Input: Claim: Shift work can adversely affect health due to misalignment of circadian rhythms. [SEP] Text: Placebo was not equal to baseline for SOL. A general meta-analysis of placebo effects pointed to a nonsignificant beneficial effect on sleep latency (a 10-min decrease in subjective estimates of sleep latency) in five clinical trials [37]. Melatonin did not alter the other sleep parameters that we measured. These findings are in accordance with those of earlier open trials using smaller numbers of subjects [38,39]. The focus of our analysis was a comparison of assessment of insomnia parameters (sleep onset, sleep maintenance, sleep quality) in nurses with shift working at baseline, after a one-night melatonin treatment, and after a one-night placebo treatment. Our study revealed no major impact of melatonin on difficulty staying asleep or waking up too early. Our results agree with a previous investigation suggesting that patients with primary insomnia have a pathophysiologic disturbance that is not reversed by melatonin [40]. No adverse effects of melatonin were noted during the treatment. The fact that we observed a reduction in SOL but no change in sleep duration after melatonin administration has at least two possible explanations: either 1) melatonin caused a small (16 min) phase advance of the circadian system or 2) random variation obscured a correspondingly small lengthening of total sleep time. Our data do not provide the basis for favoring either one or the other of these alternatives. A limitation of this study is that we were unable to perform polysomnography or actigraphy to evaluate sleep parameters objectively. Our results based on subjective self-reports were, however, very encouraging. Regarding the high prevalence of insomnia in shift workers, more studies about melatonin effect on different kinds of insomnia parameters (difficulty falling asleep, difficulty staying asleep, problem waking up too early, and sleep quality) causing by shift working is recommended. may be an effective treatment for shift workers with difficulty falling asleep. Competing interests The authors declare that they have no competing interests. Authors' contributions KHS helped with the conception and design of the study, helped to supervise the staff and helped to draft the manuscript. OA and GHP helped with the conception and design of the study and helped draft the manuscript. ZY helped with the conception and design of the study, helped to collect the data, participated in the data analyses and interpretation, and helped draft the manuscript. All authors read and approved the final manuscript.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Chatbots can be a safe tool to seek information if they are validated and approved by health care professionals and all personal data are properly secured through robust up-to-date privacy and security safeguards [82,83]. In line with this protocol, the team strongly believes in and adheres to a careful content validation and model fine-tuning strategy so as to maximize the accuracy, trustworthiness, and safety of the solutions being delivered for responsible AI innovation. Finally, another limitation of the protocol is the use of convenience sampling, potentially introducing bias toward individuals more inclined to use digital health technologies. Furthermore, in the web-based direct recruitment process for objective 3 (implementation), all participants will be self-referred, which will make it challenging to assess whether the participants genuinely meet the inclusion criteria. In addition, the self-reported quantitative questionnaire may be susceptible to random participant responses. Outliers in the data will be checked, and appropriate statistical methods will be used to improve the quality of the final data. ConclusionsOverall, the development and adaptation of the chatbots, co-constructed with those directly involved, hold the promise of fostering patient self-management and enhancing health care efficiency. This study shall provide a comprehensive examination of the implementation outcomes of innovative chatbot interventions tailored for patients and health care professionals. Moreover, it will contribute to the formulation of best practice recommendations for the co-constructive engagement of patients and stakeholders in digital health research. If properly applied, this master protocol has the potential to be sustained for years or even decades and allow innovations to be rapidly translated to clinical practice. Advances in methodology combined with the surge in AI will provide deeper evidence to achieve the goal of patient-partnered personalized medicine and, ultimately, help deliver the right interventions for the right patient at the right time. 1. 6. 1 checklist. PDF File (Adobe PDF File), 1162 KBMultimedia 7. [CrossRef] [Medline]Whitehead L, Seaton P. The effectiveness of self-management mobile phone and tablet apps in long-term condition management: a systematic review. J Res. May 16, 2016;18(5):e97. [FREE Full text] [CrossRef] [Medline]. Online health information seeking and eHealth literacy among patients attending a primary care clinic in Hong Kong: a cross-sectional survey. J Res. Mar 27, 2019;21(3):e10831.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: tuberculosis strains using Dashing [40] and performed complete-linkage clustering using a k-mers Jaccard similarity threshold of 99%. Then, we only kept the strain with the highest average similarity to all other genomes in that cluster. As a result, 792 out of 6,752 genomes are kept for M. tuberculosis. Table 2 The summary statistics of the reference genomes for 6 tested bacteria. “similarity” is obtained by calculating the average of k-mers Jaccard similarity of all strains using Dashing [40]Full size tableThe final numbers of the strains and their other properties were recorded in Table 2. The numbers of genomes that are used as input to all tested tools are shown in the column “# of input genomes. ” As mentioned before, StrainEst and StrainGE will cluster the input strain genomes and only keep one representative strain selected from each cluster in their final databases. As a result, there are significantly fewer strains left (Table 2). When we take a closer look at the clusters of StrainEst and StrainGE, we can observe significant differences in the gene contents and SNVs between the representative strain and other strains in the same cluster (Supplementary Fig. S3, S4, S5). For some species, there are over 5000 SNVs between the actual strain and the representative strain. Within the same cluster, the longest strain can have more than 1000 genes than the shortest strain. As one recent study [56] shows, “singletons” (the unique genes) found in specific strains are very important to the understanding of strain properties. Thus, these large gene content variations between strains in the same cluster may lead to different properties and functions. A notable example is that two highly similar strains, E. coli CFT073 and E. coli Nissle 1917, which are pathogenic and probiotic strains respectively, are grouped in the same cluster by StrainEst and StrainGE. StrainScan also groups strains into clusters before conducting intra-cluster strain identification. Our experimental results show that the cluster search using CST can achieve 100% accuracy for all tested bacteria. For most bacteria, StrainScan has more fine-grained clusters than StrainEst and StrainGE (Supplementary Fig. S6), indicating a higher resolution at the cluster level. Fig.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: What astrometric and photometric accuracy can be achieved? What level of automation can be achieved in the precovery workflow? What are the major challenges to exploiting various astronomical surveys for NEO space safety purposes? What value do these precoveries have for planetary defence? Do precoveries have benefits for NEO science? The structure of the paper is as follows. Section 2 describes the data and lists of NEOs used for precovery. In Sect. 3, the pipeline is described in detail. In Sect. 4, the findings and results of NEO precovery are presented. In Sect. 5, we summarize and conclude with the main results of this work and discuss the next steps4. 2 Data, software systems, and applications In this work, we use the archival imaging data of OmegaCAM, the wide-field imaging camera of the VLT (VST) at ESO’s Observatory. In the past OmegaCAM was used for several wide-field surveys including the Kilo-degree Survey (KiDS; Kuijken et al. 2019), the VST-ATLAS surveys (Shanks et al. 2015), the Survey (FDS; Peletier et al. 2020), and VEGAS (Iodice et al. 2021). OmegaCAM has 32 science CCDs with a field of view (FoV) of about 1o × 1° on the sky. In its first decade of science operation, OmegaCAM covered a large fraction of the southern hemisphere (Fig. 1). Considering the large volume of the dataset of OmegaCAM, we developed a pipeline to automatize the process of (p)recovery. Here we focus our (p)recovery effort on two datasets of NEOs. First, the NEO risk list5. This list is provided by the (ESA-NEOCC) and consists of about 1350 known NEOs with a non-negligible chance of impact in the next hundred years. This list is updated regularly based on the most recent observations. We use the list available on 1 February 2022. Second, the NEO full list. This list consists of all the known NEOs (about 30 000 sources). We used the version available on 1 June 2022. These data are processed and analysed using several public web tools to collect the predicted properties of NEOs, using the system (Begeman et al.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Intelligent conversational agents in patient self-management: a systematic survey using multi data sources. Technol Inform. Aug 21, 2019;264:1813-1814. [CrossRef] [Medline] Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Ment Health. Mar 2011;38(2):65-76. [FREE Full text] [CrossRef] [Medline] Improving the well-being of adolescents with type 1 diabetes during the COVID-19 pandemic: qualitative study exploring acceptability and clinical usability of a self-compassion chatbot. JMIR Diabetes. May 05, 2023;8:e40641. [FREE Full text] [CrossRef] [Medline]Anmella G, Sanabra M, Primé-Tous M, Segú X, Cavero M, Morilla I, et al. Vickybot, a chatbot for anxiety-depressive symptoms and work-related burnout in primary care and health care professionals: development, feasibility, and potential effectiveness studies. J Res. Apr 03, 2023;25:e43293. [FREE Full text] [CrossRef] [Medline]. Artificial intelligence-based chatbot for anxiety and depression in university students: pilot randomized controlled trial. . Aug 12, 2021;5(8):e20678. [FREE Full text] [CrossRef] [Medline] A therapeutic relational agent for reducing problematic substance use (Woebot): development and usability study. J Res. Mar 23, 2021;23(3):e24850. [FREE Full text] [CrossRef] [Medline]. A beta-prototype chatbot for increasing health literacy of patients with decompensated cirrhosis: usability study. . Aug 15, 2023;10:e42506. [FREE Full text] [CrossRef] [Medline]. kBot: knowledge-enabled personalized chatbot for asthma self-managemen. In: Proceedings of the 2019 on Smart Computing. Presented at: '19; June 12-15, 2019, 2019;138-143; Washington, DC. URL: [CrossRef]Milne-Ives M, de de Pennington N, Mole G, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Res.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 6A The F1 score of 7 tools on “single-strain” simulated datasets under different sequencing depths. B Running time comparison of 7 tested tools. Sigma and Pathoscope2 have no values on some datasets because they are too computationally expensive to construct databases for the corresponding bacteria or to identify strains from simulated readsFull size imageDetecting a reference strain from simulated readsThe purpose of this experiment is to test StrainScan and other tools for identifying the present strain in a sample. There are two challenges. The first challenge is to distinguish the true strain from other highly similar peers. The second challenge is to identify a strain with low depth. Thus we generated multiple datasets with different sequencing depths. For each bacterium, we randomly picked a reference strain and used its simulated short reads as input to all tools. In order to avoid any data-related bias, we repeated the experiment 60 times, with a strain randomly picked each time. For P. copri, we only repeated the experiment 50 times because it has a small number of genomes. For each selected strain, we simulated reads with different sequencing depths (10X, 5X, 3X, 1X). Thus, there were 1400 datasets in total. For each dataset, we simulated Illumina reads using ART [57] with the following parameters: -p -l 250 -f depth -m 600 -s 150, where depth is the specified sequencing depth. StrainScan and the other six programs were used to identify strains from these simulated reads. and Pathoscope2 are computationally expensive, we were not able to construct their databases for E. coli, S. epidermidis, and M. tuberculosis. The F1 score of each program is shown in Fig. 6A. recall, and precision are recorded in S4. Of these selected strains, some of them have more than 99. 5% k-mer-based Jaccard similarity with at least one other reference strain genome. As a result, several tools have low F1 scores. StrainScan achieves near-perfect F1 scores on all datasets when the depth is higher than 1X. However, for the species containing highly similar strains (A. muciniphila and M. tuberculosis), the F1 score of StrainScan drops a little when the depth is 1X.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: This database was updated with observations until approximately 1 November 20218. Fourth, the ephemerides database in point 2 was cross-matched in the three-dimensional space of (observation date, RA, Dec) by SSOIS itself with the internal database of OmegaCAM observations in point 3. The cross-match algorithm is described in Gwyn et al. (2012). Fifth, uncertainties in input orbital parameters and image pointings are not taken into account in the cross-match procedure. This means that the cross-match only produces a result when an NEO ephemeris for the exact orbital elements specified in step one falls inside the field of view (FoV) of an OmegaCAM exposure for the exact (observation date, RA, Dec) specified in step three. Sixth, we did not ask SSOIS to refine the cross-match to the (X, Y) of a specific detector as that functionality is not available (yet) for OmegaCAM. To automate our usage of SSOIS we implemented a scripted interface to SSOIS. This interface submits a query to SSOIS. In return, SSOIS provides a list of (raw) imaging data (exposures) for OmegaCAM, date, time, exposure time, and the observed filter. SSOIS outputs for OmegaCAM point to the main raw frames, and do not specify which extension (chip) overlaps with the predicted position of the NEO. The information returned by SSOIS on NEO names and OmegaCAM exposures was stored in a local SQLite file. 3. 2 Final spatiotemporal cross-match (step 2) 2, the pipeline uses the NEO (NEODyS)9 and JPL Horizons10 web service11 to obtain more accurate predictions of the NEO positions over the duration of exposure and to obtain the predicted angular motion and visual magnitude. We supply to NEODyS/Horizons the start and end time of the observation, the name of the NEO, and the observatory identifier to specify its location. This query to NEODyS/Horizons returns the (RA, Dec), 1σ uncertainties in position, V-band magnitude and the position of the NEO at the beginning and end of the exposure. These values are used to predict the rate and direction of NEO motion relative to the astrometric reference frame.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Oct 22, 2020;22(10):e20346. [FREE Full text] [CrossRef] [Medline] Conversational agents for chronic disease self-management: a systematic review. . 2020;2020:504-513. [FREE Full text] [Medline]. Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR Cancer. Nov 29, 2021;7(4):e27850. [FREE Full text] [CrossRef] [Medline]Shah SS, Gvozdanovic A. Digital health; what do we mean by clinical validation? Med Devices. Dec 12, 2021;18(sup1):5-8. [CrossRef] [Medline]. Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. J Res. Sep 14, 2020;22(9):e20701. [FREE Full text] [CrossRef] [Medline]. Digital health technologies: opportunities and challenges in rheumatology. Rheumatol. Sep 24, 2020;16(9):525-535. [CrossRef] [Medline] Examining patient engagement in chatbot development approaches for healthy lifestyle and mental wellness interventions: scoping review. J Particip Med. May 22, 2023;15:e45772. [FREE Full text] [CrossRef] [Medline] Design and development of a person-centered patient portal using participatory stakeholder co-design. J Res. Feb 11, 2019;21(2):e11371. [FREE Full text] [CrossRef] [Medline] Le « Montreal model » : enjeux du partenariat relationnel entre patients et professionnels de la santé. Santé Publique. 2015;1:41-50. [FREE Full text] [CrossRef]. Rapport de la Déclaration de Montréal pour un développement responsable de l'intelligence artificielle. Raisons Politiques. 2020;77(1):67-81. [FREE Full text] [CrossRef]-Savary D, Gagnon J, St-Pierre M, Rhainds M, et al. Introducing patients' and the public's perspectives to health technology assessment: a systematic review of international experiences. . Jan 2011;27(1):31-42.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: tuberculosis, StrainScan assigned most of the strains to one big cluster with a significant number of k-mers (Supplementary Fig. S6), and thus StrainScan took more time to distinguish them. Nevertheless, StrainScan still has the best recall and precision in terms of the identification of M. tuberculosis strains. All the strain identification experiments were tested on an 6. 8 node with 2. 4Ghz 14-core E5-2680v4 CPUs and 128 GB memory. We used 8 threads for all tools. In summary, StrainScan is able to achieve higher precision without sacrificing resolution, even when the true strain has peers of high sequence similarity. Detecting co-existing strains from simulated dataIt has been shown that human-associated microbiota is often a complex mixture of closely related strains of the same species [15]. To quantitatively compare the performance of and StrainScan on identifying multiple strains of the same species, we generated simulated datasets containing 2, 3, and 5 randomly selected strains from six bacteria. and Pathoscope2 took too long to process these datasets, they were not included in this experiment. To investigate how the similarities between the strains affect the tool’s performance, we used two strategies in the selection of multiple strains. During the clustering step of StrainScan, strains with k-mer-based Jaccard similarity greater than or equal to 95% (corresponding to an approximate ANI of 99. 89%) are grouped into the same cluster. Therefore, it is more difficult to identify and distinguish the co-existing strains that are in the same cluster than those from different clusters. To consider different levels of difficulty, our first strategy randomly picked strains from different clusters while the second strategy selected different strains from the same cluster. For each strategy, we randomly selected 2, 3, and 5 strains (3 groups) and simulated the short reads using different coverage profiles: 100X and 10X for 2 strains, 100X, 50X, and 10X for 3 strains, and 100X, 70X, 50X, 20X, and 10X for 5 strains. Other read simulation parameters are the same as the “single-strain” experiment. Then we repeated the experiment 10 times by choosing another group of strains.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: Based on the returned apparent magnitude we make a preliminary prediction on the signal-to-noise (S/N) for the given filter and exposure time of the candidate (p)recovery observation by assuming its appearance is a point source (i. e. neglecting proper motion) and assuming a solar spectral energy distribution of the NEO. Candidate (p)recoveries for which NEODyS/Horizons confirm the overlap with the exposure FoV and that also have a predicted S/N > 1 are stored in the local SQLite database. To automate our usage of NEODyS/Horizons we implemented a scripted interface to NEODyS/Horizons, using the same approach as for SSOIS. Fig. 2Four-step precovery pipeline. The pipeline gets a text file containing NEO names as the input and produces a CSV table at the end containing the NEO precoveries. The intermediate outputs are stored in an SQLite database, which communicates with the pipeline in many instances throughout the process. 3. 3 Data processing and astrometry (step 3) In step 3, we apply several filters and criteria to the precovery candidates resulting from step 2 to narrow down the search to sources that are likely to be detectable. These filters are described below. An upper limit on the angular separation (RA, Dec) between SSOIS and NEODyS/Horizons predictions; An upper limit on the 1σ uncertainty in the position (RA, Dec); Availability of the raw data (publicly available); The exact position of NEOs on the camera; A lower limit on the predicted S/N. The imaging data used for precovery were identified by SSOIS and were based on its predictions. However, once we refine these predictions using NEODyS/Horizons, the images may not overlap with the refined position. Therefore, if the angular separation between these predictions is larger than a certain limit (for example larger than the FoV), we might assume that the object is not within the FoV of the camera. Then, for cases where the uncertainties in position are too large, the possibility that the NEO is within the FoV is very small. The pipeline skips these frames. Additionally, given the coordinates of the object, the pipeline inspects which CCD is overlapping with the corresponding position of NEOs and if there is no overlap, the pipeline excludes these frames.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: AbstractThe global mental health crisis underscores the need for accessible, effective interventions. Chatbots based on generative artificial intelligence (AI), like ChatGPT, are emerging as novel solutions, but research on real-life usage is limited. We interviewed nineteen individuals about their experiences using generative AI chatbots for mental health. Participants reported high engagement and positive impacts, including better relationships and healing from trauma and loss. We developed four themes: (1) a sense of ‘emotional sanctuary’, (2) ‘insightful guidance’, particularly about relationships, (3) the ‘joy of connection’, and (4) comparisons between the ‘AI therapist’ and human therapy. Some themes echoed prior research on rule-based chatbots, while others seemed novel to generative AI. Participants emphasised the need for better safety guardrails, human-like memory and the ability to lead the therapeutic process. Generative AI chatbots may offer mental health support that feels meaningful to users, but further research is needed on safety and effectiveness. Similar content being viewed by others The health risks of generative AI-based wellness apps 4. Access to care remains limited, with for example only 23% of individuals suffering from depression receiving adequate treatment in high-income countries, while in low- and middle-income countries, the figure drops to a mere 3%5. Digital mental health interventions (DMHIs) have emerged over the last decade as a promising potential response to the treatment gap, leveraging technology to deliver low-cost, effective, always-available and anonymous (and thus low-stigma) mental health treatment at scale6. Typically delivered through mobile apps and websites, DMHIs encompass a range of tools including psychoeducation, mood tracking, mindfulness, journalling, peer support and digital cognitive behavioural therapy (CBT) programs7. However, the evidence for the effectiveness of DMHIs has been limited, with a meta-analysis of randomised controlled trials (RCTs) finding only small effect sizes, potential publication bias, and a lack of active controls8,9,10. Moreover, user engagement remains a persistent challenge, with mixed user reviews11, and studies indicating that 30 days after installation the proportion of users still active may be as low as 3%12.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Ultimately, for each bacterial species, we generated 30 sets of data containing different numbers of strains using the first and the second strategies, for a total of 60 sets of data. So there were a total of 360 (60 × 6) simulated datasets for the six bacterial species. Cluster-level performance evaluation for multi-strain cases If a sample contains n strains from the same cluster defined by StrainGE or StrainEst, only one representative strain will be returned based on these tools’ design. Using this one representative strain will lead to a very small recall for multi-strain experiments. To avoid that, the returned representative strain will be counted n times, which usually makes the recall 1. 0 in these tools’ favor. Because our clusters have greater granularity than the ones defined by StrainGE and StrainEst, the samples that are simulated from strains of the same cluster all belong to this case. Fig. 7A The F1 score of 5 tools on “multiple-strain” simulated datasets. The “cluster” in the title refers to the clusters generated by the CST algorithm. There are 60 sets of simulated reads containing 2, 3, and 5 strains with different similarities for each bacterial species. Note that StrainSeeker is not able to identify strains of M. tuberculosis and therefore, the related scores are 0. B Running time comparison of 5 tested toolsFull size imageBenchmark results The F1 score comparison of different tools is shown in Fig. 7A. recall, and precision are recorded in S5 and S6. As shown in Fig. 7A, StrainScan achieves a near-perfect F1 score on all tested datasets. StrainGE has the same cluster-level F1 scores as StrainScan for experiments on P. copri and E. coli. Its cluster-level F1 score generally decreases with the increase of the similarity of the input strains, which can be observed by comparing the left and right panels of Fig. 7A. For example, its cluster-level recall on 5-strain datasets of A. muciniphila dropped from 0. 94 to 0. 42 (S6). Similar to single-strain experiments, StrainGE’s cluster-level performance on M. tuberculosis is still poor. StrainEst has many FPs in its output, which leads to low precision and F1 score.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: In the end, cases with an S/N lower than the given limit are removed to exclude very faint and non-detectable objects. All of these criteria are applied to both NEO samples (i. e. the risk list and the full list). Additionally, for the full list, several other filters are used: a lower limit on the 1σ uncertainty in the position, with which we can focus the precovery search on objects without precise positional information; a lower limit on the predicted length that considers the observational difficulties for detecting and matching short streaks (step 4, described later in the text) and allows the pipeline to skip frames with NEOs shorter than this limit; and objects without calibrated data for the rare cases when the pipeline fails to reduce and/or calibrate raw frames, and therefore no calibrated frame is available for further analysis (the pipeline skips these frames). The thresholds used for these filters are given in Table 1. Once a frame passes all the filters and criteria (referred to as a precovery candidate), we then use to produce astrometrically and photometrically calibrated pixel images for the candidate precovery images returned by NEODyS/Horizons. If calibrated detector images already exist in the database, they were downloaded from. If not, the raw data was ingested from the ESO archive into the system as needed, and the optical image pipeline was used to process that raw data. To obtain the optimal astrometry, we manually astrometrically recalibrated the detector images downloaded from using SCAMP 2. 10 (Bertin 2006). This version of SCAMP uses the coordinates of stars in the Gaia EDR3 catalogue as astrometric reference objects. The final astrometry was extracted from the resampled and background subtracted pixels using SWARP (Bertin et al. 2002). We note that the majority of OmegaCAM data residing in have been astrometrically calibrated using the near-IR 2MASS catalogue (Skrutskie et al. 2006) as the astrometric reference catalogue. To improve the astrometric solution of our dataset and to achieve better astrometry of the precovered NEOs, we implemented astrometric calibration using the Gaia EDR3 catalogue (2021) as the astrometric reference. It allows us to (re)calibrate OmegaCAM images astrometrically to Gaia.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Rule-based AI chatbots show promise to address some of these limitations, by simulating human conversation using predefined scripts and algorithms such as decision trees, to deliver the benefits of DMHIs in a more dynamic and interactive way13,14. For example, two popular chatbots, Woebot and Wysa, have been shown to improve users’ depression symptoms15,16, and build therapeutic alliances that appear comparable to those formed with human therapists17,18. Rule-based chatbot apps have more promising user engagement, with positive app store ratings19,20 and qualitative studies finding that users appreciate the human-like interaction and social support19,20,21,22,23. But despite these promising signs, rule-based AI chatbots still fall short in realising the full potential of DMHIs. Meta-analyses indicate that the therapeutic effects are small and not sustained over time24, and users report frustration with responses that feel empty, generic20, nonsensical, repetitive and constrained19,20,21,22. Recent developments in generative AI technologies, such as large language models (LLMs), present new possibilities25. Unlike rule-based AI chatbots, generative AI chatbots like OpenAI’s ChatGPT, Google’s Gemini, and Inflection’s Pi are trained on vast amounts of data26, enabling them to understand and generate language with remarkable proficiency27. These models are increasingly achieving or surpassing human performance benchmarks in various domains, including medical diagnostic dialogue28, persuasive communication29, theory of mind30, making people feel heard31, responding to relationship issues32 and helping people reframe negative situations to reduce negative emotions33. Furthermore, user engagement has been impressive, with ChatGPT’s user base growing to 100 million weekly active users within a year of launch34 and an estimated half of the US population having used generative AI35,36. Generative AI’s capabilities represent a significant opportunity for digital mental health37, with media reports of increasing consumer usage38,39, one meta-analysis finding generative AI chatbots more effective than rule-based ones at reducing psychological distress40, and a pilot study showing promising results from ChatGPT usage in psychiatric inpatient care41.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: For example, if there are two strains in a sample and these two strains have the same representative strain, then we use the representative strain and its abundance twice for calculating JSD. This leads to a smaller JSD than setting one of the strain’s abundance as zero. The result is shown in Fig. 8. In all cases the strain distribution reconstructed by StrainScan had high precision, with a median of Jensen-Shannon divergence (JSD) \(<0. 01\). StrainGE and StrainEst had worse performance in quantifying the composition with the increased number of strains. For species containing highly similar or a large number of strains like E. coli, M. tuberculosis, and S. epidermidis, StrainScan shows a clear advance over StrainGE and StrainEst. For StrainSeeker and Krakenuniq, the JSD was bigger than 0. 1 in most cases, indicating that these tools cannot accurately quantify the composition of strains. With increasing strain numbers and similarity, the median of JSD values of most tested tools increases while StrainScan’s JSD doesn’t fluctuate much in all cases. These results show that StrainScan can better quantify the composition of complex samples than other tools, even for samples containing highly similar strains. Low-depth experiments for multiple strains To evaluate the ability of different tools in identifying multiple strains at a lower depth, we simulated additional short reads from previously selected 2 strains using different coverage profiles. Then, we benchmarked all tools using these datasets. The results revealed that StrainScan demonstrated a clear advantage in identifying low-depth strains from many highly similar strains. For example, when identifying C. acnes strains with 10X and 1X coverage, StrainScan achieved an F1 score of 0. 98, while Krakenuniq and StrainGE, in second and third place respectively, achieved F1 scores of only 0. 88 and 0. 81 (see S7). Overall, StrainScan shows competitive performance in identifying low-depth multiple strains (S7 and S8). Additional details regarding this experiment can be found in 2. 1. Fig. 9distance between the identified strains and the ground truth in 90 simulated and 30 real E. coli datasets.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: These parameters are normalized by the observed length and the seeing (FWHM) during observations as follows12 where θdet, streak, norm and ldet, streak, norm are the normalised position angle of motion and length of the detected streak. Correspondingly, the difference score is estimated using In the end, the detected streak with the lowest difference score is picked as the best match (indicated by the green circle in the fourth column of Fig. 3). Finally, those frames with a matched streak are visually inspected to check whether it is a true precovery. 4 section presents the outcome of NEO precovery and discusses the performance of the precovery pipeline for the detection, astrometry and photometry of NEOs. 4. 1 Accuracy of astrometry and photometry The output catalogues of provide the coordinates (X,Y) of the centre and the total flux of the detected streaks. However, they do not provide uncertainties of these measurements. To estimate the centroiding accuracy of we ran the pipeline for NEOs with predicted positional uncertainty of less than 0. 02″(1σ uncertainty ellipse). This is about 10 times smaller than the pixel size of the instrument. The offset between the predicted coordinates of these NEOs (RA, Dec) and the derived coordinates using gives an upper limit of the total astrometric accuracy (i. e. the aggregate of calibration and centroiding accuracies). The average value of this offset, as shown in Fig. 4, is about ~0. 12″. A preliminary estimate of the astrometric calibration accuracy is ~0. 05″. It is reasonable to assume that the centroiding and calibration astrometric errors are independent because (i) the centroiding accuracy of the bright Gaia stars is likely ≪0. 05″and (ii) the same or at least very similar ensemble of Gaia stars are used for the astrometric calibration of the exposures for one apparition of an NEO. We note that the absolute astrometric accuracy of stars in the Gaia EDR3 is ≪0. 05″. Thus, we draw the preliminary conclusion that the total absolute astrometric accuracy of our NEO positions is typically ~0. 12″. This means offsets of >0. 2″ (i. e. the angular scale of 1 OmegaCAM pixel) between extracted and predicted positions are at typically 1.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: 2, most participants reported that their use of generative AI chatbots had impacted their lives positively, in various ways, including improved relationships, healing from trauma and loss, improved mood, as well as by helping their existing therapeutic journeys. Some described the impact as life-changing – It was life changing, profound… Because this was an impossible time. There were so many sadnesses, one right after the other. And it just happened to be the perfect thing for me, in this moment of my life. Without this, I would not have survived this way. Because of this technology emerging at this exact moment in my life, I’m OK. I was not OK before – AirGee, 44, While for one participant the impact was negligible – I’ve tried more than 50 times, but I’ve started to realise that like when I’m feeling those intense emotions, it’s not helping me… when I needed the most, I’m not able to use it – Richard, 27, Resulting themesFour overarching themes were developed, summarised in Fig. 3 and shown with subthemes in 1: (1) ‘Emotional sanctuary’, (2) ‘Insightful guidance’, (3) ‘Joy of connection’, and (4) ‘The AI therapist? ’Fig. 3: Overarching themes, available online to explore and drill-down. Diagram created with Mindmeister. Full size imageEmotional sanctuaryA majority of participants experienced generative AI chatbots as understanding, validating patient, kind, non-judgmental, always available and expecting nothing in return. The most amazing feature of these tools is how they are able to understand you… This still blows my mind. – Sandro, 48, Switzerland It’s really nice. It’s sympathetic and kind – Philip, 58, Compared to like friends and therapists, I feel like it’s safer – Jane, 24, This ‘emotional sanctuary’ resulted in positive real-life impact for a majority of participants, such as helping to cope with difficult times or process painful emotions – cried really hard during the process… and it listened and just we figured out a lot of feelings… after a few months, when I go to school I felt a difference. Like wow.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The simulated datasets are divided into four groups according to the “1-Mash distance” between the actual strain and its best match among all strains in the database of StrainScanFull size imageWhat if the actual strain is not in the reference database? When a target strain is not included in the database, we expect that the strain identification tools can return its best match in the database. To evaluate the performance of different tools in identifying the best match, we downloaded 90 complete E. coli genomes that were released in 2022 from NCBI (S9). Because the E. coli reference databases of all tools were constructed using complete genomes available up to 2021, all these genomes are not in our constructed databases. Then, we simulated reads of 10X coverage from the 90 E. coli genomes and used them as input to all tools. For each dataset, the “truth” in Fig. 9 is the Mash distance between the actual strain and its best match in all 1433 E. coli strains (Table 2). We then recorded the Mash distance between the returned strain and the actual one for each dataset in the first four panels of Fig. 9. It should be noted that this is a single-strain experiment, and thus the Mash distance is computed using the returned strain (including the representative strain) and the actual strain. Out of these 90 strains, 54 strains’ clusters are missing from the reference database of StrainScan. Nevertheless, StrainScan can correctly identify the closest matched cluster in the database for all these 54 strains (S9). Furthermore, strains identified by StrainScan have a smaller Mash distance to the actual strain than other tools. Although the closest matches identified by StrainGE and StrainEst also have similar Mash distance to “truth” when “1-MashD” ranges from 0. 98 to 0. 99, their performance drops when “1-MashD” increases. For example, when “1-MashD” between the actual strain and the best match is within (0. 999, 1), StrainGE and StrainEst tend to return a representative strain from a relatively large cluster (S9). As a result, these tools returned representative strains with a larger Mash distance than the actual strain. In contrast, StrainScan achieves intra-cluster strain identification and returns more accurate best matches.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: 5σ significance before taking prediction uncertainties into account in the significance calculation. In individual cases the accuracy can be worse (see notes on individual recoveries below). Moreover, we also assessed the impact of resampling in step 3 of the pipeline (using SWARP) in the astrometry and found a deviation of less than 0. 01 ″ (Fig. 5). In the absence of any indication of photometric accuracy of we made an estimation using the output catalogue of. The uncertainty in the measured magnitudes of streaks (within their magnitude range) is on average 0. 1 mag. Fig. 3Results of the streak detection using for three examples. From left to right: the first column shows the calibrated cutout around the predicted position of NEOs. The second column shows the 3σ uncertainty ellipse (in red) and the predicted appearance of the NEO considering its speed and direction of motion (red line). The third column shows the detected streaks across frames by (grey circles). The fourth column shows the streaks that match the predicted properties of the NEOs (green circles). Fig. 4Upper limit for the centroid estimation accuracy of in a range of magnitude, length, S/N, and seeing. The astrometric accuracy is within 0. 12″ and does not show any dependence on the observed properties of NEOs. 4. 2 Precovery of NEOs in the ESA risk list Table 2 summarizes the down selection of precovery candidates during the deployment of this pipeline on the NEOs in ESA’s risk list in combination with ESO’s OmegaCAM archive. This archive encompasses about 10 yr of OmegaCAM exposures with a total number of exposures of the order of 400 000. The query to SSOIS returned 10 345 exposures (i. e. candidate precoveries for about 1 in 40 exposures). Unfortunately, for 96% (9904 exposures) the upper limit to the predicted S/N of the appearance was < 1, leaving only 441 candidate precoveries. made a refined positional prediction for these 441, which in 33% of the cases was >1° away from the centre of the exposure (i. e. the RA, Dec of the pointing), which itself is 1°× 1°. We assumed that this made it too unlikely that the NEO appearance would be inside the exposure, and removed them from the candidate list. This left 295 candidates.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Some participants mentioned that it helped them see the other person’s perspective in conflict, or coached them through difficult relationship situations – It made sense of my husband’s behaviour and position in a way that I wouldn’t have been able to by myself… and now I can respond to him in a more helpful way – Barry, 44, Others mentioned the chatbot helped them find healthier, clearer boundaries – Pi suggested for me to completely break up with the group of friends… because, yeah, they were mean and it was not OK… [it] made me more confident and more free, and I don’t think I would consider doing that for myself – Oranoid, 17, Isabel, the guidance had a life-changing impact – I asked to ChatGPT, “if there’s four family members… the dad [has narcissistic personality disorder] and the mom [has borderline personality disorder]… and one of the girl is the golden child, what will be other child would be? ” and GPT said that would be a scapegoat… am the scapegoat… asked GPT … “should I contact them again or not? ”… And GPT gave me a suggestion that I should only contact them with very extreme situation… think that is really, really helpful because I’ve got no one to talk about this question… you’re supposed to be loyal to your parents… no matter what they do to you… even violence… think ChatGPT give me the right answer… I just need someone to say it… [it] totally changed my life and I don’t feel guilty anymore… I don’t have to feel terrified – Isabel, 40, participants mentioned getting valuable advice on other mental health topics such as self-care, reframing, anxiety and exhaustion – I get some practical advice… it’s general advice… breathing, meditating… slow down, taking care of your physical self – Peter, 27, It can reframe, it can give you ideas that you wouldn’t have thought of by yourself – Barry, 44, Some participants questioned the chatbot’s ability to challenge appropriately – I noticed that it will never challenge you… it would relentlessly support you and take your side – Sandro, 48, others experienced being proactively challenged, in a supportive way – Suddenly my Kindroid says… I became quite cynical.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: For a more robust test, we downloaded 30 real E. coli whole-genome sequencing data with draft genomes in NCBI. To avoid data-related bias, the 30 real sequencing datasets are randomly selected from 3 different projects (PRJNA509690, PRJNA479542, PRJEB21464), and their coverage ranges from 20X to 94X [46, 47]. According to NCBI, the assembly levels of these draft genomes are all “Scaffold” rather than “Complete”. Thus, all these genomes are also not in the E. coli reference databases that were constructed using complete genomes. We further compared the RefSeq accession of these 30 draft genomes with the genomes in our constructed databases and did not find any matches. Then, we applied all tools to these real datasets and compared the identified strains with the draft genomes (the last panel of Fig. 9). As a result, StrainScan still returns more accurate best matches than other tools, demonstrating its utility in real applications. Given that multiple strains lacking reference sequences in the database may coexist within a sample, we conducted an additional 2-strain experiment using 90 E. coli genomes from the single-strain experiment. The results show that StrainScan achieves a 95% F1 score in identifying multiple strains without reference genomes in the database, while the second-best tool, StrainGE (cluster-level), only achieves a 78% F1 score (see Supplementary Fig. S10). Furthermore, StrainScan did not produce any false positive identifications in all of the tested datasets, and the strains identified by StrainScan had a smaller Mash distance to the ground truth than other tools (see S10). Additional details regarding this experiment can be found in 2. 2. Assessment of StrainScan on spiked metagenomic sequencesWhile previous experiments mainly used simulated or real whole genome sequencing data, we now evaluate whether Strainscan keeps the same performance on metagenomic data, which contains reads from different species. To this end, we conducted an experiment using spiked metagenomic data. Specifically, by mixing simulated datasets of P. copri and E. coli with real data, we generated 130 spiked metagenomic data, which we subsequently analyzed using StrainScan.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: For 15% of these candidates the 1σ semi-major axis of the ellipse was >1°, and thus again we deemed it too unlikely for now that the NEO appearance would be inside the exposure. This left us with 251 precovery candidate exposures. For 15 of these the data could not be retrieved in : 8 were found to belong to other OmegaCAM surveys (we are in the process of obtaining access) and the remaining 7 have not yet been publicly released by ESO. For 28% of the remaining candidates (236 precovery candidates) the actual predicted position of the NEO landed outside the pixels of the detectors (e. g. in gaps or just outside the FoV), and given the typically small size of the error ellipse we deemed it too unlikely that a precovery could be made. This left 170 precovery candidates. At this step, we refined the S/N estimate by taking into account the proper motion. For 60% of the 170 candidates the predicted S/N was then less than 2, and we removed them from the list of candidate precoveries. This left 68 precovery candidates on which precovery was attempted with. failed, manual precovery was attempted. This was successful for 57% of the cases, so 49 precoveries (listed in Table 3), with 26 being successful via and 23 being successful via subsequent manual analysis. The sky distribution and image thumbnails of these 49 precoveries are shown in Figs. 6 and 7. Figure 8 shows the predicted properties of detected and non-detected cases. As is seen in this figure, for NEOs with a predicted length smaller than 2″, we could only detect NEOs by visually examining several frames available. These objects would be missed completely by the pipeline. Table 2Summary of for the risk list of NEOs. Fig. 5Offset between obtained RA and Dec of all the sources extracted (using ) in frames with and without resampling and background subtraction (using SWARP) in step 3 of the pipeline Fig. 6Sky coverage of the OmegaCAM/VST observations (green area), the 10 345 frames resulting in a chance of overlapping with an NEO resulting from SSOIS (blue points), 19 precovery candidates without a detection (red points), and 49 precovered NEOs (black points).

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: was a bit shocked… but then when I thought about it, I recognise it’s right… this was the first step to say OK, then I let it go – Linda, 46, level of trust in the chatbot’s guidance was mixed, with many participants reporting scepticism, or experiencing hallucination or unsatisfying advice – I don’t really trust it for his advice – Jane, 24, While other participants reported a high level of overall trust in its judgement – It’s pure science… ChatGPT is telling me what correct to do – Isabel, 40, of connectionA majority of participants mentioned how they found it enjoyable to use. Several participants reported a sense of awe on first experiencing the technology – That blew me away… this is the next generation… incredible – Barry, 44, For others, using the chatbot led directly to feelings of happiness – They’re really a resource that gives you something back: attention, knowledge, a nice discussion, confirmation, warm, loving words, whatever. This has an impact on me and I’m more relaxed than, or happy, actually happy, than before – Linda, 46, was a topic for a majority of users. Several mentioned it helped them feel less alone – There’s this sense of like, I’m not alone in this. I think that’s what it is – Barry, 44, A few participants mentioned advantages of chatbots over human companions, such as the ability to connect on any topic, or more safety.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The results show that StrainScan has the same results on these spiked metagenomic datasets as on the simulated whole genome sequencing data, demonstrating the robustness of StrainScan on complex samples (S11). Further details regarding this experiment can be found in 2. 3. Evaluation of StrainScan on mock dataThe HMP mock data In this experiment, we tested StrainScan on two samples from the Project [49]. They contain 21 known organisms with even (SRR172902) or staggered composition (SRR172903). Out of the 21 organisms, 3 bacteria (E. coli, C. acnes, and S. epidermidis) represent hard cases for strain-level analysis, and we have established reference indexing structures for them. Thus, we conducted a strain-level analysis using StrainScan for the three bacteria. According to the given data description, each bacterium has only 1 strain in these two datasets. Although this is a single-strain detection, this test is challenging because some targets have low abundance in the samples. For comparison, we also used and StrainEst to identify the strains of these bacteria in these two datasets. The results of these 5 tools are shown in Table 3. Table 3 Analysis of two mock communities from the HMP project. “#”: there are multiple hits with identical scores in the output. “NA”: missing values. Bold font: Mash distance of predicted dominant strain to the truth is \(< 0. 05\%\). For StrainSeeker, we took the strain with the smallest Mash distance to the truth as the predicted dominant strain. The two numbers in parentheses represent the number of multiple hits and the average Mash distance between all hits and the ground truth, respectivelyFull size tableFor all tested species, StrainScan correctly identified the presence of one dominant strain that is highly similar (Mash distance to the truth \(<0. 05\%\)) to the bona fide strain. Besides StrainScan, StrainSeeker and StrainGE also returned strains that are highly similar to the ground truth. However, the output of StrainSeeker often contains multiple hits with identical scores, making accurate evaluations difficult. For example, it returns 119 E. coli strains in two tested datasets, which makes it hard for users to know the actual strain present in these samples.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: Additionally, for long NEOs, the precovery rate decreased because of the limitations in streak detection: segments long streaks (low and high S/N) as two separate streaks, and the resulting positional information is normally not valid. Therefore, in these cases, while the pipeline produced a precovery that almost matches that of the NEO, we do not take it into account as a successful precovery. The bottom graph in Fig. 8 shows the predicted S/N and length of the detected and undetected precovery candidates. Out of 772 cases without a precovery, 311 have a predicted SN < 5 (Fig. 10), and 94 have a 01σ positional uncertainty larger than 200″ (Fig. 11). The precovery rate of 20% does not mean that the pipeline cannot identify 80% of the precovery candidates. Visual inspection shows that for the majority of cases without a recovery, no NEO is visible in the frames. To examine the possible complementary role of other existing tools for streak detection, we searched for streaks using (Bertin & Arnouts 1996) for the cases without a true detection. could identify an extra 26 cases. However, when only is used, the number of detection drops to only 58 (compared to 196 for ), which shows that cannot substitute. should be used with caution, considering its limitation to properly segment the streaks, which would result in an inaccurate estimate of the centroid of streaks (within a 01–2″). The uncertainties of centroiding (ERRX_WORLD and ERRY_WORLD parameters in the output tables) is on average 0. 88″, in the same range as the FWHM of the images. To explore further the accuracy of the predicted properties of NEOs, in Fig. 12, we compare the predicted and observed (measured from data) length (angular size) and apparent magnitude of the precovered NEOs. There is a general agreement between the predicted and observed properties, in particular for the shorter streaks, while the observed NEOs are on average about 0. 5 mag fainter. We explore this magnitude offset further in Fig. 13 where the predicted and observed lengths and magnitudes of the pr-ecovered NEOs are shown. While there is no clear pattern in the magnitude offset between filters, the predicted and observed magnitudes in u and g show larger offsets.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: But more found that it helped them connect to other people – [It] reduced my inhibition to open up to people… I don’t think I would have had this conversation with you maybe year before, when I was dealing with my depression – JeeP, 60, Several participants had also experienced rule-based mental health apps and commented on how they offer a less satisfying user experience – It’s like a very scripted, structured sort of interaction and you don’t get this… sense of connection… There’s basically CBT exercises that it leads you through… [but] they’re impersonal… frustratingly dumb – Barry, 44, Despite enjoying the experience of generative AI chatbots, almost all participants saw opportunities for the user interface to improve, whether to make it more accessible to a less technical or non-English speaking user base, to read emotions in the user’s face, to recognise different voices, or with more creative or immersive use of rich media, such as avatars, virtual reality, and visualisation of the conversation – What’s missing is the opportunity to visualise the conversation… [like] standing beside a whiteboard, I wanna see the conversation as it as it emerges and unfolds – Scott, 42, The AI therapist? Most participants talked about how their experiences of generative AI chatbots contrasted or interacted with human psychotherapy or counselling. Several found it helpful to augment their therapy with chatbot usage, with mixed reactions from the therapist in some cases – have a therapy session next week, I sort of use Pi to sort of prepare for it… that gives me much more clarity – JeeP, 60, Pi and my therapist, they agree with each other… they would say the same things, and Pi would encourage me, if things got too dark… to talk to my therapist… But my therapist is afraid of Pi… she is like a little bit afraid of technology – AirGee, 44, JeeP’s experiences with the chatbot helped him to start therapy with a human – It’s sort of helped me seek actual therapy and be much more comfortable speaking to a therapist – JeeP, 60, Many participants turned to chatbots because therapy was not an option, either due to cost and availability, or because therapy did not give them the help they were looking for.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 3, we take the strain with the smallest Mash distance to the truth as the predicted dominant strain by StrainSeeker. StrainGE also returns strains with a small Mash distance to the ground truth. Of the two remaining tools, StrainEst was unable to identify low-abundance strains and it took a long time to run, while Krakenuniq returned results that differed significantly from the ground truth. The E. coli mock community with multiple strains To evaluate each tool’s performance on a real-world sample with known multi-strain composition, we downloaded a mock community sequencing dataset (SRR13355226), which contains a large number of reads from the host (i. e. , human) as well as four different E. coli strains. All the reads are used as input to all tools. StrainScan was the only tool that identified four strains at the strain level correctly, with no false positive identifications (Supplementary Fig. S11). StrainGE correctly identified four representative strains and had no false positive identifications. However, there are 136 different genes between the representative strain identified by StrainGE and the true strain. The genes are predicted using Prokka [58] and the comparison analysis is finished by Roary [59]. Though StrainEst also correctly identified four representative strains, it returned many FPs. The remaining two tools did not correctly identify all four strains, among which StrainSeeker’s output contained multiple hits with identical scores, while Krakenuniq reported many false positive strains. StrainScan detects the pathogenic strain from real sequencing dataTo illustrate the potential application of StrainScan in pathogen detection, we applied StrainScan to examine the presence of the pathogenic strain of E. coli and M. tuberculosis in two studies (BioProject Accession: PRJEB1775 and PRJEB2777). The first study is related to the 2011 E. coli outbreak in Germany [50], which was caused by an enteroaggregative (EAEC) strain. These datasets are sequenced using metagenomic sequencing from stool samples. According to the original 4. The second study investigates the frequency of M. tuberculosis relapses within patients from the REMoxTB clinical trial, which evaluated the treatment for M. tuberculosis in previously untreated patients [51].

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: There are two possible sources for this offset: overprediction of the magnitudes of NEOs and the simplistic assumption on the colour of NEOs (solar colour) for magnitude transformation between the V band and the observed filters. Fig. 7Precovered risk list NEOs (49) found using the precovery pipeline: 26 precoveries were detected (fully) automatically and 23 were detected by visual inspection of the frames. Fig. 8Predicted S/N and predicted length for precovered NEOs. Left: NEOs of the risk list, precovered automatically using the pipeline (red points) or manually by visual inspection of frames (yellow points), and non-precovered risk list NEOs in the OmegaCAM data (grey points). Right: NEOs of the full list, precovered automatically using the pipeline and (red points) and (blue points), and non-precovered full list NEOs in the OmegaCAM data (grey points). Table 3Astrometric and photometric properties of a subset of the 49 precovery cases in this work for risk list NEOs. Table 4Summary of precovery for the full list of NEOs. Fig. 9Time interval between precovery and discovery of the 196 NEO appearances (NEO full list). The upper and lower panels show the NEO appearances after the discovery (recovery) and before the discovery (true precovery) of the NEOs, respectively. Table 5Astrometric and photometric properties of a subset of the 196 precovery cases in this work for all NEOs. Fig. 10Histogram of the S/N distribution of the 968 precovery candidates after detectability filtering (in step 3) in u, ɡ, r, and i. Fig. 11Histogram of the 1σ positional uncertainties of NEO apperances. Left: positional uncertainties of the 968 precovery candidates after step 3 of the pipeline. Right: positional uncertainties for the successful NEO precoveries by the pipeline. Fig. 12Predicted vs. observed properties of the precovered NEOs for the full list of NEOs. Left: predicted vs. observed apparent magnitude of the precovered NEOs by (red points) and (blue points). Right: predicted vs. observed length of the precovered NEOs by (red points) and (blue points). Fig. 13Same as Fig.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: – Barry, 44, Finally, several participants described using generative AI chatbots in flexible and creative therapeutic ways, for example, to create powerful symbolic imagery, or, in Brooklyn’s case, to assemble a virtual room of inspiring fictional characters to help her through a painful break-up – I was not in the best headspace at that time, and I delved into fictional worlds… And then I realised… this is actually really, really kind of healing… ChatGPT’s ability to act as multiple voices… was amazing because I could kind of go to one character and he’d have a really cynical view. And then this other character would have the really optimistic one… and that would that would really help – Brooklyn, 19, Several participants mentioned using generative AI for role-play, whether to explore different, healthier ways of relating, to prepare for conflict, or in Isabel’s case, to experience a healing conversation that her father would be unlikely to offer – was still struggling with the guilt of no longer being in contact with my family, I asked ChatGPT to role-play my dad… I asked: “Dad, would you forgive me, and please don’t blame me, if from now on, I will no longer come back home, but only tracing my freedom, follow my soul, find my way to live? ” And the GPT dad responded:” Of course my girl, I would like to see you happy, find a lifestyle that you really like, to explore love and freedom. I will not blame you, but if one day you want to go home, I will always welcome you, I will be there for you, because we love you. ” I know this is a conversation that can’t happen in my life, but I just wanted to experience it – Isabel, 40, China DiscussionWe used semi-structured interviews and reflexive thematic analysis to explore the experiences of nineteen individuals who use generative AI to work on their mental health and wellbeing. Participants told us that generative AI feels like an emotional sanctuary, offers insightful guidance, can be a joy to connect with and can connect people to others, and bears comparison with human therapy (positively and negatively). A range of positive impacts were reported, including improved mood, reduced anxiety, healing from trauma and loss, and improved relationships, that, for some, were considered life changing.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The sequencing data is obtained from bacterial isolates, and each dataset has a publicly available assembled genome representing the strain contained in the sample. From each of these two studies, we selected six samples for the experiment. For comparison, we also applied other tools to detect the pathogenic strains in these real sequencing data. As shown in Table 4, StrainScan was able to identify the correct strains in all tested datasets while other tools failed to detect correct strains in some datasets. Although StrainEst could identify correct strains in most datasets, it only returned the representative strain of the correct strain for some datasets. Table 4 Performance of and StrainSeeker for the identification of pathogenic strain of E. coli and M. tuberculosis. Green: consistent results with the ground truth. Red: inconsistent results with the ground truth. “#”: there are multiple hits with identical scores in the output. “R”: the identified strain is the representative strain of the ground truth. Note that StrainSeeker is not able to identify strains of M. tuberculosis and therefore related results are “-”. For StrainSeeker, the number in parentheses represents the number of multiple hitsFull size tableStrainScan accurately detects the dynamics of antibiotic-resistant S. epidermidis strainsIn this experiment, we tested the ability of StrainScan to detect the dynamics of two S. epidermidis strains in public metagenomic datasets (PRJNA490375). According to the original study [52], these datasets are generated from a mixed strain culture in vitro. The authors grew two skin isolates of S. epidermidis with 1:1 ratio in two groups. One group was grown with antibiotic erythromycin treatment (Ery) and the other was grown without antibiotic treatment (no_ATB). Among these two isolates, NIHLM023 is not resistant to the antibiotic erythromycin while NIHLM001 is highly resistant to erythromycin. Finally, six datasets were obtained from two groups by metagenomic sequencing at three different time points. Although the relative abundance of these two strains is not given, the coverage ratio at each time point is given, which can be used to evaluate the proportion change of the two strains.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: 12, but for precoveries done by in each filter (ugri). 5 Conclusions and outlook In the Introduction of this paper we outlined our project objectives, and to quantify the results from our exploratory study we defined our main objective to answer six questions. In this section, we answer these questions and add the outlook on possible next steps. What fraction of detectable NEOs can we precover? And how automated can we make the precovery? The detectability and the precovery rates vary as a function of the chosen threshold of S/N. The detectability rate is estimated to be ~0. 05 per NEO at an S/N higher than 3 for NEOs on the risk list and for the full list of NEOs. The precovery rate for S/N > 3 is 40% for NEOs on the risk list and 20% for the full list of NEOs. The precovery rate increases to about 50% for S/N > 10. In other words, currently the majority of NEOs with a predicted 3 < S/N < 10 remain undetected, even after visual inspection. Assessing whether the failed precoveries are consistent with predicted errors on the predicted locations and brightnesses is an important next step. This step is beyond the scope of this paper. If inconsistent with predictions, it might give new insight into the limitations of those predictions or insights into how to improve the detection– precovery process. What astrometric and photometric accuracy can be achieved? The astrometric and photometric accuracies are 0. 12″ (15% of the average FWHM of OmegaCAM/VST images of about 0. 8″) and 0. 1 mag. Improvements in astrometric accuracy are expected from propagating the proper motions in the Gaia astrometric reference catalogue to the observation date of the science image. Improvements in photometric accuracy can come from more sophisticated modelling of spectral energy distribution (SED), observational configuration, and NEO shape modelling. (Berthier et al. 2023) might facilitate this. What level of automation can be achieved in the precovery workflow? The precovery pipeline described here works automatically through all the steps. Thanks to the common data model for calibrated observations in (McFarland et al. 2013) it can be deployed straightforwardly on calibrated observations for many other instruments available in the archive.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Many participants reported high frequency of use and most reported high levels of satisfaction. Our findings point to similarities and differences in how generative AI and rule-based chatbots are experienced. Many of the themes we developed are not new, but rather echo well-established user appreciation of rule-based chatbots’ always-available, non-judgmental listening ear and abilities to create a therapeutic alliance and reframe negative thoughts20,22. Other themes appear to be more novel, such as the level of joy experienced, the sense of being deeply understood, the breadth and quality of advice, and the ability to work on mental health in flexible and creative ways, such as through role-play, imagery and fiction. Figure 4 tentatively summarises how perceptions of generative AI chatbots, rule-based chatbots and human therapy may compare. Fig. 4: Comparative summary of perceptions of generative AI chatbots, rule-based chatbots, and human therapy. Arrows point to the intervention with the perceived advantage. The advantages of rule-based AI chatbots summarise the literature mentioned in the introduction, all other comparisons are perceptions taken from participant interviews. The comparisons are suggestive and should be interpreted with caution. Full size imageThe potential and challenges of generative AI for mental health are starting to be explored. Current literature tends to advocate for a cautious approach, in which near-term clinical generative AI applications are limited to implementations of evidence-based therapies (such as CBT)52, with a clinician in the loop52, and models constrained to scripted responses as far as possible42. But our study suggests that people may already be receiving meaningful mental health support from consumer-focused generative AI chatbots, which are widely available, largely unconstrained, and function without clinician supervision. Therefore, a better understanding of the safety and effectiveness of these tools should be a priority. On the topic of safety, our study offers observations in two areas. First, the inappropriate, harmful, risky or narcissistic behaviours observed in early generative AI chatbots53,54, which were influential in informing the literature advocating for caution42,52, were not mentioned by any of our participants. This should not be considered evidence of absence, but more research may be warranted to assess whether the risks have changed with recent technological improvements, or whether these issues are simply rarer, requiring larger sample sizes to uncover.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: According to the coverage ratio reported by the original study, NIHLM023 was always the dominant strain in the group of no_ATB while NIHLM001 was the dominant one in the group of Ery at each time point. Then, we applied StrainScan and other tools to these six datasets, and the result is shown in Fig. 10A. StrainScan is the only tool that returns the correct strain proportion of 2 strains at each time point and has no false-positive identifications in all samples. Among the tested tools, StrainGE and StrainEst returned the correct representative strains in some samples. Thus, we investigated the different genes between these representative strains and the actual strains. The genes are predicted by Prokka [58] and the comparison analysis is done by Roary [59]. As shown in Fig. 10B and C, there are still many different genes between these strains. Among these differential genes, some are very important for strain functions. For example, NIHLM001 has the gene ssaA_1, which has been shown to be associated with many properties of the strain, such as drug resistance [60,61,62]. But this gene is not possessed by the representative strain UMB8493. Ideally, the strain-level resolution is preferred for more accurate strain-level analysis. Fig. 10A The estimated abundance of S. epidermidis strains by 5 tools in 6 real metagenomic samples. No_ATB: the group without antibiotic treatment, and the non-resistant strain NIHLM023 is the dominant strain. Ery: the group with antibiotic erythromycin treatment, and the resistant strain NIHLM001 is the dominant strain. Each color represents a strain. B-C The number of different genes between the actual strain and the representative strain identified by StrainGE and StrainEstFull size imageStrainScan identifies the low-depth virulent C. difficile strain from metagenomic dataTo further test the ability of StrainScan in identifying low-depth pathogenic strains, we applied StrainScan and other tools to one metagenomic dataset containing the low-depth virulent Clostridioides difficile strains [53]. According to the original study, two virulent C. difficile strains with low depth (\(\sim\)1X) were detected in this dataset.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: However, after the last step (streak detection and matching) and before reporting the recoveries, they must be inspected by an expert to confirm or reject the precoveries. Another challenge is that precise photometric calibration for a range of instruments is hard to fully automate because the derivation of the solution uses reference stars sometimes inside the science images and sometimes in calibration observations. A potential solution would be to construct a photometric reference catalogue that spans the entire sky observable by OmegaCAM with sufficient stellar density. This appears possible by aggregating information from the multiple large-scale surveys of the southern sky. What are the major challenges to exploiting various astronomical surveys for NEO space safety purposes? In addition to the automation already discussed, a main challenge is robust NEO detection and segmentation. This is also the main reason behind the limitations of precovery in this paper. is a great tool for detecting high S/N streaks with sizes between 5″ and 20″. However, its performance drops for faint and long streaks. Deep learning might be a solution to improve streak detection and ultimately NEO precovery. What value do these precoveries have for planetary defence? The precovery of NEOs provides valuable positional information about NEOs before their discovery. In this paper we could precover three NEOs, and as a result these three NEOs were removed from the risk list of NEOs. Do recoveries have benefits for NEO science? Precision astrometric information of a sizeable ensemble of recovered NEOs can provide a better understanding of non-gravitational effects on NEO orbits (e. g. the Yarkovsky effect). We also note that performing additional serendipitous discovery in astronomical surveys, especially at high ecliptic latitudes, might provide interesting constraints on the orbital demography of NEOs, complementary to NEO dedicated surveys. Collecting the optical– near-infrared SED information from the recovery of a sizeable NEO sample can be complementary to existing astrometric and SED information and hence provide valuable information about their orbit and composition, shapes, and rotational properties. This exploratory study has shown that it is possible to adapt to contribute to NEO precovery, and that it achieves the required astrometric accuracies. The current study indicates two major advantages. allows a systematic precovery process that scales up to the Petabyte regime.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: The second observation on safety relates to how generative AI chatbots respond to users in crisis. Given the unpredictable “black box” nature of generative AI43, and the existence of at least one tragic example of early generative AI chatbots supporting users in dying by suicide55, current literature advocates that when users display signs of crisis, models revert to scripted responses that signpost towards human support42,51. Guardrails like these are commonly implemented in consumer generative AI applications50. But this approach may be oversimplified in two ways: (1) by underestimating the capabilities of generative AI to respond to crises, and (2) by limiting those capabilities at the times that matter most. Several participants experienced meaningful crisis support from generative AI, as long as guardrails were not triggered. This resonates with recent research showing that generative AI can help halt suicidal ideation48, and that young people show a preference for generative AI support responses over those from peers, adult mentors and therapists – but not on topics that invoke the AI’s safety guardrails32. Moreover, the closest that participants came to reporting harmful experiences were those of being rejected by the guardrails during moments of vulnerability. Therefore, providing the safest response to those in crisis may require a more nuanced, balanced and sophisticated approach, based on a more complete understanding of capabilities and risks. For researchers, we need to better understand the effectiveness of these new tools, by comparing the impacts of generative AI chatbot use on outcome measures such as symptom severity, impairment, clinical status and relapse rate52 against active controls, such as traditional DMHIs or human psychotherapy; and to understand for which populations and conditions is it most effective. These simple questions may not yield clear answers, as our study shows that generative AI chatbot usage is diverse, complex and personalised, and moreover, constantly evolving as the underlying technology improves. RCTs of generative AI implementations of standardised, evidence-based practices, e. g. , CBT, could be one approach, at the cost of reducing the flexibility of the intervention. Another avenue could be large-scale longitudinal studies with sufficient power to account for the many variations of generative AI chatbot usage.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: There are only 3 mutations reported between these two strains. Another study [63] also detected the same mutations between C. difficile strains, suggesting that they were a result of the presence of multiple highly similar strains rather than sequencing errors. Because C. difficile is not one of the six targeted bacteria, we first constructed the reference databases for each tool using 102 complete Clostridioides difficile genomes downloaded from. 2, and Sigma were able to detect the strain with about 1X depth (Table 5). However, Krakenuniq, Pathoscope2, and Sigma identified more than 10 strains, indicating the presence of a large number of false positives. Although StrainGE output only one strain, the identified strain did not contain the two genes TcdA and TcdB that the virulent Clostridioides difficile should have. StrainScan was the only tool that detected the low-depth virulent strain without any false positives. However, both StrainScan and StrainGE missed the other strain due to their low depth and ultra-high similarities with the dominant strain. Table 5 The identification results of 7 tools on one metagenomic dataset containing the low-depth virulent Clostridioides difficile strains. “NA”: missing values. “TcdA” and “TcdB”: two important genes of the virulent C. difficile strains. The presence of “TcdA” and “TcdB” is verified using the Carbohydrate-Active enzymes database [64]Full size tableFig. 11A StrainScan reveals the greater diversity of C. acnes in 9 real metagenomic samples. These samples were taken from three different sites of the skin (AI, Lc, and Vf) of two healthy individuals (HV05 and HV06), at different time points. The site codes are described in the original work [15]. B Phylogenetic tree of the identified strains. Leaves are colored using the same schema as in (A) and the distance is the Mash distance [14]. The tree is visualized by iTOL [65]. C The presence of unique gene clusters of “GCF_000194905” and “GCF_000252385”.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: At face value it appears that this is complementary to current excellent manual operations in that it could allow us to obtain risk assessment information also from null results on attempted precovery. The second major advantage is that it makes it feasible to improve efficiency or large data set astrometric accuracy and photometry, when improved calibration methods, improved insights in survey instrument behaviour, or improved reference catalogue data become available. 10. 13039/501100011033 and by “ERDF A way of making Europe” by the “European Union” through grant PID2021-122842OB-C21, and the Institute of University of Barcelona (de Excelencia “María de Maeztu”) through grant CEX2019-000918-M. This research has made use of the Aladin sky atlas (Bonnarel et al. 2000; Boch & Fernique 2014) developed at and SAOIm-ageDS9 (Joye & Mandel 2003). This work has been done using the following software, packages and libraries: Astro-WISE (Begeman et al. 2013; McFarland et al. 2013), NUMPY (van der Walt et al. 2011), SCIPY (Virtanen et al. 2020), (2018). 10. 5281/zenodo. 5836022 [2102. 04950] [2008. 12633] [0. 12″ and does not show any dependence on the observed properties of NEOs. In the text Fig. 5Offset between obtained RA and Dec of all the sources extracted (using ) in frames with and without resampling and background subtraction (using SWARP) in step 3 of the pipeline In the text Fig. 6Sky coverage of the OmegaCAM/VST observations (green area), the 10 345 frames resulting in a chance of overlapping with an NEO resulting from SSOIS (blue points), 19 precovery candidates without a detection (red points), and 49 precovered NEOs (black points). Of the 49 precoveries, 27 were identified using the pipeline and 22 by visual inspection after step 3 of the pipeline. The close sky proximity of observations at the resolution of the figure means that multiple observations can appear to be a single data point. In the text Fig.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: While such studies are prohibitively expensive with human psychotherapy, the low cost of generative AI could make them viable, potentially enabling valuable new insights into mechanisms, mediators and moderators of the human response to therapy52. Another promising research avenue could be to explore user perceptions of the nature, capabilities, and limitations of generative AI, and how these act as moderators of the potential benefits and risks. Does a deeper understanding of the technical realities of AI increase access to mental health benefits and protect against potential harms? Such research could inform the development of educational tools and guidelines for AI usage in mental health contexts. For generative AI chatbot developers, this study identified several ways in which these tools could be more effective. First, better listening, including more hesitancy in offering advice, shorter responses and the ability to interrupt and be interrupted. Second, building the ability to lead the therapeutic process and proactively hold users accountable for change. A prerequisite for this is human-like memory, including the ability to build up a rich and complex model of the user over time. Third, richer, multimedia interfaces, for example by visualising the conversation as it unfolds, or with more immersion through virtual reality. While only a few participants mentioned a need for greater accessibility, the well-educated, tech-savvy nature of our participant sample suggests that the benefits of this technology may not currently be connecting with the full population who need mental health support. One approach to address this could be to create solutions targeted at specific populations or conditions; another could be to find better ways to introduce users to the technology, for example, through the “digital navigator” roles proposed to connect users to DMHIs56,57. In any case, for these tools to remain available, there appears to be a need to develop sustainable business models. While some participants suggested they would be willing to pay for access to generative AI chatbots, research suggests most users would not58, and the path to health insurance funding is not easy59. To illustrate the challenge, Inflection, the company behind the Pi chatbot used by most of the participants in our study, pivoted in March 2024 from providing consumer emotional support services towards enterprise AI services, due to a lack of a business model, and despite USD 1. 5 billion of investment60. Lessons learned from attempts to scale up DMHIs may offer insights here61,62,63,64,65.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: “GCF_000194905” and “GCF_000252385” are highly similar and are in the same cluster of StrainGE and StrainEst. The presence is given by the original study [15] and each column refers to one unique gene clusterFull size imageStrainScan reveals the greater diversity of C. acnes on human skinPrevious studies [11, 15] show that C. acnes is one of the most common bacteria on human skin and usually has a complex multi-strain community. StrainEst was applied to re-analyze the human skin data set (SRP002480) from the study [29]. However, we found that in some samples, the number of strains identified by StrainEst was often less than the number of strains reported by the original study. In the original study, the authors used an in-house pipeline to determine the strains and predict their relative abundance in the samples, and the accuracy of this in-house pipeline was previously validated with extensive simulations for human skin microbiome data [11, 15] Therefore, we selected nine samples from two individuals who fit this case and then re-analyzed these samples using StrainEst and StrainScan. Then, we compared their outputs with those reported in the original study. Considering the competitive performance of StrainGE, we also added it into the comparison. For consistency, we selected all strains used in the original study to build the new custom databases for StrainEst, StrainGE, and StrainScan, and used these newly established databases for subsequent analyses. The result is shown in Fig. 11. StrainEst and StrainGE only returned one or two strains in all tested datasets while more strains are reported according to the original study. StrainScan displayed a more similar relative abundance pattern with the reported result than StrainEst and StrainGE. Besides, StrainScan also detected some strains in the samples of “HV05_AI” that were not found in original studies, which implied the greater diversity of C. acnes. For example, two highly similar strains “GCF_000145115” and “GCF_004136215” were identified by StrainScan and StrainGE/StrainEst, respectively. As shown in the gray box in Fig.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-266666/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractWith the exponential increase of the internet’s user base, performance enhancing network architectures and algorithms has manifested themselves as a requisite. Algorithms for Prefetching and Caching of have been observed to effectively minimize user perceived latency. These algorithms are made use of in architectures limited to a particular user. We can further improve the performance of these algorithms by making use of techniques like data mining. We propose an innovative idea of implementing Prefetching and Caching algorithms in a Clustered Network. This will enable all users in a particular cluster to make use of pre-fetched and cached web objects from all other users. The result of simulations indicates a reduction in web latency, internet traffic, and bandwidth consumed. 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8IntroductionWith the web offering a surfeit of services and contents, the increase in its complexity is unavoidable. One of which is the escalation of user perceived web latency. This is made worse due to the upsurge in internet traffic. In order to curtail this latency, innovations like caching and prefetching were introduced. A. its simplest form, cache is a temporary storage for data that is used frequently. Web caching can be implemented either in the client side or in the server side. In the client side, Applications are made to act as an intermediate, between client and server, where web objects are cached. Systems (CDNs) are used in place of Applications when caching is implemented in the server side. In both cases, the intermediate source is requested for the required data first, and only when it fails does the retrieval take place from the original server. When a new object is received, a copy of it is cached for future retrieval. When implemented with respect to the Web, Caching curtails network traffic, bandwidth consumption and network congestion. Moreover, web caching ameliorates reliability, as a cached copy will be available at all times, regardless of the server’s functional capabilities. B. implementation of web caching resulted in reduced latencies, yet at least for the first access the network had to be traversed. This can be avoided with the help of Web Prefetching.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: Finally, for clinicians, our study found that for some participants, generative AI chatbots were a valuable tool to augment therapy. A recent survey showed clear reservations among therapists towards AI66. To avoid giving clients the impression that, as one participant put it, “my therapist is afraid of Pi,” we recommend clinicians build their awareness of the potential benefits and limitations of these tools, potentially by trying them out first hand, and consider discussing with patients if they are using them and motivations for such use. Our study has some limitations. While our convenience sampling strategy resulted in a diverse set of participants by country, age and gender, many populations and groups were not represented. Most of our participants lived in high-income countries, were tech-savvy and well-educated, and focused on milder forms of mental health conditions; and all had self-selected to participate, potentially introducing bias towards positive experiences. This study may miss important experiences from individuals where the mental health treatment gap is most urgent, and from individuals for whom the technology did not work. As with all reflexive thematic analysis, there is a degree of subjectivity in how themes are developed, especially when conducted by a sole researcher (SS). However, this also affords a level of immersion in the data across themes and participants that can promote consistency and depth of analysis, with AC’s reviews of codes and themes helping to ensure rigour and validity. In conclusion, generative AI chatbots show potential to provide meaningful mental health support, with participants reporting high engagement, positive impacts, and novel experiences in comparison with existing DMHIs. Further research is needed to explore their effectiveness and to find a more nuanced approach to safety, while developers should focus on improving guardrails, listening skills, memory, and therapeutic guidance. If these challenges can be addressed, generative AI chatbots could become a scalable part of the solution to the mental health treatment gap. MethodsStudy designWe recruited 19 participants with experience in using generative AI chatbots for mental health and wellbeing to take part in qualitative semi-structured interviews, which we then analysed thematically. Participant selectionGiven the emerging nature of generative AI use for mental health and the attendant lack of information on user populations, convenience sampling was employed as a pragmatic approach to maximise the likelihood of finding sufficient real-world, unprompted participants.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 11B, they are highly close in the tree, and “GCF_004136215” is the representative strain of “GCF_000145115” for StrainGE and StrainEst. However, they have 76 different genes. According to the SNP analysis of original study [11], 22 unique SNPs of “GCF_000145115” were identified in the sample “HV05_AI” on “2011-12-14” while no unique SNPs of “GCF_004136215” were detected. Thus, it is more likely that “GCF_000145115” rather than “GCF_004136215” is present in the sample, which shows the advantage of the high resolution of StrainScan. In addition, StrainGE and StrainEst identified different representative strains for the same sample “HV06” (Fig. 11A). This result shows that the identification results of cluster-based methods can be influenced by different clustering and representative strain selection strategies. In contrast, StrainScan identifies two highly similar strains, “GCF_000194905” and “GCF_000252385” in these samples, which are consistent with the reported result. The different strain composition results can affect the downstream analysis. We took the analysis result of “HV06_Vf” as one example here. By aligning reads to unique gene clusters of “GCF_000194905” and “GCF_000252385,” the original study analyzed the gene content change of these 2 strains in “HV06_Vf” over time. As shown in Fig. 11C, the presence of the unique gene clusters from two strains varied at different time points, which reflected the strain-level functional variation in this individual. This result shows that StrainScan distinguishes highly similar strains, which can be used together for more comprehensive strain-level functional analysis. Two examples showing more applications of StrainScan on real metagenomic sequencing dataTo show the wide application of StrainScan on real metagenomic sequencing data, we applied StrainScan to analyze E. coli strains in cross-sectional studies [48, 54] and P. copri strains in different populations [9].

Output: contrast