Automating geoscience analysis Solid Earth geoscience is a field that has very large set of observations, which are ideal for analysis with machine-learning methods. Bergen et al. review how these methods can be applied to solid Earth datasets. Adopting machine-learning techniques is important for extracting information and for understanding the increasing amount of complex data collected in the geosciences. Science, this issue p. eaau0323 BACKGROUND The solid Earth, oceans, and atmosphere together form a complex interacting geosystem. Processes relevant to understanding Earth’s geosystem behavior range in spatial scale from the atomic to the planetary, and in temporal scale from milliseconds to billions of years. Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two. Techniques from the rapidly evolving field of machine learning (ML) will play a key role in this effort. ADVANCES The confluence of ultrafast computers with large memory, rapid progress in ML algorithms, and the ready availability of large datasets place geoscience at the threshold of dramatic progress. We anticipate that this progress will come from the application of ML across three categories of research effort: (i) automation to perform a complex prediction task that cannot easily be described by a set of explicit commands; (ii) modeling and inverse problems to create a representation that approximates numerical simulations or captures relationships; and (iii) discovery to reveal new and often unanticipated patterns, structures, or relationships. Examples of automation include geologic mapping using remote-sensing data, characterizing the topology of fracture systems to model subsurface transport, and classifying volcanic ash particles to infer eruptive mechanism. Examples of modeling include approximating the viscoelastic response for complex rheology, determining wave speed models directly from tomographic data, and classifying diverse seismic events. Examples of discovery include predicting laboratory slip events using observations of acoustic emissions, detecting weak earthquake signals using similarity search, and determining the connectivity of subsurface reservoirs using groundwater tracer observations. OUTLOOK The use of ML in solid Earth geosciences is growing rapidly, but is still in its early stages and making uneven progress. Much remains to be done with existing datasets from long-standing data sources, which in many cases are largely unexplored. Newer, unconventional data sources such as light detection and ranging (LiDAR), fiber-optic sensing, and crowd-sourced measurements may demand new approaches through both the volume and the character of information that they present. Practical steps could accelerate and broaden the use of ML in the geosciences. Wider adoption of open-science principles such as open source code, open data, and open access will better position the solid Earth community to take advantage of rapid developments in ML and artificial intelligence. Benchmark datasets and challenge problems have played an important role in driving progress in artificial intelligence research by enabling rigorous performance comparison and could play a similar role in the geosciences. Testing on high-quality datasets produces better models, and benchmark datasets make these data widely available to the research community. They also help recruit expertise from allied disciplines. Close collaboration between geoscientists and ML researchers will aid in making quick progress in ML geoscience applications. Extracting maximum value from geoscientific data will require new approaches for combining data-driven methods, physical modeling, and algorithms capable of learning with limited, weak, or biased labels. Funding opportunities that target the intersection of these disciplines, as well as a greater component of data science and ML education in the geosciences, could help bring this effort to fruition. Digital geology. Digital representation of the geology of the conterminous United States. [Geology of the Conterminous United States at 1:2,500,000 scale; a digital representation of the 1974 P. B. King and H. M. Beikman map by P. G. Schruben, R. E. Arndt, W. J. Bawiec] The list of author affiliations is available in the full article online. Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.
E. Board, Halina Filipowicz, Christopher Garbowski
et al.
The successful design, development, and operation of human-rated and -operated systems requires the combined effort of engineering, science, and human health disciplines. Each of these disciplines produces uniquely trained experts who approach their fields differently from fundamental work to applied practices. Human Systems Integration (HSI) is an important and vital step in the development of human-rated spacecraft and high-performance aircraft. The three disciplines of engineering, life sciences, and health/ medicine are critical disciplines that must engage with one another to ensure the health and safety of the operator. They must also include anthropometric involvement of male and female operators who are integrated into these systems or interact with them. This chapter presents some of the failures, compromises, and lessons learned in the complex field of HSI. These lessons illustrate only a few examples of how HSI is required in the design of complex systems and how its success ensures overall crew and mission safety and success.
The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.
In recent years, Mechanical Turk (MTurk) has revolutionized social science by providing a way to collect behavioral data with unprecedented speed and efficiency. However, MTurk was not intended to be a research tool, and many common research tasks are difficult and time-consuming to implement as a result. TurkPrime was designed as a research platform that integrates with MTurk and supports tasks that are common to the social and behavioral sciences. Like MTurk, TurkPrime is an Internet-based platform that runs on any browser and does not require any downloads or installation. Tasks that can be implemented with TurkPrime include: excluding participants on the basis of previous participation, longitudinal studies, making changes to a study while it is running, automating the approval process, increasing the speed of data collection, sending bulk e-mails and bonuses, enhancing communication with participants, monitoring dropout and engagement rates, providing enhanced sampling options, and many others. This article describes how TurkPrime saves time and resources, improves data quality, and allows researchers to design and implement studies that were previously very difficult or impossible to carry out on MTurk. TurkPrime is designed as a research tool whose aim is to improve the quality of the crowdsourcing data collection process. Various features have been and continue to be implemented on the basis of feedback from the research community. TurkPrime is a free research platform.
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.
The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.
K. Elliott-Sale, C. Minahan, X. J. de Jonge
et al.
Until recently, there has been less demand for and interest in female-specific sport and exercise science data. As a result, the vast majority of high-quality sport and exercise science data have been derived from studies with men as participants, which reduces the application of these data due to the known physiological differences between the sexes, specifically with regard to reproductive endocrinology. Furthermore, a shortage of specialist knowledge on female physiology in the sport science community, coupled with a reluctance to effectively adapt experimental designs to incorporate female-specific considerations, such as the menstrual cycle, hormonal contraceptive use, pregnancy and the menopause, has slowed the pursuit of knowledge in this field of research. In addition, a lack of agreement on the terminology and methodological approaches (i.e., gold-standard techniques) used within this research area has further hindered the ability of researchers to adequately develop evidenced-based guidelines for female exercisers. The purpose of this paper was to highlight the specific considerations needed when employing women (i.e., from athletes to non-athletes) as participants in sport and exercise science-based research. These considerations relate to participant selection criteria and adaptations for experimental design and address the diversity and complexities associated with female reproductive endocrinology across the lifespan. This statement intends to promote an increase in the inclusion of women as participants in studies related to sport and exercise science and an enhanced execution of these studies resulting in more high-quality female-specific data.
Robson Silva e Silva, Fábio Olmos, Edison Barbieri
Abstract Seabirds across all the seas and oceans of the planet interact with human activities and, as a result, approximately 30% of all species are in decline and threatened with extinction. The knowledge of the composition of seabird communities in both breeding and non-breeding ranges is necessary to guide appropriate conservation measures according to its status. Fisheries, oil and natural gas exploration, offshore wind farms, among other activities, require regulation and legal frameworks to protect seabirds and other organisms in this environment. The state of São Paulo already hosts numerous such activities within its territorial waters and has its own environmental protection legislation (including a list of threatened fauna species), requiring impacts on threatened species are evaluated. The present study compiles all available information on seabirds in São Paulo and update its species list based on data obtained from literature reviews, museum specimens, band recovery records, and citizen-science platforms. São Paulo has a total of 68 recorded seabird species, including the recently recognized Oceanites chilensis and the sole Brazilian records of Pterodroma externa and Pachyptila turtur. Most (50 species) are migratory, with 14 species from the Northern Hemisphere and 36 from the Southern Hemisphere. Only 18 species are resident in Brazil, of which six breed along the São Paulo coast. Among the recorded species, 24 (35%) are listed as threatened with extinction by the IUCN, MMA and/or SMA lists. Notably, three (50%) of the six resident breeding species in São Paulo are threatened. Most of the new records came from beached birds, particularly through the Beach Monitoring Program (PMP). Unfortunately, this program forwards few of the collected specimens, including rare and unprecedented records for São Paulo’s avifauna, to scientific collections. Based on the available studies on São Paulo’s seabirds, even basic data on breeding biology, home range, and diet of even the commonest coastal, and resident species is virtually nonexistent. Similarly, knowledge regarding trends in population and occupancy of breeding sites is scarce, with only outdated data available from studies carried between 1997 and 2005. Further studies and monitoring programs on the breeding areas of these resident species are necessary to fill these knowledge gaps and provide updated scientific information to support effective conservation measures.
Abstract Background Despite optimal standard therapy, residual inflammation continues to increase major adverse cardiovascular events (MACE) in patients with coronary heart disease (CHD). New immunomodulatory drugs targeting specific immune pathways have shown mixed efficacy across trials, warranting comprehensive evaluation of their role in secondary prevention. Methods We performed a systematic review and meta-analysis of 25 randomized controlled trials (RCTs) from January 1, 2014, to October 1, 2024, identified from eight databases: the cochrane library, (public medicine) pubmed, embase, web of science, china national knowledge infrastructure (CNKI), wanfang data knowledge service platform(WanFang), Weipu information database(VIP), and china biomedical literature database (SinoMed). Eligible studies assessed the efficacy of immunomodulatory agents, including colchicine, and canakinumab on MACE. Primary outcome was MACE incidence; secondary outcomes included, angina, and inflammatory biomarkers. Risk ratios (RR) with 95% confidence intervals (CI) were pooled using fixed or random-effects models. Subgroup analyses were conducted by drug class, follow-up duration, and CHD subtype (acute vs. chronic coronary syndrome). Risk of bias was assessed via Cochrane RoB 1.0, and evidence certainty rated with GRADE. Results Overall, new immunomodulatory drugs did not significantly reduce MACE (RR = 0.92; 95% CI: [0.84,1.01]; P = 0.09; I²=60%). However, subgroup analyses revealed heterogeneous effects across drug classes. Significant reductions in MACE were observed with NLRP3 inflammasome inhibitors (RR = 0.75; 95% CI: 0.65,0.86; P < 0.0001) and interleukin-pathway inhibitors (RR = 0.86; 95% CI: 0.75,0.97; P = 0.02). In contrast, no significant reduction in MACE incidence was found in the broad-spectrum immunomodulator group, Lp-PLA2 inhibitor group, or p38 MAPK kinase inhibitor group (all P > 0.05). Besides, benefits were evident only in trials with follow-up exceeding 6 months (RR = 0.89; 95% CI: [0.82,0.98]. Secondary outcomes showed significant reductions in angina (RR = 0.72; 95%CI: [0.58,0.90], P = 0.004), revascularization (RR = 0.85; 95%CI: [0.73,0.98], P = 0.03), IL-6 (SMD = − 0.82;95༅CI: [-1.62,-0.03], P = 0.02), and neutrophil count, but no effect on (cardiac arrest)CA, all-cause mortality, incidence of gastrointestinal adverse effect and high-sensitivity c-reactive protein(hs-CRP). The quality of evidence for MACE was assessed as moderate. Conclusion Targeted anti-inflammatory therapies, particularly colchicine and canakinumab, significantly reduce MACE in CHD patients when used for longer than six months. Efficacy varies by mechanism of action, supporting precision use of NLRP3 and IL-1β inhibitors. Future trials should been focus on biomarker-guided, long-term anti-inflammatory interventions in cardiovascular care. Trial Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD42024597008PROSPERO : CRD42024597008.
Diseases of the circulatory (Cardiovascular) system
Abstract Background Persistent infection with high-risk human papillomavirus (HPV) types is a well-established risk factor for various malignancies, and timely vaccination of university students is a cost-effective strategy to reduce infection rates and the burden of HPV-associated consequences. Although many studies have examined HPV knowledge, vaccine acceptance, and uptake in university students, findings remain heterogeneous, and comprehensive quantitative synthesis is limited. This systematic review and meta-analysis aims to assess global estimates of university students’ knowledge of HPV and its vaccine, willingness to receive the vaccine, and actual vaccination behaviors. Methods The study was reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2020 Checklist. A systematic search was conducted in PubMed, Web of Science, China Biology Medicine disc, China National Knowledge Infrastructure and Wan Fang Database for studies published from January 2006 through August 2024. Studies with quality assessment scores > 5 and published in Chinese or English were included for data extraction. Pooled prevalence estimates and 95% confidence intervals (CI) were calculated using the Freeman-Tukey double arcsine transformation. The heterogeneity statistic I-squared and corresponding p value were also reported. Results A total of 56 studies covering 184,351 university students from four continents (Asia, Africa, Europe and North America) were included. Among students, 68.3% (95% CI 56.4%-79.0%) and 53.5% (95% CI 53.0%-54.1%) were aware of HPV and HPV vaccine, respectively, with significant gaps in knowledge about HPV symptoms, cervical cancer screening methods, and optimal vaccination timing. Pooled HPV vaccination willingness was 52.9% (95% CI 44.2%-61.6%), with higher willingness observed among females and medical students. Only 10.4% (95% CI 6.1%-15.8%) had received at least 1 dose of vaccination and 12.4% (95% CI 3.0%-26.9%) had completed the full three-dose schedule. Vaccination coverage among females was 8.6% (95% CI 3.7%–15.1%), and among medical students, 7.7% (95% CI 1.3%-18.4%). Conclusions This meta-analysis found that university students exhibit limited awareness of HPV and its vaccine, with about half willing to be vaccinated. Actual vaccination rates remain low and vary widely by gender, major, time of year, and geographic regions. These findings highlight the need for targeted intervention strategies, such as precision education and cross-sector collaboration, to effectively increase HPV vaccination coverage in this population.