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"Human evolution Dictionaries"
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The Cambridge Dictionary of Human Biology and Evolution
Ashton reviews The Cambridge Dictionary of Human Biology and Evolution by Larry L. Mai, Marcus Young Owl and M. Patricia Kersting.
Book Review
Magnetic resonance fingerprinting
Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization—which we term ‘magnetic resonance fingerprinting’ (MRF)—that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy.
A new approach to magnetic resonance, ‘magnetic resonance fingerprinting', is reported, which combines a data acquisition scheme with a pattern-recognition algorithm that looks for the ‘fingerprints’ of interest within the data.
Raising the profile of NMR
Although nuclear magnetic resonance is a powerful analytical tool for many scientific and medical disciplines, usually only a fraction of its potential power is harnessed. Most implementations are qualitative, and restricted in the range of properties that are probed. Dan Ma and colleagues introduce a new approach — termed magnetic resonance fingerprinting — aimed at greatly enhancing the amount of quantitative information that can be obtained in one measurement. Their approach combines a data-acquisition scheme that is indiscriminate in the material properties that it probes with pattern-recognition algorithms that look for the 'fingerprints' of interest within the data. Magnetic resonance fingerprinting has the potential to detect and analyse early indicators of disease or complex changes in materials, as well as increasing the sensitivity, specificity and speed of magnetic resonance studies.
Journal Article
Deploying digitalisation and artificial intelligence in sustainable development research
Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.
Journal Article
What is epidemiology? Changing definitions of epidemiology 1978-2017
Epidemiology is a discipline which has evolved with the changes taking place in society and the emergence of new diseases and new discipline related to epidemiology. With these evolutions, it is important to understand epidemiology and to analyse the evolution of content of definitions of epidemiology.
The main objective of this paper was to identify new definitions of epidemiology available since 1978. Secondary objectives were to analyse the content of these definitions, to compare them with those used by Lilienfeld and to determine whether changes have taken place over the last forty years.
A review of grey literature and published literature was conducted to find the definitions of epidemiology written between 1978 and 2017.
102 definitions of epidemiology were retained. They helped to highlight 20 terms and concepts related to epidemiology. Most of them were already used in the definitions used by Lilienfeld. Five terms were present in more than 50% of definitions from the period 1978 to 2017: \"population\", \"study\", \"disease\", \"health\" and \"distribution\". Several developments have occurred: strengthening of the terms \"control\" and \"health\" already used, the concept of \"disease\" was less frequently encountered whereas the concepts \"infectious diseases\", \"mass phenomenon\" are no longer used in definitions from 1978 to 2017.
This evolution of content of definition of epidemiology is absent from books on epidemiology. A thematic analysis of definitions of epidemiology could be conducted in order to improve our understanding of changes observed.
Journal Article
Emotion detection from text and speech: a survey
Emotion recognition has emerged as an important research area which may reveal some valuable input to a variety of purposes. People express their emotions directly or indirectly through their speech, facial expressions, gestures or writings. Many different sources of information, such as speech, text and visual can be used to analyze emotions. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, etc., and the content of these posts can be useful resource for text mining to discover and unhide various aspects, including emotions. Extracting emotions behind these postings is an immense and complicated task. To tackle this problem, researchers from diverse fields are trying to find an efficient way to more precisely detect human emotions from various sources, including text and speech. In this sense, different word-based and sentence-based techniques, machine learning, natural language processing methods, etc., have been used to achieve better accuracy. Analyzing emotions can be helpful in many different domains. One such domain is human computer interaction. With the help of emotion recognition, computers can make better decisions to help users. With the increase in popularity of robotic research, emotion recognition will also help making human–robot interaction more natural. This survey covers existing emotion detection research efforts, emotion models, emotion datasets, emotion detection techniques, their features, limitations and some possible future directions. We focus on reviewing research efforts analyzing emotions based on text and speech. We investigated different feature sets that have been used in existing methodologies. We summarize basic achievements in the field and highlight possible extensions for better outcome.
Journal Article
Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication
Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users’ opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users’ communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users.
The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users’ Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.
Journal Article
Psychology: a Giant with Feet of Clay
The aim of the current study has been to highlight the theoretical precariousness of Psychology. The theoretical precariousness has been evidenced through a review of psychological “core-constructs” whose definitions were thoroughly searched in 11 popular introductory textbooks of psychology edited between 2012 and 2019 and in an APA dictionary of Psychology (VandeBos 2015). This analysis has shown unsatisfactory or discordant definitions of psychological “core-constructs”. A further epistemological comparison between psychology and three “harder” sciences (i.e., physics, chemistry and biology) seemed to support the “soft” nature of psychology: a minor consensus in its “core” and a minor capacity to accumulate knowledge when compared to the former “harder” sciences (Fanelli in PLoS One, 5, e10068, 2010; Fanelli and Glänzel in PLoS One, 8, e66938, 2013). This comparison also seemed to support the “pre-paradigmatic” condition of psychology, in which conflicts between rival schools of thought hamper the development of a real unified paradigm (Kuhn 1970). To enter a paradigmatic stage, we propose here evolutionary psychology as the most compelling approach, thanks to its empirical support and theoretical consistency. However, since the skepticism about “grand unifying theories” is well disposed (Badcock in Review of General Psychology, 16, 10–23, 2012), we suggest that evolutionary psychology must be intended as a pluralistic approach rather than a monolithic one, and that its main strength is its capacity to resolve the nature-nurture dialectics.
Journal Article
COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment–Based Topic Modeling
COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment-based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines.
In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved.
We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment-based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns.
Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign.
To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment-based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.
Journal Article
The Quiet Revolution That Transformed Women's Employment, Education, and Family
Women's increased involvement in the economy was the most significant change in labor markets during the past century. Their modern economic role emerged in the United States in four distinct phases. The first three were evolutionary; the last was revolutionary. The revolution was a \"quiet\" one, not the \"big-bang\" type. The evolutionary phases led, slowly, to the revolutionary phase. First, this article discusses the three evolutionary phases and how they led to the revolutionary phase. It then describes the changes that occurred during the revolutionary phase and end with whether the revolution, as some have claimed, is stalled or being reversed. The terms \"evolution\" and \"revolution\" are not used lightly. By the term evolution and the shift to revolution, something quite specific is meant. The distinction between the two pertains to three aspects of women's choices and decisions. The first concerns \"horizon,\" that is, whether at the time of human capital investment a woman perceives that her lifetime labor force involvement will be long and continuous or intermittent and brief. The second concerns \"identity,\" that is, whether a woman finds individuality in her job, occupation, profession, or career. The third concerns \"decision making.\" Here the distinction is whether labor force decisions are made fully jointly, if a woman is married or in a long-term relationship, or, on the other hand, whether the woman is a \"secondary worker\" who optimizes her time allocation by taking her husband's labor market decisions as given to her.
Journal Article