How does loneliness affect your brain?

Spreng et al. The default network of the human brain is associated with perceived social isolation. Nature Communications. December 2020.

A recent study found fundamental structural and functional differences in the brains of lonely people. Researchers examined the magnetic resonance imaging (MRI) data, genetics, and psychological self-assessments of over 40,000 middle-aged and older adults in the UK Biobank medical database. They then compared the MRI data of participants who reported often feeling lonely with those who did not. There were several major differences in the brains of lonely people which were primarily found in what is called the “default brain network,” a group of brain regions that are involved in inner thoughts such as remembering, future planning, imagining, and thinking about others. Detailed analyses of these regions showed that surprisingly, the default networks of lonely people were more strongly wired together and their grey matter volume in regions of the default network was greater. Moreover, bundles of nerve fibers called fornix that connects the hippocampus to the default network were better preserved in the brains of lonely people. These findings suggest that since lonely people are more likely to use imagination, memories of the past, or envisioning the future to overcome their social isolation they strengthen memory-based functions of their default networks through internally-directed thoughts and imagining social experiences. Loneliness has been increasingly turned into a major health problem, as other studies showed that older people who experience loneliness have a higher risk of cognitive decline and dementia. As COVID-19 related social distancing continues, isolation and loneliness could affect our society even more dramatically. Understanding how loneliness manifests itself in the brain at the structural and functional level, and how these paradoxical findings translate into late-onset brain pathologies, would be critical to prevent both neurological diseases and related social problems.

Human-made mass is about to exceed total global living biomass

Elhacham et al. Global human-made mass exceeds all living biomass. Nature. December 2020.

Humanity is rapidly approaching a new milestone in the history of our planet. The amount of manmade objects on Earth will soon outweigh all living biomass. A new study finds that each person alive today produces approximately the amount of manmade mass equivalent to their bodyweight every week. Our daily life objects such as roads, houses, cars, and clothes now weigh in at around 1.1 trillion metric tons, which is equal to the combined dry weight of all plants, animals and microorganisms on the planet. The production and accumulation of manmade objects, also known as anthropogenic mass, has accelerated since the early 1900s. The world’s plastics alone now weigh twice as much as the planet’s marine and terrestrial animals.

About 50% of the current anthropogenic mass is concrete. Bricks, asphalt, metals, plastic, and other materials make up about 19% of the total. Three major problems will arise from the outproduction of antropogenic mass. First, manufacturing consumes resources which will not be available for future generations unless objects are recycled or new raw materials are discovered. Second, even if we can achieve 100% recycling, pollution is generated and energy is used during manufacturing, so resources are still consumed. Third, many of the manufactured items will eventually be discarded which will cause serious disposal issues in the future. This is particularly alarming for the future. Nature is not infinite like so many of us would like to believe. If the current trend continues, anthropogenic mass will grow to three times the world’s biomass by 2040. In the next 20 years, we will generate as much waste as from the last 110 years together. These huge waste flows could lead to massive environmental catastrophes. This study demonstrates the brutal scale and impact of human activities on our planet. Humans are modifying the planet to such an extent that we might have already started a new geologic epoch likely called the Anthropocene.

Drug repurposing by artificial intelligence

Liu et al. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data, Nature Machine Intelligence. January 2021.

Researchers have developed a machine-learning method that analyzes very large datasets to discover which existing medications could work for diseases for which they were not prescribed. This process is called “drug repurposing,” a popular strategy to find new purposes for existing drugs that offers a rapid transition from research to clinical care. Drug repurposing can lower the risk associated with safety testing of new medications and dramatically reduce the time and money required to get a drug into the marketplace for clinical use. However, discovering new uses for existing medications still requires time-consuming and expensive randomized controlled trials to prove that a drug that is effective for one disorder will also be useful to treat another disorder. To overcome this challenge, a team designed a computational framework that works in two steps. First, it searches enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates for a given disease. Second, it calculates and estimates effects of those existing medications on a defined set of clinical outcomes. As a proof-of-principle, researchers decided to focus on repurposing of drugs to prevent heart failure and strokes in patients with coronary artery disease. The edge of the machine learning approach is that it can analyze and compare thousands of human differences within a large population that could influence how a drug will work in the body. These confounding factors such as age, gender, race, and disease severity function as parameters in the deep learning computer algorithm on which the framework is based. This information is streamed from “real-world evidence,” which consists of longitudinal observational data about millions of patients captured by various sorts of electronic medical records. The team used insurance data for more than 1.2 million heart-disease patients. The algorithm analyzed each patient’s drug prescriptions and diagnostic tests for every visit and models input for drugs based on their active ingredients. The model yielded a total of 9 drugs with potential therapeutic benefits, three of which are currently in use and six new candidates for drug repurposing. Interestingly, two diabetes medications, metformin and escitalopram, have been found to lower the risk of heart failure and stroke in the model patient population. This study shows how artificial intelligence can speed up hypothesis generation and clinical trial processes. While this study focused on heart failure and stroke, the framework is flexible and could be applied to most complex diseases.

Andersen KG et al. The proximal origin of SARS-CoV-2. Nature Medicine, March 2020.

Cases of Covid-19 first emerged in December 2019, when a mysterious illness was reported in in the city of Wuhan, China. The cause of the disease was soon confirmed as a new kind of coronavirus, and the infection has since caused a large-scale epidemic and spread to more than 70 other countries. Coronaviruses are a large family of viruses that are related to a broad spectrum of illnesses, the first of which was the 2003 Severe Acute Respiratory Syndrome (SARS) epidemic in China. A second outbreak of severe illnesses began in 2012 in Saudi Arabia with the Middle East Respiratory Syndrome (MERS). On December 31 of 2019, Chinese authorities alerted the World Health Organization of an outbreak of a novel strain of coronavirus named SARS-CoV-2 causing severe illness. As of February 20, 2020, nearly 167,500 Covid-19 cases have been reported, though many milder cases have likely gone undiagnosed. More than 6,600 people have already died as a result of contracting this virus – and the numbers will be much higher when you will be reading this article. Chinese scientists sequenced the genome of SARS-CoV-2 very shortly after the epidemic began and made the data available worldwide. The analyses of genomic sequence data have shown that Chinese authorities rapidly detected the epidemic and that the number of Covid-19 cases have been increasing because of human to human transmission after a single introduction into the human population.

Recently, a group of scientists used this sequencing data to explore the origins of SARS-CoV-2 and how it has become the version that it is now. The scientists specifically focused on the genetic codes for spike proteins, the mechanical framework on the outside of the virus that it uses to grab and penetrate the outer walls of human and animal cells. There are 2 major parts of the spike proteins: the receptor-binding domain (RBD), a molecular hook that grips onto host cells, and the cleavage site, a molecular can opener that allows the virus to crack open and enter host cells. The scientists found that the RBD portion of the SARS-CoV-2 spike proteins mutated to effectively target a molecular feature on the outside of human cells called ACE2, a receptor normally involved in regulating blood pressure. The SARS-CoV-2 spike protein was exceptionally effective at binding to human cells, and the scientists concluded this could only be a product after a natural selection process and not the product of human-designed genetic engineering. This evidence was further strengthened by data on SARS-CoV-2’s backbone molecular structure. If someone were to engineer a new coronavirus as a pathogen, they would have constructed it from the backbone of a virus known to cause illness. But the scientists found that the SARS-CoV-2 backbone differed substantially from those of already known coronaviruses and mostly resembled related viruses found in bats and pangolins. These two features of the virus, the mutations in the RBD portion of the spike protein and its distinct backbone, basically ruled out laboratory manipulation as a potential origin for SARS-CoV-2. Based on their genomic sequencing analysis, scientists came up with two possible scenarios as the most likely origins for SARS-CoV-2.

In the first scenario, the current pathogenic state of SARS-CoV-2 has emerged naturally in non-human hosts such as bats or pangolins and then jumped to humans. Coronaviruses are well known to undergo genetic recombination. In fact, this is exactly how previous coronavirus outbreaks have emerged, with humans contracting the virus after direct exposure to civets (SARS) and camels (MERS). The researchers proposed horseshoe bats as the most likely reservoir for SARS-CoV-2 as it is very similar to a bat coronavirus. There are no documented cases of direct bat-human transmission so far, suggesting that an intermediate host was likely involved between bats and humans.

In this particular scenario, both of the distinctive features of SARS-CoV-2’s spike protein and the cleavage site would have mutated to their current pathogenic state prior to entering humans. In this case, the current epidemic would probably have emerged rapidly as soon as humans were infected, as the virus would have already equipped with the features that make it pathogenic and able to spread between people.

In the second proposed scenario, a non-pathogenic version of the virus jumped from an animal host into humans and after a mutation process it has acquired its current pathogenic state within the human population. For instance, some coronaviruses from pangolins, armadillo-like mammals found in Asia and Africa, have a spike protein very similar to that of SARS-CoV-2. A coronavirus from a pangolin could possibly have been transmitted to a human, either directly or through an intermediary host such as civets or ferrets.

In this scenario, only the cleavage site could have mutated within a human host, possibly via limited undetected circulation in the human population for months or maybe years prior to the beginning of the epidemic. The researchers found that the SARS-CoV-2 cleavage sites have similarities that resemble strains of bird flu that can transmit easily between people. In the case of SARS-CoV-2, such a virulent cleavage site could have been formed in human cells and soon the current epidemic got initiated, as the coronavirus would possibly have become far more capable of spreading between people.

At this point, it is almost impossible to know for sure which of the scenarios is most likely. If the SARS-CoV-2 entered humans in its current pathogenic form from an animal source, it raises the probability of future outbreaks, as the illness-causing strain of the virus could still be circulating in those animal populations and might come back to humans again. It is still noteworthy that a non-pathogenic coronavirus entering the human population and then acquiring properties similar to SARS-CoV-2, the second scenario, is less likely than the first scenario.

In conclusion, this study brings an evidence-based view to the baseless rumors and conspiracy theories that the SARS-CoV-2 was deliberately manufactured in a lab and concludes that the virus has emerged after a natural process that took place in multiple hosts over time. These genetic findings are also consistent with how SARS-CoV2 is currently behaving. The virus has a low fatality rate (1% to 3.4%) and does not seem to act like a bioweapon compared to pathogens such as anthrax or Ebola. Given the previous coronavirus epidemics and the persistence of the culture of eating exotic mammals in China and other parts of the world, the current COVID19 epidemic is unfortunately not a big surprise for scientists and experts. We have to take necessary measures to be more prepared for such outbreaks that may take place in future.

Recent advances in science have led to longer lives, better health care, and healthier societies. Science develops through trial and error. However, irreproducibility still remains a problem, with issues related to bias, a high level of competition, a lack of appropriate statistical analysis, and preference to publish novel and positive findings instead of negative or additive results.

Irreproducibility issue

One of the pillars of scientific work is its reproducibility. Repeated proofs with a statistically significant number of experiments that show the finding is true under defined conditions further consolidate its acceptance by the scientific community. Positive findings often need reproducibility from different scientists and laboratories to be accepted as scientific fact. This does not mean, however, that they are completely discredited when they cannot be reproduced. Each bit of research is significant and worth praise by itself.

Scientists often do not find the means to repeat somebody else’s work given that large amounts of effort and money need to be spent. The practical way to test someone else’s findings usually happens through industries that could gain commercially from a certain finding. Unfortunately, this often comes with a price, even if research is found to be irreproducible. Bruce Booth, a venture capitalist, states that at least half of the findings in publications cannot be reproduced in industry settings. This might be an underestimation, given recent reports from different biotech companies.

According to various reports the reproducibility of published articles, in biomedical sciences for instance, is as low as 10-30 percent. Amgen, a biotech company with a team of around 100 scientists, tried to reproduce the results of 53 key cancer research articles in top journals, and found only 6 of them were reproducible (11% reproducibility). Bayer’s scientists found that only 14 of 67 projects related to oncology, cardiovascular medicine, and women’s health were reproducible (21% reproducibility).

Another example is the PsychFileDrawer project related to experimental physiology, which showed only 6 out of 21 articles were reproducible (28% reproducibility). In addition, one study to reproduce findings drawn from 18 articles regarding gene microarray analysis found in Nature Genetics largely failed. Similarly, when protein samples of identical proteins, as shown in article by Bell et al (2009), were sent to different laboratories, those labs failed to reproduce the initial findings. Dr. Asadullah, Vice President and Head of Target Discovery at Bayer, points to the increased trend of failures in Phase III trials, which also suggests a serious problem in the way research is vetted and published.

These numbers regarding the reproducibility raise an important question regarding the system of scientific research. If these alarming findings are true, it could mean that 70-90% of money spent on science does not bring about anything that is reproducible. It is intriguing that although the technologies used for scientific discoveries are now much improved, which would be expected to make date more precise and rigorous, the opposite is happening. This tends to suggest that the issue is not with the technology, but with the attitude and atmosphere of the larger scientific community.

Table 1: Major issues regarding today’s science

Publish or perish

A scientific publication is not only a tool for the transfer of knowledge between scientists, but also the basis for promotion and the awarding of grants. A rule in scientific communities regarding finding money to perform research is “Publish or Perish.” This refers to the cycle of publishing a high impact paper to get a grant, or losing one’s ability to compete with other labs.

The bar for success in the scientific community is set very, very high. This forces fledgling scientists to publish quickly and in high impact papers. Remarkably, in the major science journals, there has been a 10-fold increase in the retraction of articles in the last ten years, while there has only been a 1.4-fold increase in the number of journals published (Fang et al, 2012, Misconduct accounts for the majority of retracted scientific publications, PNAS).

Selective publication issue

Providing the best figures for publication, and selective interpretation of data, can also lead to incorrect results within scientific literature. A lack of blind experiments in basic science also plays a role in these mistakes. Some researchers may design their experiments, know what the control and experimental groups are, and may have biases in terms of wanting their hypothesis to be correct – thus they may interpret their data in favor of their idea.

Dr. Begley, head of global cancer research at Amgen, tells of a case where they tried to reproduce scientific findings in a paper but failed to reproduce the findings, even after 50 trials. Finally, they went to the author of the article and discussed how to solve this issue. Dr. Begley learned that the authors had tried the experiment only 6 times and it worked only once, but they put it in the paper since it made the best story. This might be seen as an extreme case, but similar selective publication of “good data” is not uncommon.

Scientists are under a great deal of pressure to publish the “best story” – both to advance their own careers and to fulfill their own competitive ambitions. This is increasingly becoming an important issue, and it stresses the need for verifying the results of others and publishing findings that negate previous publications.

Lack of incentive for verification

A majority of journals require authors to publish novel and positive findings and disregard negative findings and repetitive studies. There are a few journals that publish negative results, such as the Journal of Negative Results in Biomedicine, the Journal of Pharmaceutical Negative Results, and the Journal of Interesting Negative Results, etcetera. But publishing negative findings is still a low priority for most publications, which seek new and exciting discoveries to attract more attention. But a renewed focus on verifying existing science, and publishing negative results, will create more reliable scientific literature.

Another issue is the lack of incentives for verification. Since the easiest way to get funding is novelty and publishing in high impact journals in the current hypercompetitive environment, there is almost no room for verification, if any. Under these conditions, many scientists tend to write up their own observations and seek to get published with little concern for reproducibility. Why do they need to find out whether it is wrong or not? If it is wrong, this would make it tougher for them to get funding.

But is this proper scientific conduct? Given these issues within the community, how can we be certain their research and its findings are sound?

Given the potential amount of misinformation in scientific literature, we need a scientific renaissance. We could have a reward system, such as funding or recognition that improves reproducibility, robustness of data, and an increased publicity of data and protocols, as Dr. Ioannidis suggested. Other approaches to increase reliability could be decreasing the requirements for getting grants, and providing a reproducibility parameter in the citation index.

Toprak is a freelance writer, living in Texas, studying Genetics.

References

1. In cancer science, many “discoveries” don’t hold up. Retrieved from http://www.reuters.com/article/2012/03/28/us-science-cancer-idUSBRE82R12P20120328 on 4/28/13.
2. Reliability of ‘new drug target’ claims called into question. Retrieved from http://blogs.nature.com/news/2011/09/reliability_of_new_drug_target.html on 4/28/13.
3. Retrieved from http://www.psychfiledrawer.org/view_article_list.php on 4/28/13.
4. John Arrowsmith. Trial watch: Phase III and submission failures: 2007–2010. Nature Rev. Drug Discov. 10, 87; 2011.
5. Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124
6. Ioannidis et al. 2009. Repeatability of published microarray gene expression analyses. Nature Genet. 41, 149–155; 2009
7. Fang FC, Steen RG, Casadevall A (2012) Misconduct accounts for the majority of retracted scientific publications. Proc Natl Acad Sci U S A 109: 17028–17033. doi: 10.1073/pnas.1212247109
8. Bell et al. 2009. A HUPO test sample study reveals common problems in mass spectrometry–based proteomics. Nature Methods 6, 423–430; 2009
9. Bruce Booth. Academic bias & biotech failures. Retrieved from http://lifescivc.com/2011/03/academic-bias-biotech-failures/#0_undefined,0 on 4/28/13.

New developments in scientific thought and hypotheses testing are changing the ways we think about truth and certainty.

Not only do scientific decisions rely heavily on tools and procedures of quantitative analysis, but also on social life and daily decisions. I want to start with a story from the judiciary system about the misuse of an important quantitative tool of the scientific community, Hypothesis Testing. It’s about the overturned Amanda Knox case in Italy. In 2007, she was accused of killing her roommate and sent to jail. The odds of DNA matching in the case of accidental death were reported as incredibly rare by a forensic data analyst, who examined data with regard to incidents of crime, concluding that the accident is not statistically an accident. The judges then made their decision based on this rareness, a single value, named p-value. Later, this was overturned (New York Times, March 27, 2013), because of a misinterpretation by the judges and lawyers, of the rare probability. What factors forced the judiciary system to make this decision based on a single value alone? Honestly, this is the story of the century and a story of Hypothesis Testing. This article introduces the logic behind their decision, draws attention to the misuse of data, and mentions alternative logics to this traditional approach on the matter (in March 2013, Italy’s Supreme Court ordered a retrial and found the two suspects guilty on January 30, 2014).

What is Hypothesis Testing?

We live in a data-driven world. Statistical inference is the process of drawing conclusions or decisions from data. The Hypothesis Testing procedure in the Neyman-Pearson paradigm is one of the procedures of this process, widely used in the scientific community for over a century. In this mindset, two complementary hypotheses are first defined: one is the conventional thesis (the null hypothesis) accepted to be true by default; the other is the alternative thesis (the alternative hypothesis) that needs evidence from data to falsify the null hypothesis. It results in a single value, called p-value, which is calculated from the data using theoretical model assumptions. A p-value is a measure – in probability sense, ranging from 0% to 100% – of how much evidence you have against the null hypothesis. The smaller the p-value, the more evidence you have against the null hypothesis. One may combine the p-value with the significance level to make decisions on a given hypothesis. This is also called significance testing. It has an accept-reject mindset (dichotomous decision or binary logic); in such a case, if the p-value is less than some threshold (usually .05) then the null hypothesis is rejected. Basically, it is a black or white decision, without considering the contrasts between them. This interpretation has been widely accepted in the twentieth century of scientific research, and many scientific journals routinely publish papers using this interpretation for the results of hypothesis tests, even though there are current tendencies not to use this. Let’s take a look at the history of this black-white logic.

History

The history of the process of hypothesis testing starts with Fisher (1890-1962), around the early twentieth century. Later, the contributions of Pearson (1857-1936) and Neyman (1894-1981), who were early fathers of statistics, about the interpretation of the hypothesis tests were integrated into present-day applications. Fisher’s approach focuses on inductive inference about a single hypothesis, whereas the Neyman-Pearson approach informs future behavior based on a test using two complementary hypotheses (Newman 2007).

These approaches are strongly influenced by Popper’s logic of falsification. Falsification can be defined as the act of disproving a proposition, hypothesis, or theory. This logic asserts that sufficiently improbable events can be considered impossible. A p-value suggests whether a null hypothesis is sufficiently improbable to be considered practically falsified in the sense of a logical refutation (Newman 2007). When we come back to the Amanda Knox case, the decision the judges made and justified is followed from this falsification logic in hypothesis testing. Another criticism with this mindset is: how fair it is to rely on a single value that yields a rare result? Are we going to generalize one lucky drawing of raffle tickets to believe all tickets are winners?

Logics and proofs

We use logical flaws and biases in our daily life. Finding the logical flaw in scientific papers is something most readers aren’t interested in. Instead, the results are believed as a fact. One of the widely used flaws/biases is in seeking or interpreting evidence in ways that are partial to existing beliefs or a hypothesis in hand (Mercie and Sperber, 2011). We tend to prove or show the accessibility or superiority of arguments by bringing evidence. Failure to prove that a treatment – say, a low fat diet – is effective is not the same as proving it is ineffective.

Let me give another example to clarify this: The New York Times editorial news reported on February 9, 2006, “Millions of Americans have tried to reduce the fat in their diets, and the food industry has obligingly served up low-fat products. Yet now comes strong evidence that the war against all fats was mostly in vain.” Actually, in the hypothesis testing mindset, the evidence collected from research should either reject the null hypothesis which is “diet is ineffective,” or fail to reject it. The mindset in testing is not about finding evidence to support the null statement. It is also not about proving the alternative hypothesis, “diet is effective.” Rather, it is basically looking for evidences to falsify the null statement. In the report, the “evidence” the reporter meant should be against the null hypothesis, not against the alternative hypothesis. Data is collected to falsify the null hypothesis, not to prove its truthiness, because the null is already accepted to be true unless convincing evidence is collected against it.

Let me give another example of this paradigm. As Newfoundland (2013) described, a person is innocent until proven guilty by bringing convincing evidence. The jury can’t say “he is innocent,” instead, the jury uses evidence that produces reasonable doubt to reject his innocence.

Developments in inferential paradigms

The accept-reject paradigm in inference represents a conventional wisdom. There are other, or currently developing, paradigms in inference, too. One of the dominating paradigms is the Bayesian-likelihood approach. After enjoying much wider acceptance in social and natural sciences, the Bayesian method suggests different views of hypothesis testing. The Bayesian approach is a method of data analysis in which subjectivity, conditionality, or past information is used to update the hypothesis as additional evidence is acquired. This approach in hypothesis testing offers a dynamic structure in probability calculation so hypotheses, and decisions, are not seen as a static truth; instead, they are updated with current data and the decision is stated in the sense of likelihood.

In our court room example, the Bayesian inference is applied to all evidence presented, with the past information being combined with the current evidence. The benefit of a Bayesian approach is that it gives all historical information so the decision is unbiased all along as the past data (prior knowledge) is used correctly. This paradigm changes the way statistics are calculated and how the result and inference are interpreted. Currently it is widely appreciated in quantitative data analysis, especially after convenient software exists for its implementation. However, the criticism to this approach, made by many, is found in its subjectivity. Objectivity and handling prior knowledge is a concern here so that different people, having different opinions, may arrive at different results.

Another developing paradigm: Fuzzy Logic

Fuzzy logic is another method in quantitative decision making. In contrast to binary logic (yes-no, or accept-reject), fuzzy logic can be thought of as gray logic, which allows a way to express in-between data values. It emerged in the development of the theory of fuzzy sets, by Lotfi Zadeh (1965). Fuzzy logic is mostly seen as a branch of artificial intelligence that deals with reasoning algorithms used to emulate human thinking and decision making. It handles the concept of partial truth using linguistic variables, where the truth value may range between completely true and completely false. The concept of partial truth would be subjective and would depend on the observer. For example, for the temperature of the weather, we want to determine when to say cold, warm, and hot. The meaning of each of these concepts can be represented by a certain membership (called a fuzzy set). The concept of each would be subjective. One might consider cold for all values up to 40 0F. In Figure 1, the meanings of the expressions cold, warm, and hot are represented by functions mapping a temperature scale to “truth values” ranging from 0 to 1. A point on that scale has three truth degrees, one for each of the three functions (expressions). The vertical line in the figure represents a particular temperature that the truth values (see three arrows) are measured. Since the red arrow points to zero, this temperature may be interpreted as “not hot.” The orange arrow (pointing at 0.2) may describe it as “slightly warm,” and the blue arrow (pointing at 0.8) “fairly cold” (Fuzzy Logic, 2013, para. 6). A rule could then be adopted, like for example, if “slightly warm” then stop the fan.

Picture was obtained from

Fuzzy logic uses truth degrees as a mathematical model of the vagueness phenomenon, and it summarizes data analysis in facts with truth degrees, and leaves the decision to the observer. Its advantage is its ability to deal with vague situations with respect to linguistic variables. While the significance testing for a hypothesis declares one accept or reject, fuzzy logic allows for degrees of acceptance or rejection. However, in fuzzy logic, the notion of truth doesn’t fall by the wayside, but it is expressed in degrees and offers possibilities for different situations. Regarding inference, fuzzy logic uses the mindset ‘everything is a matter of degree and open to interpretation,’ and this mindset is also adopted to machine learning, which is considered a more suitable mindset with human reasoning instead of binary logic. The constraints in fuzzy logic are found in its tools and interpretations when complex inputs are considered.

The developments of paradigms in statistical inference have similar fates as in the developments of mathematics, geometry, and physics. According to the Euclidean parallel postulate, in space, there exist no two parallel lines that intersect each other. However, after Non-Euclidean geometry was developed in the nineteenth century, a wider geometrical and mathematical reasoning stemmed from it; accordingly, the geometry of the physical universe and particles came to be understood better. The development of Riemannian geometry, offering that distance properties might vary, resulted in the synthesis of diverse results concerning the geometry of higher dimensional surfaces and the behavior of geodesics on them. It also made Einstein’s theory of general relativity justifiable. Likewise, as time passes, we witness wider paradigms or logics that abandon or correct former approaches in scientific decision making tools. This is a good reason why teachers and professors should update their current teachings as to be consistent with convincing trends, as well as to train students to be ready for wider paradigms in the future.

Conclusion

In today’s scientific community, the way decisions are made and fallacies proved, are changing as new paradigms emerge. In order to make better decisions or to validate claims, many aspects and methodologies should be considered. One way to avoid mistakes as much as we can would be to expect fallacy points to exist in human mental processing during decision making, and improving and seeking better alternatives with well-established wisdoms.

(Thanks Ugur Sahin for reviewing the preliminary copy of this article.)

References

• Fuzzy Logic. In Wikipedia. Retrieved August 1, 2013, from http://en.wikipedia.org/wiki/Fuzzy_logic
• Kass, Robert E. 2011. Statistical Inference: The Big Picture 1. Statistical Science, 2011, Vol. 26, No. 1, 1-9, DOI: 10.1214/10-STS337, Institute of Mathematical Statistics.
• Mercier, Hugo, Dan Sperber. 2011. “Why do humans reason? Arguments for an argumentative theory.” Behavioral and Brain Sciences. 34, 57-111.
• Newfoundland, Jason. 2013. “The Cell Phone-Brain Cancer Controversy.” The Fountain Magazine, Jan-Feb, Issue 91.
• Newman, Michael C. 2008. “‘What exactly are you inferring?’ A closer look at hypothesis testing.” Environmental Toxicology and Chemistry, Vol. 27, No. 5, pp. 1013-1019, 2008.

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

The first is the preservation of characteristic, long term data imprints that describes the development of an organism in the stable DNA sequences.

Second is the storage of medium term epigenetically featured data that is carried a couple of generations further down the cellular level. Epigenetic information is not stored within nucleotide sequences but in the chemical modifications of these sequences (like the methylation of repeated strings of GC dinucleotide).

Third is the storage of data generated as a result of dynamic interactions between proteins, RNA and DNA in order to adapt to the events and changes during cellular life cycle in the form of nucleoprotein or DNA-protein complexes.

The data generation and storage capacity of DNA in three different hierarchical levels and time periods demonstrates that genome plays a plethora of roles in cellular activities and heredity. Formatting of genome for its generation and storage of data is carried out via DNA sequences of various features. Genomic system is composed of repeating DNA sequences. DNA sequences (satellite) function as a marker as they repeat numerous times in various frequencies. Genome includes genomic folders similar to that of computer systems. These genomic folders, also known as the epigenetic index of genomes, are responsible for the remodeling of chromatin and the coordinated control of genomic functions. Repeating DNA sequences play a critical role in replication of genome (making a copy of DNA), dispersal of copied DNA into daughter cells and construction of support systems that enable organization of chromatins.

It is possible to better understand genomic functions in relation to examples such as memory sticks and hard drives that are used in electronic information systems. The difference between a genome as a basic data-information storage medium from a hard disc is that it can be replicated as required by its nature and these replicas can be transferred to daughter cells. Following examples could be given to illustrate that a genome gains function only when it interacts with various data processing modules in the cell.

1. A copy of genome is produced by cellular DNA replication system
2. Correct localization of each genome copy towards daughter cells is only possible when chromosome segregation system works (the centrosomes and microtubules)
3. The central transcription system is responsible for the copy of data from DNA to RNA. Different gene expression patterns are developed via regulation of transcription time and level with the help of transcription factors and a web of cell signalization.

Very intricately organized genomic system structures are designed through the successive combination of protein encoding sequences, signals distributed in various places and repeating DNA sequences. Formatting of genome resembles formatting of computer programs. Various repeated serial commands of computer software are used to allocate addresses to files independent of the original data contained; different computer systems use different signals and structures to manage programs. In a similar fashion, diverse living species often utilize repeating DNA sequences and chromosomal structures to organize the encoded information and to format their genomes.

Diversity and variation of repeating DNA sequences are building blocks that are constructed into different genomic system structures. Genomes of different organisms bear characteristic system morphology just like computers with various operating systems and hardware. For instance, animal cells are created as a good model to take and incorporate foreign DNA into their genomes. Genetic data transfer among organisms of the same kind is referred to as “vertical gene transfer” whereas transfers between different species, genuses and classes are called “horizontal gene transfer.” Mobile DNA sequences like transposons are very effective horizontal gene transfer agents.

Cellular differentiation and morphogenesis (formation of tissue and organ from cell) is not programmed completely in the primary structure of the DNA sequence. Components of modular programs are encoded in a flexible way and a continuous renewed and recombined arrangement is enabled when needed.

The reason behind creation of different organisms from a single genome is this utilization of such genomic structure. Metamorphosis, that is the development of different organisms like invertebrates such as a caterpillar and a butterfly, is a good example of this feature.

Two organisms from the perspective of the same genomic protein and RNA codes can be considered as two different species. Different genomic structures and repetition of sequences among different organisms are distinctive criteria for the identification of species since these features can lead to mismatch of reproductive cells, different expression patterns of genetic code sequences, and may cause ecological diversity as well. That is why repeated DNA sequences are very important in studying parental relationships. Today, microsatellite DNA as repeated DNA sequences are used to configure biological relations among individuals in forensic sciences. Plant species vary in respect to the repeated sequences in centromeres in their chromosomes; these variations are used for identification of species. Main determinants of genomic system structure are diversity, frequency, and genomic localization of repeated DNA sequences. To explain this with examples, we could say that successively repeated sequences at centromeres, telomere repetitions and transcription, packing of chromatin, repeated sequences that are spread throughout genome in charge of cellular functions like nucleus localization are the main elements of the genome system structure. Genome is a single integrated system that is controlled closely and remotely via communication webs that use repeated sequences.

While explaining the Qur’anic concept of the Manifest Record (36:12) Bediuzzaman Said Nursi, the great renovator of Islamic thought in Turkey in the twentieth century, wrote that the Manifest Record expresses one aspect of Divine knowledge that is related “more to the past and future than to the present. It is a book of Divine Destiny that contains the origins, roots, and seeds of things, rather than their flourishing forms in their visible existence” (30th Word, Second Aim).

Inspired from this view, a seed can be considered as a tiny adorned form of Divinely creative command as programs and indexes and as a determinant for those programs and indexes in the organization of an entire tree. Since the Manifest Record book, as a title of Divine knowledge and command, observes the past and the future rather than the present, the genome of a grain or a seed acts like a library and an archive in which the future and past of an organism is written.

Sequences encoding different information in DNA

Different information types corresponding with various DNA sequences exist in the genome. These DNA sequences that were considered junk for a long time because they were not coding proteins, have in fact been found to be responsible for an amazing array of functions in genomic structure. Some of these sequences include:

1. Group determining sequences that enable coordinated or successive expression of genes,
2. Sequences acting as a marker in charge of initiation and termination during transcription of DNA to RNA ,
3. Signal sequences responsible for conversion of primary immature RNA, sequences into smaller functional RNA molecules,
4. Transcription control sequences that determine the expression frequency of genes,
5. Sequences that identify and mark the initiation regions for intensification and remodeling of chromatins,
6. Sequences that make binding regions which affect the relocation of genome in nucleus or nucleolus,
7. Sequences that target regions where covalent DNA modification (methylation) with functional groups like methyl takes place,
8. Sequences that control and identify the regions responsible for initiation of DNA replication,
9. Sequences that make the structures which enable completion of replication at terminal ends,
10. Sequences at the segregation points that enable equal distribution of copied DNA molecules into daughter cells and centromere sequences,
11. Sequences responsible for guidance during repair of DNA bound errors and damages,
12. Start point sequences used for repackaging of genomes,

Recurring sequences exist in the genomes of many organisms and shows great structural diversity. Recurring elements function as an initiator or terminator for heterochromatin regions. Furthermore they form an important scaffold and binding spots for folding of DNA structure. As if they carry out the job of an architectural mold in specific shaping of genome to be packed into a very limited area. The ratio of repeating sequences in genome (60-90%) is much more than sequences that are encoding proteins and RNA (10-40%). To explain it with an example, chromosomes in human genome are made up of packages of protein-DNA such as heterochromatin and euchromatin. Heterochromatin regions usually make up the regions with no transcription whereas euchromatin regions feature DNA transcription.

The ratio of protein encoding sequences to the entire human DNA is approximately 1.2%. Around 43% of euchromatin regions are composed of recurring and mobile DNA elements. 18% of heterochromatin region is also made of satellite (dense repeating sequences) and mobile DNA elements. Therefore almost 50% of human genomic DNA is composed of these repeating DNA sequences. In bacteria however, these only make up around 5-10% of the genome. These sequences were described as parasitic and junk individual DNA structures up until today and still continues to be described thus by many researchers and scientist. Nevertheless, even today, mobile DNA elements and repeating sequences are accepted as genomic parasites. Recent advances in the last ten years that have demonstrated this is not true, have instead revealed the vital importance of repeating sequences in genomic functions.

Repeating DNA sequences affect chromatin (dense pack of DNA and protein) structure in two ways. Irregular repeating DNA sequence copies contain binding regions for proteins that organize DNA. Heterochromatin (darker since it is densely packed chromatin) inhibits transcription and recombination, delays replication, and generally blocks the reading of information in DNA sequences that contain genetic coding. Heterochromatin regions are distributed throughout the chromosome. Because of this, presence of regions with coupled successive repeated sequences triggers heterochromatin formation.

In fruit flies, placement of protein encoding loci required for eye pigmentation near the heterochromatin blocks in centromeres (phenomenon of position effect) is provided via organization of chromosomes and thus, formation of phenotypic characters are inhibited. The “phenomenon of position effect” is convincing evidence that genome is a major system which is integrated with composition of partially repeating DNA sequences. When heterochromatin amount is increased in XYY male fruit flies, reorganized pigmentation of eye expression decreases. In XO males, when heterochromatin amount decreases, inhibition becomes severe. Changes in levels of protein which binds to special heterochromatin specific DNA regions generate opposite effects. Decrease in these proteins reduces or suppresses “phenomenon of position effect.” Surplus synthesis of these proteins also enriches this effect.

Repeating DNA sequences play an important role in the transfer of genome into daughter cells. For instance, they function in formation of the centromeres as chromosomal binding regions for microtubules, during gamete formation as linear terminals of chromosomes are replicated, and during chromosomal matching. Distribution of repeating sequences plays a major role in configuration of genomic functions. Each genome has genomic system structure that is shaped dependent on the amount of repeating DNA sequences to a major extent.

Going back to Nursi’s explanation of the Manifest Record, we can draw a parallelism between the book of the universe and the book of revelation, the first of which shows us that certain sequences in the genome are repeated for significance and necessity, just as many verses are repeated frequently in the Qur’an with nuances to refer to different meanings, benefits, and purposes, opening a wider space for many interpretations.

A genome is not only a book that contains protein and RNA codes, but also has a complex system structure with many functions for cellular vitality. The most needed sequences are those that are repeated more frequently. They are not pieces of junk DNA as predicted, they are jewels Divinely constructed.

References

• Shapiro J. A. 2001. “Genome Formatting for Computation and Function :Genome Organization and Reorganization in Evolution: Formatting for Computation and Function.” Presented at a symposium on “Contextualizing the Genome,” Ghent University, Belgium, November 25 – 28, 2001 (Ann. N.Y. Acad. Sci., in press)
• Shapiro, J.A. 2005. “A 21st century view of evolution: genome system architecture, repetitive DNA, and natural genetic engineering.” Gene 345, pp: 91–100.
• Shapiro J. A. and Sternberg R. V. 2005. “Why repetitive DNA is essential to genome function.” Biol. Rev., 80, pp. 1–24. Cambridge Philosophical Society.

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.

To date, many have spoken for Islam: experts, apologists, extremists, politicians, religious leaders, even fiction writers. In this cacophony of voices everyday Muslims have remained silent. Many have speculated on their silence, promoting causes and foreseeing the clash of civilizations and the end of world.

The silenced majority of Muslims have spoken for Who Speaks for Islam? written by John L. Esposito, professor of Islamic Studies at Georgetown University and Dalia Mogahed, a senior Analyst and Executive Director of the Gallup Center for Muslim Studies, provides a comprehensive analysis on a multiyear, mammoth, Gallup poll data – “the largest study of its kind” – which attempts to answer popular questions such as: Is Islam to blame for extremism? Why is there so much anti-Americanism in the Muslim world? Who are the extremists? What do Muslim women really want?

The Gallup Institution surveyed over 55,000 Muslims in over 35 Muslim populated countries. When we ponder on the role this data may play in the betterment of relations with 1.3 billion inhabitants of the world, we can’t help, but appreciate in depth the painstaking efforts shown in this project. Readers can also read about the challenges and method of the survey at the end of the book.

Most of the answers of the surveyed go against the popular perceptions and shed light on the level of misinformation and distortion we all are exposed to, reminding us one more time of Einstein’s advice that a man should look for what is and not for what he believes should be. Constructed in a comprehensive design and plain language, the data can easily be grasped by say, even the statistics illiterate. Every effort is made to ensure a wider readership. The book is organized in five chapters under five crucial questions:

Who are Muslims?

After exploring the five pillars of Islam (i.e. profession of faith, prayer, fasting during Ramadan, almsgiving, and pilgrimage to Mecca), the books touches upon sensitive issues such as the meaning of jihad, the role of family and culture in Islam, nostalgia for a glorious past, and politics.

Democracy or theocracy?

Many important questions are tackled in this chapter such as: Why is democracy absent in so much of the Muslim world? Is Islam the problem? How do Muslims view democracy? Should support for Sharia make the West panic? What do Muslims believe the chances are of improving relations with the West? Many Muslims view democracy as necessary for progress but skeptical about America and Europe’s intentions in promoting democracy in Muslim countries.

What makes a radical?

Extremists and terrorists were thought to be uneducated, poor, religious fanatics who reject modernization and technology, yet within weeks of 9/11, the media reported the “stunning discovery” that many of the attackers were, like Bin Laden and Dr. Ayman al- Zawahiri, well educated (some of them in western universities), middle to upper class, and from stable family backgrounds. Investigating diagnosis and misdiagnosis, this chapter explores the causes of extremism based on poll data.

What do women want?

When asked by the Gallup Poll of American households, “What do you admire least about Muslims or Islamic world?” American women answer “gender inequality” as among the top responses. Many have engaged in struggle to liberate the Muslim woman, yet her voice rarely makes its way in the discourse. Interesting answers are given by Muslim women to questions, like: How do Muslim women feel about Islam and the Sacred Law (Sharia)? Do they want to be liberated by the West? How do they view gender equality?

Clash or coexistence?

Confronting myths vs. realities, this chapter offers insights into what can be done to end global terrorism. The ability to move beyond presuppositions and stereotypes in our attitudes and policies and to form partnerships that transcend an “us” and “them” view of the world is critical to this cause. According to data findings, public diplomacy, defined as winning minds and hearts, is required. Finally, here is a trial size bite from the book without spoiling it. There is a prevalent assumption about Islam and democracy being incompatible. Yet that the majority of the respondents declared democracy as one of the things they admired most about West. Another impressive finding of this book was the answer Muslims gave to the open ended question: What can the West do to improve relations with Muslims? The answer was: Show respect. Respect comes with better understanding and reading Who Speaks for Islam? may be an important step toward it.

What do you think?

As is the case in many scientific arenas, the first studies in the field of algorithms were conducted by Muslim scientists. The concept of algorithm was used for the first time by the worldwide renowned Muslim scientist Al-Khwarizmi, who lived between 780–850 AD in Baghdad. Al Khwarizmi put his name to historic studies in the fields of mathematics, astronomy, and geography. His work entitled Hisāb al-jabr wa-l-muqābala (Calculation by Completion and Balancing) constitutes the first collection of algorithms. The Latin translation attracted great attention in Europe. The Europeans used the term algoritmi, rendered from “Khwarizmi,” to refer to rules for solving arithmetical problems by using Arabic numerals.

Having first developed as a branch of mathematics, algorithms have been described as a sequence of instructions that is used to solve a certain problem. These finite number of steps,begin at a certain point and come to an end with a result. But, today when we talk about algorithms, what comes to mind are the methods pursued in the operations of sequencing in computer programming; though algorithms are used in many fields, including physics, chemistry, biology, and music.

When different algorithms are used for solving the same problem, this enables us to reach the same solution through different means. There are, for instance, a great number of sorting algorithms (selection sorting, merge sorting, quick sorting) that have been developed for the purpose of incrementally sorting a given set of numbers. The same also applies to the search algorithms which find a given number in a set of numbers. What does this imply for us? Different people can attempt to solve the same problem with different algorithms or approaches that are better, truer or more “beautiful”. Thus, it is always important not to reject an idea without a prior examination or understanding, to welcome proposals of different opinions and to try to benefit from better alternatives or solutions. Thus, we can understand from an algorithmic approach that establishing dialogue and showing mutual respect are essential moral virtues in transforming differences into wealth.

Computer algorithms solve certain problems according to pre-defined parameters. Thus, we should never forget that computers cannot carry out a spontaneous operation, but can only carry out functions that have previously been programmed into them by human beings. Although some researchers, by relying too much on recent developments in computer sciences, and particularly in fields like artificial intelligence, attribute human characteristics to computers. They even estimate that they can produce human-like things in the near future, which seems rather unfeasible when we consider countless physical and spiritual features of humans. What does this imply for us? Pursuing a systematic method when solving problems in both computer sciences and daily life would considerably increase one’s success rate in solving any problems encountered. The most critical stages in problem solving are obviously a correct diagnosis and full description of the problem. The algorithms are developed and the most coherent and suitable are then chosen.

After an algorithm is designed in a flowchart, it is translated into software and tested against verifiable data prior to its usage in real operations. This test is necessary to prevent probable flaws in the software. What does this imply for us? By utilizing all the talents and especially the intelligence granted to us, human beings are always able to find what is better or truer. Research has been conducted in scientific studies by keeping in mind that there is always a next step in any development that has been achieved; the experience of the history of mankind demonstrates that a method that has been developed today might prove to be inadequate tomorrow.

When analyzing a number of different algorithms that are used to solve the same problem, the cost is assessed, with the least costly and most suitable one being selected. The cost of an algorithm is determined according to the number of operations carried out in solving any problem. An algorithm which solves the same problem with a greater number of steps has a lesser efficiency and a lesser performance.

Sorting algorithm by insertion

Sorting algorithms are one of the most frequently used algorithms in computers. As sorted data is more easily processed and used, data are usually sorted first with a sorting algorithm before it is processed within more costly operations.

In insertion sort (i.e., sorting by insertion), numbers to be sorted are inserted into a new “sorted” set. The new sorted set is empty at the beginning, and the numbers are inserted one by one into the right position of this set. During each insertion, the number at hand is compared to the numbers of the new set from the smallest to the largest. During a comparison, if the term (number) at hand is smaller than the term in the new sorted set, then the term is inserted right before the term in the sorted set. As each term is generally compared with all the terms which precede it, sorting of n numbers is done by about n2 comparisons. Thus, the cost of such a sorting algorithm is O(n2).

The quick-sort algorithm

This algorithm is used to find the shortest way between two points. Let us suppose that we have a limited number of points, some of which are inter-related. During this type of calculation, the shortest way is systematically found by checking all the combinations that have been able to be established among given points. The web sources that are provided for planning routes and mapping services with PNDs (Portable Navigation Devices) for travelers can be given as examples; here the minutest details of every highway and road have been recorded to the virtual media. In accordance with certain criteria, by taking into consideration all the probable routes between the two addresses provided by the user, the optimum driving route, including road and street names, is suggested.

Use of algorithm in medicine

Any research or inquiry used in the diagnosis and treatment/curing of a disease is defined as a medical algorithm. The logic of a decision tree is used in diagnosing, curing and following up diseases with these algorithms. Guiding algorithms, for example, “if symptoms A, B, C are observed in patient, then, the patient is probably suffering from the disease D, so, use the treatment E” are used in medical expert systems. The purpose of such algorithms is to standardize the medical services provided and thus minimize potential uncertainties and errors that likely to arise. Moreover, such algorithms are also used for educational and training purposes and as a guide to approaching the patients, diagnosing and curing diseases and towards solving their problems with greater ease. A great number of medical information that has been published has been transformed into numerous algorithms, ranging from those that use simple calculations to those that make highly complicated decisions. For instance, in the algorithm that was developed to measure the Body Mass Index (BMI), age, sex, height and weight are among the questions that are asked of the patient. The data obtained from the patient is later applied to a formula developed from the experiences of medical science, and the said index can thus be calculated. However, doctors should compare the results obtained from such algorithms with their existing knowledge, for even such simple algorithms as BMI become undependable in different cases, for example an athlete or an expectant mother. Algorithms only provide guidance for physicians, but the final decision should be taken by the physician after having individually assessed the status of each and every patient.

Algorithms and music

The algorithmic composition has been used in music for centuries to produce music with a methodological approach. Canon, for example, is a form of music produced with an algorithm in which a melody is replicated by one or more imitations of it played after a given duration.

Human brain, nature, and algorithms

Although the secrets of brain have not yet been fully explained, vital information has been compiled about its biological structure and functioning, thus proving that different sections of the brain fulfill different roles. Such sophisticated tasks as controlling the bodily organs, mental discernment, functions corresponding to the use of tongue and other sensory activities, the perception of colors, calculation of actions and the perception of physical conditions, are all carried out by use of brain mechanisms and faculties. All these tasks are accomplished by algorithmic processes which have not yet been fully explained. The issue of the unification of the data or images received by the eyes is, for instance, one of the matters that scientists do not yet fully understand. Although many have tried to imitate the human brain, such simple cerebral functions as walking and communicating have, to date, not been convincingly imitated.

What algorithms imply for us

As science and technology develop more and more, new inventions are being made, algorithms are being proposed for unsolved problems, while algorithms of already solved problems are being further developed. For example, many ciphers, which in the 1970s were believed to be unsolvable within an acceptable span of time, are being easily solved today thanks to the algorithms that have been developed and the ever-increasing operational power of computers. Consequently, developments in algorithms suggest to us that we should always search for that which is better and more effective; also we should remember that there are a number of different ways that lead to the very same target. Thus, we should always be open to the ideas of others and willing to consult with them before making a decision about any subject.

Ahmet Isik has a PhD in mathematics and is a freelance writer.

References

• Cormen, T. H., C. E. Leiserson, R. L. Rivest, Clifford Stein. Introduction to Algorithms, MIT Press, 24th Edition, 2000.
• http://www.nist.gov/dads/termsType.html#P

Chaos Theory

Over the last fifty years Chaos Theory has evolved as a new science which assumes that the natural order is irregular, discontinuous and erratic (Gleick, 1987). Newtonian physics implies that there is a rational order to everything, that we can predict the events of any system if we are able to plug in enough variables. The idea behind “chaos theory” is that we can predict what systems might do, but we cannot be sure. Everything exists as a series of possibilities.

Since the beginning of the twentieth century several other sciences have evolved that have sought to solve the problems in prior models. For example, the theory of relativity eliminates the illusion of absolute time and space. Quantum mechanics eliminates the Newtonian dream of controllable measurement processes, as well as the fantasy of deterministic predictability. Chaos theory, the third new science, embraces irregularity as a norm. Scientists from different fields have begun to observe the regular patterns within the irregularity of the natural world.

In the old views of nature, notes Gleick (1987), it was held that simple systems behave in simple ways while complex systems imply complex causes. In the new view, it is believed that simple systems give rise to complex behaviors and complex systems give rise to simple behaviors (Snyder, 1995).

The new science of chaos centers around two points. The first is the exploration of the hidden order that exists within the chaotic systems. The second is the study of how self-organization emerges from chaos (Hayles, 1990).

The three principal conditions for a chaotic system are: (1) that it operates in a non-linear way; (2) that it is iterative (the output of one cycle becomes the input of the next); and (3) that small variations in initial conditions lead to large differences in outcomes. Many systems within educational organizations appear to meet these conditions (Cunningham, 2000).

The concepts of chaos theory can explain the way schools work. For example, teachers do not exist as separate entities, but are affected by the relationships that exist within schools. It may also shed some light on how we can deal with and understand how things in our classrooms, schools, and entire communities are interrelated and all reflect in some manner upon each other.

There are several aspects of chaos theory such as nonlinearity, complexity, butterfly effect, fractals and feedback mechanisms that may have significance for educational settings.

Nonlinearity

In a linear system there is a simple cause and effect relationship; A causes B which causes C, and so on. However, a chaotic system is nonlinear. A may not necessarily cause B at all times. Lots of variables come into play and interact with each other. School systems look like nonlinear chaotic systems, too. In school district A, the purchase of new computers might have a positive impact on student achievement, while in school district B, this might bring little or no gain in student achievement.

It is widely believed that experienced teachers have better classroom control. If you have a veteran teacher in a classroom, you will have an orderly environment and the administrators, thinking in a linear way, might believe that the more veteran teachers in a building, the more orderly the environment will be. That might not be the case in every school district, especially in urban schools; there are instances where young and inexperienced teachers contribute positively to the school environment much more than veteran teachers.

Complexity

Chaotic systems take complex forms, making their precise measurement difficult if not impossible (Glickman, 2001). Different measurement instruments have been put in place to evaluate and compare the performance of a school. However, due to the complex nature of schools, none of these assessment methods seem to measure precisely the school performance and have very limited validity for the following reasons (Cunningham, 2000):

• The prior achievement of pupils is not taken into account and this is a major factor in pupil achievement at a later stage.

• Schools are differentially effective in different subjects and with pupils of different ability, which is not reflected in a single figure.

• Schools change over time; however, the achievement data used reflects only one group and is essentially historical data.

• Student mobility between schools is not reflected in the assessment.

• Social factors, sex of students, ethnic origin and social background are not taken into account. These factors are out of the school’s control.

Therefore, assessing school performance and comparing one to another have become increasingly difficult given the complex nature of today’s schools.

The Butterfly Effect

The butterfly effect means that a small and seemingly unrelated event in one part of a system can have enormous effects on the other parts of the system. Theoretical meteorologist Edward Lorenz made the term ‘butterfly effect’ famous when he argued that a butterfly stirring its wings in Bejing today could unleash powerful storms in New York city next month. One implication of sensitive dependence on initial conditions is the impossibility of predicting not only next year’s weather, but the long term future of any chaotic system (Glickman, 2001).

In terms of school improvement, what we understand from the butterfly effect is that it is impossible to predict the long-term effects of school improvement efforts. Planning in a chaotic system like a school should be medium range (one or two years) rather than long range (five to ten years). Formal planning in an unpredictable system needs to focus on process rather than product with the goal of producing “a stream of wise decisions designed to achieve the mission of the organization” (Patterson, Stewart and Purkey, 1986).

The butterfly effect ensures that no lesson will ever go completely as planned, or have the same effect on any two students. It indicates the need for teacher flexibility in teaching, as well as the need for individual attention to students, each of whom is experiencing a given lesson within his or her own personal context (Glickman, 2001).

Fractals

A fractal is a geometric shape that is similar to itself at different scales. Mid-sized branches of a tree are remarkably similar in shape to the larger branches from which they come. Smaller branches, in turn, are the same shape as the mid-sized branches from which they come, and so on.

Through work with fractal generations, it has become apparent to scientists that predictability does exist (known shapes re-appear), and randomness plays an important and unexpected role. What has been learned is that, within chaotic and seemingly unpredictable systems, structures of order exist through which the system recreates itself.

Complex social systems can also reveal self-similarity on different scales: at each level of the system, specific patterns of organization and culture reappear. Like fractals in nature, schools reveal self-similarity in different scales. For example, a school-wide staff development day, a department meeting, a classroom lesson, and a halfway interaction between a teacher and student might all reveal the same cultural characteristic. Thus, reflective inquiry at the school, team, classroom and individual level can help educators better understand their school culture, change needed, and pathways to improvement (Glickman, 2001).

By being reflective practitioners, teachers can develop their teaching skills, acquire more insightful experience in their fields and learn to look at problems from a different perspective. They also understand their weaknesses, areas of strengths and recognize the repeating patterns of their teaching styles.

Feedback Mechanisms

Chaotic systems contain feedback loops enabling outputs to feed back into the system as input. Feedback can bring stability or turbulence to a system. For example, a thermostat is a feedback mechanism that causes temperature stability. Conversely, when the sound from a loudspeaker feeds back through a microphone, it is rapidly magnified to create a disruptive shriek (Gleick, 1987). Feedback can also cause a system to move toward greater levels of complexity.

Feedback in schools can take the form of student performance data, survey results, quality circles, third party reviews, and so forth. The important thing is that meaningful data on the results of change efforts be made available to teachers, and that they be given opportunities to reflect on the data and redirect their change efforts accordingly.

With all the unpredictability present in classrooms, beneficial feedback is critical for both teachers and students. For teachers, student performance data, direct student feedback, and classroom observation data can all assist them to improve classroom instruction. For students, feedback on their cognitive and affective performance-from teachers, parents, and peers-is an essential part of the learning process. The fact that in chaotic systems like classrooms output becomes input means that the artificial distinctions we often draw between learning and assessment need to be removed: in reality, learning and assessment cannot be separated (Glickman, 2001).

Conclusion

The deterministic view of education that schools are simplistic, cause-effect systems which can be easily manipulated, quantized and controlled is not addressing the problems of today’s schools. From an alternative perspective, chaos theory gives us an understanding that the things we consider unimportant or trivial in our daily lives might have an equal weight in terms of affecting the results as the things we consider important. Just as it is characterized in the Qur’anic teaching that every minute thing or action is recorded in a Book regardless of its proportion. “Whatever your preoccupation (O Messenger), and whatever discourse from Him in this (Qur’an) you may be reciting, and whatever work you (O people) may be doing, We are certainly witness over you while you are engaged in it. Not an atom’s weight of whatever there is in the earth or in the heaven escapes your Lord, nor is there anything smaller than that, or greater, but it is (recorded) in a Manifest Book” (Yunus 10:61). It further suggests that everything we do has a significant impact on us, our communities and ultimately society as a whole.

In conclusion, chaos theory has the potential to offer deeper understanding of how today’s schools function in an ever changing world of our times.

Aydin Kara is a graduate student at University of Dayton. He studies educational leadership and administration. He can be reached at aydinkara33@hotmail.com.

References

• Cunningham, R. 2000. Chaos, Complexity and the study of Education Communities. Institute of Education.
• Gleick, J. 1987. Chaos: Making a new science. New York: Penguin Books.
• Glickman, Carl D. 2001. Supervision and Instructional Leadership: Allyn and Bacon
• Lortie, D. C. 1975. Schoolteacher. Chicago: University of Chicago Press.
• Patterson, J. L., Purkey, S. C., and Parker, J. V. 1986. Productive school systems for a nonrational world. Alexandria, VA: Association for Supervision and Curriculum Development.