Blockchain Papers

Elliot Yates

No abstract is available for this record.

Sean T. Manion

What is the brain that it can understand science?What is science that it can understand the brain?These two basic questions (with homage to Warren McCulloch in the framing) have guided my career, aiming to understand the brain and an effort to understand science. This journey has taken me from academic lab work to clinical research oversight and government policy to the emerging health & science technology industry and back to academia. It has now led me to co-lead, along with Dr. Jennifer Lovejoy of the Institute of Systems Biology, this section of Frontiers in Systems Biology -Systems Concepts, Theory and Policy in Biology and Medicine. Our journal Chief Editor, Dr. Yoram Vodovotz, has laid out the overarching vision for this and the other sections (Vodovotz, 2021). This Grand Challenge is an effort to add another layer of detail to the portions of that broad scope contained in our Systems Concepts section (Lovejoy, 2024).Systems biology and systems medicine have roots going back to at least World War II, when biologists and physiologists were recruited into the war effort in the United States and Britain, trained in computational approaches, and joined with engineers and mathematicians to solve complex problems with communications, radar, anti-aircraft guns and more (Churchill, 1949). This alignment led to the foundation of the field of cybernetics and the related Macy Conferences in the U.S. postwar, while in Britain, "This coalescing of biological, engineering, and mathematics frameworks would continue to great effect a few years later as the Ratio Club," (Husbands, 2008). In the decades that followed, this robust milieu of ideas would foster the development of everything from general systems theory and information theory to artificial intelligence (AI) and cognitive science (Pickering, 2010). Despite this early alignment, it would be decades before systems biology and systems medicine arose as formal fields of inquiry (Green, 2017).Science has arguably been the most effective way of generating and validating new knowledge for the past few centuries. New technologies and computing approaches now provide us with novel tools to accelerate this process. While early work is being done to explore the use of these new tools for science, these have been limited in success to real-world application to detailed aspects of biology and medicine (McCoy, 2024). A comprehensive conceptual framework may be a more effective way to realize the value of technology in accelerating science. Modern science is not a simple holistic process, but an amalgam of processes and interests that have accumulated over centuries.By analyzing this system of science, we can better synthesize a new approach to using the array of emerging technologies now available. This will require us to revisit the current human and institutional processes that govern the creation of new scientific knowledge. A human and machine hybrid approach, aligned with a governance in the classic cybernetic style (i.e. control and communication in humans and machines), may allow us to optimize our scientific efforts and advance knowledge for the betterment of all of humanity.There has been much excitement about the potential of emerging technologies applied to science in recent yearsfrom AI to applications of blockchain technologies and web3 applied as decentralized science (DeSci) (Weidener, 2024). In these nascent efforts there has often been an oversimplification of science in order to capture technical requirements to automate or simulate biomedical research.Science is not done by a single person or organization. Science embodies the contribution of multiple individualswhose brains are themselves collections of dozens of subsystems (Kirby, 2024)processed through a series of refinement and testing. The results of these are moved through a longitudinal process of validation, contextual framing against prior accumulated knowledge, and consensus determination of evidence level and confidence in the results. Only then does this new knowledge contribute to the body of generalized knowledge we applied to the real world.Creating a new technology-accelerated knowledge system for biomedical science -what I'm calling here Scientia Machinamay be best approached through first articulating the conceptual and epistemological framework of the current system of biomedical science as it moves from data to information to evidence to knowledge and its application. Along the way it passes through layers of trust and is eventually captured in the artifacts of biomedical science we have come to rely on and expect. For applications of emerging technologysuch as the automated complex information processing of AI and the automated trust and governance of blockchainto be most beneficial to science, we should use them to systematically augment and accelerate these processes and creation of the artifacts of science while maintaining or improving the basic conceptual framework of biomedical knowledge discovery and implementation. Eventually parts of the current system may be sundowned leading to an even greater acceleration of science.This Scientia Machina framework starts with identifying key layers of trust in the biomedical bench to bedside process of evidence based medicine. Here I have proposed five layers of trust along with examples of their current artifacts and processes, plus potential approaches to augmenting these with technology and related adjustment to the current workflow (Figure 1).Data Layer -Data are collected in experimental and/or clinical context, often based on specific methodology. The principal investigator (PI) and team, along with the equipment and techniques used, are trusted to produce and capture explainable and reproducible data. This layer is only sometimes made transparent and rarely validated.Future of Data -Data are verifiable through trackable provenance and alignment with related metadata (e.g. demographics, treatment delivery details, device and equipment specifications, etc.). Data can be accessed for querying and algorithm training without moving, copying or exposing the data.Information Layer -Data are combined in datasets with contextual meta-data (e.g. demographics of research participants). The PI and team are trusted to compile, store and manage this data. It is increasingly becoming requested by funders and publishers to be made available. Some programs promote dataset sharing through centralized repositories or direct PI to PI contact.Future of Information -Data confidence fabrics allow sorting combined datasets based on confidence levels for each data point related to their associated metadata, with deployable programming to temporarily convert non-standard data into a calculable or trainable standard.Evidence Layer -Analysis of the datasets and testing hypotheses produces results that interpreted as findings. These are presented as novel assertion, backed by the data and methods, and put into the context of previously identified findings in the field in the form of a manuscript submitted for peerreview. The journal editors and peer-reviewers are trusted to confirm the assertions are supported by the evidence, fit (or convincingly contradict) previously established knowledge in the field.Future of Evidence -Swarm approach, i.e. networked, auditable crowd-sourcing, to peer review with a wider array of contributors with inputs weighted based on preset governance and continuous crowd feedback for nearer to real-time review with broader, multi-discipline input.Knowledge Layer -Combined sets of published articles are reviewed by a group of experts against certain criteria to answer specific questions about the state of evidence in the field as systematic reviews and meta-analyses to provide the most up-to-date knowledge in the specific area of focus.The groups of authors along with editors and peer-reviewers of those systematic reviews and metaanalyses are trusted to have executed and validated, respectively, a thorough and sound assessment of the evidence for the area in question to provide new knowledge.Future of Knowledge -Swarm approach (see above) to systematic review with network on demand request for new or updated reviews of existing evidence along with evidence threshold signals (i.e. sufficient new evidence in a particular areas prompts new or updated systematic review).Applied Knowledge Layer -Applications of knowledge can come in various forms, including pharmaceuticals, devices and procedures. The application of knowledge is periodically assessed for incorporation into clinical practice guidelines (CPG) and similar clinical guidance documents. The CPG group is trusted to have found and appropriately graded all of the available evidence and refined knowledge on a topic area to best inform clinicians how to address the area optimally.Future of Knowledge Application -Networked clinical practice guideline wiki (collaboratively edited living document) allowing for continuous, network refereed input and update of new knowledge.Each of these layers and their future states can be augmented, enhanced, accelerated and potentially replaced with appropriate applications of an array of automated processing and trust technologies. Additional administrative areas of biomedical research such as gap analysis, funding, regulatory review and more can be similarly improved.The call to action for this Grand Challenge is to: a) Consider the core elements of what we need to maintain and continue to elevate from our past and current successful biomedical research and knowledge translation effort, along with areas where those efforts have been flawed, corrupt or unsuccessful. b) Critique (and adjust or replace as needed) the Scientia Machina framework proposed here as the backbone for the layers of trust that are the core elements to be maintained as we continue to bring new technologies into biomedical research to accelerate and improve science. c) Capture and assess those current pilots to apply emerging technology -especially within AI (complex information processing) and DeSci (automated governance, auditing and/or incentivization) as umbrella categories for these effortsand place them in the context of a broader framework of what we are trying to achieve with biomedical research. d) Conceptualize gaps in our current efforts along with bridges from the current status quo to the desired future that may give us a better chance of success at transformational change to the systems of biomedical research and knowledge translation. e) Communicate all aspects of the above areas in appropriate venues of biology, medicine, technology and policy. This includes formal submissions of manuscript on any related topics to this journal section and its partnered sections as appropriate.This proposed conceptual framework is merely a jumping off point for broader consideration of how to maintain the core elements of the trust we have imbued in biomedical research as we continue to explore applications of emerging technology to improve its quality, manage its costs, and accelerate its contribution to the health and well-being of everyone. In the not so distant future, it is conceivable that we may be able to make all available relevant data on a topic or a patient accessible to any researcher to make AI-augmented and blockchain-audited hypothesis testing to provide near realtime, peer-validated contributions to evidence-based medicine. This could allow clinicians to query and access this near real-time evidence as part of compressing the 17 years it takes to go from bench to bedside by a factor of 10,000xgiving us new, actionable evidence-based precision medicine for patients in under a day. This future is within reach. Aligning behind a shared framework like Scientia Machina can bring it into our reality even faster. Better science. Cheaper research. Faster Miracles.STM is the sole author, having conceived, written, and edited this manuscript.

Sheri M. Markose

Despite prolific innovations and diversity in economic and biological systems, the theoretical impasse on novelty production has led to a longstanding reliance on randomness or statistical white noise error terms. Extant Decision Sciences and Game Theory, respectively, conflate rationality with an optimal choice from a prespecified action set and rule out Nash equilibria with strategic innovation or ‘surprises’. In contrast, the Wolfram-Chomsky schema implies that only digital software systems incorporating Gödel Incompleteness can produce novelty. Advances in gene science and neuroscience show how this relates to genomic intelligence which reaches its apogee in general-purpose highly protean human intelligence. Key developments with the Adaptive Immune System (AIS) and the Mirror Neuron System (MNS), latterly mostly in primate brains, involve distinctive Gödelian features for eukaryote intelligence of self-reference (Self-Ref) and offline virtual self-representation (Self-Rep) for complex self-other interaction with prodigious open-ended capacity for anticipative malware detection and novelty production within a unique self-referential blockchain distributed ledger. This initially developed in the AIS, which from the get-go accounts for somatic hypermutations for novel anti-body production and in humans as unbounded proteanism for novel extended phenotypes in the form of artifacts outside of ourselves. Thus, models of bounded rationality, extant Decision Sciences and Complexity Economics that overlook human proteanism for novelty production may have no basis in the evolution of human intelligence and complexity. Clearly, radical rethinking is needed to navigate the burgeoning digital world.

Abraham Nash

Decentralized Health Intelligence Network (DHIN) extends the Decentralized Intelligence Network (DIN) framework to address challenges in healthcare data sovereignty and AI utilization. Building upon DIN's core principles, DHIN introduces healthcare-specific components to tackle data fragmentation across providers and institutions, establishing a sovereign architecture for healthcare provision. It facilitates effective AI utilization by overcoming barriers to accessing diverse health data sources. This comprehensive framework leverages: 1) self-sovereign identity architecture coupled with a personal health record (PHR), extending DIN's personal data stores concept to ensure health data sovereignty; 2) a scalable federated learning (FL) protocol implemented on a public blockchain for decentralized AI training in healthcare, tailored for medical data; and 3) a scalable, trustless rewards mechanism adapted from DIN to incentivize participation in healthcare AI development. DHIN operates on a public blockchain with an immutable record, ensuring that no entity can control access to health data or determine financial benefits. It supports effective AI training while allowing patients to maintain control over their health data, benefit financially, and contribute to a decentralized ecosystem. Unique to DHIN, patients receive rewards in digital wallets as an incentive to opt into the FL protocol, with a long-term roadmap to fund decentralized insurance solutions. This approach introduces a novel, self-financed healthcare model that adapts to individual needs, complements existing systems, and redefines universal coverage, showcasing how DIN principles can transform healthcare data management and AI utilization while empowering patients.

Nicolò Vallarano, Claudio J. Tessone

ChainScience 2024, the second edition of the interdisciplinary conference, brought together academics, practitioners, and industry experts to explore novel developments in the realm of distributed ledger technologies. The conference aimed to bridge diverse fields such as informatics, business, economics, finance, regulation, law, mathematics, physics, and complexity science. The papers presented in these conference proceedings address emerging topics such as AI/ML applications to blockchain, DLTs interoperability, decentralized financial services, and tokenomics, alongside ethical, societal, and governance aspects of blockchain and DLTs. With a focus on promoting high-quality research and interdisciplinary collaboration, ChainScience24 aimed to unlock the collective potential of its diverse participants, embodying the ethos that the whole is greater than the sum of its parts.

Suzanne O'Halloran

No abstract is available for this record.

Alfred C. Chin

OPINION article Front. Blockchain, 01 December 2020 | https://doi.org/10.3389/fbloc.2020.606413

Authors unavailable

Early in 2020, a deadly new virus emerged and suddenly seemed to threaten the world with a pandemic. Like the plague of times gone by. What exactly were the risks? How possibly severe? What were the appropriate precautions? What were potential solutions? Reports in the media varied. Sometimes they even conflicted. With life and death possibly at stake, how would an average citizen know which claims were trustworthy? The COVID-19 crisis has dramatically underscored the need for functional scientific literacy.Now that the initial shock has passed, we are well positioned to reflect on recent history. What information seemed persuasive, but was misleading or not true? What apparently unlikely claims later turned out to be reliable? What can we learn from experience about how to assess any particular scientific claim?The customary wisdom – this month's Sacred Bovine – is that we should judge the arguments ourselves. With hucksters and ideologues everywhere, isn't it best to think for yourself? Namely, if we can equip someone to reason scientifically, do we not help them achieve intellectual independence? According to this view, argumentation is central to all science education (see Allchin & Zemplén, forthcoming).Yet a conundrum emerges in pursuing this strategy. To fully assess an argument, you need the evidence. However, “cherry-picked” data or biased samples can be misleading. To know whether you have enough relevant information, you have to be an expert already. To interpret a statistical analysis, you first need to know if the appropriate statistical model was used. That requires expertise as well. To assess experimental results, you need to know if the methods were sound – for example, if all the appropriate controls were included. And that, too, requires an expert's background knowledge. A “simple” assessment of an argument seems to involve an extraordinary level of expertise. But, of course, that very deficit is why the nonexpert seeks an answer in the first place. That's the conundrum: can you assess an argument on its own merits without also possessing all the expertise needed to make it?Indeed, the theme of expertise vs. argument reappears in the most prominent questions about the coronavirus. Consider a few examples – organized here in the form of an inquiry lesson, using history as data instead of students' own laboratory results (see → for questions to pose for student discussion).When the pandemic first threatened, shoppers soon emptied store shelves of hand sanitizer and face masks. No one needed much science to spur people's desire to protect themselves from possible harm. In that environment of fear, many websites (and a televangelist) offered products with the prospect of protections and cures. “VitalSilver,” a colloidal silver solution. Elderberry tincture. Boneset tea. Oregano oil. Antiviral essential oil aromatherapy. Frankincense. →Were any of them effective? How would you know?One did not have to wait long for an answer. None of these treatments was approved by the U.S. Food and Drug Administration (FDA), and the government quickly stepped in (Brewster, 2020). In Britain, the market was flooded with face masks with purported N95 protective status. They were labeled with known brand names, logos, and certifications. However, many were counterfeits (Daragahi, 2020). In India, Rwanda, Kenya, and elsewhere, people were caught selling fake hand sanitizer. All false claims. And all outright fraud. Easily exposed, perhaps. But these “simple” cases indicate that science con-artists and bogus claims are everywhere (Sacred Bovines, Nov., 2012; Oct., 2018). In assessing scientific claims, honesty matters as much as the content of the argument.From a perspective of scientific literacy, fraud is not so easily dismissed as one might imagine. It poses a critical epistemic problem. Namely: →How do you detect fraud?Fraud does not announce itself. It is not part of the argument. One needs to attend to the context, not just the content of the claim. Who is the speaker? Why are they making the claim? Is there a conflict of interest? Attention must shift from directly assessing what is claimed to analyzing who makes the claim, and why. That involves evidence, too, but of a very different kind. It turns out that the evidence for social context is just as important for other scientific claims as well.Another element of context revealed by cases of fraud is the psychological status of the recipient. →Why – or when – do we trust others? How does trust about reliable information differ specifically from other forms of trust – about moral guidance or personal loyalty?Many factors contribute to our sense of trust. We tend to believe those who speak with confidence and self-assurance (whether what they say is ultimately true or not). Emotions matter, too. Fear, or a desire to believe in a certain outcome, can distort the judgment of otherwise reasonable people. In addition, we tend to trust friends and allies. Or those who share our beliefs, our sense of identity, or a common enemy. We empathize with those who suffer innocently. Our wariness is quieted and our confidence lifted by assurances of credibility (even if it is a lie), by appearance, by familiar contexts, or by the appearance of a consensus. All these increase the susceptibility to fraud and science con-artists (Sacred Bovines, Nov., 2012; Oct., 2018).Science advocates often remark how science is founded on skepticism – that is, viewing claims by others with a measure of doubt. But perhaps, given the emotions just noted, we should equally focus the skeptical attitude on ourselves. Our own psychological vulnerabilities may strongly shape what “arguments” or “evidence” we accept as adequate. We should examine our own motives critically.Cases of fraud, even if infrequent, thus provide an important lesson. The social and psychological context of any scientific claim is critically important to consider, especially in a social setting.Early on in the history of COVID-19, it seemed that there might be no need to worry about a global pandemic at all. Many civic leaders characterized the virus as nothing more serious than the seasonal flu, and they assured the public that things were “totally under control.” Some called it a hoax. In mid-February, after dozens of cases had been detected in the U.S., the president told governors, “I think it's going to work out fine. I think when we get into April, in the warmer weather, that has a very negative effect on that, and that type of a virus.” In late February, he commented that of 15 cases, “within a couple of days is going to be down to close to zero.” In Brazil, even as late as early May (when most states had already been under lockdown for weeks) the president portrayed media claims about the threat as irresponsibly overstated. Typically, we expect government officials to monitor and heed scientific advice. Was it prudent to believe their claims, here? In retrospect, we can clearly see the answer. The messages minimizing the risks were tragically misguided. The pandemic did indeed become very serious. But could anyone really have known that in advance? →From a historically situated perspective, what would have been an appropriate basis for belief? Expertise or argument?In retrospect, we can see that trust in the experts was warranted. Assess the people, not the arguments or whatever evidence you are given. Experts, even with very simple data about transmission and travel, can build models to anticipate how a disease will likely spread. So, as early as the first week of January, 2020, the World Health Organization (WHO) voiced alarm, and by the end of the month it had formally declared a “public health emergency of international concern.” (That deceptively modest phrase was the technical label for a truly significant threat, such as the episodes of Ebola, SARS, and MERS in recent years, each with hundreds of deaths.) But many elected public officials seemed to disagree. →Whose view should one accept? Who is really qualified to know? What is expertise?Of course, as noted above, we may be strongly inclined to believe those who agree with us politically. But this does not qualify those individuals scientifically. Rather, the trust one needs is epistemic. One needs people who understand disease transmission and epidemiology. In this case, experts from the U.S. Centers for Disease Control and Prevention (CDC), along with other recognized authorities from around the world, concurred with WHO. Again, as the history now confirms, they were the trustworthy voices. The moral? Expertise matters (Oreskes, 2019).Consider, then, new model projections that were announced March 22 indicating that deaths in Italy were peaking and that coronavirus cases in the U.S. would be dwindling much sooner than most health experts had reported (Guzman, 2020). Promising news! →Were the claims credible? On what basis?In this case, they came from Michael Levitt, a Nobel laureate at Stanford University. His credentials certainly seemed to reflect expertise. Weeks later, however, deaths in Italy were still climbing, and cases in the U.S. showed no signs of abating, even as governors extended sheltering policies well into May. Unfortunately, perhaps, Levitt did not have the relevant expertise. His Nobel Prize was in chemistry. He was recognized for modeling the molecular structure of proteins and nucleic acids, not for modeling pandemics. Levitt's original comments to the Los Angeles Times revealed, perhaps, a telltale bias: “What we need is to control the panic.” In the grand scheme, he urged, “we're going to be fine.” Levitt's models were never formally published, nor endorsed by health experts. One needs not just any expertise, but the relevant expertise (Oreskes, 2019).What about cures? One rural doctor reported remarkable results using a mixture of hydroxychloroquine (or HCQ, an antimalarial drug), azithromycin (an antibiotic), and zinc sulfate (Roose & Rosenberg, 2020). It was an inspirational story of scientific discovery: a modest local physician, coping with a sudden barrage of COVID-19 cases, tries an obscurely reported cure and finds that it seems to work miraculously on all his patients. That was the narrative that attracted the attention of the U.S. president, who on March 20 touted it during a nationally televised news briefing. While acknowledging the drug's unproven status, he said, “I feel good about it. And we're going to see. You're going to see soon enough.” Anthony Fauci, one of the nation's top officials on infectious diseases (with decades of experience), then observed that the evidence for the cure was “anecdotal” at best, and cautioned against unwarranted hope or action. Several news commentators took Fauci to task for challenging the president's authority. Despite Fauci's remarks, the president continued to repeat his claims for the next few weeks, portraying HCQ as potentially “one of the biggest game changers in the history of medicine” (Crowley et al., 2020; Reuters, 2020). Meanwhile, other medical researchers echoed Fauci's skeptical posture. Again: →What should one believe? Miracle cure or tantalizing hype? Argument or expertise?One might imagine, as a purported ideal, that an ambitious citizen would investigate and assess all the evidence on her own. This assumes, of course, that such a person could interpret all the subtleties of clinical trials. But that level of medical expertise and background is beyond even most well-educated consumers. For example, if you found one study reported in a French journal, would you appreciate its limitations, based on a meager sample size of 20? Also, there was no control group, to ensure that any observed effect was due to the drug, rather than exhibiting the normal course of patients in the sample (Sacred Bovines, May, 2020). Soon, other doctors in Paris could not replicate the results. Indeed, concerns surfaced about how this paper was reviewed and whether it met customary standards for publication (Retraction Watch, 2020). What about the New York doctor's study? Well, he reported his results in a video addressed directly to the president, later posted on YouTube. There were no formal records documenting the course of treatment. And again, no controls. Without systematic evidence, can one justify bold claims? Still, several clinical studies were promptly begun to address the question. In early April, however, a study with 81 patients in Manaus, Brazil, was halted when fatal heart complications developed among many patients (Thomas & Sheikh, 2020). Although the FDA had approved HCQ to treat other conditions, it is not safe for use in the recommended doses for COVID-19. In the meanwhile, the career professionals at CDC had removed comments about the prospective use of the drug from its website, restating its earlier position that “there are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19” (Reuters, 2020). “Oversold false hope” seems to be the historical judgment on hydroxychloroquine.Of course, most ordinary people would not have access to the resources, nor devote the time, to analyze HCQ so thoroughly. Nor would such effort really be needed. As in the cases above, a claim's credibility is most directly and effectively established by expertise. Yet despite their professional status, we should not have trusted those two doctors. →Why not?Reliable medical knowledge is not established by one person or by a few, or by a few loose studies. Experts must also agree. The local doctor may have been an expert on practicing medicine. But he proved not to be an expert in medical research. The French doctor who led the now disputed study seems to have largely retreated from professional discourse since, to promote his cause on YouTube instead (Sayere, 2020). The consensus of experts is essential (Oreskes, 2019). In this case, the collective of informed, qualified experts never endorsed HCQ for COVID-19. As the history now bears out, that expert consensus was a sufficient basis for belief.Other contested claims about COVID-19 have entered public discourse. For example, the whether one should a face in Some it was a to the of the the time, a said, can do it. have to do it. I not to do it. a it's So, how important was For example, the of a from the but when would it be In 2020, that it would be “we're very historical in and given the when it be safe for to to to to and we expect a what would have been trustworthy at the to reliable from claims? 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Still, can a social It a sense of of or a social can the of qualified experts. The by can and for the scientific expertise. Namely, based on emotions of and trust can and claims can so on for other a pandemic – just when for expertise is most needed – false can and 2020; 2020). the virus in as the make a as Was the a (or a to or cure the All these false claims may have seemed from →What of would help you by these false What would you use to someone who found these in education has been intellectual for all. Namely, if can arguments and evidence then science But, such an attitude an of that to expertise. knowledge is to by. we the intellectual work among of expertise. we on whether or doctors or or or or That that the for intellectual may be may need to how to with intellectual and when to expert about recent history and possible in judgment truly are isn't this all just in the of Is it any more than the of need other significant cases science has been (and still and among For example, is as have to the a a or a fraud (Sacred Bovines, April, The science is as sound as that to the global coronavirus pandemic long it the of modeling seems the COVID-19 pandemic us it might be that we can no the of the experts. And this trust might with the science of if not will likely be even more than or The of recent COVID-19 history seems to be on the not own personal assessment of the evidence. course, that the potentially of who is a and who is an And how do you know Yet conundrum in 2020).

Shuaicheng Ma, Yang Cao, Li Xiong

BACKGROUND: Genomic data have been collected by different institutions and companies and need to be shared for broader use. In a cross-site genomic data sharing system, a secure and transparent access control audit module plays an essential role in ensuring the accountability. A centralized access log audit system is vulnerable to the single point of attack and also lack transparency since the log could be tampered by a malicious system administrator or internal adversaries. Several studies have proposed blockchain-based access audit to solve this problem but without considering the efficiency of the audit queries. The 2018 iDASH competition first track provides us with an opportunity to design efficient logging and querying system for cross-site genomic dataset access audit. We designed a blockchain-based log system which can provide a light-weight and widely compatible module for existing blockchain platforms. The submitted solution won the third place of the competition. In this paper, we report the technical details in our system. METHODS: We present two methods: baseline method and enhanced method. We started with the baseline method and then adjusted our implementation based on the competition evaluation criteria and characteristics of the log system. To overcome obstacles of indexing on the immutable Blockchain system, we designed a hierarchical timestamp structure which supports efficient range queries on the timestamp field. RESULTS: We implemented our methods in Python3, tested the scalability, and compared the performance using the test data supplied by competition organizer. We successfully boosted the log retrieval speed for complex AND queries that contain multiple predicates. For the range query, we boosted the speed for at least one order of magnitude. The storage usage is reduced by 25%. CONCLUSION: We demonstrate that Blockchain can be used to build a time and space efficient log and query genomic dataset audit trail. Therefore, it provides a promising solution for sharing genomic data with accountability requirement across multiple sites.

Douglas Heaven

Scientific publishing is increasingly adopting the technology underlying cryptocurrencies. Scientific publishing is increasingly adopting the technology underlying cryptocurrencies.

Shady Gomaa Abdulaziz, Norizan BintiMohd Yasin, Asmaa Hatem Rashid

Cancer is the major public health problem in developing countries. According to the international Agency for Research on Cancer (IARC). The purpose of the study Coordination of activities within the Arab world and collaborative in cancer research among cancer research Institutes and healthcare organization based on the health information systems.
 Also, many approaches have been proposed for the some Arab countries to establish the health information systems and starting the cancer registry project in order to provide a good treatment for patients and how to develop effective cancer control programs, enhance cooperation in medical research. However, most of these researches did not address gaps in the decentralized and autonomous in healthcare organizational units. This study cover the recent review the relevant PubMed literature and cancer incidence data from various sources in the Arab world, and describes the health information systems precisely cancer registry project status. The objective from the current research was describes the cancer development in Arab world, the level of adoption the health information systems and Barriers to adapt in Arab world. The analysis of the collected data shows there is cancer incidence in Arab countries is increasing. It has been found that there is a need to adapt the health information system cancer control and prevention, planning strategy among healthcare organizations and research institutes. Is an essential step in facilitating this process, because it can improve cancer registries, create robust infrastructure, improve skills of personnel and lead to effective cancer control and prevention.
 © 2018 JASET, International Scholars and Researchers Association
 Author Biographies
 
 Shady Gomaa Abdullaziz
 Department of Information Science , Faculty of Computer Science and IT, University of Malaya, Kuala Lampur, Malaysia
 Norizan Binti Mohd Yasin
 Department of Information Science, Faculty of Computer Science and IT, University of Malaya, Kuala Lampur, MalaysiaAsmaa Hatem Rashid
 Department of Information Science , Faculty of Computer Science and IT, University of Malaya, Kuala Lampur, Malaysia

Richard Kemeny

No abstract is available for this record.

L. N. Chavali, N. L. Prashanti, K. Sujatha, G. Rajasheker · 5 authors

The emergence of blockchain technology is regarded as 4th industrial revolution i.e., the integration of cyber-physical systems since the advent of Internet with far reaching applications in Banking, Insurance and Government, may impact other sectors especially life sciences. The fourth revolution is characterized by fusion of technologies that is blurring the lines between the physical, digital, and biological spheres. Therefore, it is vital to understand the opportunities and threats of adopting this technology in life science/pharmaceutical research. The proprietary databases of today's institutions interact with some form of interface either human or otherwise. Integrating blockchain with life science/pharmaceutical applications will decentralize the interface as well as the data exchange, resulting in high efficiency, greater speeds, low marginal cost, and infinite scalability.

V. S. Dhillon, David Metcalf, Max Hooper

Evidence-based clinical sciences are currently suffering from a paralyzing reproducibility crisis. From clinical psychiology to cancer biology, recent metaresearch indicates a rise in researchers failing to replicate studies published by their peers. This problem is not just limited to benchwork that happens in a lab; it also plagues translational research where the transformation from bench to bedside happens. Treatments, tests, and technologies are converted from simple lab experiments to government-approved devices and assays that affect hundreds of lives. Therefore, replicability is crucial to converting scientific breakthroughs into pragmatic remedies.

Norman G. Anderson

My 90 years have witnessed a basic transformation in the understanding of disease in terms of molecules, largely through the application of new instruments and technologies. The ultimate distillation of what really works at this level—the quantitative measurements that generate clinical insight from specimens like blood—is clinical chemistry. This field has fascinated me for a long time, partly because of my interest in inventing or improving analytical instruments, and partly as an anchor to real-world biology that is frequently missing in academic research. A second thread of interest to me is how successful research gets done, and how to know when a solitary inventor is needed and when it takes an army. Here I recount some personal experiences relevant to these interests, ranging across several fields and in organizations of widely varying scale, all ultimately linked to clinical chemistry and the human proteome. Interdisciplinary R&D has always fascinated me, and my introduction to it occurred in unusual times, during World War II. I was on active duty in the US Navy before Pearl Harbor as a Photographer’s Mate 2nd Class, and was discharged at the war’s end as a Lieutenant (jg) line officer, with zero instruction in between on how to be a naval officer. Despite (or because of) this fortuitous absence of formal tuition, I found that much of the fun and adventure in life lies in the cracks between disciplines, and that these cracks can be wider in large organizations (like a Navy in wartime) than smaller ones. Flying in blimps off the Carolina coast during the height of antisubmarine warfare, it occurred to me that maybe, lacking a bombsight, we couldn’t actually sink a German submarine if we found it. After developing proper instrumentation, I found experimentally this was largely true, and a proper bombsight was developed. This was the start of a series of projects that put together all sorts of technologies, raised interesting questions, and whose results were usually translated into immediate action. Transferred to the Pacific and the submarine service, I worked as a movie photographer on a project to be called “The Silent Service.” This was authorized by a personal letter from Franklin Delano Roosevelt, which proved to be a magical passport to getting things done far from home. As I was shooting background footage of 2 submarines I had arranged to do the required postrefit maneuvers, a radioman came topside to say that Truman had announced use of the atomic bomb. This ended the war and with it my introduction to interdisciplinary work with effectively unlimited resources. Suddenly I found myself at Duke University immersed in the culture of Little Science. I was taught (by a future president of the National Academy of Sciences) that proteins and nucleic acids were too complex to ever be sequenced, that chromatography, while interesting, could never be quantitative, and that no one knew for certain where and how genetic information was stored. The general attitude was very different from the “win at all costs” approach adopted in war—it was painstaking and slow, but it was biology. I began to realize I had been contaminated by the notion of Big Science, but felt I should learn to be comfortable at both ends of the Big Science–Little Science spectrum (1). This pendulum has swung back and forth for me several times, and is an invigorating oscillation. Returning to the Big end, I obtained an Atomic Energy Commission (AEC)1 postdoctoral fellowship in the Biology Division of the Oak Ridge National Laboratory (ORNL). My PhD thesis had concerned subcellular components isolated using very simple centrifuges, and my hope at Oak Ridge was to extend this work to proteins in different subcellular particles using some new type of centrifuges, yet to be conceived. ORNL’s unprecedented facilities, with staffs running into the tens of thousands, included almost all disciplines of science and engineering. Almost anything one could reasonably imagine was either available or could be designed and built quickly, even if it happened to involve nonstandard laboratory supplies like large titanium forgings. The saying, “Why use lead when gold will do?” reflects a little of the flavor. Separation, either physical (as in the case of uranium isotopes) or chemical (as was the case for plutonium), and accurate analysis were the key technologies at most of the Manhattan Project facilities. My initial laboratories were in the same valley that housed more than a thousand giant Calutrons (preparative mass spectrometers) used to enrich kilograms of U-235. After World War II, this facility was used to go straight through the atomic table, isolating and characterizing all the stable isotopes. I wondered if the same sort of effort and philosophy could be adapted to the comfortable field of biology? Could one ever separate the components of living cells into a “parts list” for man? If so, it should provide a powerful way to study and ultimately understand disease. As it happened, the major nuclear weapons laboratories needed new missions after the success of the Manhattan Project. I suggested one in the winter of 1959–60 entitled “The Cell Fractionation Project,” an effort to separate and characterize all the molecules in cells, which much later became the Molecular Anatomy Program. It appealed to nearly everyone at ORNL except my fellow biologists, who did not like big projects (unless it was mouse genetics). We had thought about sequencing DNA but were assured by biochemists that, while RNA could in theory be sequenced, DNA simply could not be for purely chemical reasons (this was before the discovery of restriction enzymes or dideoxy sequencing). So the thinking focused on proteins. Protein fractionation had been advancing on multiple fronts during the preceding decades. In the 1930s and ’40s, Svedberg had developed the analytical ultracentrifuge which showed, unexpectedly, that proteins had well-defined masses, and Tiselius, who once described to me how he had inadvertently left his gardening shoes on when he went to hand out Nobel Prizes, had developed electrophoresis by which plasma proteins could be classified into 4 discrete groups (albumin and the famous α, β, and γ globulins). By the mid-1950s, Sober and Peterson had begun to fractionate proteins on cellulose columns, and Waldo Cohn, who had pioneered separating fission products on ion-exchange columns at Oak Ridge, began to work on nucleic acids, convincing Moore and Stein to use ion exchange in place of starch columns for amino acid analysis. Precipitation was explored in parallel by Gerhard Schwick at the Behring Institute in Germany. He isolated dozens of human plasma proteins, made antibodies to them, and distributed these worldwide. This approach with distributable reagents allowed specific protein assays to be performed on clinical samples, thus starting immunodiagnostics on the present road to broad coverage of the human proteome. While largely forgotten in the field of proteomics, this effort has survived through multiple commercial marriages with Hoechst, then Dade Behring, and finally Siemens Diagnostics. My own work really began with the invention of the zonal centrifuge (2) to fractionate subcellular particles. In this device, the volume limitation inherent in swinging bucket gradient separations was surmounted by using large, hollow, bowl-shaped (zonal) rotors. In these, gradients and samples were caused to flow through rotating seals into a rotor spinning at low speed and then accelerated to maximum speed to effect a separation based on either sedimentation rate or isopycnic banding density (or, in later designs, both). This was followed by deceleration to a low speed and recovery of the gradient as isolated fractions by displacement from either the center or the edge. I had designed and built a slow and crude proof-of-principle zonal rotor and then had arranged to have one built commercially, which was unfortunately unstable at high speed. Instability of a large rotor at 40 000 rpm, especially if it leads to catastrophic self-disassembly (a phrase we adopted from Los Alamos, which knew about such things) is undesirable. We needed real engineering expertise in rotating systems, an unusual discipline but one that was by chance very popular at Oak Ridge. Gas centrifugation for uranium enrichment had been tried and abandoned in 1943 because of its high cost. Subsequently it was discovered that a captured German Luftwaffe engineer named Guernot Zippe had designed for the Russians a remarkably simple centrifuge that used very little power and was surprisingly efficient. The need to catch up with this development accounted for the presence of an engineering staff working at top speed (in all meanings of the phrase) in Oak Ridge. The resulting urgency, money, and minimal administration helped as usual to eliminate the curse of delayed gratification, chief destroyer of creativity. We built (and sometimes blew up) a lot of centrifuges, and they became progressively better at separating biological materials. In the early ’60s, Robert Huebner of the National Institute for Allergy and Infectious Diseases and others found that many animal cancers were caused by viruses, especially if the viruses were given to newborns. Numerous groups were set up across the US to attempt to isolate cancer viruses, grow them in culture, test them in primates, and see if a cancer vaccine was possible. When these efforts failed to find culturable human cancer viruses, I suggested to Huebner that we try to isolate them by physical means, using density gradient centrifugation, instead of relying on growth in culture. If this were successful, then similar physical methods could be used for large-scale purification of virus for a vaccine. The US Food and Drug Administration (FDA) was insistent that any killed virus vaccine should contain no (or at least very little) cancer cell DNA to be sure that the vaccine itself did not cause cancer. To make a pure virus vaccine for large-scale human use by physical means would require a liquid centrifuge of a size never before built. Testing these systems required large quantities of virus, and neither Sabin nor Salk, who were very cooperative, had poliovirus in the quantities we needed (milligrams rather than infectious doses). Initially we settled on seawater obtained from the Woods Hole laboratory and discovered to our surprise that the ocean has about the same viral load as a viremic human’s blood (3). For more realistic development, though, we obtained a batch of human viral vaccine that did not meet FDA standards and thus could not be sold. To avoid risk of viral contamination to ORNL’s enormous mouse genetics facility, we relocated the centrifuge development program to the most distant site available on the Oak Ridge reservation, which was, fortunately, right next to the giant Oak Ridge Gaseous Diffusion Plant, locus of the gas centrifuge project. Our “lab” was a mothballed power plant, whose Manhattan Project pedigree was visible on the wall as a framed single-page purchase order for “One coal-fired steam-driven electrical generating plant, 237 megawatt.” It had railroad tracks coming in one end of the 100-yard long main floor and a 30-ton overhead crane for moving large equipment, among other conveniences. We needed a general theory on which to base our search for viruses in tissue homogenates. To see the possibilities of such a separation, I plotted the sedimentation coefficient S against the banding density ρ for viruses and for the major subcellular particles and discovered that viruses generally are found in the middle of this plot in an otherwise thinly populated area away from nuclei, mitochondria, proteins, etc. (4). This plot was key to the whole project, and it suggested that we combine sequentially rate and banding techniques into one 2-dimensional (2D) S–ρ separation. This theoretical plot was converted into a real one in which bacteriophage were recovered from rat liver and other tissue homogenates (5), perhaps the first integrated high-resolution 2D separation in biology. As it became clear that no cancer viruses were being found around which to design a vaccine purification system, I decided that we should work on an existing vaccine that required better purification. We would thus be ready if a human cancer virus was actually found. At that time, egg-grown influenza vaccines contained appreciable amounts of egg proteins, resulting in many deaths from anaphylactic shock each year and the requirement that they be given under close medical supervision. We approached Eli Lilly about designing a centrifugal system specifically to purify influenza vaccine. Their batch size was 100 L, and the purification run had to be completed in an 8-h day. Knowing these parameters and both the sedimentation coefficient and banding density of influenza, it was possible to design a rotor system that used continuous flow to band the virus from 100-L batches in a narrow gradient that could be recovered at the end of a run. The result was the K-II continuous-sample-flow-with-banding ultracentrifuge (6). Use of this centrifuge essentially eliminated vaccination deaths from anaphylactic shock and allowed vaccination in supermarkets under minimal supervision. Almost 40 years later, it is still in use around the world with minimal modifications for vaccine manufacture, and we have recently proposed its use to isolate the viral load from 100-L batches of pooled diagnostic serum discarded in clinical reference laboratories each week (7). The viral DNA and RNA, concentrated and free of host nucleic acids, could then be shotgun-sequenced to screen for new viruses, while providing a running index of the known viruses “going around.” separations of cell components many To specific across these we used from clinical chemistry. I once to had most of the that be some other way to clinical chemistry. He that this was not possible. I thought about this a It was my introduction to clinical and clinical chemistry. The was to a system for between samples and reagents in parallel rather than It out that centrifugal is an way to and liquid while at the of a rotor spinning a provide measurements that very accurate The rate was in that we needed to a like the to it. these in the early was given ORNL’s but we that a a caused no The resulting was named the Energy centrifugal It was a commercial success for and and in many it did to the and it still be the system for very accurate The the rotor of an early centrifugal system At right is a of the system used to and measurements from at the of the spinning in during one of the Despite the success of zonal and the centrifugal the National that human cancer was to viral with interest in me to to the University of I was to be in by my who had completed a PhD at the University of under Nobel and done a with had famous on 2D electrophoresis and had the we set up a laboratory and a research to the most we ever had worked out a which was, of a In a system and the major plasma proteins by with the whole of Behring The of plasma proteins, called was many and Protein on were clear We found the 2D of plasma proteins to be and to an but it was more to a than a clinical 2D plasma and serum from the same The and the and on with This was in of plasma proteins, The plasma and genetic 2nd It became clear that to 2D we needed once the of a National and this with an to the biology at For several we worked in during the week and at on designing together what we called the system for and running large of 2D in parallel Our initial analysis system was an designed to 2 by between them, a used by to the This was by an and large for these we explored the protein called of human as as rat liver and many other We were to host the first 2 major on 2D first at and the second at the both as of (in and The results at these 2D are in some surprisingly similar to the of with the of protein using mass perhaps was before DNA we felt that of all the human proteins by cell fractionation and 2D electrophoresis was the way to in biology the effect that the had in chemistry. This was as the Protein at providing a for and ultimately what is systems biology. an effort would require large and so, with several we suggested of a Protein the general we had in Oak Ridge, to and this who was the of the US at the time, was in research with on and on in his in the on these a Protein was and in a was out the and size of a on the human and a new of much to and more to the the National Laboratory it that study of proteins was to a in the that time, the in biology to the Big Science approach of the National We left in and set up Biology to 2D and protein index and the the years a with in protein and finally a successful initial in the year an 2D electrophoresis system running 100 we explored in rat the of and to the of human a approach we had developed at Oak Ridge we the first columns that the plasma proteins used as the of the system columns with fractionation the of 2D from the most plasma proteins to more than The of mass for protein allowed finally to all the or we had in of and analysis quantitative of in specific protein this I to a in centrifugal systems, developing a large-scale centrifugal for and a centrifuge for viruses from clinical samples, banding them or them in to a plasma This has to and concentrated viruses from serum in about 2 in quantities that the of thus the way and sequencing of human viral it is a little for my own the broad of a approach to understanding the human and it for has recently begun to To start the of a human has the means to the proteins, and perhaps most that are really about of them rather than the 100 000 we were once to the and to on a protein of each this like a to at a or several large-scale are with an effort to the of all the proteins. because is really this is being done at Big Science like the at (in a large The resulting should provide a for understanding and thus the of cell and In and are of a project antibodies to each human and then to see where these proteins are in and success in this a broad for major clinical in will be the to be protein real with clinical to be into A new of mass for is that can ultimately in terms of and while and this it possible to specific assays for proteins starting from a and project to quantitative, and specific assays for all human proteins a of assays in the present protein and even into the clinical laboratory mass is for better of and It to me that this of project, up basic clinical research and clinical chemistry at the same time, is even more than the human and for a of Big Science thinking in the protein If all this to it will a in clinical it at the of biological and at the of clinical would be Atomic Energy Oak Ridge National US Food and Drug Energy Protein Biology initial and they have to the of this and have the to the and of or analysis and of or the for and of the of of any of of The organizations no in the design of of and of or or of I in to the of I have not and I my many and for at Oak Ridge, and through the especially of which extend through his