Ageism in academic jobs in India in Nature India Blog

Ageism is a serious problem for Indian academia.
Several issues in policy are daunting the progress of Indian academia as mentioned :
1. "Many academic institutions have no guidelines on the role, involvement and career development of academic fellows."
2. "Many academics fail to understand the role and potential of fellows like me and often consider them just as an extended postdoc — not as a long-term prospect or potential collaborator. Hence, I did not get enough of an opportunity to teach and to mentor PhD students."
3. surely some vogus and callous advices are there without judging the nature of research: "for improving my faculty application and to enhance my chances of a secure job — this was to publish my current research: i) without foreign authors; and ii) as senior or first author in more prestigious journals such as Science, Nature or PNAS. Whilst the first is possible, the second is easier said than done."
 &&& "collecting and publishing groundbreaking ecology data in top journals can take years longer than other disciplines".
4. and the most serious problem "The journals I have been publishing in are not familiar to some of the members of recruitment panels I’ve met with. I have even been asked if ‘Ibis’ and ‘Parasites & Vectors’ were proper journals."

see Subhra Priyadarshini post in Nature India Blog indigenus, dated 20-July-2017

Blue or red pill: Jobs in the age of artificial intelligence - Need for Alternative Models To Built Talent is Vital

As artificial intelligence (AI) becomes more sophisticated, the threat that this automation will displace a wide number of jobs is very real. This obviously creates a distressing picture for many of us.

However, evidence shows that if technology really destroyed jobs, there would have been no work today for anyone. The technological revolution we have seen in the past 30 years has been unparalleled and exponential, and yet there are more jobs today and probably better salaries than before. Therefore, we need to shift the dialogue from the type of jobs that can be protected to a conversation about jobs that can and will be created.

As an example, the banking industry has undergone changes over the years, where some of the traditional tasks like passbook updating, cash deposit, verification of KYC details and salary uploads have been automated for operational efficiency. The focus is shifting from transactions to advisory and consultation. In the near term, AI is not going to replace 'judgment' aspects and therefore dealing with complex cases, which are high-value in any domain including banking, will be what is required. Employees will need to build skills on reading data, making sense out of the reams of analysis that will become available and be able to provide solutions that work.

Across industries, though some jobs will be automated in the next few years, jobs with higher skill levels will still be in demand. This would mean a focused approach towards reskilling the existing workforce and preparing for the future. A good education will be imperative to acquire skills that are competitive in the evolved labour market. This also means that the current system of education will need a rather extensive, and much needed, overhaul.

We will need to think about what this will mean for us as a society from the policy point of view. What can we do to ensure that opportunities for upward mobility are not hindered with the change in market dynamics? We will probably need to look at making it easier for entrepreneurs to start new firms and employ people in new forms of work.

As employers, we would need to think of alternative models to build talent. In his book Humans Need Not Apply, Jerry Kaplan also proposes a so-called "job mortgage" as a new type of financial instrument through which employers, vocational schools and colleges would have an incentive to collaborate in a new way. He suggests (among other things) that employers can commit to an intent to employ an individual in the future if that person commits to acquire a specific set of skills over a certain time frame.

In the new future, we would have to look at more such innovative thinking to manage the challenge. Governments and businesses will need to come up with a concerted approach on education, skills and employment and will have to work together. This itself can create a talent revolution that we need for an AI-driven future.

Companies are already using or testing AI and machine-learning systems and the emergence of entire categories of new, uniquely human jobs has been identified. These roles are not replacing old ones. They are novel, and require skills and training that have no precedents.


It is important to realise that humans are not really being left behind. As computers become more and more intelligent, humans will evolve in parallel, possibly with the help of embedded intelligent chips and BCIs (Brain-Computer Interfaces). The focus, therefore, should be to create systems that let humans combine what they are good at — asking the right questions and interpreting results — with what machines are good at: computation, analysis, and statistics using large datasets. We cannot peer into the uncertain future but we can certainly think about an exciting present which has a real potential of creating great value for the business.

What individuals can do is to be constantly in touch with the progress of technology in their field. This will help identify how our roles could possibly evolve and the next step is then to make sure we develop the skills required to step into the newly created role requirements.

Jobs in the AI age will clearly be violet pills!

Times of India Jul 12, 2017, 05:40 PM IST By Madhavi Lall  

 Do administrators in education sector is concern or competent to judge this?

Nature Index: over 50% of China’s high-quality research involves international co-authors

Nature Index: over 50% of China’s high-quality research involves international co-authors, 2017-May-26

The Nature Index 2017 China supplement, published today, reveals that as China’s total number of articles included in the Nature Index increased since 2012, the percentage of articles with international co-authors has continued to rise year-on-year. By 2016, papers with international co-authors comprised more than 50% of the country’s articles in the index.

In 2016, the Institute of High Energy Physics, Chinese Academy of Sciences (CAS) and the National Institute for Nuclear Physics in Italy formed the strongest bilateral collaboration between a Chinese institution and an international institution, followed by Peking University and Harvard University. Out of the top 10 international bilateral collaborations in China, the Germany-based Max Planck Society participated in five.
However, the depth of collaborations between institutions domestically remains far greater than internationally. There are 56 such partnerships between Chinese institutions that are stronger than the leading global pairing.
In terms of China’s inter-city collaborations, as well as institutions in Beijing having the highest contribution to the index overall, last year they formed the largest number of partnerships across China. Beijing was followed by Shanghai and Nanjing in this respect. But beyond these metropolises, institutions in smaller cities have expertise that makes them strong collaborators in their own right.
For instance, Kunming, well-known for its unique biodiversity, is a boon for scientists interested in studying plants and ecology. Whereas Changchun in China’s northeast has a long history specialising in chemistry research. Last year institutions in Kunming formed 190 domestic research partnerships with other Chinese institutions to co-author papers included in the index, ranking 16th of the 184 Chinese cities examined by the index. More than two-thirds of Kunming’s output on the Nature Index is the field of chemistry, with many articles investigating the chemical structures and processes of plants. Changchun’s strongest partnership last year was between Changchun Institute of Applied Chemistry (CIAC) and University of Chinese Academy of Sciences (UCAS) in Beijing, both part of the Chinese Academy of Sciences.
For the first time, the Nature Index China supplement took a look at China’s performance beyond the 68 journals tracked by the index and expanded into the larger Web of Science database, from Clarivate Analytics. “This helps our readers and index users gain a broad picture of China’s research output overall and in some specific fields. By making comparisons, they can find some interesting facts to better understand the trend,” said David Swinbanks, Founder of the Nature Index.
As indicated by the Nature Index, international papers make up a significant portion of China’s high-quality research, but this level of international co-authorship does not extend to all papers published in China. The country’s share of articles with international co-authors indexed in the Web of Science remains below 25%. “The slower growth rate in papers from international collaborations was because China’s overall research output had grown so dramatically,” said Dr. Yue Weiping, Chief Scientist of China for Clarivate Analytics. “This slower rate may be attributed to language barriers and allocation of institutional resources. Researchers in top universities have more opportunities or resources to collaborate with peers worldwide.” Yet there are signs that this overall rate of international collaboration will rise over the next decade, due to government policies – such as the national World-Class 2.0 project – aimed at making Chinese research more global, including generous funding schemes to promote collaboration.

 

Movement (conference) - Oxford University

Dear colleagues,
I am honored to  invite you to attend the world conference on Movement sponsored, in part, by the Harvard University School of Medicine’s Spaulding Rehabilitation Hospital, the M.I.N.D. Institute at M.I.T., the Hebrew University of Jerusalem, the Wingate Institute for Sports and Exercise Science, the National Institute for Brain and Rehabilitation Sciences, Nazareth, Israel, the Institute for Neurology and Neurosurgery, Havana, the University of the Medical Sciences Facultad ‘Manuel Fajardo’ Havana, the School of Public Health of the University of Havana, and Bielefeld University in Germany.
The purpose of the conference is to share knowledge of all those whose interests lie in the nature of human movement. The conference will address issues related to gait, motion, kinesiology, disorders of movement, movement rehabilitation, motion and balance, movement and cognition, human factors and ergonomics, as well as optimized movement in elite athletes, developmental issues of movement and coordination. Workshops on physiotherapy of movement impairment will also be provided.
You are hereby invited to join us  and participate in this unique conference.
The abstracts of the conference as well as selected principal papers will be proceedings and will be published in the journal Functional Neurology, Rehabilitation, and Ergonomics published by Nova Scientific publishers.
We welcome your participation in this event that addresses the relationship between movement and cognition and I personally welcome your enquiries and suggestions. In the meantime, please check out our website at: www.movementis.net  (Our mail: office@movementis.com)
Should you have any questions about the nature and form of the abstracts or pertaining to the larger papers, please connect with me at: g.leisman@alumni.manchester.ac.uk
I hope to meet each of you at Oxford University in July 2017
Wish very best wishes,
Gerry Leisman
Chair Scientific Committee, MOVEMENT 2017
Director, The National Institute for Brain and Rehabilitation Sciences, Nazareth, Israel
Professor, Human Factors and Rehabilitation Sciences
O.R.T.-Braude College of Engineering, Karmiel, Israel
Profesor de Neurología restaurativa
Universidad de Ciencias Médicas de la Habana
Facultad Manuel Fajardo, Havana, Cuba

Some opinion of Rolf M. Zinkernagel for those who want to venture into newer fields of biomedical branch

Some notable facts are very important as reflected in the comments by Rolf. M. Zinkernagel

Here some opinion of him is important for those who want to venture into new fields of biomedical branch.

We are given to understand that you had difficulty in getting a postdoctoral position despite applying to as many as 50 institutions. A disappointment of this magnitude could possibly annihilate even the most spirited aspirants.
That's just normal! You must realize that nobody is waiting for you. Why do we go to an institution? We do that to learn, to gain more experience and knowledge. Sure it was busy with all of the rejections because I had to keep applying without a stable position, but it was normal. Just set your goals; keep a
straight mind; and address the principal question that you are trying to answer.

How about disagreements from the scientific community about your work?
Disagreement is normal. It takes some time for hypotheses and even results to be accepted. However, getting published in journals, such as Nature, does provide reassurance. There is an entire article on comments counter arguing the opinions that I had published in an issue of the Scandinavian Journal of Immunology. However, I am never worried about the hypotheses that I publish because many of these are testable.

Do you believe that being in big institutions ( such as the Ivy League institutes) enhances the prospects of one winning the Nobel Prize as against, say, being the head of a smaller laboratory early in your life?
There is no such standard rule, provided that you have the facilities needed to carry out your research. At the end of the day, you simply need to be lucky. You can get lucky in a small institution or a big one. When Peter and I were working on solving the problems of MHC-restricted immune T-cell recognition, the Eureka moment came in Canberra, Australia. It was so unexpected, so serendipitous, that the most important thing we did was not to miss it! Had we not discovered it, somebody else would have surely done so sooner or later. Discoveries can occur anywhere.

===> Note: It was the 3rd volume of the Journal [Characteristics of the interaction in vitro between cytotoxic thymus-derived lymphocytes and target monolayers infected with lymphocytic choriomeningitis virus, Scan J Immunol, 1974, 3:287-94] and in the following year another publication in the 1st volume of Lancet, [A biological role for the major histocompatibility antigens, Lancet 1975, 1:1406-9]. Possibly at that that time, corporate culture had not pervade into the academy of science. In present scenario the journal could not have indexing status and as par UGC journal criteria that pioneering scientist would not able to pursue his further research and hence, such fundamental discoveries would not be possible in India due to existing academic policy of Indian science. Let's see what are big difference with respect to the development of bio-medical scenario in Indian context.
 
Does medical education equip one better to deal with the challenges of research?
You need both. Laboratory work teaches you to be more analytic in your approach. By studying medicine, you realize the importance of quality control, and can better apply it. If I were to advise a student, it would be better to get a basic medical background and then acquire molecular skills, for that's much easier. Also when you start off with medicine, the road ahead is wide open: There is a variety of paths that you can pursue.
===> Note: In Indian context diseases are addressed by scientists who do not have any exposure to clinical scenario. Ironically, they holds different policy making bodies. Let's see how it affect the paradigm change.

What advice would you give to students who are at the start of their scientific careers?
You have to make a choice to take big risks, or to safeguard yourself by earning good money. However, you can only make a significant discovery by taking risks, because you will have to go somewhere where no one has gone before. Everybody will give you advice, but I personally believe that once you reach the
age of 16, you don't change. You just pick the suggestions that fit your character. You learn from experience. Pick out the advice that has worked for you early on, and leave out what hasn't.
===> Note: Due to bad policy and poor economic structure Indian students avoid minimal level of risks as risks may impose them to get hand-to-mouth, good money is far away. In most of the academic jobs, there is very stringent bar and reservation system

 

that actually do not nurture for the development of a mind-set to learn from failures.

Ref. Science and Sensibility: An Interview with Rolf M. Zinkernagel published in MedGenMed 2007, 9:24
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Durjoy Majumder, Ph.D.
Secretary, SSBTR

Fake Journal

Recently fake journals are polluting the advancement of science. Here some  interesting articles like
"Fake Journals: Their Features and Some Viable Ways to Distinguishing Them" published in Sci Eng Ethics (2015) 21:821–824, DOI 10.1007/s11948-014-9595-z
and "New year's resolution: Say no to fake journals and conferences" published in Obesity (2016) 25:11-12 DOI: 10.1002/oby.21738

have been found where several procedures are suggested to identify fake journals. Some of them are as follows:
1. "work in a multidisciplinary way" and no specific objective. 
2. charges for publication
3. publish fake science
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Durjoy Majumder, Ph.D.
Secretary, SSBTR

Ageism "as bad as racism"

Though different persons have different physical and mental and cognitive ability irrespective of biological age. Moreover different works need different maturation time frame to grow specially in scientific research. However in Indian context there is a blunt age bar that actually deny suitable person to get into the academic and scientific research. As a result nation may suffer. Interestingly, the issue is now being discussed in Nature Jobs Blog by Jack Leeming dated 27 Oct 2016.
SSBTR thinks the age bar issue has detrimental effect towards the propagation of multi-/interdisciplinary research. For details please visit Ageism "as bad as racism".
Durjoy Majumder, Ph.D.
Secretary, SSBTR

Natural Language Processing Use in Radiology: A Systematic Review

Following document is important with respect to Electronic Health Record -

"Natural Language Processing Use in Radiology: A Systematic Review" by Dorothy A. Sippo, MD, MPH, CIIP, Johns Hopkins University School of Medicine; Daniel Rubin, MD, MS; Paul G. Nagy, PhD, FSIIM; Brandyn Lau, MPH presented in the Annual Meeting of Society for Imaging Informatics in Medicine, SiiM2015

Hypothesis: The performance of natural language processing used in radiology has improved over time. The types of applications of this technology in radiology have expanded over time.

Introduction: The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 has spurred greater adoption of electronic health records (EHRs), with their use nearly doubling from 2010-2011 (1). With more clinical data available in an electronic format, the goal is to be able to leverage this data to assess the quality of care and guide future decision making. A significant proportion of EHR data is stored in a narrative, unstructured format. Natural language processing (NLP) employs a computer to extract meaningful information from human language. In the setting of the EHR, it is typically used to extract information from unstructured report text. A 2008 review of research on the extraction of information from text documents in EHRs revealed that performance of these systems has improved since a prior systematic review in 1995 (2).
Radiology is a medical subspecialty with a rich collection of text documents reporting the results of imaging examinations. Frequently, these documents have been stored in an electronic format over significant periods of time. They represent an archive that is ripe for information extraction to identify imaging findings and disease diagnoses. There are numerous descriptions in the literature to NLP being applied within radiology (3). A systematic review of the literature to investigate the use of NLP in radiology would be helpful to summarize the progress in the field and to identify gaps. The goal of this work is to evaluate the performance of NLP over time in radiology. We will identify the types of information being extracted from radiology reports and the clinical applications of this informatics tool. We will also address the computer science methods being used for NLP in radiology. From our review, we will identify gaps in functionality and opportunities for future work.

Methods: We will use a systematic approach to searching the literature to minimize the risk of bias in selecting articles for inclusion in this review. Searching the literature will involve identifying reference sources, formulating a search strategy for each source, and executing and documenting each search. For our searches of electronic databases, we will identify relevant medical subject heading terms. 

Sources Our comprehensive search will include electronic searching of peer-reviewed literature databases and grey-literature databases as well as hand-searching. We will run searches of the MEDLINE®, EMBASE®, Cochrane Library, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, INSPEC, and Compendex databases through September 15, 2014. We will design search strategies specific to each database to enable the team to focus the available resources on articles that are most likely to be relevant to the key questions about the performance and application of NLP over time within radiology. We will develop a core strategy for MEDLINE®, accessed via PubMed, on the basis of an analysis of the relevant medical subject heading terms and text words of key articles identified a priori. The PubMed strategy will form the basis for the strategies developed for the other electronic databases.

Management of Literature Search With the assistance of the Johns Hopkins University Welch Medical Library, all references will be downloaded into ProCite® version 5.0.3 (ISI ResearchSoft, Carlsbad, CA) and de-duplicated prior to initiating the review. We will use this database to store full articles in portable document format (PDF) and to track the search results at the title review, abstract review, article inclusion/exclusion levels.
Title Review Two team members will independently reviewed all titles. For a title to be eliminated at this level, both reviewers must indicate that it is ineligible. If the first reviewer marks a title as eligible, it will be promoted to the next level, or if the two reviewers do not agree on the eligibility of an article, it will automatically promoted to the next level.
Abstract Review We will exclude an abstract at this level if the abstract does not apply to one of the key questions or for any of the following reasons: does not address NLP used in radiology, has no original data (e.g., letter to the editor, comment, systematic review), or is not in English. Abstracts will be promoted to the article review level if two reviewers agreed that the abstract could be applicable. Differences of opinion will be resolved by discussion between the two reviewers.
Article Review Full articles that were selected for review during the abstract review phase will undergo independent review by two members of the study team to determine whether they should be included in the full data abstraction. If both reviewers determine the articles have applicable information, the articles will be included in the data abstraction.
Data Abstraction We will sequentially review each article to abstract data from the final list of articles. For all articles, reviewers will extract information on general study characteristics, including: study design, location, clinical topic of interest, inclusion and exclusion criteria, description of the population under study, and description of the NLP applications. In this process, the primary reviewer will complete all relevant data abstraction forms. A second reviewer will check the first reviewer’s data abstraction forms for completeness and accuracy. We will form reviewer pairs to include personnel with both clinical and methodological expertise. We will resolve differences of opinion through consensus adjudication between the reviewers.

Results: We will summarize the different types of NLP approaches used on radiology reports and their reported performance. We will describe how NLP is being applied to radiology and assess if applications have expanded over time. We will present the types of computer science methods used for NLP in radiology and, where possible, categorize these methodologies. We will also identify gaps in functionality and applications of NLP as it relates to modern challenges of quality assurance, business intelligence, decision support, and scientific discovery.

Discussion: We will discuss types of applications of NLP in radiology and how this has the potential to enable knowledge discovery to inform future healthcare decision making. We will highlight NLP applications with superior performance and factors that may have contributed to their success. We will discuss how an understanding of NLP methods can inform future development of NLP applications within radiology.

Conclusion: A systematic review of the literature of the use of NLP in radiology demonstrates how its performance and scope of applications have evolved over time and suggests new opportunities for research.

References:

1. DesRoches CM, Charles D, Furukawa MF, Joshi MS, Kralovec P, Mostashari F, Worzala C, Jha AK. Adoption Of Electronic Health Records Grows Rapidly, But Fewer Than Half Of US Hospitals Had At Least A Basic System In 2012. Health Aff (Millwood). 2013 Aug;32(8):1478-85.
2. Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008:128-44.
3. Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp. 2000:270-4.

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Durjoy Majumder, Ph.D
Secretary, SSBTR