Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: We found that some tasks that receptionists routinely perform in one practice were performed by administrators at another practice. The practices we observed differed considerably in their organisational forms, from single-site practices working independently to larger groups or ‘super-practices’ who share services across multiple sites. The issue of ‘task fit’, that is, allocating tasks to the appropriate person, involves both matching the task to the most appropriate pay grade and making sure clinicians are shielded from unnecessary administrative work. The driving force behind the assigning of tasks to occupations in this study was to accurately represent the core identity and ‘scope of work’ of each observed occupation. The third issue is the fundamental difference between ‘clinical’ and ‘administrative’ tasks. There is an abundance of administrative and clerical tasks that occur in primary care. While the field researcher observed, catalogued and categorised nearly every clinical interaction that takes place in the domain of primary care, it was often hard to distinguish administrative tasks from those that are purely clinical. Also, the O*NET database that was used for our analysis provided more terminology and language to describe administrative tasks and office work than it did for nuanced clinical work. ConclusionEvidence from our study show there is great potential to impact workloads in primary care by automating certain administrative tasks (approximately 44%, broadly). We believe that careful introduction of currently available technologies has considerable potential to reform administrative workflows and increase productivity in primary care. However, this should not be seen as a magic bullet that will solve the workload crisis in the immediate future. Introduction of new forms of automation should proceed cautiously with ongoing evaluation of their wider impacts. 2016. Available: Understanding pressures in general practice, 2016. Available: M, Everington S. Tackling the crisis in general practice. BMJ 2016;352:i942. doi:10. 1136/bmj. i942pmid: Engl. General practice forward view, 2016. Available: H, Stern R. Making time in general practice. Foundation, 2015: 1–83. ↵. association survey of GPs in England. London, 2016. ↵ Eighth national GP Worklife survey, 2016. Available: Clinical workload in UK primary care: a retrospective analysis of 100 million consultations in England, 2007-14. Lancet 2016;387:2323–30. doi:10.

Output: contrast


Input: Claim: Many supposed sightings of cryptids lack verifiable evidence. [SEP] Text: PeerJ 11(4):e16622 Pérez-Hernández CX, Zaragoza-Caballero S, Romo-Galicia A. 2022. Updated checklist of the fireflies (Coleoptera: Lampyridae) of Mexico. Zootaxa 5092(3):291-317 Center. 2019. Mobile connectivity in emerging economies. Ramírez-Manzano S, Cano-Santana Z, Cibrián-Tovar J, Luna-Cavazos M, Romero-Manzanares A, Ruiz-Posadas L, García-Moya E. 2023. Influence of abiotic factors and vegetation type on the abundance of Photinus palaciosi adults (Coleoptera: Lampyridae) in Nanacamilpa, Tlaxcala, Mexico. de Biodiversidad 94:e945091 R Team. 2017. R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. da Silveira LFL. 2021. A comprehensive review and call for studies on firefly larvae. PeerJ 9(1):1-24 Soga M, Gaston KJ. 2016. Extinction of experience: the loss of human-nature interactions. Frontiers in Ecology and the Environment 14:94-101. 2007. The habitat requirement of the genji-firefly Luciola cruciata (Coleoptera: Lampyridae), a representative endemic species of Japanese rural landscapes. 15:177-189 Theodorou P, Radzevičiūtė. 2020. Urban areas as hotspots for bees and pollination but not a panacea for all insects. 11(1):576. 2021. Global assessment of artificial habitat use by amphibian species. 257(6412):109129 van. 2020. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368(6489):417-420. 2016. Design principles for engaging and retaining virtual citizen scientists. 30:562-570. 2022. Physiological responses of the firefly Pyrocoelia analis (Coleoptera: Lampyridae) to an environmental residue from chemical pesticide imidacloprid. Frontiers in Physiology 13:879216. 2021.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Consequently, microbes with smaller genomes expel unused genes (Giovannoni et al. , 2005). For example, in a well studied group of archaea, Methanosarcina mazei has the smallest genome of the sequenced Methanosarcina and has clearly shed transferred genes found in other members of the genus (Garushyants, Kazanov & Gelfand, 2015). Gene loss is more common than gene gain over the evolution of both bacteria and archaea (Wolf et al. , 2012). On the other hand, a large genome is an adaptive strategy in which excess genes are retained longer (Chang et al. , 2011) in order to allow greater versatility (Guieysse & Wuertz, 2012). Thus, counting the absolute number of transferred genes present in a genome does not necessarily reflect the rate of horizontal transfer into that organism. This, however, could happen at a much larger scale when comparing organisms from very different environments. In order to compare bacteria from a range of living conditions, we look for bacteria with unusual levels of HGT with archaea than predicted by their genome size. total of 448 bacterial and 57 archaeal genomes (Table S1) were compared using reciprocal BLAST hits (BLAST version 2), where each BLASTp hit had to have an e-value of less than or equal to 1e−10 and the alignment length was >50% of the query (Table S2). Genomes were downloaded from the NCBI ftp site in 2009 and in cases where a microbe had one or more plasmids, those plasmids were included with the genome. All genomes were 100% complete and closed and no two genomes are from the same species. Information about each organism and its temperature, oxygen, and salt p1. code and RBH dataset necessary to reproduce the regression modeling and visualization is freely available at In addition, we examined the Korbel normalization, which includes the genome size of both bacteria and archaea: weighted average = 2 × A × B A 2 + B 2 where A and B are the genome size of the archaeal and bacterial genomes (Korbel et al. , 2002). fit was similar to our log–logistic function, but was found to be less accurate for bacteria with >5,000 genes (Fig. S2). Therefore, we used the log–logistic function in all our analyses.

Output: pass


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This fusion has resulted in grading systems that not only measure performance but also enhance the learning process by delivering pedagogically sound feedback matched with educational goals, such as increasing critical thinking and problem-solving skills. 2. 5 The current landscape: AI-driven adaptive learning systems2. 5. 1 State-of-the-art AI systems in educationToday’s automated grading landscape is dominated by powerful AI-driven adaptive learning systems. Darvishi discusses the current innovations in this arena, emphasizing how these systems leverage advanced algorithms to offer individualized feedback and adapt to different learning patterns [11]. Capable of identifying learning gaps, personalizing content, and giving tailored interventions, these technologies dramatically increase the learning experience. They reflect the result of years of evolution in educational technology, blending AI breakthroughs, language processing, and educational theory to create a dynamic, responsive learning environment. Advertisement 3. Principles of AI in gradingThis 3. 1 AI algorithms in education3. 1. 1 Core algorithms and their capabilitiesAI algorithms have transformed student assessments in schools. Key algorithms such as machine learning (ML) and deep learning are pivotal in this change. Crompton, Burke, and Jones note that these algorithms excel in processing and learning from big datasets, allowing for extremely accurate evaluation of student replies [12]. They are capable of evaluating patterns in student answers, delivering personalized feedback, and modifying evaluation criteria based on individual student performance. 3. 2 Evolution of AI in grading systems3. 2. 1 Technological advancements in NLP and MLAdvancements in natural language processing (NLP) and machine learning (ML) have greatly pushed the advancement of AI in grading systems. Kompa, Hakim, and Palepu describe how NLP enables AI systems to interpret and analyze human language, which is critical for judging written responses [13]. Meanwhile, ML allows these systems to learn from data, improve over time, and make more accurate assessments regarding the quality and content of student work. 3. 3 Accuracy and reliability of AI grading3. 3. 1 Ensuring fairness and consistencyEnsuring accuracy and reliability is a fundamental priority in AI grading. Patel underlines the necessity for AI systems to be not just efficient but also fair and consistent in their assessments [14]. This involves the constant development of algorithms to decrease errors and biases and validation against broad datasets to assure equitable grading across various student populations. 3. 4 Ethical considerations in AI grading3. 4.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Main textThe natural history of tobacco dependenceNicotine is among the least rewarding of all addictive drugs: only 20 % of first time users find smoking to be relaxing [1]. Smoking provides an image, a shared social activity, and something to do when bored. These factors motivate smoking until dependence takes over. Prior to the onset of dependence, sporadic non-daily use is typical. The classic withdrawal syndromeThe seminal research on tobacco withdrawal focused on individuals with advanced dependence [2–4], as was the case with research on withdrawal associated with other substances [5, 6]. Because of this history, the term ‘withdrawal’ has traditionally been used to describe the symptoms experienced by individuals with advanced physical dependence [7, 8]. In this report, the historical connotation of the word ‘withdrawal’ is expanded to accommodate recent advances in scientific knowledge afforded by the study of individuals with emerging dependence. The emergence of physical dependenceMany addictive drugs cause a physical (physiologic) dependence that manifests as withdrawal symptoms. [7] (The term physical dependence is introduced here to indicate that the discussion is about to focus on that aspect of dependence related to withdrawal phenomena. The author acknowledges that there are psychological aspects to dependence and that the physical and psychological may be tightly entwined. ) Tobacco withdrawal symptoms are identifiable as such because (1) they recur upon each withdrawal from tobacco, (2) they appear in a characteristic sequence, (3) they appear after a characteristic latency, and (4) they are relieved immediately upon using tobacco. Based on these criteria, it has been shown that physical dependence on tobacco develops through a characteristic sequence of stages in all addicted smokers [9]. As physical dependence begins to develop, the earliest symptom is withdrawal-induced ‘wanting’ of a cigarette [10]. ‘Wanting’, by definition, is mild, short-lived and fairly easy to ignore. It does not intrude upon the patient’s thoughts. For individuals in the first stage of physical dependence, wanting is the only withdrawal symptom experienced. The second stage of physical dependence is characterized by withdrawal-induced ‘craving’. Craving is a more intense and persistent sensation than is wanting, but what distinguishes craving qualitatively from wanting is the fact that craving intrudes upon the patient’s thoughts. Smokers often describe craving as if something inside of their head is telling them that it is time to smoke.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: , 2006). Sequences were aligned, and distances were calculated with mothur (Schloss et al. , 2009). The purpose of the Mantel test was to test whether deeply-branching bacteria share more genes with archaea than more divergent bacteria. Therefore, for the Mantel test, only the distances between bacteria and the archaeal root were included in order to remove the phylogenetic signal from bacteria-bacteria relationships. The phylogenetic distances between each bacteria and the archaeal root were used to create a bacteria-bacteria Euclidean distance matrix with Primer. test compared a bacteria-bacteria matrix of phylogenetic distance to archaea with a bacteria-bacteria matrix of shared genes with archaea. Mantel tests were performed with the R package “vegan”. DarkHorse algorithm DarkHorse is a statistical method to look for horizontal gene transfer, using lineage probability index ranking scores (LPI), which inversely reflect phylogenetic distance between the query amino acid sequence and its closest database match (Podell & Gaasterland, 2007; Podell, Gaasterland & Allen, 2008). The lineage of each gene is compared to the lineage of the entire organism. The genes flagged by the DarkHorse algorithm have not been checked with phylogenetic trees, so they should be considered potential horizontally transferred genes. However, the phylogenetic distance between bacteria and archaea is very large, so we are examining genes with very low LPI numbers (below 0. 2 (Podell & Gaasterland, 2007)), which increases the robustness of our analysis. Scores at the lowest end of the LPI range are the most likely to be real horizontally transferred genes because their best hit is quite distant from the query organism (Podell & Gaasterland, 2007). Files including LPI numbers for all available proteins in each of the archaeal and the non-pathogenic bacterial genomes were downloaded from the DarkHorse database at at the genus level comparison (Podell, Gaasterland & Allen, 2008). Using a genus level comparison to find transferred genes means that genes shared at a family or class level would not be considered horizontally transferred. This setting may bias our results away from ancient transfer events. For example, the transfer of bacterial genes that allowed the class Haloarchaea to form from methanogens (Nelson-Sathi et al. , 2012) would not be marked as transferred in our scheme.

Output: corroborate


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: 1 Addressing biases and ethical concernsThe employment of AI in grading brings forward important ethical problems. Goel underlines the significance of facing potential biases in AI systems, which may emerge from biased training data or incorrect algorithm design [15]. It is vital to guarantee AI systems are transparent and their decision-making processes are accessible to educators and students, sustaining trust and fairness in AI-assisted grading. 3. 5 The future of AI in educational assessment3. 5. 1 Projections and potential developmentsLooking forward, AI in educational assessment is predicted to endure considerable breakthroughs. Chiu thinks that future AI systems would not only grade more efficiently but also deliver more detailed and insightful feedback to people [16]. The combination of AI with other developing technologies, such as augmented reality and virtual reality, could further increase interactive learning and tailored education. Advertisement 4. Enhancing feedback with AI4. 1 Personalized feedback through AI4. 1. 1 Customized responses and their impactThe integration of AI in education has ushered in a new era of individualized feedback, significantly altering student interaction with learning materials. Igbokwe studies AI systems that assess individual student replies, indicating distinct learning patterns and opportunities for improvement [17]. This individualized method enables feedback to be adapted to each student’s distinct learning style and pace, resulting in more effective support for specific educational needs. The impact of this personalization is enormous, leading to higher student performance, enhanced retention of knowledge, and a deeper comprehension of the subject matter. 4. 2 Immediate feedback and its advantages4. 2. 1 Benefits of real-time feedbackOne of the most transformational elements of AI in education is the provision of immediate feedback. Al Ka’bi addresses the advantages of real-time replies in educational environments [18]. Immediate feedback allows students to promptly rectify mistakes and reinforce accurate concepts, encouraging a more efficient and continuous learning process. This immediacy is especially advantageous in areas requiring a strong basic grasp, as it minimizes the building of misconceptions and bolsters confidence in learning. 4. 3 AI-driven feedback and student engagement4. 3. 1 Impact on student motivation and participationAI-driven feedback dramatically influences student engagement and motivation. George studies how AI systems, by offering timely and meaningful feedback, might dramatically enhance student participation in the learning process [19]. This greater engagement is linked to the interactive aspect of AI feedback, which tends to be more immediate and tailored compared to older techniques.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: The third and final stage in the development of physical dependence is withdrawal-induced ‘needing’. The needing stage is characterized by a desire to smoke that is so intense and urgent that it cannot be ignored and the individual is so distracted that he or she is unable to function normally [10]. In the words of one teen, “You really want one. You know you need it. You know you’ll feel normal after smoking, and you have to smoke to feel normal again” [10]. When smokers report that they need to smoke, it is not a need to experience pleasure, it is an acute and urgent need to relieve withdrawal symptoms [10]. This description of the Wanting-Craving-Needing stages is not based on, or predicted by any specific theory of addiction, it is a clinical description based on case histories, validated by larger surveys [9–12]. To the author’s knowledge, stages in the development of physical dependence on other drugs have not been identified. Traditionally, it had been assumed that long-term heavy daily use of tobacco was a prerequisite to dependence [13], and the DSM has suggested that prolonged heavy use was a prerequisite for withdrawal syndromes generally [14]. However, in a national survey, 16 % of adolescents who used tobacco only one or two days per month reported strong cravings to use tobacco, and this symptom of physical dependence increased proportionate to the frequency of tobacco use to 78 % among those who had initiated daily use [15]. Irritability and restlessness during withdrawal were reported by 13 % of adolescents that used tobacco 1–2 days/month and by 70 % of daily users [15]. Close to two dozen peer-reviewed studies document symptoms of physical tobacco dependence in nondaily or very light daily smokers [16]. Symptoms can appear after only a few uses of tobacco. [17] Studies demonstrate that physical withdrawal symptoms can be elicited by the administration of an opioid receptor antagonist after a brief exposure to morphine [18, 19]. These studies raise the possibility that clinically significant physical dependence on other drugs is present in some form soon after the onset of infrequent use. If so, we should not expect their symptoms to be identical to those seen during withdrawal among individuals with far advanced dependence. It is likely that only a mild ‘wanting’ to use the drug manifesting at a predictable interval after the last use might be the first sign of physical dependence.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Proteins in reciprocal best hit files were matched to LPI numbers from DarkHorse for both the bacteria and archaea. If the LPI number was below 0. 2 for either the bacteria or the archaea, the reciprocal best hit was tagged as horizontally transferred (Table S4). For each archaeon, each bacterium with a horizontally transferred gene count more than 1. 96 standard deviations from the mean was defined as significantly enriched in horizontally transferred genes and considered to form an enriched pair with that archaeon. This technique is susceptible to multiple testing issues, but enrichment with three or more archaea is considered robust. Three bacteria were >1. 96 standard deviations from the mean for only one archaea and were not substantiated by enrichment in shared genes (above). These three bacteria were removed from further analysis as potential false positives. COlinking proteins to COGs or Clusters of were downloaded from NCBI ftp site. The COG collection used was originally created in 2003 (Tatusov et al. , 2003) and then used to identify COGs in subsequently sequenced genomes. The numbers of RBH in each category can be seen in Table S5. Fifteen categories had enough shared genes in them to examine statistically. COG categories were examined both with and without removal of the genome size effect. analyses, a four-parameter log–logistic function was fit between bacteria and each archaea for each COG category to remove the effect of genome size, as described above. The removal of the correlation of RBH and genome size was checked for each COG and archaea and was successful in all cases (R2 < 0. 001). Additionally, to further take GC into account, the effect of genome size and GC were simultaneously removed from the residuals for each archaea by multiple linear regression using the number of genes in the bacterial genome, reciprocal BLAST hits, and GC content. (Clarke, 1993) analyses were done with the Primer 6 program ( Results and the 448 bacterial genomes used in this study, 273 are from non-pathogens, 81 were anaerobes, 42 were thermophilic or hyperthermophilic (optimum growth temperature >50 °C; abbreviated as “(hyper)thermophilic”) (Fig. 1; Table S1). Regarding salinity, there were 72 moderately halophilic bacteria and 6 extremely halophilic bacteria (Table S1).

Output: corroborate


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: Such participation not only enhances motivation but also produces a more dynamic and participative learning environment, enabling students to actively engage in their education. 4. 4 Impact of AI feedback on learning outcomes4. 4. 1 Educational benefits of AI feedbackThe effect of AI-generated feedback on learning outcomes is significant. Chen presents many studies indicating how AI feedback can increase academic achievement and expand subject matter understanding [20]. This efficacy is largely owing to AI systems’ capacity to deliver precise, specific feedback addressing the complexities of a student’s responses. Furthermore, AI feedback generally includes ideas for development and extra learning resources, making it a full educational tool. 4. 5 Challenges and ethical considerations4. 5. 1 Addressing potential hurdles in AI feedback systemsDespite its many advantages, the deployment of AI in feedback systems offers problems and ethical considerations. Chen covers numerous topics such as potential biases in AI algorithms, the importance of data protection, and the need for transparency in system operations [20]. These problems underline the need for careful design, continual monitoring, and regular upgrades to guarantee AI feedback systems are egalitarian, fair, and respectful of student privacy. Additionally, they underline the significance of human oversight to complement AI feedback, ensuring the educational process stays sympathetic and student-focused. Advertisement 5. Efficiency and scalability in AI-enabled grading5. 1 Enhanced resource management in education5. 1. 1 The role of AI in optimizing educational resourcesEffective resource management is vital in education, directly impacting the quality and efficacy of teaching and learning. AI technology plays a vital role in this field. Alqahtani analyzes the impact of AI on grading systems, stressing not only the rapidity of the grading process but also a considerable improvement in the efficiency of resource allocation [21]. AI-enabled systems can efficiently process massive volumes of student work, a task that normally demands significant time and effort from educators. This quick processing ability frees up significant time for teachers, allowing them to focus more on direct educational activities such as lesson planning, individual student interactions, and pedagogical research. Furthermore, these tools assist detect specific areas where students require additional support, enabling teachers to adapt their education and interventions with greater precision. Beyond the classroom, AI increasingly influences resource management at the administrative level. Schools and educational institutions can gain large cost savings by decreasing the need for extra grading staff or outsourced grading services.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Wanting has not been previously recognized as a withdrawal symptom, probably because of the perceived difficulty in distinguishing withdrawal-induced wanting from the wanting that we all experience as a part of daily living. The key to recognizing withdrawal-induced wanting is to apply the following criteria. Does the wanting recur predictably upon each withdrawal from the drug? Does it appear after a characteristic latency (time interval)? Is it relieved immediately upon using the drug? Is it followed by other symptoms in a predictable sequence? As physical dependence develops in new smokers, the symptoms of wanting, craving and needing develop in that order. During each episode of withdrawal, symptoms emerge in this same order: wanting, then craving, then needing. For individuals in the first stage of physical dependence, the desire to smoke never proceeds beyond wanting. For those in the second stage, the desire to smoke never proceeds beyond craving, while for those in the third stage, the desire to smoke will always progress to needing if abstinence is maintained. Thus, as physical dependence develops, it progresses through stages of wanting, craving and needing, and during each episode of withdrawal, symptoms escalate from a wanting phase, to a craving phase, to a needing phase. Table 1 presents a validated measure of the stages of physical dependence [20]. Individuals answer the three questions and the stage or level of physical dependence is determined by the most advanced symptom endorsed. Table 1 A clinical measure of the Stages of Physical DependenceFull size tableThe classically recognized symptoms of nicotine withdrawal such as irritability, anxiety, restlessness, moodiness, impatience, difficulty concentrating and trouble sleeping [7, 8], do not appear in a standard order as physical dependence develops but are usually associated with the needing stage of physical dependence and the needing phase of withdrawal. In practical terms, this indicates that the seminal work on tobacco withdrawal focused on individuals who had already advanced to the needing stage. Earlier stages of physical dependence were not discovered for many decades because light smokers had been systematically excluded as research subjects. Seminal studies on other withdrawal syndromes have similarly focused on treatment seeking individuals with advanced dependence [2, 3]. This raises the question as to whether similar clinical stages of wanting-craving-needing in the development of physical dependence to other drugs have gone unnoticed because of the natural tendency to focus on treatment seeking individuals with the most advanced dependence. Table 2 lists symptoms selected by the DSM as criteria for a tobacco withdrawal syndrome [7].

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: The outer ring represents the frequency of archaea-bacteria pairs enriched in reciprocal best hits (RBHs), where green represents 0–18 pairs, orange 19–25 pairs, and red 25–31 pairs. Download full-size image DOI: 10. 7717/peerj. 3865/fig-2 Figure 3: Genome size and number of reciprocal best hits fit using non-linear regression. Examples of (A) hyperthermophilic, (B) anaerobic, and (C) extremely halophilic archaea. The number of shared genes between archaea and bacteria were fitted using non-linear regression (black lines) and 1. 96 standard deviations from than the mean were calculated for the residuals (grey lines) to visualize the range. Bacteria-archaea pairs with reciprocal best hit (RBH) counts more than 1. 96 standard deviations greater than the mean are defined as enriched in RBHs. Green marker indicate bacterial endosymbionts where their oxygen utilization was unclear. axis is identical in all three graphs and labels are found in part (C). Download full-size image DOI: 10. 7717/peerj. 3865/fig-3 Eliminating the genome size effect Genome size can confound the quantification of HGT because the number of genes an organism accumulates is an adaptive strategy influenced by many other factors such as nutrient regime. A small genome allows a microbe to use fewer nutrients and to have a favorable surface to volume ratio, so microbes adapted to smaller genomes expel unused genes (Giovannoni et al. , 2005). A large genome in which excess genes are retained longer allows a microbe greater versatility (Chang et al. , 2011; Guieysse & Wuertz, 2012). Merely counting numbers of transferred genes does not take into account these lifestyle differences. In this paper we endeavored to determine which bacteria have engaged in more HGT with archaea than predicted by their genome size. A nonlinear genome size effect has been observed for pairwise comparisons of shared genes between bacteria and archaea (Snel, Bork & Huynen, 1999; Fuchsman & Rocap, 2006). The genome size of the bacteria is a much more important factor than the genome size of the archaea in the nonlinear genome size effect (Fuchsman & Rocap, 2006).

Output: corroborate


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This efficient deployment of human and financial resources can be channeled toward strengthening many educational facets, including student support services, technological improvements, and professional development opportunities for educators. 5. 2 AI’s role in expanding educational horizons5. 2. 1 Adapting AI systems for large classes and online learningThe scalability and adaptability of AI systems are especially significant in big classes and online education, where the student-to-teacher ratio might be disproportionately high. Bauer highlights the inherent obstacles in these situations, such as ensuring equitable and uniform evaluation for a diverse and extensive student body and how AI effectively mitigates these issues [22]. In big classroom environments, traditional assessment procedures typically become unworkable due to the excessive volume of assignments needing examination. AI systems, unencumbered by the limits imposed by human graders, may adeptly manage thousands of assignments concurrently, assuring rapid and uniform grading. This scalability is critical for sustaining academic standards and insuring that students receive fast feedback, essential for their academic growth and progression. In online education, AI systems demonstrate extraordinary versatility. They can adapt to numerous course styles, academic fields, and diverse levels of student academic readiness. This flexibility is crucial in online platforms that usually serve an international and culturally heterogeneous student community. AI grading systems can interpret and evaluate responses across different languages and dialects and can be adjusted to adapt to specific curriculum standards and cultural situations. Moreover, in online education environments, AI systems can offer a level of customization that is impossible to attain in traditional classrooms. By evaluating individual student data, these systems can provide individualized feedback and learning ideas, enhancing the educational experience for each learner. In summary, the efficiency and scalability of AI-enabled grading systems constitute a significant leap in educational technology. They provide solid solutions for managing resources in education, successfully addressing the particular issues provided by big classroom environments and online educational platforms. As these systems improve, they hold the potential to significantly revolutionize the delivery and assessment of education, increasing learning experiences worldwide. Advertisement 6. Beyond traditional testing: AI in creative and subjective assessment6. 1 Evaluating creative work through AI6. 1. 1 Advanced techniques for assessing creativityThe application of AI in grading has predominantly been linked to objective assessments with clear right-or-wrong answers. However, recent advancements have extended AI’s capabilities to include the evaluation of creative work.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: As this list is limited to symptoms that would be experienced only by individuals at the needing stage, the DSM criteria are not a sensitive indicator of physical dependence. Table 2 DSM-5 tobacco withdrawal criteria [7]Full size tableThe stage of physical dependence, as measured by the instrument in Table 1, correlates with progressive changes in the neural architecture of the anterior cingulate gyrus [21, 22]. As the stage of physical dependence advances, neural pathways between the anterior cingulate gyrus and the precuneus increase in number, while those linking to the frontal lobe decrease substantially. Neural activity in networks involving the anterior cingulate increases in proportion to the strength of craving reported by individuals in withdrawal [22, 23]. Although the symptoms of tobacco withdrawal are primarily psychological, the correlation of both the stage of physical dependence and the intensity of withdrawal-induced craving with measures of brain structure and function confirm that physical processes are involved in the development and expression of physical dependence on tobacco. [21, 22] Changes in neural architecture have also been identified in conjunction with other forms of addiction [24, 25]. The progressive changes in neural architecture in parallel with stages of symptom development suggest that some of these changes represent neural adaptation rather than non-specific toxicity [26]. The identification of these changes among very light smokers suggests that it would be worthwhile looking for neural changes among users of other drugs who do not show the classic signs of physical dependence. As with the clinical research, imaging research has missed the opportunity to identify the progression in changes because studies have focused on individuals with advanced dependence. The latency to withdrawalThe latency to withdrawal is a measure of the time elapsed between the last use of tobacco and the onset of withdrawal symptoms. [10, 27, 28] As the symptoms of wanting, craving and needing emerge in that sequence during withdrawal, there is a latency to wanting, a latency to craving, and a latency to needing. The latencies vary between individuals, ranging from 4 weeks to as short as several minutes [27–29]. An 18 year-old woman reported wanting a cigarette after 30 min but being able to go two days before she absolutely needed one, while a 19 year-old man would want a cigarette in 2–3 h, and need one in 6–7 h [10].

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: After determining shared genes from reciprocal best BLAST hits (Table S2), we explored two ways to predict the expected number of shared genes for each bacterial genome size: (1) the Korbel weighted average that utilizes both archaeal and bacterial genome size and (2) an empirical log–logistic function. While the fits were similar for bacterial genome sizes <5,000 bp, the empirical log–logistic function was superior for genomes >5,000 bp (Fig. S2) so was used for further calculations (Fig. 3, Fig. S1). Here, we denote archaea-bacteria pairs that have significantly more (>2 standard deviations from the mean calculated with the log–logistic function) reciprocal best hits (RBHs) than expected based on genome size to be enriched in shared genes (Table S3). This technique is particularly useful for identifying unusually large numbers of shared genes in bacteria with small genomes. Identifying horizontal gene transfer Genes can be shared between archaea and bacteria either due to horizontal gene transfer or due to vertical inheritance from their common ancestor. To test for the extent to which vertical inheritance can explain the numbers of shared genes between archaea and bacteria, we used a Mantel test to compare a similarity matrix derived from each bacterial genome’s number of shared genes with each archaeal genome to a similarity matrix derived from each bacterial genome’s 16S rRNA genetic distance to archaeal 16S rRNA. The correlation between the two matrices is significant (R = 0. 176 p < 0. 0001), but the R value is not high. This result implies that some, but not all, of the shared genes between archaea and bacteria were inherited from the last common ancestor, which is not surprising. To disentangle vertical inheritance and horizontal transfer, we used the DarkHorse algorithm, which searches for horizontally transferred genes using a probability-based, lineage-weighted method in which the lineage of each gene is compared to the lineage of the entire genome (Podell & Gaasterland, 2007). The phylogenetic distance between bacteria and archaea is very large, so we are only examining genes with extremely large lineage differences from the entire genome, which increases the robustness of our results. By cross-referencing our reciprocal best hit data with the DarkHorse results, we can flag some genes as potentially horizontally transferred (Table S4).

Output: corroborate


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This shift involves intricate processes wherein AI algorithms are trained to discern and assess elements such as creativity, originality, and artistic expression. Johnson and Lee have investigated the methods through which AI can appraise creative assignments [19]. These methods often utilize advanced machine learning techniques, including neural networks and deep learning, capable of analyzing patterns, styles, and techniques in various creative works. For instance, in evaluating written creative work, AI systems can analyze aspects like narrative structure, language use, and originality. In visual arts, they can examine color usage, composition, and artistic techniques. The subjective nature of creativity presents a unique challenge. Creative works, unlike objective tests, are open to various interpretations. Therefore, AI systems in this field aim to provide a structured evaluation, focusing on technical aspects and widely recognized standards of creativity while avoiding subjective judgment. This approach requires a delicate balance, ensuring that AI assessments both nurture and support creative expression, avoiding the imposition of overly rigid standards. 6. 2 AI’s flexibility in meeting diverse educational needs6. 2. 1 Tailoring learning to individual p7. Fairness and bias in AI grading7. 1 Tackling inherent biases in AI systems7. 1. 1 Identifying and reducing biasesThe challenge of inherent biases in AI grading systems is a key issue in educational technology. AI systems inherently reflect the biases existing in their data and algorithmic basis. Zhang investigates the ways biases might penetrate AI systems, such as through prejudiced training data, the subjective opinions of developers, or the simplifying of complicated human actions [24]. Identifying these biases involves a complete investigation of the AI system’s training data, algorithmic structure, and outputs. This process demands a multidisciplinary approach, incorporating not only technologists but also educators, sociologists, and students. Regular audits and updates are crucial to ensure in fairness and accuracy. Mitigating these prejudices is a continual undertaking. It needs broadening the training data to span a broad spectrum of student work across varied ethnicities, backgrounds, and learning styles. Additionally, adding feedback mechanisms where educators and students can review the AI system’s performance and fairness is crucial. This inclusive strategy assures the AI system is continually learning and changing, gradually decreasing biases. 7. 2 Establishing fair and inclusive grading practices7. 2. 1 Guidelines for equitable AI gradingCreating solutions for fair and inclusive AI grading is vital to guarantee these systems serve all students equitably.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: The observation that latencies in some smokers are measured in weeks, while in others they are measured in minutes, reflects the fact that the latencies shorten with repeated exposures to nicotine [10, 29]. Since the short half-life of nicotine results in its elimination from the body within a day, it may seem paradoxical that withdrawal symptoms may not appear in novice smokers until several weeks have passed since their last cigarette. The mechanism that triggers tobacco withdrawal symptoms is unknown, but the idea that it is triggered by nicotine levels dropping below a threshold level is incompatible with the clinical evidence. Similar to the phenomenon whereby delirium tremens may not be experienced until several days after alcohol has been cleared from the blood [3], newly addicted smokers may not experience withdrawal symptoms until weeks after their last cigarette [10, 27–29]. In contrast, chain smokers report the need to smoke within minutes of having smoked [27, 28] while blood nicotine levels are still quite high [30]. Nicotine alters the expression of 162 genes in the brains of adolescent rats [31], triggers the release of a half-dozen neurotransmitters [32–35], and alters the production of neurotransmitters and neuronal responsivity for up to four weeks following a single dose [34, 36]. It is likely that mechanisms other than nicotine levels trigger withdrawal. Since nicotine withdrawal in humans is not triggered by the administration of an antagonist as is the case with opiate withdrawal, the mechanisms responsible for withdrawal symptoms may be drug-specific. Admittedly, this suggests that lessons learned about tobacco addiction may not apply to other drugs. On the other hand, non-addictive drugs can produce withdrawal symptoms, so some withdrawal symptoms may have little relevance to addiction, suggesting that differences in withdrawal mechanisms between drugs may not be relevant. Shortening latencies and the trajectory of tobacco useThe clinical implication of shortening latencies is that the length of time an individual remains comfortable after putting out a cigarette decreases over time. This increases the frequency at which cigarettes must be smoked to maintain comfort. [28] An increase in the frequency of smoking is one of the earliest signs of dependence [37]. At a latency to craving of two days, a person could keep withdrawal at bay by smoking one cigarette every other day. Fifteen cigarettes would be sufficient to keep withdrawal at bay for 30 days.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: In general, the numbers of potentially horizontally transferred genes identified by DarkHorse are greater in bacteria that form archaea-bacteria pairs enriched in shared genes (Fig. 4 and Fig. S5). Over half (55%) of the bacteria in pairs with enriched RBHs also had greater than two standard deviations above the mean potential horizontally transferred genes identified by DarkHorse. Figure 4: Comparison of shared and transferred genes. Examples of HGT for anaerobic hyperthermophile Desulforococcus kamchatkaensis (A), anaerobic mesophile Methanosarcina acetivorans (B), aerobic mesophile Nitrosopumilus maritimus (C). In the first column the number of potentially horizontally transferred genes as determined from the DarkHorse algorithm is compared to the number of reciprocal best hits between bacteria and each archaeon. A positive value on the x-axis indicates the RBH count was greater than 1. 96 standard deviations from the mean. In the second column, the number of potentially transferred genes is compared to the number of those genes transferred from archaea to bacteria. In the third column, the number of potentially transferred genes is compared to the number of those genes transferred from bacteria to archaea. Color-coding describes whether the bacterium forms a pair with the archaeon listed (red), with any archaea in this study (blue), or not with any archaea (green). Download full-size image DOI: 10. 7717/peerj. 3865/fig-4 Correlations with growth conditions After the effect of genome size is removed, interesting trends emerge between the number of shared genes and the lifestyles of archaea and bacteria. For example, the enriched archaeal-bacterial pairs involving Desulfurococcus kamchatkensis, an anaerobic hyperthermophilic archaeon, predominately include (hyper)thermophilic bacteria (Fig. 3A). Similarly, Methanosarcina acetivorans, an anaerobic mesophilic archaeon, primarily forms enriched archaeal-bacterial pairs with anaerobic bacteria (Fig. 3B). Contrastingly, the number of archaea-bacteria pairs involving extremely halophilic bacteria and Haloarcula marismortui, an extremely halophilic aerobic mesophilic archaeon, are not significantly more than expected for their genome sizes (Fig. 3C).

Output: corroborate


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: This integration is redefining how instructors and students interact with educational content and assessments. LMS platforms are rapidly adopting AI capabilities that provide real-time feedback on student assignments, suggest resources for improvement, and adapt learning content depending on individual student performance. This integration provides a more tailored and responsive learning experience, making education more flexible to individual learner needs. 9. 2 Preparing for advanced AI integration in education9. 2. 1 Infrastructure, ethical use, and trainingPreparing for the advanced integration of AI in education demands a diverse approach, focused on infrastructure development, ethical application, and extensive training. points out, securing the successful application of AI systems in education extends beyond just technological improvements [29]. Infrastructure development: Educational institutions must invest in the essential digital infrastructure to support advanced AI systems. This involves powerful internet connectivity, suitable hardware, and secure cloud storage solutions. Additionally, there needs to be a focus on designing interoperable systems that can combine diverse educational resources and platforms effortlessly. Ethical use: The ethical application of AI in education is crucial. Institutions need to develop explicit standards and policies that regulate the usage of AI systems. This includes guaranteeing data protection, eliminating potential biases in AI systems, and assuring openness in AI-driven judgments. Ethical use also requires ensuring that AI does not replace the key human parts of teaching but rather complements and enriches them. Training and professional development: Educators and administrative personnel need to be trained not only on how to use AI systems efficiently but also on understanding their limitations and potential biases. Continuous professional development programs should be created to keep educators current of the newest AI advances and best practices in integrating AI technologies into their teaching. The future of AI in grading appears optimistic, with technology growing more sophisticated and integrated into educational procedures. However, this future also involves careful planning and deliberation, ensuring that AI tools are used responsibly and efficiently to enhance the educational experience. Advertisement 10. Conclusion10. 1 Recap and future prospects10. 1. 1 Summarizing key themes and looking aheadAs we conclude our exploration of AI in grading, it is necessary to recall the key aspects and discuss future opportunities. This book has traveled the landscape of AI in education, from its earliest implementation in grading systems to the ethical, privacy, and technical difficulties it raises. We have seen how AI can boost the efficiency and fairness of grading, provide tailored feedback, and adapt to varied educational demands.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: But when the latency to craving shortened to 45 min, one would have to smoke every 45 min to keep withdrawal at bay, which would entail smoking >500 cigarettes over a 30 day period. A 21 year-old woman described a latency-to-craving of two days after having smoked for about six weeks at age 16. Her latency decreased to four hours by age 16½, to two hours by age 17, to 1. 5 h by age 18, to one hour by age 19, and to 30–45 min by age 21. Over this time, her intake increased from 5 cigarettes/day to 15 [10]. Individuals addicted to opiates do not have to use their drug with such frequencies, but is it possible that at the onset of opiate dependence, a single dose may keep withdrawal symptoms at bay for many weeks, but a shortening of the latency to withdrawal demands a gradual escalation in the frequency of use? Aware of their latencies, smokers sometimes smoke in anticipation of a period of abstinence to postpone the onset of withdrawal (e. g. , prior to going to sleep, school or work). Even addicted smokers can smoke for pleasure, or to relieve stress or boredom. This is reflected in the fact that cigarette consumption correlates only moderately with the latencies (rho = −. 53, r = −. 53, and Kendall’s tau b = −. 53 in three studies of adolescents) [27–29]. This level of correlation indicates that only a proportion of cigarettes are smoked for the purpose of relieving withdrawal symptoms. Physical dependence need not be the only reason why addicted individuals self-administer their drug of choice, but it may put an outside limit on how long they can comfortably refrain from use. A new withdrawal period begins each time a smoker finishes a cigarette. From the moment the cigarette is finished the timer starts on the latency period during which withdrawal symptoms are not experienced. This asymptomatic phase of withdrawal may last from a few minutes to several weeks. The asymptomatic phase of withdrawal is followed by a symptomatic withdrawal phase during which wanting, craving, needing and the DSM withdrawal symptoms (anger, anxiety, restlessness, etc. ) emerge. The act of smoking aborts one episode of withdrawal and initiates another, analogous to hitting the snooze button on an alarm clock.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: In general, the bacteria that share unusually large numbers of genes with archaea (i. e. , after correction for genome size) are predominantly anaerobic and/or thermophilic (Fig. 5A). Bacteria isolated from hot springs, oil wells, sediments and activated sludge were over-represented in this set of bacteria (Fig. 5B). This result is remarkable considering that most of the bacterial genomes used in this study were from aerobic mesophiles (Fig. 5A). These trends are also true in the converse; i. e. , anaerobic and (hyper)thermophilic archaea are more likely to have large numbers of RBHs with bacteria (Fig. S6). In contrast, aerobic mesophilic archaea have a small number of RBHs with bacteria and form the fewest archaea-bacteria pairs enriched in RBHs (Fig. S6). Figure 5: Comparison of bacteria enriched in shared genes with archaea to the composition of the database. Pie charts comparing the bacterial database, excluding pathogens, to the bacteria that formed pairs enriched in shared genes (RBHs) with at least one archaeon. (A) Color coded with oxygen utilization and growth temperature of the bacteria (B) Color coded with the environment where the bacteria was isolated. Download full-size image DOI: 10. 7717/peerj. 3865/fig-5 To investigate more precisely which types of archaea and bacteria are more likely to share genes, we examined whether the archaea and bacteria in each enriched pair shared their growth conditions and habitats (Fig. 6). Indeed, in general, anaerobic bacteria share unusually large numbers of genes with anaerobic archaea, and (hyper)thermophilic bacteria share unusually large numbers of genes specifically with (hyper)thermophilic archaea (Fig. 6). One explanation of this result is that bacteria and archaea that live in similar environmental conditions are more likely to share genes with each other. However, this does not seem to apply to aerobic mesophilic bacteria. While all hyperthermophilic bacteria, 80% of thermophilic bacteria were enriched with at least one archaeal partner, and 56% of anaerobic bacteria were enriched in shared genes, Very few aerobic mesophilic bacteria were ever included in enriched pairs, even with aerobic mesophilic archaea (Fig.

Output: corroborate


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: The incorporation of advanced AI technologies like NLP and machine learning into educational procedures, particularly in grading, signifies a fundamental shift in how student achievement is judged. The opportunities of AI in this field are immense. As Pedró and Subosa imply, we are likely to see more advanced AI systems that not only evaluate with high accuracy but also provide nuanced feedback that helps lead students in their learning path [30]. Moreover, the incorporation of AI in grading is projected to expand in line with emerging educational technology. This includes the possible convergence of AI with augmented reality (AR) and virtual reality (VR) for more immersive learning experiences, as well as the usage of big data to get deeper insights into learning patterns and outcomes. However, as AI systems become more incorporated into educational settings, attention about ethical considerations and data protection becomes increasingly crucial. The future of AI in education will be influenced not just by technology breakthroughs but also by the laws and frameworks designed to guide its ethical and fair use. 10. 2 Embracing change and innovation10. 2. 1 The broader impact of AI in educationThe broader significance of AI in education extends beyond grading. and Kodikal emphasize, AI has the ability to alter the entire educational scene [31]. It can lead to more personalized and adaptable learning settings, where instruction is adapted to individual student requirements, talents, and learning styles. This customization could help bridge gaps in education, delivering support where it needs most and challenging people to realize their full potential. AI’s significance in education also necessitates a reevaluation of established teaching methods and curriculum frameworks. The data-driven insights offered by AI can guide more effective teaching practices and curriculum development, leading to a more responsive and dynamic educational system. However, embracing this transformation demands a coordinated effort from educators, lawmakers, technologists, and students. It includes not only accepting new technologies but also adapting to the cultural and educational shifts they bring. The role of educators, in particular, will shift, with a greater focus on guiding and mentoring students through a technology-enhanced learning journey. In conclusion, the future of AI in education is not just about technological innovation; it is about utilizing this technology to produce more equal, effective, and inspiring educational experiences. As we welcome these developments, it is vital to handle them intelligently, ensuring that AI is used as a force for good in defining the future of education. 10. 2.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Traditionally, when researchers referred to withdrawal they were referring to advanced withdrawal symptoms, as described in the DSM. However, under this new conceptualization of withdrawal, smokers are in a state of withdrawal (asymptomatic or symptomatic) anytime they are not actively smoking. In other words, the term ‘withdrawal’ is used to describe a physiologic state rather than specific symptoms. Analogously, when the cause of was unknown, symptomatic individuals were diagnosed with AIDS. After the underlying cause was better understood, individuals were described as carrying the human immunodeficiency virus, which is often an asymptomatic state. Smokers are in a state of withdrawal anytime they are not smoking. If they go too long without smoking they may experience withdrawal symptoms of increasing severity as time passes. Individuals with short latencies may experience symptomatic withdrawal dozens of times each day even though they are making no effort to maintain abstinence. Shortening latencies compel smoking at progressively shorter intervals and the resulting escalation in cigarette consumption can be mathematically modeled as a smooth trajectory [38–40]. The latencies shorten at different speeds and to different degrees in different individuals, determining whether that person will plateau as a light, moderate or heavy smoker. Longer latencies allow some physically dependent smokers to remain light smokers over a lifetime [41]. In others, the latencies shorten so much that they feel compelled to chain smoke. The fact that latencies shorten is the key to understanding the pathophysiology of tobacco dependence, its behavioral manifestations and its clinical course throughout the lifespan [42]. To the author’s knowledge, latencies have not been systematically studied in relation to any other drugs. Clinical manifestations of shortening latenciesThe trajectory of the frequency of tobacco use is very different from that of a drug like alcohol. When adolescents are drinking to achieve intoxication, the amount of alcohol consumed on weekends may increase substantially without a concomitant increase in the number of drinking days per week. With tobacco, the opposite pattern is seen. As the latency shortens, smoking frequency increases from monthly to weekly, to several days per week, and finally to daily smoking. All the while, the number of cigarettes smoked on smoking days remains around one to two [43]. Only when the latency shortens from days to hours is there a gradual increase in the number of cigarettes smoked per day [44].

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: 001 Low vs significant pH (2 way w/Temp) Not significant with 16S Matrix 0. 176 0. 000 DOI: 10. 7717/peerj. 3865/table-1 Like the number of reciprocal best hits, the number of potentially transferred genes is highest among anaerobic or thermophilic bacteria and anaerobic or thermophilic archaea (Fig. 4 and Fig. S5). sp. BAV1, 12–31% of total shared genes were horizontally transferred, and this was fairly typical for bacteria enriched in shared genes. The numbers of transferred genes were larger than the residual from the upper confidence interval for shared genes, but these two numbers are not necessarily comparable. However, it does seem likely that a large proportion of the extra shared genes in enriched archaea-bacteria pairs (i. e. , those identified by our correction for genome size) were horizontally transferred. Figure 7: Examination of horizontal gene transfer in individual bacteria. Bacteria that form pairs with >3 archaea for either the number of residual reciprocal best hits (RBH) or potentially horizontally transferred genes, as determined by DarkHorse. The color coded bar graphs represent the number of archaea with which that bacterium forms enriched pairs (similar to Fig. 6). Bacterial names (left) are also color coded by bacterial oxygen utilization and growth temperature as in legend. Circles next to bacterial names indicate bacterial isolation environment. The column of numbers indicates the average number of horizontally transferred genes from archaea to bacteria for each category of archaea in the order aerobic thermophilic, anaerobic thermophilic, anaerobic mesophilic, and aerobic mesophilic archaea. The average number of HGT genes from archaea to bacteria for the entire dataset is four. Download full-size image DOI: 10. 7717/peerj. 3865/fig-7 The DarkHorse results on horizontal gene transfer can be evaluated to determine the direction of transfer (i. e. , from bacteria to archaea or from archaea to bacteria). Most documented cases of HGT between bacteria and archaea are predominantly unidirectional from bacteria to archaea (Wiezer & Merkl, 2005; Kanhere & Vingron, 2009; Nelson-Sathi et al. , 2015).

Output: corroborate


Input: Claim: Natural language processing (NLP) is used in education to develop intelligent virtual assistants for student support. [SEP] Text: Max Silberztein2 233 Accesses AbstractNowadays, most Processing software applications use empirical “black box” methods associated with training corpora to analyze texts written in natural languages. To analyze a sequence of text, they look for similar sequences in a corpus, select among them the most similar one according to some statistical measurement or some neural-network-based optimization state, and then bring forth its analysis as the new sequence analysis. Here, I first show that the limited size of the corpora used and their questionable quality explain why most NLP applications produce unreliable results. Next, I examine the principles which are at the basis of corpus-based methods and uncover their linguistic naiveté. I finally dispute the scientific validity of empirical approaches. I propose solutions to various problems that are based on the use of carefully handcrafted linguistic methods and resources. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 119. 00 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e159. 99 Price excludes VAT (USA) Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Notes1. Available at: See (Nelson & Kucera, 1979). 2. Available at: See (Davies 2009). 3. Available at: at: See (Taylor et al. 2003). 5. By wordforms, we mean contiguous sequences of alphabetic characters delimited by non-letters (a. k. a. delimiters). For instance, “Monday”, “soon” and “stepfather” are wordforms, whereas “week end”, “as soon as possible” and “father-in-law” contain respectively two, four and three wordforms. 6.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: Individuals with very short latencies will begin to feel uncomfortable within minutes of finishing a cigarette, prompting chain smoking. Such individuals will spend a great deal of time smoking (Table 3, Diagnostic and 5 (DSM 5) criterion 3). Table 3 DSM 5 criteria [7]Full size tableAs the latency shortens, smokers may experience difficulty in refraining from smoking in situations where it is not allowed and may avoid such situations because the emergence of craving and needing makes them uncomfortable (Table 3, DSM 5 criterion 7). Individuals with latencies shorter than the time spent in bed may smoke just before going to bed and still feel the need to smoke immediately upon arising. The latency explains why the time to the first morning cigarette is a valid measure of dependence [15, 45]. Insomnia is a symptom of tobacco withdrawal and individuals with very short latencies may awaken during the middle of the night needing to smoke [46]. The discomfort of individuals in the needing stage of withdrawal may be such that they will smoke even when sick in bed because the discomfort of withdrawal makes them feel even worse, and why, even some nondaily smokers feel compelled to go outside in severe weather to smoke [37]. A note on medical and psychiatric approaches to diagnosisThe medical approach to identifying and diagnosing conditions is based on evidence of a disruption of normal anatomy or physiology [47]. The observation that the three stages of physical dependence (wanting, craving and needing) correlate with structural and functional changes in the brain establishes that physical dependence represents a disruption of normal anatomy and physiology [21]. As such, tobacco dependence can be diagnosed as a medical condition when symptoms of withdrawal-induced wanting, craving or needing are reported [47]. In practical terms, tobacco users fulfill the criteria for a medical diagnosis of tobacco dependence when they reach the wanting stage of physical dependence as indicated by endorsement of the first symptom on the instrument in Table 1. While medical conditions are diagnosed on the basis of indications of altered anatomy or physiology, the psychiatric approach to diagnosis is based on indications of impairment. Under the definition offered by the DSM 5, substance use represents a mental disorder when a combination of specified symptoms causes “clinically significant impairment or distress” [7]. Dissimilar to intoxicating and illegal drugs, tobacco use rarely causes incarceration, job loss or divorce.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: The number of transferred genes from bacteria to archaea may be five-fold larger than the number of genes transferred from archaea to bacteria (Nelson-Sathi et al. , 2015). Our results agree with this trend for “normal” archaea-bacteria pairs (i. e. , those that are not enriched in shared genes); these pairs generally have more genes transferred from bacteria to archaea (Fig. 4 column 3; Table S4). However, of the 58 bacterial genomes that form pairs for enriched numbers of shared genes, over half had 11 or more genes horizontally transferred from archaea to bacteria (Fig. 7), whereas the mean number of archaea to bacteria transfers for all genomes is only 4 ± 7. Anaerobic and (hyper)thermophilic bacteria have more transferred genes from anaerobic and (hyper)thermophilic archaea, respectively, than from aerobic or mesophilic archaea (Fig. 7). Some bacteria, such as Dictyoglomus and Dehalococcoides, have 30–50 horizontally transferred genes from archaea (Fig. 7). These results suggest that the unusually large numbers of shared genes in our enriched archaea-bacteria pairs is primarily due to an unusually large number of gene transfers from archaea to bacteria in anaerobic and (hyper)thermophilic conditions. Examination of halophily Given that the numbers of shared genes are strongly associated with bacterial growth temperature and oxygen utilization, the lack of any significant correlation with halophily is surprising (Table 1) though consistent with results from Rhodes et al. (2011). There appears to be no difference in the numbers of shared genes between bacteria and archaea for non-halophilic and moderately halophilic bacteria. Of the six extremely halophilic bacteria, only two, Salinobacter ruber and Chromohalobacter salexigens, were ever enriched in RBHs. Salinibacter ruber, which is known to share genes with haloarchaea, including rhodopsins and a hypersalinity island (Mongodin et al. , 2005), was enriched in shared genes with each of the six extremely halophilic archaea. Salinibacter also possessed many horizontally transferred genes from archaea (Fig. 7).

Output: corroborate


Input: Claim: AI will fully replace human teachers in the next decade. [SEP] Text: 5 found that, like humans, it tends to overestimate how widespread its opinions are in the general population, a bias known as the false consensus effect. But unlike humans, the model showed little hesitation in taking risks and tempting fate. Marcel Binz, a cognitive scientist at the Institute for Biological Cybernetics, says AI might need to physically interact with the world to exactly mimic human participants; it’s hard to learn all the nuances of intelligent behavior just through passive reading. But he thinks AI will progress quickly in any case. “It is plausible that we will have a system within a few years that can just be placed into any experiment and will produce behavior indistinguishable from human behavior. ” A critical question is whether language models will not just reproduce existing findings, but generalize and predict new ones. When models appear to match published psychology studies, they could be regurgitating training data in response to memorized questions. As a result, many researchers are taking pains to phrase questions in novel ways. Another lingering issue is whether models reflect what people would actually do or just what they say they’d do. People often lie to researchers—and even themselves. co-founder suspects the models state true p3. She and her colleagues found that, like the people in the original experiment, the model didn’t begin to let off the button until 300 volts. Aher thinks the models could provide guidance in other sensitive arenas that are difficult to study, for example, what to say to a person who is suicidal. Gray says researchers could study ostracism, or the role of negative feedback on self-esteem. Or, he says, they could study dehumanization of the kind seen in the Vietnam War’s My˜ Lai massacre by describing the situation and asking a model what it would do—provided the models aren’t too sanitized. Argyle says she doesn’t know of anyone yet who has replaced human participants with language models. “To be honest, this is all still pretty much a hypothetical,” she says. “First we have to demonstrate the language models can do the work. ” believes the shift is inevitable. It reminds him of a similar transformation a decade ago, when many social science experiments moved from in-person to online surveys. “People were like, ‘How can you run experiments online? Who are these people?

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: The DSM does not explain what impairment or distress might mean in relation to tobacco use, and researchers have always assumed that anyone who satisfies the diagnostic criteria has “clinically significant impairment or distress” [48]. Table 3 lists the diagnostic criteria for Disorder under DSM-5 along with some criterion-specific observations. Only two criteria must be met to satisfy the DSM-5 requirements for a psychiatric diagnosis of tobacco use disorder. Tobacco users satisfy the DSM-5 tobacco use disorder criteria when they reach the craving stage of physical dependence. The presence of craving would satisfy criterion 4 “Craving, or a strong desire or urge to use tobacco” and smoking to alleviate that craving would satisfy criterion 11b “Tobacco (or a closely related substance, such as nicotine) is taken to relieve or avoid withdrawal symptoms. ” Since all individuals at the craving stage of physical dependence smoke to relieve their craving, simply establishing that an individual has reached the craving stage of physical dependence is equivalent to a DSM-5 diagnosis of tobacco use disorder. Although the medical and psychiatric approaches to diagnosis come from different perspectives, in practical terms, using the instrument in Table 2, tobacco dependence as a medical disorder can be diagnosed at the wanting stage of physical dependence, while DSM-5 tobacco use disorder can be diagnosed at the craving stage. The implication here is that researchers should recognize that dependence as a medical disorder may be present in individuals who do not meet DSM criteria for a psychiatric disorder; researchers should not limit their focus to individuals who meet DSM criteria. Dependence onset in relation to smoking frequencyCraving after having smoked only a few cigarettes is common. [17, 28, 49, 50] In a survey of 34,000 adolescents who had tried smoking, craving or other dependence symptoms were reported by one third of those that had smoked 3 or 4 tobacco cigarettes, and by half of those that had smoked 10–19 cigarettes [28]. By the reasoning described above, half of adolescent smokers meet DSM-5 diagnostic criteria for tobacco use disorder before they have smoked a whole pack of cigarettes. This should not be surprising given the rapidity with which nicotine triggers enduring neuroplastic changes in the brains of experimental animals (see [26] for a review). Neuroplastic changes have been observed after a single dose of nicotine in animal studies [51]. Each of the first 100 cigarettes appears to promote shortening of the latencies and the appearance of additional symptoms.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Salinibacter was isolated from a salt cistern, which is the saltiest habitat known to harbor bacteria (Mongodin et al. , 2005). None of the other extremely halophilic bacteria in this dataset reach the ultra-extreme halophily of Salinobacter (Hirschler-Rea, 2003; Hou et al. , 2004; Mongodin et al. , 2005; Auman et al. , 2006; Hoeft et al. , 2007). is an exception among the six halophilic bacteria examined here in its extensive sharing and transferring of genes with archaea, and this may be related to its extreme halophily. We do not deny that halophilic archaea have been changed by horizontal gene transfer with bacteria. However, the ancient transfer of 1,000 bacterial genes that allowed the class Haloarchaea to form from methanogens (Nelson-Sathi et al. , 2012) would not be marked as transferred in our scheme because the genes are present in several genera. Additionally these genes don’t appear to be shared specifically with other modern sequenced halophilic bacteria. Correlation with genome %GC Along with oxygen utilization and growth temperature range, bacterial genome %GC content was also a significant factor correlated with the number of shared genes (Table 1). As has been noted previously (Naya et al. , 2002; Wu et al. , 2012), in our dataset aerobic bacterial genomes have a significantly higher %GC (59%) than anaerobic bacterial genomes (45%; p < 0. 001) but GC content does not correlate with oxygen utilization in archaea. Similar to Hurst & Merchant (2001), GC content does not correlate between temperature groups in either archaea or bacteria in our dataset. To verify that %GC is not confounding the effect described above for anaerobes and (hyper)thermophiles, we repeated our analyses after correcting for both genome size and genome %GC by multiple linear regression (Fig. S4). The magnitude of the %GC correction (average of 2. 6 per 10% GC change) was much smaller than that for the original genome size correction (average 45 per 1,000 genes). Interestingly, a relationship between GC content and reciprocal blast hits was only significant for aerobic archaea (p < 0. 01).

Output: corroborate


Input: Claim: AI will fully replace human teachers in the next decade. [SEP] Text: AI replace doctors’ ‘gut instincts’? Published on December 20, 2023 Doctors’ intuition plays a key role in healthcare, even when computers suggest another treatment approach. But with AI advancing, is that all about to change? Use + rapid advances in the field, AI won’t supplant human healthcare workers in 2024. : Pexels: to use -Edquist DOI 10. 54377/02d2-eea4 Doctors’ intuition plays a key role in healthcare, even when computers suggest another treatment approach. But with AI advancing, is that all about to change? The value of healthcare workers’ intuition in effective clinical care has been verified by reports around the world again and again. From doctors’ ability to spot sepsis in critically ill children, to ‘nurse worry‘ as a ‘vital sign’ predictive of patient deterioration, to helping GPs navigate complex patient care — intuition appears to play a large role in supporting high risk patients — even when data or computer outputs suggest another treatment approach. (AI) has already begun to transform healthcare, and the health sector will only continue to consider AI innovations in 2024 and beyond. In this increasingly technological world, questions swirl on what is the role of these human hunches in healthcare practice and whether AI is about to overtake doctors’ ‘gut feelings’ entirely. What is AI in healthcare and when is it used? Davenport from and Deloitte consultant explain elsewhere, AI healthcare includes ‘rule-based expert systems’, which use prescribed knowledge-based rules to solve a problem, and ‘robotic process automation’, which uses automation technologies to mimic some tasks of human workers. Such technology can help with automated patient monitoring, where alerts are signalled once a rule criterion is met, patient scheduling reminders and medicine management. Other forms of AI used in healthcare include robots, natural language processing and machine learning. Robots can help move and stock medical supplies, lift and reposition patients and assist surgeons. hospital has launched a €7 billion project, set to be completed in 2028, which will engage robots to collect patient data typically reliant on human physical touch: from measuring pulse, to taking temperature and calculating oxygen saturation. The release of ChatGPT in 2023 marked a leap forward for AI in popular consciousness.

Output: contrast


Input: Claim: The study of folklore can provide insights into cultural perceptions of nature. [SEP] Text: [17, 28, 37, 49] The prevalence of craving and other symptoms increases to about 95 % among youth who have smoked 100 or more cigarettes [17, 52, 53]. In relation to smoking frequency, symptoms of physical dependence are reported by 82 % of youth who smoke at least once per week but not every day, and by 95 % of daily smokers [52]. Consistent with reports of latencies of four weeks or more, 46 % of youth who were smoking less than once per month experienced symptoms of physical dependence [17, 29, 49, 54]. Similarly, in a national survey nearly half of youths who smoked as few as 1–3 days per month reported having experienced at least one symptom of nicotine dependence [43]. Growth in the number of symptoms of dependence reported is greatest at the lowest levels of exposure in terms of days smoking per month or cigarettes smoked per day [43]. Symptom development tends to plateau when daily consumption reaches 6–15 cigarettes [43]. PrognosisThe prognosis for adolescent tobacco dependence is poor. Early symptoms of dependence predict continued and escalating smoking [37]. Shortening latencies promote an escalation in smoking frequency, while more frequent smoking promotes the progression of dependence in a mutually reinforcing cycle [45, 53, 55]. The number and intensity of the symptoms experienced by very light smokers is disproportionate to their tobacco consumption, with some nondaily smokers describing their withdrawal symptoms as unbearable [56–58]. Many nondaily smokers have failed at one or more attempts to quit, and, in cessation studies, nondaily and daily smokers relapse at the same high rates [54, 59–62]. The first insights into how tobacco dependence develops came from observing that youths who used tobacco infrequently reported significant difficulty in stopping their use. It might be a fruitful first approach to ask infrequent users of other substances if they have ever failed in an attempt to stop using the drug. Because relapse rates are very high even before the onset of daily use, craving is a grave prognostic indicator even at minimal levels of tobacco use [59, 60]. Youth become aware of craving, on average, when they are smoking two cigarettes per week [59, 60]. In a 12-year longitudinal study, smoking two cigarettes per week at age 12 increased the risk of progressing to heavy adult smoking with an odds ratio of 174 [38, 63].

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: A two-way of the secondary residuals indicated that oxygen utilization and growth temperature of the bacteria were still significant factors affecting numbers of RBHs (aerobic vs. anaerobic R = 0. 382 p = 0. 001; mesophilic vs. hyperthermophilic R = 0. 676p = 0. 001). address the question of whether bacteria and archaea from the same habitats are more likely to engage in HGT, we categorized the DarkHorse and RBH results by the environment from which the archaea and bacteria were isolated. Habitats with similar temperature and oxygen characteristics, such as terrestrial hot springs and marine hydrothermal vents, have similar numbers of horizontally transferred genes and RBHs between bacteria and archaea (Fig. 8). A similar correspondence was observed with animal guts and sediments, both of which harbor a high proportion of anaerobic organisms involved in the degradation of complex organic matter (Fig. S7). These findings suggest that metabolic and physiological similarities could be more important in determining successful gene transfers between archaea and bacteria than co-habitation. However, our database does not include bacteria and archaea isolated from exactly the same area, which would be needed to examine this question more thoroughly. Figure 8: Comparison of transferred and shared genes with habitat. Comparison of transferred (A) and shared (B) genes with habitat. Particularly, we compare genes in hotsprings and hydrothermal vent bacteria to hotsprings and hydrothermal vent archaea. HGT stands for horizontally transferred genes, and RBH stands for reciprocal best hits or shared genes. Download full-size image DOI: 10. 7717/peerj. 3865/fig-8 Our findings do suggest that HGT is more common in some habitats than others. Oil wells, hot springs, and sediments are environments where bacteria and archaea share unusually large numbers of genes (Fig. 5B). 30% of bacteria with HGT genes >2 standard deviations above the mean were from hot springs, 32% were from sediments and 20% were from soil (Fig. 7). Thermophiles employ various mechanisms to mitigate DNA degradation at high temperatures, so thermophile-derived DNA may be more likely to persist both inside and outside of cells.

Output: corroborate


Input: Claim: AI will fully replace human teachers in the next decade. [SEP] Text: This type of AI, which requires training on large data sets (supported by human feedback), focuses on giving computers the ability to read, support and manipulate human language. Such natural language processing has changed the communication landscape with its language mimicry. While some note that the hype hasn’t quite been reflected in reality, professionals in a range of sectors — including healthcare — now use ChatGPT for correspondence, such as with drafting “sick notes”, medication management, or to manage healthcare information. There are predictions that healthcare Processing will be a $USD7. 2 billion business by 2028, with this type of AI being deployed to help translate complex published papers for public consumption, for analysis of electronic health records to help identify at-risk patients, and to interact with patients to help with triage or answer healthcare questions. In 2024, some say this type of AI is likely to focus on more sophisticated language models that power chatbots and virtual assistants, and will be built into word processing programs. gives computers the ability to learn without explicitly being programmed for a given task. The algorithms driving these types of AI are based on statistical and predictive models. often relies on ‘training’ from existing data sets, which have been human reviewed and annotated. Essentially, doesn’t automatically know what to look for, and without human-informed training this type of AI tends to provide lots of noise and useless predictions. Once trained, can take previously unseen patient information and apply its prior ‘training’ to analyse the data and predict outcomes, or make recommendations. In healthcare, can recognise patterns which may be missed by humans such as described for AI’s role in patient survival of gastric cancer, identification of primary causes of cancers, and reducing breast cancer false positives. In 2024, algorithms are likely to continue to be used for probing healthcare data analytics, with vast medical data provided by wearables, medical devices and electronic health records. With all forms of healthcare AI — it is clear that humans are still needed for AI training, evaluation of outputs, and considering the impacts of the AI recommendation. Artificial versus human intelligence: Who has the edge? Given the global healthcare workforce shortages, the proverb may be true: Necessity breeds invention.

Output: contrast


Input: Claim: Natural environments can reduce stress and improve mental health. [SEP] Text: 1 BackgroundMental health crisis is defined as “occurring when the level of distress and risk presented by a young person is not adequately supported or contained by the system that is in place for them. ” [1] (p. 13). Although over half of mental health disorders arise during adolescence, and the greatest benefits of mental health interventions are attained when implemented as soon as possible after onset of symptoms [2], it is estimated that only 25% of children and young people with a diagnosable mental health disorder are receiving treatment in the UK [3]. With the lack of early support being accessed by young people experiencing adverse mental health, it is unsurprising that the number reaching crisis point is growing. Unfortunately, in the UK, Child and (CAMHS) are not always able to meet the demand for their services, leading to long wait times for specialist mental health support, and ultimately leading to more young people reaching crisis point [4]. Possible ramifications of not addressing poor mental health in childhood and adolescence are the likelihood of mental disorders continuing into adulthood, impacting both mental and physical well-being, which could potentially limit opportunities to live a fulfilled life [5]. However, not only can poor mental health and mental health crisis have long-term consequences, but also severely damaging short-term effects, including suicidal ideation, self-harm, and currently one of the leading causes of death for children and young people in the UK, suicide [6]. In 2015, the Commission’s report, Right here, right now [7], reviewed the quality and effectiveness of crisis services in the UK and found them to be insufficient, often leading those in crisis to attend A&E as they had nowhere else to go. Furthermore, when attending A&E in crisis, less than half of young people reported a positive experience [7], along with other crisis services, A&E was scrutinised for being “under-resourced, understaffed, and overstretched” [8]. As a result, crisis services have been at the forefront of the mental health service reform agenda in the England, with the call to improve crisis services evident in key health documents, including the Concordat [9], Future in Mind [3], and the for [10]. The development of crisis mental health services continues to be at the forefront of policy in the UK and internationally [11].

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: This biochemical characteristic of thermophiles has been previously suggested as a possible cause for the greater prevalence of HGT events between thermophiles compared to halophiles (Rhodes et al. , 2011). Furthermore, oil wells and hot springs are typically dominated by biofilm communities (Blenkinsopp & Costerton, 1991; Meyer-Dombard et al. , 2011; Marks et al. , 2012), which are known to facilitate HGT due to the high density and close physical proximity of metabolically active cells (Molin & Tolker-Nielsen, 2003; Aminov, 2011). Sediments and soil also have a high density of microbial cells (Christensen, Hansen & Sørensen, 1999; Schippers et al. , 2012). Additionally, viral abundance and production in sediments is high compared to the water column (Danovaro et al. , 2008), and more than half of the DNA in sediments is extracellular (Dell’Anno & Danovaro, 2005). Therefore, all of these factors (cell density, viral abundance, and extracellular DNA) may promote HGT in sediments. Functional characterization of shared and transferred genes To gain more insight into the types of genes that may be shared more frequently between anaerobic and (hyper)thermophilic organisms, we examined the shared genes (RBHs) by functional category using the COG database (Tatusov et al. , 2003) (Table S5). Some COG categories are more likely than others to contain genes shared between bacteria and archaea. Specifically, the categories Energy Production, Translation, Nucleotide Metabolism, and contain a higher proportion of archaea-bacteria RBHs than that found in the total set of genes in all genomes (Fig. 9). Curiously, HGTs identified by DarkHorse are not enriched in these same categories; instead, they are more abundant in Transport and Metabolism (such as metal or sodium transporters), and Conversion, and Transport and Metabolism (Fig. 9B).

Output: corroborate


Input: Claim: AI will fully replace human teachers in the next decade. [SEP] Text: It may not be long until healthcare includes integrated AI forms where a robot greets you in your native language for your annual check-up using Processing, takes your vital signs, and sends a recommendation to the doctor on which patient to prioritise and what investigations need to be ordered using algorithms designed to analyse collected vital signs. What AI can’t do is replace the natural ‘gut feel’ of a healthcare professional. And this won’t change in 2024. The clinical reasoning and thinking process that healthcare providers engage is so complex, and the sources of information that the human brain considers in patient care are too numerous to capture with current algorithms. The implicit knowledge an expert relies on for effective clinical care is so deeply embedded in human automation, that methods to get at these data points often fail. On top of this, AI-accessible data and the AI algorithms themselves can have flaws. Machine learning can be overly sensitive, leading to over-diagnoses in some patients. Processing AIs can act as healthcare trojan horses, where the technology is so convincing in its communication approaches that it tricks the user into thinking it is knowledgeable in the same way a human is. There are also privacy concerns with such AI applications. AI typically relies on data input to continue learning — and the clarity about what happens to confidential patient information when input into AI remains an open question for many platforms. There are also challenges that the healthcare AI field is tackling related to bias and responsibility, and questions about what we consider ‘mundane’ and ‘repetitive’ tasks which AI can truly take off humans. In reality (ironically), all existing AI is devoid of the necessary context within which healthcare occurs. It misses the complexity, the empathy, and important data points that human intelligence has access to, and can therefore only replicate specific human tasks. Our healthcare future: Humans leading, AI supporting A better description of the role of AI in healthcare in the future might be: “AI won’t replace the doctors, but those doctors will be replaced who don’t use Artificial Intelligence,” as Reddy, director at India’s Apollo, has put it. Healthcare AI is increasingly taking on roles of “clinical decision-making support”, meaning the healthcare provider is in charge and human intelligence is prioritised, while AI augments this.

Output: contrast


Input: Claim: Natural environments can reduce stress and improve mental health. [SEP] Text: Despite this current policy discourse, little is actually known about the current organisation of crisis services in the UK, or the experiences of those accessing them [12]. A recent systematic review explored the currently available evidence on the organisation, experiences, effectiveness, and aims of crisis services for children and young people [13]. The review demonstrated a wide range in crisis services available globally, with many having positive effects on those that utilise them, and established three categories of organisation of crisis services; triage/assessment-only; digitally mediated support approaches; and intervention approaches and models [13]. This research aims to contribute to the growing field of research conducted on crisis mental health services, to help fill gaps identified within the field. The context of the current research is Greater Manchester, England, an area that has witnessed devolution of its health and social care budget, a process whereby power and funding is transferred from central to local government [14]. As a part of the devolution process, has made a significant investment into mental health services for children and young people, with a newly created Pathway, to offer a solution to the calls for better crisis service provision across the 10 localities of the city-region. In 2017, the Pathway was established in Greater Manchester, with the aim to improve the experiences of children and young people accessing crisis care services and avoid A&E admissions [1]. The pathway comprises six newly created and enhanced established services (see Table 1). Table 1 Crisis care pathway servicesFull size tableAs part of the Pathway’s plan, these crisis services should work together, and with wider mental health services in the community, to support young people on their path to wellness. A key intention of the current study is to examine the extent to which this aspiration has been realised in practice. Since the creation of the Pathway in 2017, these services have been slowly implemented across the 10 boroughs of and have evolved according to need. The information provided in Table 1 is the most relevant and up to date description of services as they currently stand. Methods of effective and consistent data collection were not designed into the implementation of the Pathway, making it difficult for managers to evaluate the effectiveness of this new crisis service delivery system. However, funders and managers of the Pathway wanted to evaluate this new pathway, so that services can continue to improve for the benefit of the young people accessing them. They asked the University of Manchester to conduct a third-party study to investigate this for them.

Output: pass


Input: Claim: Horizontal gene transfer allows bacteria to acquire new traits. [SEP] Text: Consistent with previous findings that transferred transporters were enriched in Methanosarcina genomes (Garushyants, Kazanov & Gelfand, 2015), transporters were especially enriched in HGTs in our dataset: 15% of HGTs involved transporters of some kind compared to 5% in the whole dataset of all genomes. Indeed, transporters can be expected to confer immediate benefits to a cell after horizontal transfer. Figure 9: COG categories of genes shared between archaea and bacteria. (A) COG categories of all genes in all genomes, (B) all genes shared between bacteria and archaea as measured by RBHs, and (C) all genes identified as HGTs by DarkHorse. (D) Boxplot comparing COG cateogories that are enriched in HGTs. (E) Boxplot comparing COGs that are enriched in RBHs. In the boxplots, the lines represent the mean, points represent outliers, the edges of the box are the 25th and 75th percentiles and the whiskers represent the 90th percentile. Values in the boxplots are normalized by the appropriate total numbers of genes. Download full-size image DOI: 10. 7717/peerj. 3865/fig-9 Interestingly, archaea and bacteria share many genes in the Translation category, but these genes are not horizontally transferred (Fig. 9C). This is consistent with translation proteins being highly conserved between archaea and bacteria (Koonin et al. , 2012) and is likely an example of genes shared from vertical descent. and Metabolism genes are also frequently shared by archaea and bacteria but not horizontally transferred (Fig. 9C). Notably, Transcription and genes do not seem to be shared between bacteria and archaea (Fig. 9). The lack of shared genes in these categories is not surprising (Cordero & Hogeweg, 2009), considering that archaea and bacteria employ completely different transcription machinery (Jun et al. , 2011) and signal transduction pathways (Ulrich, Koonin & Zhulin, 2005). The abundance of shared genes in each COG category was normalized for genome size using the empirical fit log–logistic function (Table S6). As with the total dataset, temperature, oxygen utilization and GC content were all significantly correlated with the number of shared genes in many COG categories (Table 2).

Output: corroborate