli bacteria so that the recurring sections of spidroin 1 and spidroin 2 efficaciously come to form. Others theorize about the likely gene introduction into fungi and soya plants. It may also be possible to modify the silk genes for precise intentions.

Why a spider’s house is the frailest of houses

Spider silk is stronger than steel, but the Qur’an (29:41) states that the flimsiest of houses is the spider’s house. The per unit weight of the dragline silk of the golden orb spider is one of the world’s hardest fibers. Webs are combinations of many kinds of spider silk, all able to be produced by the same spider. The web radials are strong, but the somewhat feebler circumferential (quasi-circular concentric) fibers are flexible and gluey to absorb the energy of a flying insect and hold it in place. The strongest of all is the fiber, which the spider uses for transport, the dragline silk. In summary, the spider fabricates both sturdy as well as feeble fibers and the web it weaves to catch flying insects is weaker; this may be the reason why it is referred to in the Qur’an as the “frailest” of houses.

Conclusions

Scientists are foreseeing many potential uses for biosilk. Textile usages are noticeable one. The flexibility and potency of prevailing merchandises such as spandex and nylon have to be improved. Since it is lightweight, hardy and flexible, biosilk may also have uses in satellites and aircraft. More prominently, the new group of progressive things that spider silk investigation may cause has the prospective to alter our lives in innumerable manners that we can barely imagine. More than 72 years have passed since the inventions of Wallace and Carothers that gave the world nylon that led us into the age of polymers. Artificial spider silk may help produce super-performing clothes of the future. Earthquake resistant suspension bridges hung from cables of synthetic spider silk fibers may someday be a reality. [1]

References

1. Syed, I. B. : Spider Silks http://www.irfi.org/articles/articles_1_50/spider_silks.htm
2. Romer, L and Scheibel, T.: The elaborate Structure of spider silk, PRION, Oct-Dec. 2(4) 154-161, 2008. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2658765/
3. RIKEN Center for Sustainable Resource Science (CSRS). Scientists discover key mechanism behind the formation of spider silk. Materials Science. May 29, 2018, https://phys.org/news/2018-05-scientists-key-mechanism-formation-spider.html

4. Nur Alia Oktaviani, Akimasa Matsugami, Ali D. Malay, Fumiaki Hayashi, David L. Kaplan, Keiji Numata, “Conformation and dynamics of soluble repetitive domain elucidates the initial β-sheet formation of spider silk”, Nature Communications, 10.1038/s41467-018-04570-5 https://en.wikipedia.org/wiki/Riken
5. Service, Robert F. (18 October 2017). “Spinning spider silk into startup gold”. Science Magazine, American Association for the Advancement of Science. Retrieved 26 November 2017. https://en.wikipedia.org/wiki/Spider_silk
6. Vivienne Li, University of Bristol, Spider Silk and Venom. Molecule of the Month – July 2002. http://www.chm.bris.ac.uk/motm/spider/page4.htm

In the past, the area missing a tooth could be filled by bridge implants by eroding the enamel on neighboring teeth. In cases where there was a large gap or no teeth to use as connectors, removable implants were used. In other words, present healthy teeth would be treated to compensate for the missing teeth; if the number and health of the remaining teeth was not sufficient, removable implants would be produced that gave the “ready to fall out” feeling. The imperfections of this type of implant led scientists to pursue new ideas.

Scientists have tried to fill in missing teeth with artificial roots and with implants mounted over these by mimicking the existing tooth root. This way, there is no need to treat and erode the healthy enamel of neighboring teeth in the gap. These artificial roots, which are positioned into the jaw bone by a surgical operation, are called a “tooth implant.” Modern-day implants are generally composed of two main parts.

One of these is the fixture part which is secured into the jaw bone by screws; the other is the support part (abutment) which mimics the visible parts of the tooth in the mouth. Approximately 3-4 months of curing time is required for the fusion of bone and implant (osseointegration), which is screwed together by a surgical operation. Towards the end of this time, the implant is built over the abutment. The length of time necessary for the implant to integrate with the bone to replace a tooth which can be extracted so quickly is rather noteworthy despite ongoing studies to reduce the wait time and successful attempts to do so.

Employed materials

Even though the main logic for implants is the mimicry of the tooth and the root, the materials utilized are quite variable. The history of such implants goes back thousands of years. The employment of bamboo sticks in tooth restorations could be seen in some civilizations, such as ancient China. Mayans used sea shells, which were recently shown to be biologically suitable as implant material. Maggiolo produced a tooth root by using gold, in 1809. In the beginning of the 20th century, Lambotte prepared implants using materials such as aluminum, silver, brass, copper, and gold.

These newer implant methods have failed because of the insufficient strength of the majority of the materials and the failure to fit into biological tissues. The initial employment of titanium and its alloys in the 1950’s almost opened a new age in implantology and enabled the successful use of these materials up until today. However, these titanium implants, which are considered successful, have a very limited ability to mimic tooth roots:

• Once the implant is in place, for reasons such as aging and improper tooth brushing, tooth gum recession and resurfacing of the bright faces of the implants which mimic the root surface can take place. This leads to undesirable displays. Even though gum recession can also occur in natural teeth for similar reasons, this situation is not as disturbing as with implants. This is because the tooth root color is similar to that of the tooth surface.
• Under normal conditions, implants should be fused to integrate with the bone (osseointegration). Integrated implants are considered successful clinically. A similar case to this bondage, which is unwanted clinically, is the fusing of tooth and bone which can be seen generally in dead teeth and is called ankylosis. Many hardships can be encountered when the tooth that is fused with the jaw bone is required to be removed. The same thing also applies to the implant. If an implant is set to be extracted for a reason, even if just a small amount must be removed, the surrounding jaw bone must be removed as well. However, a natural tooth is not bonded so tightly to the jaw bone, as if the tooth is healthy, it is suspended over the jaw bone through small suspension-like structures. When a tooth is needed to be pulled as a result of trauma or tooth rotting, only these suspensions are detached; thus any damage to the jaw bone is averted. Present efforts to design similar mechanisms for implants have not been successful yet.
• When a disease develops in our natural teeth, we feel pain and take action before it is too late. However, diseases within implants are rarely encountered as pain; generally, when the pain is felt, the implant is already disconnected from the bone and it is lost. Toothache often felt as a strong pain is a blessing granted to us. If the implants were built with pain mechanisms, just like in our natural teeth, we could take action at the beginning of the disease and avoid the time and financial losses.
• Even a healthy tooth, which we think of as immobile, can move via microscopic ligaments for 0.1 mm around its root on a type of pad. The tooth root is created in a perfect fashion to resist the strong chewing power that can be generated from many directions in the mouth through these ligaments, which are wrapped around different sides and which come from different angles. However, these ligaments, which act as a suspension, do not exist in between the implant and jaw bone. Therefore, implants are almost immobile. Even though it may seem like a good thing for them to be immobile and tightly fixed, there may be intense power spots generated on the implant or jaw bone since these forces acting on the implants are transmitted to the jaw bone without being dispersed. Forces densely acting on small areas can lead to fractures of the material, including tiny screws or porcelain plating, and can damage the bone tissue. Investigators once understood that these tiny, suspension-like structures won’t form around the implant, so they engaged in mounting tiny Teflon or spring pieces inside the implant to generate suspension. However, these imitated suspensions failed to resist the chewing forces that can go up to 400 kg at times, and their use was aborted after a short time.
• Teeth can be examined by the method of tapping with small hand tools (percussion). These small impacts cause pain in case of an abscess at the tooth root. By taking necessary actions, the tooth can survive longer. It is impossible to do such determination and diagnosis with implants.

As it is seen, making less successful artificial implants as opposed to real teeth is quite a cumbersome, costly, painful, and time consuming job. Despite all of these imperfections, because of their numerous advantages over traditional implants, the popularity of dental implants continues to increase, and is preferred by dentists and patients. In addition, implants are seen as an inevitable treatment option when tooth loss occurs, and may ultimately provide patient relief in the long run.

Despite progress made in implants, it’s clear that our teeth were created in such a perfect way, down to the very smallest detail, that they cannot be replaced. Science is well aware of the fact that we will never fully be able to replicate tooth structure. Therefore, studies are shifting towards stem cell research. This way, instead of foreign substances, the missing tooth will be compensated as if by planting tooth seeds by using human’s own cells. Nonetheless, the opportunities that are discovered via all these efforts will never replace the natural teeth that were so perfectly created for us.

Observing Majorana fermions in the ferromagnetic atomic chains on a superconductor. Nadj-Perge et al. Science, October 2014.

In the universe, matter and antimatter particles are always produced as a pair and, if they come in contact, they destroy each other in a flash of energy. In 1937, an Italian theoretical physicist named Ettore Majorana had proposed that there can be unique exceptions to this rule: a stable particle could exist in nature that is both matter and antimatter. Scientists have been looking for that indefinable particle, also known as the “Majorana fermion,” for seventy years. A group of researchers recently reported that they were able to detect the Majorana particle which behaves simultaneously like matter and antimatter. Researchers designed an experimental system allowing them to observe an emergent particle inside a material. They first generated an extended chain of pre magnetic iron atoms on a superconductor made of lead. Then, they cooled the material to -272 C, just about one point above absolute zero, and monitored it using a giant two-story-tall scanning-tunneling microscope, which can track electrical signal changes with very high precision. Finally, they were able to capture a glowing image of an electrically neutral particle at the ends of atomically thin iron wires. The Majorana particle was surprisingly stable and the opposing properties make the particle neutral so that it interacts very weakly with its environment. The discovery of the Majorana particle has exciting implications for several areas of modern physics, engineering, and astrophysics. For example, Majorana particles are very similar to neutrinos, as they both have very weak interactions with the matter. Neutrinos are thought to make up most of the dark matter that fill the Cosmos. Perhaps, neutrinos are simply Majorana-like particles and Majorana particles are also a candidate for what dark matter is. As an industrial application, Majorana particles can be utilized in quantum computing which aims to create computers to handle incalculable systems. The current quantum computing technology uses electrons, but they are known to be very unstable due to high interaction rates with surrounding materials. However, since Majorana particles are neutral and highly stable, they can be engineered into a variety of materials to produce more reliable and powerful quantum computing applications.

The Bitter Side of Artificial Sweeteners

Artificial sweeteners induce glucose intolerance by altering the gut’s microbiota. Suez J. et al. Nature, September 2014.

There have been conflicting and confusing findings about the health effects of artificial sweeteners over the past several decades. Some studies found that they cause weight loss and others found the exact opposite. Some studies linked them to diabetes and other studies argued otherwise. A recent study provided a series of experimental evidences that artificial sweeteners disrupt the body’s ability to regulate blood sugar, and thus may cause metabolic diseases and diabetes. Researchers, using animal models and human studies, found that sweeteners significantly alter the gut’s microbiome – the collective name of bacterial colonies living in our intestines. The composition of our gut microflora plays a critical role protecting us from pathogenic bacteria, the metabolism of indigestible components of our diet, and modulating development and regulation of the immune system. Sweeteners – in the form of saccharin, sucralose, or aspartame – are found to alter the mix of microbes in our intestines and consequently change how our bodies metabolize glucose. Constant use of sweeteners in mice and human test groups caused typical glucose intolerance symptoms in which glucose levels rose higher after eating and declined more slowly than expected. Glucose intolerance can ultimately lead to serious illnesses like metabolic syndrome and Type 2 diabetes. Although this study will cause a lot of discussions and headaches in the food industry, the link identified between microbiome and glucose intolerance will definitely inspire novel therapeutic approaches to metabolic disorders such as diabetes.

Brainy Fingertips

Edge-orientation processing in first-order tactile neurons. Pruszynski JA and Johansson RS. Nature Neuroscience, August 2014

A new study found that neurons in human skin are able to perform advanced calculations that scientists thought only the brain was capable of performing. A group of sensory neurons that extend into the skin and record touch are called first-order neurons in the tactile system. Each nerve ending branches in the skin to form about 5mm2 elliptical receptive field, with up to 8 highly sensitive zones that are unevenly distributed within the field. It turns out that these neurons not only transmit information about when and how intensely an object is touched to the brain, but they also send complex information about the touched object’s shape. Researchers found that the sensitivity of individual neurons to the shape of an object depends on the layout of the neuron’s highly-sensitive zones in the skin. Computations that require untangling geometric shape information are classified as feature extraction computations in neuroscience and are typically attributed to the immensely complex circuits of the cerebral cortex. This study showed that neuronal populations localized outside of the brain, such as first-order tactile neurons, can have advanced processing capacity similar to brain neurons. These results can also potentially improve treatments for nerve injury and rehabilitation, as scientists previously assumed that the cerebral cortex was doing all the work.

Heart Failure

The heart is basically a pump that provides the force needed to circulate blood and its contents to the body. It consists of four chambers: left ventricle, left atrium, right ventricle, and right atrium. The right ventricle and atrium collect the blood from the whole body and pump it to the lungs for removal of carbon dioxide and replenishment of oxygen. On the other hand, the left ventricle and atrium are responsible for collecting the blood from the lungs and pumping it to the body through the aorta (main artery). Non-stop blood circulation requires life-long and unfailing heart muscle power. Common symptoms of heart failure include waking up at the middle of night with shortness of breath and decreased ability to walk a few steps upstairs. Heart failure could arise due to any condition that decreases efficiency of the myocardium (heart muscle – Figure 1) through myocardial infarctions (death of muscles due to lack of oxygen, also known as heart attack) or overloading, such as hypertension, that requires increased contraction force. Loss of function in the ventricles (lower chamber of the heart) may require the use of ventricular assist devices (VAD).

Ventricular assist devices

A VAD is a mechanical pump that is implanted into the chest of patients to support the heart function through bridging the blood flow from the lower chamber to the aorta (Figure 2).^2,3 A VAD is usually useful during or after cardiac surgeries until recovery of the heart or while waiting for a heart transplant. VADs could also be used long term if the patient is not eligible for a heart transplant due to other complications. A VAD has several components, including a tube carrying blood out of the heart into a pump, a pump with another tube carrying blood to the aorta, and a power supply connecting to a control unit that monitors the VAD`s functionality.

There are different designs of VADs such as HeartMate, HeartWare, DuraHeart, and so on. Some VADs pump like the heart does, using a pumping action, and others use continuous blood flow. Intriguingly, VADs with continuous flow lead to a loss of normal pulse, but this has been found to decrease complications and increase survival. Two types of VADs include left ventricular assist device (LVAD) or right VAD (RVAD). LVADs are the most commonly used ones due to higher incidence of left ventricular function loss. If both LVAD and RVADs are used together, they are called a biventricular assist device (BVAC). VADs nowadays could be used not only for people with end stage heart failure, but also earlier stages of heart failure, including children. A VAD takes ninety percent of the pumping function of the heart. When using a VAD, your heart will still beat and have a rhythm. If none of these works for a patient, this requires the use of a mechanical or artificial heart, also known as total artificial heart (TAH).

Total artificial hearts

An artificial heart is a mechanical device that substitutes for the failing heart.^4,5,6 They are commonly used during heart transplantations to bridge the blood flow temporarily or to replace the heart permanently during a shortage of transplantable hearts. Early studies with artificial heart trials go back to the 1940s. Since then, various groups worldwide have invested in development of artificial heart prototypes and performed animal and human trials. Total artificial heart prototypes include, but are not limited to, SynCardia, ABIOMed (AbioCor), and Carpentier (CARMAT).6

SynCardia is developed from the Jarvik-7. It was first implanted in 1982. Dr. Robert Jarvik originally designed the Jarvik-7 (Figure 3). Barney Clark underwent the first artificial heart implantation at the University of Utah and survived for 112 days. This was followed by other implants. The longest survival with the Jarvik-7 is 620 days. However, the device is more commonly used on patients as a bridge during heart transplants. It has two pumps that resemble the two ventricles of the heart and is pneumatically (air) powered. The pump of SynCardia is covered with polyurethane. A pneumatic driver used in the US is a non-portable console, and requires patients to stay in the hospital. However, a portable version has been developed in Europe that could be carried a backpack while a patient waits for a donor heart.

The ABIOMed (AbioCor) is a completely self-contained, total artificial heart, which avoids the need for an external console or having wires or tubes piercing the skin to power the device. It uses a wireless energy transfer system, also known as a transcutaneous energy transmission system. This decreases the risk of developing infections due to implants.

CARMAT, on the other hand, is designed by Alain F. Carpentier.⁷ It is a fully implantable artificial heart with embedded biomaterials that make the device more biocompatible. In addition, the CARMAT includes valves made from cow heart tissue and has internal pressure sensors. This allows a person to adjust the flow rate in response to increased demand, such as during exercise. This feature distinguishes the CARMAT from other artificial hearts that provides a constant flow rate.

Biological artificial hearts

Synthetic replacement of organs is one of the long-sought dreams of modern medicine. To this end, there are efforts to develop biological artificial hearts in the laboratory. One recent study took the approach of producing a decellurized (empty from cells) scaffold of a mouse heart and recellurized it with human cardiac cells, and showed that lab-grown human heart tissue can beat on its own (about 40-50 beats per minute) in as short as a few weeks (Figure 4).⁸ They took the advantage of induced pluripotent cell (iPS cells) technology to produce multi-potential cardiovascular progenitor (MCP) cells, which could give rise to all three types of cardiac cells found in the heart. This area of research is still in its infancy but in the future, at least, it may provide tools to generate patches of heart tissue to replace damaged parts of the hearts. Given the success of a mouse heart cellurized with human cardiac cells, it’s possible that scientists will also try to decellurize the heart of a monkey or another animal and then cellurize it with human cardiac cells to produce a beating human heart in the laboratory as an alternative source to a full heart transplant.

Another approach to a biological artificial heart is to genetically modify animals in a way that their hearts will be compatible with a human body. Genetic engineering is a rapidly evolving field that one day could provide such tools for scientist to grow necessary organs in monkeys, dogs, or maybe even horses. Genetic modification may overcome tissue rejection issues when they are transplanted into a human body. For instance, one study tested the possibility of a heart transplant from genetically modified pigs into monkeys and showed the applicability of heart transplants between different species.^9,10

Until the development of biological artificial hearts, ventricular assist devices and mechanical artificial hearts seem to the best options. However, artificial heart implants have had various complications, including infections, pneumonia, high fevers, and multiple organ failures with variable survival rates based on the type of device and materials used. Another issue is the necessity of artificial heart to meet requirements of the body in terms of heart flow rate. For instance, the required flow rate of someone walking or exercising is different than someone at rest. In addition, the possibility of mechanical or computerized systems to fail may be a source of distress in patients implanted with artificial hearts or assist devices. This reminds us of that as long as we take care of our heart’s health, we won’t have worry whether its battery could fail – and what a mercy that is.

It is stunning that even with so much need and effort we are still not able to develop something that completely replaces all the functions of a heart. This clearly points that the heart is a marvelous gift granted to us. We are counting on every beat of the heart for our survival, and we should give thanks, with every beat, for what an incredible gift we’ve been given.

References

1. Bui, A. L., Horwich, T. B. & Fonarow, G. C. Epidemiology and risk profile of heart failure. Nat Rev Cardiol 8, 30-41 (2010).

2. What Is a Ventricular Assist Device? NHLBI, NIH. Retrieved from www.nhlbi.nih.gov/health/health-topics/topics/vad/ on 1/2/14.

3. Ventricular assist devices (VADs) Definition – Tests and Procedures – Mayo Clinic. Retrieved from www.mayoclinic.org/tests-procedures/ventricular-assist-devices/basics/definition/PRC-20020578 on 1/2/14.

4. Artificial Hearts. Retrived from www.umasswiki.com/wiki/Artificial_Hearts on 1/2/14.

5. Artificial heart. Retrieved from en.wikipedia.org/wiki/Artificial_heart on 1/2/14.

6. Heart Assist Devices. Texas Heart Institute. Retrieved from http://texasheart.org/Research/Devices/index.cfm on 1/12/14

7. Carmat artificial heart patient in good condition: hospital. Retrieved from www.reuters.com/article/2013/12/30/us-carmat-patient-idUSBRE9BS07O20131230 on 1/2/14.

8. Lu, Tung-Ying, Bo Lin, Jong Kim, Mara Sullivan, Kimimasa Tobita, Guy Salama, and Lei Yang. “Repopulation of decellularized mouse heart with human induced pluripotent stem cell-derived cardiovascular progenitor cells.” Nature communications 4 (2013).

9. Heart of genetically modified pig ‘successfully transplanted into monkey’, South Korea scientists claim. Retrieved from http://www.dailymail.co.uk/news/article-2164964/South-Korea-scientists-successfully-transplant-heart-genetically-modified-pig-monkey.html on 12/1/14

10. Pig to human transplants. Retrieved from http://www.theguardian.com/world/2002/jan/03/qanda.simonjeffery on 12/1/14

Despite advances in technology, computers still can’t come close to the power of the world’s most remarkable computer – the human brain.

Computing machines have seen three phases: the tabulating phase, the programmable phase, and now the new era of computing, the cognitive phase [1]. Tabulating machines performed a fixed task, whereas programmable machines could be reprogrammed to execute different tasks without any change in the hardware. Cognitive machines, however, promise learning and reasoning capabilities.

The possibility of such machines raises the question: is cognition the ultimate test for conscious existence? What do we know about cognition? How do we define intelligence? The questions go on and on. One thing, however, everyone seems to agree with is the fact that understanding the mechanism of human cognition is the key to developing advanced artificial intelligence, and cognitive machines.

Cognitive machines have the ability to learn, and they employ artificial intelligence to “reason.” Artificial intelligence is defined as, “The science of making machines do things that would require intelligence if done by men” [2]. The good news is that cognitive computing is no longer an esoteric pursuit of some futurists. It is, and has been, essential for the operation of many big-data driven processes. The 21st century has been flooded with data coming from almost every aspect of our lives. Technology has made it possible to generate data at an exponential rate. Temperature distribution throughout our buildings, the number of people diagnosed with cancer in the last six months, real-time changes in customer preferences, ethnic profiles of college applicants, and the top three words trending in online conversations at this very moment, are some examples of the kind of data available today.

As the amount of data generated increases, it also becomes harder and harder to process and make sense of the data collected. Our “greedy and ambitious” human nature does not want to waste, and it wants to use every bit of available data. This is where it becomes imperative to have a computing machine that goes beyond performing pre-programmed tasks and learns as it goes, without human interference. For this kind of computing, we need cognition.

The biggest challenge in imitating human cognition is to understand how cognition happens in the brain. It is obviously beyond our ability to monitor such activity that is constantly taking place in our brains, let alone recreating such marvels in the first place. Nevertheless, it will be a great achievement if we can manage to somewhat imitate human cognition, even partially. It would open a whole new era in terms of what can be achieved from a computing standpoint. For instance, the entire curriculum of a college degree can be processed by a cognitive machine in a fraction of a second; such machine can digest the whole of medical literature in a short period of time, provide human doctors with second opinions on their diagnoses [3].

Despite the fact that there have been substantial improvements in designing “intelligent” computing machines, mimicking the hardware of the human brain and simulating its decision-making processes, have posed three fundamental challenges: a hardware with comparable processing power and memory, a software algorithm to implement intelligent behavior, and the necessity of both being self-adapting and self-improving.

First of all, human intelligence has not been fully characterized – its capabilities and limitations are still unknown. This lack of knowledge makes it difficult, and perhaps even impossible, to reduce such intelligence to smaller, or simpler, modules. Therefore, we don’t have a good handle on how to mimic the human brain in a behavioral sense.

The second major problem is that we are still far away from having the hardware on which our “intelligence” software could run. Implementing intelligence in conventional computing machines, evidently, seems to be a futile undertaking. Programmable machines are no match for human brains; even the fastest supercomputers, taking advantage of thousands of processors, is able to mimic just one percent of one second worth of human brain activity-and even that takes 40 minutes [4]. Therefore, cognitive computing machines must incorporate different hardware architecture from conventional computers to achieve cognition comparable to humans. IBM’s SyNAPSE chip is one example of hardware inspired by the brain, and it has the potential to carry out the required, intense computations.

Lastly, the human brain and its cognitive power are constantly changing. Depending on various factors and experiences, our brains can improve or deteriorate; this is also true of our cognitive power. However, such improvement or deterioration could be in the form of a change in the physical structure or the amount of capacity utilized [5]. Such dynamic flexibility, also called Brain Plasticity [6], is essential to our intelligence. At this time, no self-evolving computing hardware has been worked out. However, promising developments have been reported with respect to cognitive computing machines that can learn – that is, they can make deductions and reach conclusions that are not preprogrammed.

Along the way, human supervision will be the ultimate guide in perfecting such imitation. Therefore, human cognition, taken for granted in our daily lives, remains to be the final frontier for our thousands-years long technological journey. Once again, the creation set the boundaries for human development.

Adem G. Aydin holds a Phd degree in Electrical and Computer Engineering. He works as an engineer scientist at IBM.

References

[1] Virginia Rometty, 2013, http://smarterplanet.tumblr.com/post/32816006311/i-b-m-chief-on-watson-cognitive-computing-and-her

[2] Marvin Minsky, 1968, http://www.akri.org/ai/defs.htm

[3] “WellPoint and IBM Announce Agreement to Put Watson to Work in Health Care”, http://www-03.ibm.com/press/us/en/pressrelease/35402.wss

[4] “Largest neuronal network simulation achieved using K computer” http://www.riken.jp/en/pr/press/2013/20130802_1/

[5] William James, The Principles of Psychology

[6] Bryan Kolb and Ian Q. Whishaw, Brain Plasticity and Behavior, Annual Review of Psychology, Vol. 49: 43-64

In spite of the developments in science and technology, we are still so powerless at predicting phenomena; there is so little we can control and uncertainties are so many. We do not face the challenge of uncertainties at predicting happenings but also at decision-making. While we make decisions in daily life we try to minimize uncertainties. Any decision that holds uncertainties in its nature is risky. Therefore, decision-making processes are a kind of risk management at the same time. All the efforts are directed toward reducing the possible risks and mistakes. High-risk problems have a higher ratio of mistakes.

Studies of forecast need collecting numeric data and analyzing them along with obtaining relevant information based on observations. The data to be collected should be objective and the statistical methods appropriate. Otherwise, unsuccessful forecasts will lead to waste of time, money, and resources. Decision problems with uncertainties can be of different nature. For example, it is possible to reckon when an apple which broke off from its branch will reach the ground by knowing physical laws. As factors get more complicated, possibility of surprises is much higher. The discipline which systematizes these studies is econometrics. Statistics, mathematics, economics, optimization, decision-making, and computer science fall within the studies of econometrics. Forecast problems are mainly studied with numeric and extrapolative analyses.

In recent years, intuitive methods have been added to these two major approaches. Along with other issues, numeric analysis is frequently used in artificial neural networks, computer simulations, and computer-based learning methods. Artificial neural networks are formed by trying to model the millions of neurons in human brain work. The artificial cells whose number varies between five to ten are educated by using a certain mathematical transformation function, with recurring algorithmic processes. At the end of the process of education with past data, the performances of the calculations in new situations are evaluated. This method, which is used at various forecast problems, is really meaningful in terms of reflecting what can be done by merely copying a few of the millions of human nerves. However, the greatest obstacle before the studies of artificial neural networks are the computers with insufficient capacity. Even super computer systems have difficulty in an optimization problems with more than a hundred cells and the data obtained is far from useable. One cannot help but amaze at the perfect capacity of human brain and how it tackles hundreds of parallel processes incessantly. In some decision-making problems, due to insufficient numeric data, the parameters targeted to be forecast may not be reliable and present a peculiar nature. The studies about very rare diseases and forecasting technological developments are examples to that. In such cases, numeric analyses do not help because of not having any reliable numeric data. Therefore, extrapolative analysis methods are preferred at such forecast problems. At forecasts based on extrapolation, the expert’s level of knowledge, intuition, experience, capacity of processing information, and power of judgment are important but they remain limited. There are lots of forecast problems about socioeconomic life such as economic fluctuations, price moves, and commercial size. Healthy forecasts on how and when to make investments are very important in order for the investors’ determining how to utilize their resources. However, the investors’ decisions will inevitably have uncertainties to a great degree. Therefore, it can be said that studies of forecasting are still in the cradle.

No matter of what quality, forecasts are always needed, since expectations for the future need to be determined and met. One of the basic assumptions of the economic system that make its effect felt in the global scale is that, human beings are self-centered creatures who act on opportunist drives. Such philosophies have always impelled people to be egocentric. However, people can listen to their conscience instead. They can simply choose to be altruistic; they can choose to care about others and help them. It is possible to reverse such an understanding of selfishness by giving charity and fulfilling other responsibilities. Thus, the forecasts about the future depend on what is to be given priority. In other words, the science of forecast can serve a more humane philosophy.

Everything is finely balanced with appropriate measures in this universe. Everything is created with wisdom. From the perspective of forecasting studies, our efforts can in a way be seen as guesswork to learn about destiny. Although we can try to make predictions within certain limits, we can never ignore possible surprises which take place totally out of our control. Human beings are equipped with an ability to make decisions in the face of uncertainty. We are supposed to comprehend the philosophy of creation and try to discern the meaning of the pattern being woven by Providence.

Ertugrul Deniz is a freelance writer from Turkey with a PhD degree in mathematics.

References

• Makridakis, Spyros; Wheelwright, Steven; Hyndman, Rob J. (1998). Forecasting: methods and applications, New York: John Wiley & Sons.
• Fama, Eugene (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work”. Journal of Finance 25 (2): 383–417.

“The human brain, then, is the most complicated organization of matter that we know.”
Isaac Asimov

If someone asks what you recall when you look at the following pictures, I can hear you say ‘President Obama’ and ‘Statue of Liberty’. You just see a fragment of the pictures and remember them.

So, how does it happen? This is just a simple task for the brain. It stores an image and retrieves it whenever a part of it is seen. Amazing features of the brain, especially its power to learn and make decisions, inspires computer scientists in the field of artificial intelligence.

In computer science, an artificial neuron is a simple computational model of a neuron in the brain that excludes biological properties. Artificial neural networks (ANNs) are composed of artificial neurons, and they are utilized to solve specific problems, especially those that require learning and decision-making. ANNs may not be the best solutions in various machine learning problems; however, they are accepted as strong alternatives. Although ANNs don’t claim to be so, currently they are not even close to producing a simple model of the brain. Let’s take a short journey into the world of ANNs to experience the extreme difficulty of modeling the brain.

Some Applications of ANNs

Figure 1: Overview of ALVINN structure. Images obtained from the camera installed on the vehicle are provided to the ANN, and ANN decides the steering angle.

(adapted from: http://virtuallab.kar.fei.stuba.sk/robowiki/images/e/e8/Lecture_ALVINN.pdf).

ANNs have various applications in very large spectrum of problems that require learning, such as the Autonomous Land Vehicle in a Neural Network (ALVINN). The structure of ALVINN is shown in Figure 1. The ALVINN project by Carnegie Mellon University started in 1986 and aims to make a vehicle without a driver (Mitchell, 1997). In this project, ANN learns the steering habits of a driver. A camera is mounted on the vehicle to capture the images of the road. With respect to the continuous images provided, ALVINN determines the steering level with 45 different angle positions from sharp left to sharp right. Steering is updated 15 times per second so that it allows real-time control while driving at 55 mph. The system is trained by the data obtained from a human driver in a simulator and a real vehicle. ALVINN was able to speed up to 70 mph and successfully drive at 55 mph for 90 miles.

Figure 2: Handwritten zip codes (LeCun, et al., 1989)

Another example is handwritten zip code recognition (LeCun, et al., 1989). Zip codes from US Mail written by various people with large variety of styles and sizes were used in the experiments. Figure 2 presents some examples of zip codes in the experiment database. After the ANN was trained with more than 7,000 digits in the zip codes, it was 99% successful in recognizing around 2,000 digits in new zip codes.

Learning and Decision-making in ANNs

In order to understand the challenges better, we will first examine learning and decision-making in neurons and ANNs on simple examples.

Figure 3: (a) A typical neuron (adopted from http://commons.wikimedia.org/wiki/File:Neuron_-_annotated.svg), (b) artificial neuron in computer

Figure 3(a) illustrates a typical neuron which is the constituent of brain’s complicated network structure. Each neuron receives information as signals via dendrites, then evaluates it and generates a signal that is transmitted through its axon. A neuron has many connections between its dendrites and the axons of various other neurons. Figure (b) demonstrates an artificial neuron in computer science. It is considered a function: dendrites as the inputs of the function and generated signal via the axon as the output of the function.

Let’s see an example of an artificial neuron that understands if a given produce is a red apple or not. Think about how you understand whether a produce is a red apple or not. You see the shape and the color. However, it might be an artificial one for decoration. Then you can taste it and you get the sweetness of the apple. Similarly, our neuron receives three pieces of information as the input; ‘has circular shape?’, ‘is sweet?’, and ‘has red color?’. If the output is ‘yes’, that means the neuron recognizes the produce as a red apple. Otherwise, it will be ‘no’, which means the produce is not a red apple (Figure 4). Here, the neuron’s function is defined in such a way that it only generates ‘yes’ when all the inputs are ‘yes’.

Figure 4: Example inputs and outputs for the artificial neuron.

In an artificial neuron, some of the information can be more important than the others. For instance, to have red color may be more valuable in determining the price of produce. Assume that round shape and sweetness has equal value of $1; however, having red color is $2 – twice as valuable as the other features (Figure 5).

Figure 5: Artificial neuron with different input weights. Arrow thickness indicates the importance.

So, what is the big fuss about artificial neurons if they are only functions? In fact, the main feature of artificial neurons is learning. Considering the last example above, the neuron initially does not know the importance of the dendrites, i.e. the weights of inputs are all the same. If not trained, the neuron will generate the following answers which are sometimes wrong as indicated in Table 1.

Table 1: Artificial neuron before training; highlighted answers are wrong.

In real life, a teacher trains students. For instance, the teacher asks a question and if the received answer is not correct, she provides the right answer. Students learn the right answer and use this correct information in their lives. It is similar in artificial neurons as depicted in Figure 6. When the response of the neuron is incorrect, it adjusts the importance of the dendrites with respect to the correct answer hence it answers the same question correctly next time. During the training session, the neurons will be continuously asked the values of all the produce until it learns them all.

Figure 6: The learning process of the artificial neuron.

What if the problem gets complicated? Then one artificial neuron will not be sufficient, and we will need a network of neurons; ANNs. A more complex problem, ‘learning the digits’ is indicated in Figure 7.

Figure 7: (a) Digit learning problem, (b) ANN structure that learns digits. Due to the difficulty, only the connections between the input layer and first / last neurons in the middle layer are shown.

ANN has 3 x 5 = 15 input units like receptors of an eye retina. Each input unit corresponds to one square in the digits; either filled or blank. Each input unit is connected to the dendrites of all neurons in the middle layer. The output of each cell in the middle is connected to the dendrites of all neurons in the output layer. There are 10 output neurons corresponding to the digits from 0 to 9. After the ANN is trained, it provides a correct answer to the given digit as input. When digit ‘3’ is provided to the network, the neuron labeled with number ‘3’ in Figure 7(b) is triggered and outputs ‘yes’ whereas the rest of the neurons output ‘no’.

Figure 8: Faulty digit ‘3’ with a missing black square on the top right side.

Initially, all neurons in the network have equally weighted dendrites. After a reasonable amount of training, neurons adjust their weights, and ANN is able to identify digits. Here, we have some major challenges: what does ‘reasonable amount of training’ mean? When the ANN is undertrained, it will not always answer correctly to the digits given in Figure (a). In the other case, when the ANN is overtrained, it will memorize the digits provided during the training and will not recognize the faulty ones such as the one in Figure 8.

Challenges of ANNs

Beyond the mentioned the overtraining / undertraining problems, ANNs have a bigger challenge – how to determine the structure of ANN that fits the problem? In the digit learning example, we’re lucky because the structure is provided in Figure 7(b). However, the outcome of the solution may drastically depend on the number of neurons and the connections among them which is indeed a hard problem for ANNs.

The huge capability of the brain in learning and decision making comes from the huge number of neurons – around 100 billion – and the enormous amount of connections among them – from 100 to 500 trillion. The challenge to design such a huge network requires huge computation power. With the increasing number of neurons, ANN dramatically slows down especially during the learning process. Here, our example is a simple learning task of 3×5 pixel digits compared to the brain’s acquisition capacity of hundreds of images in our daily life. 

When the number of neurons gets larger, the reliability of network also reduces. Small adjustments in weights may change the entire behavior of the network hence it is easy to lose control of ANN. In contrast, the brain has a robust system, and its fault tolerance is admirable. Although neurons die every day, this doesn’t affect its performance significantly. The training method and how to update the weights are other hard problems leading to many different approaches in the neural computation field.

We have presented some simple tasks that can be solved using a few neurons and their challenges. On the other hand, consider the thousands of problems, various and incredible amount of information we have learned, and the thousands of decisions we make. The brain is truly amazing from the computer science perspective.

Bibliography

• Hertz, J. A., Krogh, A. S., & Palmer, R. G. (1991). Introduction To The Theory Of Neural Computation. Reading, MA: Addison-Wesley.
• Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational properties. Proceedings of the National Academy of Sciences of the USA , 79, 2554-2588.
• LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., et al. (1989). Backpropagation applied to handwritten zip code recognition. 1 (4), 541-551.
• Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.

Original Article: Gibson, D.G. et al., Science Express (2010).

A team of genome researchers at the J. Craig Venter Institute in the U.S recently announced that after almost 15 years of work and with a budget of $40 million, they had finally built the first bacterial strain with a completely synthetic genome. In the study, researchers chopped the genome of Mycoplasma mycodies into 1,000 pieces in the computer, chemically synthesized these fragments and assembled them into an artificial chromosome in yeast cells. The reconstructed artificial genome was subsequently transferred to a closely related bacterium Mycoplasma capricolum, whose genome was removed. Remarkably, the strain with the artificial genome was able to guide the protein machinery of the host cells, produce the necessary enzymes and macromolecules for a bacterium to survive and most importantly to grow and divide. Team leader Prof. J. Craig Venter, best known for his pioneering efforts in human genome mapping project, commented on their findings as “we created a ‘synthetic cell’ and it is the first self-replicating species we’ve had on the planet whose parent is computer.” Many media sources also publicized the study as the first successful creation of the artificial life. As much as the scientific community agreed that the synthesis, transfer and retention of a functional synthetic genome is a breakthrough, most of the scientists have found Prof. Venter’s comments and the media’s reflection on the study to be somewhat of an overstatement. It would be quite unfair to call the new bacteria an example of “artificial life.” The synthesized genome was a copy of another living bacterium with slight modifications. The genome is a blueprint, whereas the proteins perform the actual cellular functions. This new approach shows that we can copy the book of cellular blueprints reliably but it brings no new parts to our inventory. Moreover, the synthetic genome had to be assembled in live yeast cells, processed with biochemical extracts from mycoplasma cells and finally transplanted into another (closely related) live cell. In other words, “natural life” was absolute prerequisite for the so-called “artificial life.” The generation of a fully functioning organism directed by machine-synthesized genome certainly represents a major step in our ability to manipulate large chunks of genetic material. It is clear that this study will positively influence many scientists, especially synthetic biologists, to try writing novel “synthetic” software to recruit the variety of organisms’ cellular hardware for solving various global problems like energy shortage or environmental pollution. However, the philosophical questions that probe the essence of life, like: “Can we reduce life to material? Is the human being ever going to be able to build a live cell from only a few chemicals?” will likely remain as major controversial issues for many years in the age of molecular biology.

2- Sharing the powers of Spiderman

Original Article: Vogel, M.J. & Steen, P.H., PNAS (published online before print on February 4, 2010).

The adhesive powers of Spiderman, jumping from one building to another and walking on the walls, attracted most of our interests. The recent invention of scientists from Cornell University brings this power from science fiction cartoons/movies to the real life. Inspired from a little creature, leaf beetle, which can stick to leaves by generating a force exceeding 100 times its body weight, these researchers designed a device which can stick to surfaces by using the adhesive powers of water. The device consists of a plate not thicker than a credit card with hundreds of tiny holes on it. The water is pumped through these holes, which builds liquid bridges between surfaces and thus generates a strong adhesive force. Simply pushing back the water un-sticks the device in a controllable and switchable manner. There are no solid moving parts nor any kinds of glue used in the system, and this makes device even more promising. The capabilities of the device are not at the level of the leaf beetle yet, but the inventors believe that it can be improved by building on the same principles. The system can potentially be used in many practical applications, such as robotics, and it can also be implemented into shoes and gloves allowing them to stick to surfaces. Accordingly, it is no longer improbable to imagine sharing the sticky-powers of Spiderman and walking on the walls very soon.

3- Igniting a Supernova

Original Article: Gilfanov, M. & Bogdan, A., Nature 463, 924 (2010).

upernova: the Rosetta stone that may help us put together the missing pieces of the cosmic jigsaw puzzle; one of the most energetic and most luminous explosions in the universe, putting out energies equivalent to what our sun could produce in 10 billion years. Yet the mechanism that produces these explosions still eludes us. Once our sun consumes its remaining fuel in another 5 billion years, it will shrink into a “white dwarf.” These compact stars are believed to produce subsequent explosions leading to supernovas if they reach beyond a critical limit of mass. One way to gain mass is to steal material from a companion star through an “accretion” process. Accretion was thought to be the most common means that might help push the mass of a white dwarf beyond the critical mass limit, until a recent study revealed that two clashing (in-spiraling) white dwarfs might be the missing fuse that ignites supernovas. German astronomers measured the X-ray flux of four nearby elliptical galaxies and the core of the Andromeda Galaxy to see whether the amount of X-rays from these galaxies are consistent with predictions based upon the accretion mechanism. Contrary to expectations, the observed X-rays were 2–3% of the amount that would have been produced if accreting white dwarfs were the primary trigger of supernova explosions. Hence, perhaps merging white dwarfs are more commonplace in the cosmos after all.

4- Strategy of bats finding their way

Original Article: Yovel Y et al., Science 327, 701 (2010).

Bats, dolphins, shrews and swiftlets use sound waves for navigation and hunting. They emit short sonar pulses and listen to the echoes reflecting back from solid objects. Microsecond differences in the arrival times of echoes are coded by detector neurons and used as a main cue for positioning objects in an environment. This phenomenon is known as biosonar. A recent study published in Science reveals one unknown part of this perfect sound processing strategy. The study shows that bats do not center the sonar beam on the target. Instead, they aim to match the maximum slope of the beam to the target in order to increase the signal-to- noise ratio. Around the sharp edge, small variations of the target position can be detected as a clear signal change in reflected sound intensity. Furthermore, the researchers showed that if the environment is very noisy, bats could bias this critical point to increase amplitude of the echoes. This powerful technique has already been employed by humans in engineering and used in various technological tools such as atomic force microcopy. Whether this strategy is used in general by other echolocating animals remains to be answered.

In general, the most common approach in tissue engineering is to develop tools as needed. Physicians treat patients and define the requirements for a better cure. Then, biologists study the targeted problem and learn what the mechanism is that caused the failure. Later, chemists and materials scientists manufacture the tools needed to treat the problem. Finally, the tools are delivered to doctors to treat the patients. Thus, tissue engineering requires a good understanding of how body parts work and come into existence, and this involves precise and sensitive application. Precise, aware and regular study of the interactions involved in tissues and organs must be practiced by the researchers who are interested in developing techniques for the manufacture of potential body parts. One of the first scientific approaches used for tissue engineering is to simply inject the body with molecules, such as growth factors, which are known to promote organ formation.

The growth factors are naturally occurring proteins which are assigned for cell proliferation and differentiation. Different parts of the body require different types of growth factors to signal to the cells to multiply or to replace the cells which have died or have been damaged. For example, it has been discovered that bone morphogenic proteins are responsible for the beginning of bone cell reproduction. For someone with a fractured bone that can not heal on its own within a reasonable period of time, the injection of bone growth factors to the site can direct the body to where bone cells are needed to be produced to repair the fracture.

In more severe conditions, the body may not receive the signal only with a simple injection of the growth factors. In this case, there is a need for more intricate treatment. Another way to treat organ malfunction starts with the harvesting of cells from the patient. The harvested cells can be multiplied in an artificial scaffold to eventually be implanted into the wound site. Because cells inhabit a different world than we do, we need a way to speak their language. The artificial scaffold should provide everything a cell needs and be able to direct the targeted cells toward the desired purpose. Basic knowledge gained from biology can help us to design potential artificial environments for cells.

A critical challenge in tissue engineering is how to design and make the artificial scaffolds. The cells must be fed through the blood vessel and are grown in the scaffold by the body; the scaffold should be able to communicate with the cells and finally the scaffold should disappear when its mission has been completed. The best example of a perfect scaffold is the natural environment of the cells, the extracellular matrix (ECM). The ECM provides support and anchorage for the cells and regulates communication between cells. There are various biological signals found in the ECM that help cell survival. For example, proteins called collagens provide mechanical support for cells through adhesive proteins in the ECM and the handles on the cell surface, known as integrins. Cell adhesion is crucial for cell survival and proliferation. Growth factors are also found in the ECM for cell organization. Some growth factors promote blood vessel formation, which can provide nutrients for cells. Therefore, a simple artificial environment should include various biological signals found in the ECM.

Currently, there are natural and synthetic scaffolds that are being used to generate the optimal environment for cells. Natural polymers such as collagen, chitosan or glycosaminoglycans, and synthetic polymers, including polylactic acid, polyglycolic acid, polycaprolactone or self-assembled nanofibers, are some of the materials used or considered for scaffold production. Natural polymers can be obtained easily, however biological contamination is a concern since they are produced using components from animals or microorganisms. Synthetic polymers can usually avoid the problem of contamination. Sometimes the ability to process the polymers can be problematic. Researchers have developed self-assembled nanofibers to overcome the problems that arise with synthetic and natural polymers. These nanofibers are composed of small molecules which are programmed to come together under control and to form larger structures. The nanofibers in the solution can form a three-dimensional network and convert into a self-supporting gel which can encapsulate cells as an artificial scaffold. In general, small bioactive molecules can be conjugated to the self-assembled molecules or can be encapsulated in situ in the 3-D network of fibers.

One of the recent uses of tissue engineering is to replace tissue that has been damaged by cancer. Cancer surgery is one of the most challenging types of surgery in that the defective tissue must be reconstructed afterwards. Improvement in surgical technology gives the chance of transferring a tissue from different sites of the body but unfortunately most of the time it is not the same tissue, and does not have the same texture or function. Reconstructing a resected tongue or the feeding tube is possible with the use of skin from the leg or forearm. But this skin does not provide the normal mucosal function, so it does not enable taste or sense to be perceived in the same way nor does it produce mucus in the same way. Together with advances in tissue engineering surgeons have started using the tissue-engineered mucosa of patients to reconstruct the mouth and feeding passage defects, instead of using the skin from chest, leg or forearm skin. These clinical applications of tissue engineering are in their very early stages, but it would not be surprising if we were able to reconstruct a lost organ from a similar one in the future. It would be exciting to be able to replace the tongue of a tongue cancer patient with a brand new tongue grown from his/her own tissues produced in a laboratory. Tasting the same…sensing the same…moving and even articulating the same…instead of having a piece of meat from another part of the body…

Innovative and imaginative work which has been inspired by natural materials demonstrates how the treatment of organ malfunctions is feasible. Efforts in biotechnology to develop tissue-engineered products will benefit many people who are searching for a healthier life. Potentially, in the near future, tissue-engineered products will be more widely used to treat bone fractures, serious skin burns, spinal cord injuries, diabetes, and heart diseases. Before implanting the tissue-engineered products, it is vital that there be extensive testing of the materials to be used. Toxicology and efficacy studies should be performed on the materials to prevent damage to the original healthy cells, and the new cells and regenerated tissue must be compared to original healthy cells and tissue.

Mustafa Guler has a PhD in chemistry. He is currently a research associate at Northwestern University, Chicago, IL. Joseph Coreman is a medical doctor at the Ohio State University Medical College, Columbus, OH.

References

• Khariwala SS, Vivek PP, Lorenz RR, Esclamado RM, Wood B, Strome M, Alam DS. Swallowing outcomes after microvascular head and neck reconstruction: a prospective review of 191 cases. Laryngoscope. 2007 Aug; 117(8):1359-63.
• Sauerbier S, Gutwald R, Wiedmann-Al-Ahmad M, Lauer G, Schmelzeisen R. Clinical application of tissue-engineered transplants. Part I: mucosa. Clin Oral Implants Res. 2006 Dec; 17(6):625-32.
• Hotta T, Yokoo S, Terashi H, Komori T. Clinical and histopathological analysis of healing process of intraoral reconstruction with ex vivo produced oral mucosa equivalent. Kobe J Med Sci. 2007;53(1-2):1-14.
• Ratner, Buddy D. “Biomaterials Science – An Introduction to Materials in Medicine” Elsevier, 2004.
• Lanza, Robert P., Robert S. Langer, William L. Chick, “Principles of Tissue Engineering”, Academic Press, 1997.
• Alberts, Bruce, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, Peter Walter, “Molecular Biology of the Cell” Garland Science, 2002.

The theorem published in 1931 by the 25 year old Austrian scientist, Kurt Godel, made a great impact on the scientific circles. Not only did it destroy the hopes of many scientists, but it also initiated a new point of view concerning AI and the mind. This theorem is one of the most important ones to be proven this century, ranking alongside Einstein’s Theory of Relativity and Heisenberg’s Uncertainty Principle. However, very few people know about it. In this article, we will examine in detail the effects of Godel’s theorem on AI.

What is Godel’s Incompleteness Theorem?

As a formal definition, proof is a sequence of well-formed-formulas (wff), each of which is either an axiom or a wff that is derived from preceding wff’s. Godel’s contemporary Hilbert, one of the most famous mathematicians, thought that all proofs in mathematics can be obtained in an automated way (with an axiomatic system) and he started to work on this project. He believed that if he derived all wff’s in basic arithmetic from its own axioms, then he could derive all facts in mathematics using these axioms.

Unfortunately, Godel demonstrated the impossibility of this. First of all, he found a method of translating the syntax of a formal system into arithmetic. Then he formulated the statement, “This formula is improvable in the system,” (G) in arithmetic. Using the same method, he also formulated the negative of the statement G (“This formula is provable in the system”). For the next step, he showed that if the truth value of G was calculated, the truth value of negation of G could also be calculated, causing a contradiction. At the end of his calculations, Godel arrived at two very important consequences:

1. If a formal system that contains minimal arithmetic is consistent, then it is incomplete.

2. Consistency of any formal system containing minimal arithmetic is not internally provable (by using the system’s own rules and formulas).

Surprisingly, even if G were added as a further axiom into the system, a new Godel sentence could be easily found. In other words, no matter how many axioms we add, one can find a Godel sentence that will make the truth value undeterminable.

What Does the Theorem Imply for Artificial Intelligence vs. the Mind?

By examining Godel’s Theorem, one can determine very important consequences for artificial intelligence. An English mathematician, Turing, described an abstract machine called the “Turing Machine.” This is an abstract machine which has an unlimited amount of storage space and which can go on computing forever without making any mistakes. This machine can compute any type of algorithmic problem. According to the Turing Theorem all computers are Turing equivalents. After proposing this, Turing went on to observe that some type of problems have no algorithmic solutions. In the meantime, “the Halting Problem” emerged – the problem of deciding those situations in which a Turing Machine action fails never comes to a halt because of the consequences of the Godel’s Incompleteness Theorem.

It has been proven that a halting problem is computationally insoluble. This leads us to an important conclusion; a computer cannot be the same as the human mind because the non-computational physics of the mind is not available for Turing equivalent machines and the nature of the algorithms is not compatible with the thinking process due to the halting problem.

The argument of the Godelian Case problems made great sense to AI supporters. Godel’s Theorem started a great debate between supporters of AI vs. those of the human mind.

Reviews of the Theorem on AI vs. Mind

Penrose claims that the human mind cannot be compared to artificial intelligence. Penrose bases his claim on Godel’s Incompleteness Theorem. By appealing to the results obtained by Godel (and Turing), mathematical thinking (and hence conscious thinking generally) is something that cannot be encapsulated within any purely computational model of thought. This is the part of Penrose’s argument that his critics have most frequently taken issue with. In addition, he states that there are certain classes of problems that do not have any algorithmic solutions (R. Penrose, 1994, p.29). In fact, Turing described this as the halting problem. Penrose gives an example of the completely deterministic, but non-computable “tiling problem” (R. Penrose, 1994, p.30-33).

Penrose asserted that some mathematical relations required long chains of reasoning before they could be perceived with certainty. But the object of a mathematical proof is to provide such chains of reasoning that each step is indeed something that can be perceived as being “obvious.” He concluded that the endpoint of such reasoning is something that must be accepted as being true, even though it may not, in itself, be at all obvious. One might imagine that it would be possible to list all possible “obvious” steps of reasoning once and for all, so that from that time on everything could be reduced to computation. But, what Godel’s argument shows is that this is not possible. There is no way to eliminate the need for new “obvious” understandings. Thus, mathematical understanding cannot be reduced to blind computation (R. Penrose, 1994, p.56).

Penrose claims that the results of Godel’s

theorem established that human understanding and insight cannot be reduced to any set of computational rules (R. Penrose, 1994, p.65). In the chapter entitled “The Godelian Case” of his book Shadows of the Mind, Penrose supported his idea with Turing’s Halting Problem and showed sound examples on non-computability. At the end of the chapter he answered possible technical objections to his idea based on Godel’s Theorem in details (R. Penrose, 1994, p.64-116).

Penrose believes that there is something beyond computation in the human mind. In Chapter 3 of Shadows of the Mind, he examines the thinking process and non-computability in mathematical thought carefully and uses formal representations (R. Penrose, 1994, p.127-209). Godel’s theorem states that in any sufficiently complex formal system there exists at least one statement that cannot be proven to be true or false. Penrose believes that this would limit the ability of any AI system in its reasoning. He argues that there will always be a statement that can be constructed which is unprovable by the AI system. However, Penrose believes that somehow the human mind can see the truth of such Godel statements directly (R. Penrose, 1989).

Along the same lines as Penrose, Lucas believes that Godel’s theorem seems to prove that the idea of “Mechanism” is false, that is, that minds cannot be seen in terms of machines. He claims that Godel’s theorem must apply to cybernetics, because the essence of being a machine is that it should be a concrete instantiation of a formal system. It follows that for any given machine which is consistent and capable of doing simple arithmetic, there is a formula which it will be incapable of producing as being true (i.e., the formula is improvable in the system but which we can see to be true). It follows that no machine can be a complete or adequate model of the mind, that minds are essentially different from machines. This does not mean that a machine cannot simulate any piece of the mind; it only says that there is no machine that can simulate every piece of the mind. Lucas says that there may be deeper objections. Godel’s theorem applies to deductive systems, and human beings are not confined to making only deductive inferences. Godel’s theorem applies only to consistent systems, and one may have doubts about how far it is permissible to assume that human beings are consistent. Godel’s theorem applies only to formal systems, and there is no a priori bound to human ingenuity which rules out the possibility of our contriving some replica of humanity which is not representable by a formal system (J. Lucas, 1970).

Chalmers examines the situation when a formal system F, which understands the consequences of Godel’s Theorem, is given. According to his claim, F may not be sound, so Godel’s theorem cannot be applied. He specifies that the crucial point of Godel’s argument is not to know “a formal system is sound”; but to determine “if we know that our system is sound.” It follows that we perhaps have a sound system, but we can not conclude that “we know that we have a sound system” (D. J. Chalmers, 1995).

Like Chalmers, McCullough claims that not only artificial intelligence, but also the human mind is tightly related with Godel’s theorem. Godel argument did not prove that human reasoning had to be noncomputable – it only proved that if human reasoning was computable, then it had to either be unsound, or it had to be inherently impossible for a human to know both what a human’s own reasoning powers were and to also know that they were sound. And adds, Penrose dismisses the possibility that a human knows its reasoning powers, but does not know that they are sound. In his paper, McCullough also examines the appliability of Godel’s theorem on non-computable systems and the human mind. According to him, both are possible, by the way he asserts that Penrose’s idea is wrong. Consequently, McCullough agrees with Penrose that human reasoning cannot be formalized in some sense, because humans do not understand their reasoning system well enough to formalize it. This limitation is not due to a lack of human intelligence, but is inherent in any reasoning system that is capable of reasoning about itself. (D. McCullough, 1995).

As a short conclusion, it seems that the discussion between AI vs. mind will last for a long time. But, considering the present situation, AI has a long way to the go in order to achieve the expected skills.

References

• Chalmers, D.J. (1995). “Minds, Machines, and Mathematics”. http://psyche.cs.monash.edu.au/v2/psyche-2-09-chalmers.html

• Godel, K. “On Formally Undecidable Propositions of Principia Mathematica”. http://www.ddc.net/ygg/etext/godel/

• Lucas, J.R. (1970). “Minds, Machines and Godel”. The Freedom of the Will, Oxford: Oxford University Press. http://users.ox.ac.uk/jrlucas/mmg.html

• Maudlin, T. (1995). “Between The Motion And The Act…”. http://psyche.cs.monash.edu.au/v2/psyche-2-02-maudlin.html

• McCarthy, J. (1995). “Awareness and Understanding in Computer Programs”. http://psyche.cs.monash.edu.au/v2/psyche-2-11-mccarthy.html

• McCullough, D. (1995). “Can Humans Escape Godel?”. http://psyche.cs.monash.edu.au/v2/psyche-2-04-mccullough.html

• Penrose, R. (1989). The Emperor’s New Mind. New York: Oxford University Press.

• Penrose, R. (1994). Shadows of the Mind. New York: Oxford University Press.

• Penrose, R. (1996). “Beyond the Doubting of a Shadow”. http://psyche.cs.monash.edu.au/v2/psyche-2-23-penrose.html

• Pysche (1995). An Interdisciplinary Search of Consciousness, Vol. 2, Symposium on Roger Penrose’s Shadows of the Mind. http://psyche.cs.monash.edu.au/psyche-index-v2.html