Top-10 List Research Articles

Greenwashing debates on Twitter: Stakeholders and critical topics

This study explores how the concept of greenwashing has evolved on social media and identifies the most dominant themes and stakeholders shaping these debates. Drawing on the extant literature on greenwashing and stakeholder theory, an in-depth empirical analysis was conducted on how greenwashing debates unfolded during 2006–2022 on the Twitter (now ‘X’) platform. The analysis is based on 496,276 unique tweets, which provide a detailed account of the main themes and stakeholders involved in online greenwashing debates. The findings indicate that greenwashing debates on social media are dominated by a limited number of themes, sectors, companies, and stakeholders. Over the last 10 years, three companies have consistently been in the top-10 list of tweets addressing greenwashing. Likewise, greenwashing debates on social media are often propagated by small groups of individuals, non-governmental organisations, and media outlets. This study provides new insights into the issues and stakeholders dominating greenwashing debates on social media and highlights the dynamic interplay between the accusers, accused, accomplices, and allies involved in these discussions.

Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination.

Prenatal diagnosis of a rare disease on ultrasound relies on a physician's ability to remember an intractable amount of knowledge. We developed a real-time decision support system (DSS) that suggests, at each step of the examination, the next phenotypic feature to assess, optimizing the diagnostic pathway to the smallest number of possible diagnoses. The objective of this study was to evaluate the performance of this real-time DSS using clinical data. This validation study was conducted on a database of 549 perinatal phenotypes collected from two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed ('ascertained') diagnosis, was performed by a panel of external experts. The proportion of ascertained diagnoses within the software's top-10 differential diagnoses output was evaluated, as well as the sensitivity and specificity of the software to select correctly as its best guess a syndromic or isolated condition. The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and 84%. The stepwise approach required an average of 13 queries to reach the final set of diagnoses. The DSS showed high performance when applied to real-world data. This validation study suggests that such software can improve perinatal care, efficiently providing complex and otherwise overlooked knowledge to care-providers involved in ultrasound-based prenatal diagnosis. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Identification and Prioritization of Research Questions on Paraplegia Caused by Traumatic Spinal Cord Injury with Those Affected, Their Relatives and Health Care Professionals

Objective With expertise based on experience, paraplegics, their relatives and health care professionals can contribute to the development of research questions relevant for those affected and those in health care practice. For this purpose, the James Lind Alliance (JLA) has provided a methodological approach. The aim of this study was to develop a research agenda for paraplegia resulting from traumatic spinal cord injury with an adapted JLA approach. Four consecutive online surveys of people with paraplegia caused by traumatic spinal cord injury, their relatives and caregivers were conducted. In the first survey, the respondents freely formulated research questions unanswered from their point of view. These were synthesized and checked to see if they can already be answered by available evidence. The unresolved questions were prioritized stepwise in the subsequent surveys. In the second survey, the relevance of questions was rated on a five-point rating scale (1-5). Questions with a mean value of 4 or higher were taken up in the third survey, in which the 10 most relevant questions were determined. These were ranked in the fourth survey as a top-10 list. Based on the first survey (n=52), 38 unresolved research questions were identified. Of these, 26 questions were rated as important (2nd survey; n=53), from which 10 questions were selected (3rd survey; n=17) and ranked (4th survey; n=12) as a top-10 list. Four prioritized questions related to treatment of spinal cord injury or associated health issues, three to aspects of the health care system with regard to assistive devices and the implementation of new therapies, two to possibilities of those affected to improve their own situation, and one to research on the course of disease. Nine priorities focus on research that could help improve the life and health care situation of paraplegic patients and one on curative treatment. The prioritized questions should be taken up by researchers and research funders for the benefit of patients and to help health care providers. For some priorities, a need for research was consistently identified in present guidelines or systematic reviews.

The Future of Causal Inference.

The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high-dimensional data and precision medicine, causal machine learning, causal discovery, and others. These methods are not meant to be an exhaustive list; instead, we hope that this list will serve as a springboard for stimulating the development of new research.

Investigating the Influence of Feature Sources for Malicious Website Detection

Malicious websites in general, and phishing websites in particular, attempt to mimic legitimate websites in order to trick users into trusting them. These websites, often a primary method for credential collection, pose a severe threat to large enterprises. Credential collection enables malicious actors to infiltrate enterprise systems without triggering the usual alarms. Therefore, there is a vital need to gain deep insights into the statistical features of these websites that enable Machine Learning (ML) models to classify them from their benign counterparts. Our objective in this paper is to provide this necessary investigation, more specifically, our contribution is to observe and evaluate combinations of feature sources that have not been studied in the existing literature—primarily involving embeddings extracted with Transformer-type neural networks. The second contribution is a new dataset for this problem, GAWAIN, constructed in a way that offers other researchers not only access to data, but our whole data acquisition and processing pipeline. The experiments on our new GAWAIN dataset show that the classification problem is much harder than reported in other studies—we are able to obtain around 84% in terms of test accuracy. For individual feature contributions, the most relevant ones are coming from URL embeddings, indicating that this additional step in the processing pipeline is needed in order to improve predictions. A surprising outcome of the investigation is lack of content-related features (HTML, JavaScript) from the top-10 list. When comparing the prediction outcomes between models trained on commonly used features in the literature versus embedding-related features, the gain with embeddings is slightly above 1% in terms of test accuracy. However, we argue that even this somewhat small increase can play a significant role in detecting malicious websites, and thus these types of feature categories are worth investigating further.

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

Diversity and Discrepancies in Water‐related University Rankings: Is There a Need for More Consistency or Is There Value in Breadth?

Diversity and Discrepancies in Water‐related University Rankings: Is There a Need for More Consistency or Is There Value in Breadth?

Reflection (With Some Coaching) on a Quality Improvement Initiative: Finding Our Collective Voice Along the Journey.

The Quality Improvement Success Story article by Roberts et al. (1) titled “Screening for Mental Health Comorbidities in a Pediatric Diabetes Clinic Setting” is a wonderful example of how the quality improvement (QI) lens can be focused on different kinds of outcomes. The clinic at Seattle Children’s Hospital elected to pursue improvement of its rate of mental health screening given the association of depression and diabetes distress with poor clinical outcomes such as A1C. Much is to be learned by their initiative and energies. I present below a top-10 list (in no particular order of importance) of my reflections on this project. The team used the plan-do-study-act (PDSA) methodology to create change. The PDSA process enables practices to create many small, iterative changes rather than focus on one large change, hope for the best, and remeasure at some point in the future. The multiple months between intervention, data collection, and change in the initiative by Roberts et al. was quite long. Ideally, small samples of data would be collected weekly to determine whether the intervention needed to be redesigned or deployed in a different manner. To gain that knowledge quickly, I encourage practices to have shorter cycle times between PDSA cycles. Much can be learned from simply looking at five randomly selected patients per week who were exposed to the intervention to see whether the intervention behaved as was intended. At the end of each week, team members can look at the results and ask themselves (the “A” in PDSA): “Should we Abandon the initiative, do it Again” (i.e., collect data for another week), Adapt it (i.e., tweak the design of the intervention and then study that change going forward), or Adopt it as a standard process because enough data have been collected to show that it is working …

Application of Latent Factor Model on a Restaurant Menu Recommendation System

A restaurant menu recommendation system is becoming a necessity following the trend of eating out at the restaurants. The task of such a system is to generate a top- N list of menus that may be of interest to a customer, in which the customer previous rating behavior is used as the learning model. In this paper, we apply the SVD (Singular Vector Decomposition) latent factor model as the learning technique of the recommendation system. Beforehand, we implement the mean imputation technique to fill in the missing rating entries so that SVD can also deal with the new customer that has no rating record in the system. Evaluation on a real-world restaurant menu recommendation dataset shows that our recommendation system is able to generate a top-10 list of menu recommendations to a target customer and that the results of the low-rank approximation using SVD are comparable with that of the full-rank.

The Beginning of the Kharkiv Pantheon Formation (on the Materials of Travel Guides of the Second Half of the 19th – Early 20th Centuries)

The author explores the issue of forming the "pantheon" of the most prominent figures in the history of Kharkiv. To this end, Soviet city guides were analyzed. The names found on the guide pages have been recorded in the appropriate database. The author stated the principles of construction of this database and possible interpretations of the obtained results. It contains two fields - "faces" and "mentions". In the first box, those names found on the guide pages were recorded, and in the second box, those ratings that accompany a specific name were recorded. Along with fixing a person's name, a social or professional characteristic (scientist, military, architect, statesman, etc.) was also mentioned. In the "mention" field, depending on the nature of the mention of a person, a coefficient was stated: if the name was mentioned without a rating, it is a factor # 1 (1 point), a combination with such epithets as "known", "prominent" and the like – a factor # 2 (2 points), more or less a detailed characteristic of the activity, first of all from the point of view of impact on the development of the city – factor # 3 (3 points). As a result, it allowed to create a certain hierarchical name system and to separate "TOP-10" of them. In the long run, it will allow you to see a system of names in the dynamics, when some persons will fall out of the list of leaders at certain periods and others will be included in this list. It will also be important to capture changes in terms of increasing or decreasing attention to a particular category of people (social and professional characteristics). In total, more than 650 people were included in the database. The author concluded that in the early 20th century the guides less mentioned statesmen and local administrators, and more of artists, architects, and scientists. Also, the article states that in the late 19th and early 20th centuries the list of "local heroes" in Kharkiv has just begun to form. This can be explained by the absence of some known persons in the TOP-10 list. This is partly due to the fact that the guide genre itself has not yet been completed, and the first guides rather resembled information directories and were thematically imbalanced. Nevertheless, this material can be used in studying the initial stage of assembly of the "Kharkiv Pantheon".

Microbial bioremediation of oil contaminated seawater: A survey of patent deposits and the characterization of the top genera applied

Petroleum has been the world's major source of energy since the middle of the twentieth century, leading to positive changes but also social, political, and environmental problems worldwide. Oil contamination affects all ecosystems, and the remediation of polluted sites using environmentally friendly strategies is crucial. Here, we report an analysis of the patent documents of potential petroleum bioremediation techniques that use microbes to clean seawater. The patent search was performed using Orbit Intelligence®, SciFinder® and the Derwent World Patents Index®. A group of 500 patent documents were validated according to the search objective and carefully studied. Increases in patent deposits coincide with periods following widely reported oil spills, suggesting a relationship between media disclosure and stimulation of innovation activities. China leads the list of countries with patent applications in bioremediation with 152 deposits, followed by Russia with 133 and the US with 48. These three countries have completely different temporal deposit profiles, influenced by their historical, political, and economic scenarios. A total of 368 patents described degradation of the oil compounds exclusively by bacteria, 24 by fungi and yeasts, 1 by Archaea, 1 using a microalgal strain, and 32 by mixed consortia. The leading microbial genera found in the patents are Pseudomonas (114 patents), Bacillus (75), and Rhodococcus (60). In the top-10 list of microbial strains mostly cited/claimed, no genera are obligate hydrocarbonoclastic bacteria. This fact, together with the broad pattern found in the main International Patent Classification (IPC) codes, suggest that most of the documents are general bioremediation approaches and not focused on oil-polluted seawater. This work highlights the importance of stimulating the development of innovative environmentally friendly strategies focused on the degradation of oil hydrocarbons in marine ecosystems.

Searching for the best conditions

Organic Chemistry The vastness of the archival chemistry literature is both a blessing and a curse. The reaction that you're looking for is probably in there, provided you take enough time to search for it. Gao et al. trained a neural network model on 10 million known reactions to speed up this process. Specifically, the model was charged with predicting a catalyst, reagents, solvents, and temperature to achieve a given transformation. When tested, the model's top-10 list of suggestions produced a close match to actual conditions nearly 70% of the time, with a ±20°C error margin in temperature. ACS Cent. Sci. 4 , 1465 (2018).

Developing a protocol to identify and prioritize research questions for psoriasis: a James Lind Alliance Priority Setting Partnership.

Psoriasis affects over two million people in the U.K. It has a significant psychological and social impact on individuals and an associated high economic cost to the U.K. National Health Service. There are many unanswered questions about psoriasis. To develop a protocol in order to work with patients, families, carers and healthcare professionals to identify psoriasis uncertainties; to agree by consensus a top-10 list of psoriasis uncertainties; and to disseminate prioritized unanswered questions to researchers and funders so as to promote work that will focus on answering the uncertainties considered most important by stakeholders. A Psoriasis Priority Setting Partnership has been established to gather psoriasis uncertainties following the transparent methodology advocated by the James Lind Alliance. A steering group composed of stakeholders has disseminated an initial survey to patients, families, carers and healthcare professionals to collect information on important psoriasis questions. After removing duplications, uncertainties will be collated and checked against existing evidence to determine whether any have already been resolved. 'True uncertainties' will be circulated to stakeholders in a second survey where they will be ranked by importance. At a final workshop, information will be distilled to generate a top-10 list of uncertainties. By following the protocol outlined in this paper a prioritized list of uncertainties will be identified that will be used to inform the psoriasis research agenda. Research targeted to address priorities identified by a range of stakeholders is imperative. This project will inform policy makers and research funding bodies about what really matters to these groups.

Comparative analysis of weighted gene co-expression networks in human and mouse.

The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally central genes in the human and mouse networks.

P071: Choosing Wisely in the emergency department: exploring the reach, support and potential for the Choosing Wisely Canada® campaign among emergency physicians

Introduction: Choosing Wisely Canada® (CWC) launched in April 2012. Since then, the Emergency Medicine (EM) top-10 list of tests, treatments and procedures to avoid has been released and initiatives are on-going. This study explored CWC awareness and support among emergency physicians. Methods: A 60-question online survey was distributed to Canadian Association of Emergency Physicians (CAEP) members with valid e-mails. The survey collected information on demographics, awareness/support for CWC as well as physicians’ perceived barriers and facilitators to implementation. Descriptive statistics were performed in SPSS (Version 24). Results: Overall, 324 surveys were completed (response rate: 18%). Respondents were more often male (64%) and practiced at academic/tertiary care hospitals (56%) with mixed patient populations (74%) with annual ED volumes of >50,000 (70%). Respondents were familiar with campaigns to improve care (90%). Among these respondents, 98% were specifically familiar with CWC and 73% felt these campaigns assisted them in providing high-quality care. Respondents felt that the top-5 EM recommendations were supported by high quality evidence, specifically the first 4 recommendations (>90% each). The most frequently reported barriers to implementation were: patients’ expectations/requests (33%), the possibility of missing severe condition(s) (20%), and requirements of ED consultations (12%). Potential facilitators were identified as: strong evidence-base for recommendations (37%), medico-legal protection for clinicians who adhere to guidelines (13%), and support from institutional leadership (11%). Conclusion: CWC is well-known and supported by emergency physicians. Despite the low response rate, exploring the barriers and facilitators identified here could enhance CWC’s uptake in Canadian emergency departments.

The association between shift work and treatment-seeking migraine in Denmark

In Europe, the one-year prevalence of migraine is 14.9% and migraine is on the top-10 list of leading causes of years lost to disability. Sleep disturbances and irregular daily routines are considered triggers of migraine and these factors are well-known consequences of shift work. We studied the association between treatment-seeking migraine and shift work, categorised as fixed evening work, fixed night work and variable working hours with and without night work in a Danish working population of 5872 participants. When compared with fixed day workers, only participants with fixed evening work were found to have significantly increased odds of reporting treatment-seeking migraine after adjustment for socio-demographic and behavioural covariates (OR = 1.56; 95% CI 1.05–2.32). Participants with seniority of 10 years or more notably accounted for this association. Due to the cross-sectional design, selection mechanisms may have biased the results.Practitioner Summary: The study showed higher odds of treatment-seeking migraine among evening workers even when taking a range of potential confounders into account. Due to the cross-sectional design, we cannot draw any causal inferences, but potential mechanisms underlying the present study are discussed, with an emphasis on possible selection into evening work.

Real-World Examples: Developing a Departmental Alumni Network

We describe the context for and implementation of a departmental alumni network. More than a database compiling facts about graduates, this network provides students with information and inspiration. It also offers a wonderful opportunity to support lifelong learning through the development of collaborative relationships between alumni and faculty and students. We suggest several features and progression steps for such a network. As an integral component of these efforts, we describe the creation of “alumni spotlight” profiles featured on our departmental website and include a top-10 list of objectives and benefits of such a resource. In addition, we offer ideas for possible enrichment and extension of the network to serve different programmatic purposes.

Analyzing Delays of Road Construction Projects in Cambodia: Causes and Effects

AbstractThis research analyzes the delay factors in road construction projects in Cambodia and their effects on project time, cost, and quality. A questionnaire survey was conducted with engineers from contractor and consultant firms in assessing the frequency and severity of delays so that an importance index for delay factors could be computed. In general, there was no significant difference between contractors and consultants in assessing the delay factors. The top-10 list was dominated by factors related to the contractor and the project. However, two external factors, rain and flood, were the first two factors on the list, which also had a high impact on the three project objectives. The remaining factors on the list were land acquisition, award of project to lowest bidder, equipment breakdowns, poor site arrangement, management and supervision, unexpected ground condition and terrain, low quality of the contractor human resources, late progress payments, and low productivity of labor. Recommendation...

A data mining approach to selecting herbs with similar efficacy: Targeted selection methods based on medical subject headings (MeSH)

Ethno-pharmacological relevanceNatural products have long been the most important source of ingredients in the discovery of new drugs. Moreover, since the Nagoya Protocol, finding alternative herbs with similar efficacy in traditional medicine has become a very important issue. Although random selection is a common method of finding ethno-medicinal herbs of similar efficacy, it proved to be less effective; therefore, this paper proposes a novel targeted selection method using data mining approaches in the MEDLINE database in order to identify and select herbs with a similar degree of efficacy. Materials and methodsFrom among sixteen categories of medical subject headings (MeSH) descriptors, three categories containing terms related to herbal compounds, efficacy, toxicity, and the metabolic process were selected. In order to select herbs of similar efficacy in a targeted way, we adopted the similarity measurement method based on MeSH. In order to evaluate the proposed algorithm, we built up three different validation datasets which contain lists of original herbs and corresponding medicinal herbs of similar efficacy. ResultsThe average area under curve (AUC) of the proposed algorithm was found to be about 500% larger than the random selection method. We found that the proposed algorithm puts more hits at the front of the top-10 list than the random selection method, and precisely discerns the efficacy of the herbs. It was also found that the AUC of the experiments either remained the same or increased slightly in all three validation datasets as the search range was increased. ConclusionThis study reveals and proves that the proposed algorithm is significantly more accurate and efficient in finding alternative herbs of similar efficacy than the random selection method. As such, it is hoped that this approach will be used in diverse applications in the ethno-pharmacology field.

The Big Question

The Big Question