Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: It is the technique of fetching, followed by caching, of web objects before user requests for them. This involves deducing the user requests which is based on referring the spatial locality of [17]. It utilizes the client’s unused time, which is the time gap between the requests, in order fetch Web Objects. Prefetching algorithms are based on clustering of users, probability of access and popularity of web objects. With the help of prefetching, latency is reduced, and bandwidth under-consumption is avoided. C. is the process of organization of the data into clusters, to maintain closeness among the objects of the same cluster but difference with the objects of the other clusters. It can also be considered as a form of data compression, as a cluster of data objects can be treated as one group. The analysis of clusters provides information about the nature of the data in each cluster and the placement of data. Efficient bandwidth usage decreased latency and lower network congestion is made possible when cluster analysis, along with web caching and web prefetching, is applied to the users of the web. In this paper, we propose a scheme which utilizes the techniques of along with the help of Clustering, to enable users in the same cluster to access cached Web Objects. Literature SurveyIn [17], the author presents a prediction design based on analytical interrelation among web objects and proposes away to integrate caching and prefetching algorithms by including the prefetching technique within a caching algorithm. Author proposes to perform this combination in the intermediate server applications. Author proposes a unified algorithm that performs both prefetching and caching, called Pre-GDSF. Which looks at prefetching aggressiveness, increased network traffic and replacement policy. From simulations, it is shown that the algorithm increases the web caching performance with increase in traffic. Also, the author advises about the choice of selecting a fitting prefetching range as it directly affects the conduct of the system. Finally, the author concludes that small-scale caches perform better with prefetching. In [2], the author presents an innovative prefetching technique based on semantics. Prediction of subsequent requests is based on the priorities set because of previously obtained records, instead of temporal rapport among Locators. Because the client navigates based on keywords in Locators. Author refers to the event as “semantic locality”.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: We advertised the study on various user forums (Pi, reddit and the IFS guide app), to King’s students and staff, and on LinkedIn. Participants were required to have had at least three separate conversations with an LLM-based generative AI chatbot on mental health and wellbeing topics, each lasting at least 20 minutes. The intention behind these criteria was to ensure that participants had enough breadth and depth of usage to create meaningful experiences, while not excluding light users who may also have valuable insights to share; with thresholds informed by our experience of using these tools. Additionally, participants were required to be over 16 years old; and to be comfortable being interviewed in English. There were no geographical restrictions, and no compensation was offered for taking part. Interested participants were directed to an online information sheet and consent form, provided through Microsoft Forms. The consent form was signed by 35 individuals, of which 19 subsequently booked and attended an interview. Data collectionWe collected data using semi-structured interviews, as a well-established approach to enable participants to express diverse perceptions and focus on topics most meaningful to them, in particular for complex or emotionally sensitive topics that they may not be used to discussing with others67. Following the framework from Kallio et al. 67, the first author (SS) drafted a topic guide, inspired by existing qualitative research in this area, reviewed with the second author (AC), and piloted with a research collaborator before starting the interviews, resulting in helpful feedback to the interview technique but no material changes to the topic guide. Questions included “tell me about your first experiences of AI chatbots,” “what mental health and wellbeing improvements were you looking for,” “how many conversations did you have and how long did they last,” “what did you like and not like,” “what changes did you see in your daily life as a result of those conversations,” “what might have made the conversations more helpful for you,” and “how does the AI chatbot experience compare with other approaches you’ve experienced for working on your mental health and wellbeing? ” SS conducted all 19 semi-structured interviews. AC, an expert in qualitative methods, reviewed and quality-checked the video of the first interview.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: The analysis results show that StrainScan can accurately identify strains at a higher resolution from metagenomic samples, which can lead to more comprehensive biological insights. For example, StrainScan is the only tool capable of distinguishing E. coli strains in samples from three countries into three distinct groups (Supplementary Fig. S12). Similarly, by analyzing P. copri strains identified by StrainScan, we observed that strains from Omnivores samples are clearly separated from Vegans samples in terms of the phylogenetic relationship, while strains from Vegetarians samples lie somewhere in between (Supplementary Fig. S12). Additional details regarding these two experiments can be found in 2. 4 and 2. 5. DiscussionIn this work, we presented StrainScan, a new strain-level composition analysis tool for short reads. We designed a novel tree-based k-mers indexing structure to strike a balance between strain identification accuracy and computational complexity. Then, by applying informative k-mers and the elastic net model to identify strains and predict their abundance, StrainScan improved the resolution of the strain-level analysis and the accuracy of abundance estimation. StrainScan shows higher accuracy and resolution than other tested tools across all benchmark datasets with different complexity. In particular, StrainScan outperforms all other tools on datasets containing strains with higher similarity and various sequencing depths. This level of high resolution can be achieved by alignment-based tools such as Pathoscope2 and Sigma. But StrainScan is at least 10 times faster than them. The experimental results of mock data and real data further demonstrate that StrainScan can provide more comprehensive strain-level composition analysis. Novel strains that are not in the reference database tend to appear often. While tools like StrainGE and StrainEst return representative strains that are most similar to the present strains in a sample, StrainScan returns a strain that is the closest match. As a result, the returned strains by StrainScan can represent the actual strains with higher accuracy. Additionally, StrainScan offers information on the identified clusters in the output, enabling users to consider the identified cluster(s) as the subject for downstream analysis in cases where the genome sequence of the actual strain is unavailable. As shown in our benchmark experiments (Fig. 9 and Supplementary Fig. S10), StrainScan is able to identify the strain that has a smaller Mash distance to the actual strain in the sample, which can provide more accurate reference strain for the downstream analysis.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: Search engines accept keywords and provides a series relevant documents, on the contrary this type of prefetching systems obtains priorities based on the user’s previous usage patterns and fetches new URLs while the user is browsing the current site. Additionally, new URLs that were never visited can be prefetched because of semantics. Neural network is used to build a collection of URLs and to prefetch user’s requests. It also includes a good adaptability to the varying nature of the user’s browsing pattern. The technique was tested by simulating it in the daily browsing of various news sites. In [3], the author noted the outcomes of condensation of the cache performance by measuring a squid cache for various compression ratios, where the cache condensed the data, and the decompression takes place in the client-side. The performance was determined by benchmarking it in the Best- and Worst-case scenarios. Also, the author demonstrated a point of crossover where expense of cache compensated the performance gains. Advantages of caching condensed content are greater compression ratios and performance of the cache. Compressed content requires less space, therefore caching them will improve the hit ratio as more objects can be stored in the system’s main memory. / Output is also sped up, which will increase the throughput and decrease the delay. Content compression is useful as an improvement means as its features can be combined with transmission latency reduction. In [4], the author proposes a prediction model based on clustering of web user sessions. Author makes use of various data mining techniques on the logs in the server to predict the next page. The browser can predict and prefetch the next page which will decrease the user perceived latency. In this way the analysis of predicting the consequent pages for user is performed. The author claims that this model provides better accuracy than the present prediction models. It is essential to maintain the order of the accesses made by the user to predict the next page queried by the user. Additionally, sequence alignment was used to measure the integrated distance among 2 terms. In [5], the author explains the group of prefetching algorithms which are aimed at improving the efficiency of the system. The terms used for comparison are obtained by combining the hit rate and the bandwidth. The author also proposed a set of algorithms that included the ratio. The proposed algorithms are called “Objective-Greedy prefetching”.

Output: pass


Input: Claim: AI-powered chatbots can provide mental health support and patient engagement. [SEP] Text: & Insights (2023). Pandya. of Generative AI: Over half of Americans have used generative AI and most believe it will help them be more creative. Adobe blog (2024). Torous, J. Paradox: the Time to Unlock the Potential? Harv. Rev. 23, (2023). Broderick, R. People are using AI for therapy, whether the tech is ready for it or not. (2023). Robb, A. ‘He checks in on me more than my friends and family’: can AI therapists do better than the real thing? (2024). Li, H. , Zhang, R. , Lee, Y. -C. , Kraut, R. E. & Mohr, D. C. Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. Npj Digit. Med. 6, 236 (2023). 10. 55922/001c. 92367 (2024). De Freitas, J. & Cohen, I. G. The health risks of generative AI-based wellness apps. Nat. Med. (2024). Department for Science, Innovation and Technology, AI & Bengio, Y. Report on the Safety of Advanced AI - Interim Report. UK Government (2024). Chung, N. C. , Dyer, G. & Brocki, L. Challenges of Models for Counseling. Preprint at (2023). Milne-Ives, M. , Selby, E. , Inkster, B. , Lam, C. & Meinert, E. Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digit. Health 1, e0000079 (2022). 10. 18653/v1/2023. emnlp-main. 698. Braun, V. & Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006). 2402. 01822 (2024). Heston, T. F. Safety of Models in Addressing Depression. Cureus (2023). Stade, E. C. et al. Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Besides, as shown in Fig. 11C, highly similar reference strains can still contain strain-specific genes and thus using all of them (rather than a representative) can reveal more comprehensive strain-level functional changes in the comparison of multiple samples. These results indicate that using strains identified by StrainScan for downstream analysis has a great potential to generate new biological insights compared to tools that only return representative strains. When the strains are highly similar, the sequencing depth is the main factor affecting the resolution of StrainScan. Thus, when the strains have high similarities and low depths, our cluster search using CST will fail to identify some clusters due to the low coverage. As a result, the performance of StrainScan will drop rapidly if the depth of the strain is lower than 1X. Moreover, we investigated the minimum sequencing depth required by StrainScan and the likelihood of detecting a strain in low-depth samples (2. 6). We tested the limits of StrainScan on building the CST for 25,349 complete and draft E. coli genomes. The program requires > 1TB of memory. Our empirical tests show that StrainScan is efficient for building the CST for less than 5000 genomes (see S12). However, for bacteria with many genomes and high intra-species diversity (e. g. , E. coli), the construction of CST can take a longer time. Thus, we recommend users to use only complete genomes for constructing the indexing structure if there are more than 5000 genomes. Our first step based on cluster search can efficiently reduce the search space. However, if all the reference genomes are highly similar and only differ by a handful of bases, they tend to be grouped in one cluster. In this case, the cluster search still returns a large search space for the second step, which does not take full advantage of the cluster indexing structure. M. tuberculosis has a large cluster, which slows down the strain identification. It should be noted that the quality of the reference genomes can affect reference-based methods [43] including StrainScan. StrainScan allows users to build the database with their own genomes. Thus, pre-processing can be conducted to mitigate data contamination or bias problems in the input genomes. As discussed above, the scalability of input reference genomes can be a limitation of StrainScan.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: The algorithm fetches objects in a greedy fashion, that is, more than those that are finally required. The result provided by the paper are obtained using the comparison between the “Objective-Greedy Algorithms” and the current algorithms based on the various metrics specified above. The highest hit rate is obtained by the “Hit Rate-Greedy” and the lowest bandwidth is obtained by the “Bandwidth-Greedy” techniques. The simulations performed resulted in the conclusion that the proposed techniques provided better results than the previously proposed techniques. In [6], the author analyzes the effect of the architecture of web prefetching in the reduction of the latency perceived by the users. The author also examines prefetching architectures with respect to the aspects that restrict the predictive power and evaluate these hypothetical limits in terms of number of requests and the latency identified by the user. The main goal of the study is to analyze the architecture to decrease the latency and get analysis on the cap of performance. The location was analyzed based on the number of requests and the latency observed by the user. Predictors based on clients/servers were found to be favorable up to a certain extent. Analysis was performed on “Collaborative predictors” too. In [7], the author proposes a prefetching technique wherein the operation is performed on the client side and the user chooses the objects to fetch. The simulations provided the result that this technique can reduce latency “by up to 81% in a homogeneous environment” and “63% in a heterogeneous environment”. This technique does not increment traffic, but on the contrary, it reduces latency. It can be used on the side of the clients with any help from the other parts of the network infrastructure. In [8], the author proposes an analysis of performance of web servers based on “distribution-aware caching”. document caching is performed using “request distribution”. The author uses “cooperative caching” and “exclusive caching” to avoid producing object copies. Author explores the interests of “cooperative caching algorithms” to drive them towards “general purpose cooperative caching algorithms”. The kernel of Linux was exercised for the caching technique’s implementations. In [9], the author documents a technique which integrates caching and prefetching.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: AMA Journal of Ethics® Illuminating the Art of navigation 179. doi: 10. 1001/amajethics. 2019. 167. Citation PDF Altmetric email Abstract Background: As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems’ data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve for all. Methods: Two case studies are examined using a machine learning algorithm on unstructured clinical and psychiatric notes to predict intensive care unit (ICU) mortality and 30-day psychiatric readmission with respect to race, gender, and insurance payer type as a proxy for socioeconomic status. Results: Clinical note topics and psychiatric note topics were heterogenous with respect to race, gender, and insurance payer type, which reflects known clinical findings. Differences in prediction accuracy and therefore machine bias are shown with respect to gender and insurance type for ICU mortality and with respect to insurance policy for psychiatric 30-day readmission. Conclusions: This analysis can provide a framework for assessing and identifying disparate impacts of artificial intelligence in health care. Bias in health care is an inherently data-driven field, most clinicians operate with limited evidence guiding their decisions. Randomized trials estimate average treatment effects for a trial population, but participants in clinical trials often aren’t representative of the patient population that ultimately receives the treatment with respect to race and gender. 1,2 As a result, drugs and interventions are not tailored to historically mistreated groups; for example, women, minority groups, and obese patients tend to have generally poorer treatment options and longitudinal health outcomes. 3-9 Advances in artificial intelligence (AI) and machine learning offer the potential to provide personalized care by taking into account granular patient differences. Machine learning using images, clinical notes, and other electronic health record (EHR) data has been successful in several clinical tasks such as detection of diabetic retinopathy10 and distinguishing between malignant and nonmalignant skin lesions in dermatoscopic images. 11 Prior research has established that machine learning using clinical notes to supplement lab tests and other structured data is more accurate than an algorithm using structured data alone in classifying patients with rheumatoid arthritis12 and in predicting mortality13 and the onset of critical care interventions14 in intensive care settings.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: However, many efficient data structures [66] or methods [12] have been developed to increase the scalability of microbiome strain-level analysis. For example, one tool called mSWEEP [67] can identify strain lineages accurately from large-scale reference genomes by utilizing pseudoalignment technology. Thus, for future work, we plan to extend pseudoalignment-based methods or efficient data structures such as HyperLogLog [30] to increase the scalability of StrainScan, and speed up both the database construction and identification process of StrainScan. ConclusionsIn conclusion, we developed an accurate, efficient, and high-resolution strain-level composition analysis tool for short reads. The experiment results on simulated and mock datasets show that StrainScan can achieve more accurate strain-level microbiome composition analysis than existing tools while keeping the high resolution. The results of these benchmark experiments also prove StrainScan’s robustness with complex samples, low-abundance strains, and strains that are not in the reference database. Furthermore, the analysis results of real metagenomic datasets show that StrainScan can benefit pathogenic strain identification, strain-level composition and functional analysis, and meta-analysis across different studies or datasets. Based on these results, we believe that StrainScan is an important contribution to the field and offers improved performance over state-of-the-art tools. Availability of data and materials All data and codes used for this study are available online. The source code of StrainScan is freely available at 5. 20. CAS PubMed 50. PubMed 54. 17. Nayfach S, Rodriguez-. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 2016;26(11):1612–25. 4. 5-year study of a patient with Crohn’s disease. mSystems. 2019;4(1):e00337–18. - copri complex comprises four distinct clades underrepresented in westernized populations. Microbe. 2019;26(5):666–79. 53. PubMed 239. 64. 8. 20. 132. PubMed Pub66. 960. PubMed Pub52. 38. 7. 2392. 8. CAS PubMed 71. PubMed Pub1925. PubMed Pub252. 86. CAS PubMed 506. PubMed Pub248. 74.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: The prefetched URLs are dependent on the “documents”, “cache contents” and the space in the network. To manage user inclinations, “Preference lists” are used. Cryptographic methods are used for keeping track of the account of URLs. The prediction engine is assisted by a monitor to determine the objects to be obtained and the bandwidth. The weight information is also stored by making use of cryptographic techniques. In [10], the author focuses on predictive prefetching. Author identifies two factors, “page accesses” and “noise”. The noise influences the technique for processing the accesses made by the users which is then used for measuring the efficiency of the algorithms. Using the above-mentioned factors, the author presented a framework describing the “markov predictors”. The author also described a new way to interpret the prefetching algorithms, called “markov predictors”. This way displayed the important aspects that alter the performance of the algorithms. Additionally, the author develops an innovative method and proves formally that it is indeed a “generalization of the current methods”. A broad investigative and exploratory examination of all calculations shows that the proposed calculation beats existing ones by consolidating their p100. We used a college website for 50 pages and commercial websites for 75 and 100 pages. On average, each page was found to be of the size 3 MB. We obtained the log files from their proxy servers with the size of 150 MB. Each website’s log file was collected after one (100 Users), two (200 Users), four (500 Users) and thirteen weeks (1000 Users) which were used as training set. After each of these weeks, the prefetching algorithm suggested some pages, which were then prefetched and cached in the proxy server. The successive week’s log file was analyzed for the number of accesses of the cached pages using which the bandwidth saved and the hit ratio were calculated. ConclusionThis paper has presented an innovative approach for the prefetching of pages. It clusters users based on their historical p2009. [2] Cheng-.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: We expand on the first author’s previous research, discussed above, on bias in ICU patient mortality prediction using the same MIMIC-III data set cohort with gender and insurance type in addition to race as demographic groups. We also analyzed potential bias in 30-day psychiatric readmission prediction for the same demographic groups. Because unstructured clinical notes from the EHR contain valuable information for prediction tasks—including information about the patient’s race, gender, and insurance type—we focus on clinical narrative notes in EHR data available for each stay. We examine bias, as measured by differences in model error rates in patient outcomes between groups, and show that in the ICU data set, differences in error rates in mortality for gender and insurance type are statistically significant and that in the psychiatric data set, only the difference in error rates in 30-day readmission for insurance type is statistically significant. Data and Methods Data. We analyze prediction error in psychiatric readmissions at a hospital in a data set containing 4214 deidentified notes from 3202 patients, collected from stays between 2011 and 2015. We extracted notes, patient race, gender, insurance payer type, and 30-day psychiatric readmission from every patient stay. The data set is racially imbalanced but has relative gender parity. We use the insurance payer type—public, private, and other insurance—as a proxy for socioeconomic status. (1. 4 data set with the cohort selection explained earlier. (1. 4 and our analysis code are made publicly available to enable reproducibility and further study. 35 Results: Enrichment of note topics. White patients had higher topic enrichment values for the anxiety36 and chronic pain topics, while black, Hispanic, and Asian patients had higher topic enrichment values for the psychosis topic. 37 Male patients had higher topic enrichment values than female patients for substance abuse (0. 024 v 0. 015), whereas female patients had higher topic enrichment values than male patients for general depression (0. 021 v 0. 019) and treatment resistant depression (0. 025 v 0. 015), reflecting known clinical findings. 38,39 Previous work has shown that those with serious mental illness are more likely to have public insurance than private39; we similarly find that private insurance patients have higher topic enrichment values than public insurance patients for anxiety (0. 029 v 0. 0156) and general depression (0. 026 v 0. 017).

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Aedes aegypti microbiome composition covaries with the density of Wolbachia infection access Published: 17 November 2023 Aedes aegypti microbiome composition covaries with the density of Wolbachia infection Jane Pascar1, Henry Middleton1 & Steve Dorus1 Microbiome 94. 6% of the nearly 21 million reads that mapped to bacterial genomes and greatly expanded upon previous microbiome characterizations in this species, (2) quantified microbiome variation while also identifying core bacterial species at the population-level, (3) identified substantial variation in the density of wMel infection across individuals and (4) revealed that wMel was a central hub of negative interactions within a larger Ae. aegypti microbiome interaction network. Methods Ae. aegypti genome sequencing dataDouble digest restriction-site associated DNA sequencing data (ddRAD-seq) were utilized from a study of Ae. aegypti populations from six geographic locations near Cairns, Australia (412140) [20]. Releases of Ae. aegypti transinfected with the D. melanogaster strain of Wolbachia (wMel) were conducted in four of these locations: North and Westcourt (2013 releases); Bungalow and West (2014 releases). Schmidt et al. [20] conducted Illumina HiSeq2500 (100 bp reads) paired-end ddRADseq library sequencing of DNA isolated from 172 female mosquitoes reared using standardized conditions from eggs collected in the wild. We have taken advantage of the fact that whole mosquito genomic DNA isolation also captures microbiome DNA. Additional information about the samples can be found in the metadata for 412140. Taxonomic classification Ae. aegypti microbiomeThe Kraken2 pipeline [44] was used to assign taxonomic classifications to all raw ddRAD-seq reads that met standard quality metrics. The Kraken2 search database included all available RefSeq genomes from bacteria (n = 21,333), fungi (n = 60), protozoa (n = 40), viruses (n = 10,388), archaea (n = 390) (200 on August 26, 2020) and the recently reannotated Ae. aegypti reference genome (AaegL5 [45]). The database was built with 512 GB of available RAM on 4 threads using the default Kraken2 parameters for minimizer length and spaces, and a conservative k-mer length of 31.

Output: contrast


Input: Claim: Data mining techniques are employed to classify astronomical objects from large surveys. [SEP] Text: Ibrahim, Towards Semantics-to Access Latency, Proceedings of the 2003 Symposium on Applications and the Internet, 2003 [3] Sadhna Ahuja, On the Effects of on Performance, on Information Technology: [4] by Clustering and Measure,2012 IEEE/ACM on Advances in Analysis and Mining, 2012. [5] Bin Wu, Ajay D. Kshemkalyani, Objective-for Long-term on and Applications, 2010. [6] Josep Dome`nech, Julio Sahuquillo, Jose A. Gil, Ana Pont, of the Architecture on the Limits of Reducing User’sWIC/ACM on Web Intelligence, 2006. [7] Aviploum N. W; via Client-Side Prefetching, 2000 [8] Walter F. Tichy, Request Distribution-in Cluster-Servers, third on and Applications. [9]. caching with cumulative prefetching for large multimedia data. 2000; 33(1–6):645–55. [10] Alexandros Nanopoulos, Dimitrios Katsaros, and Yannis Manolopoulos; a Algorithm for and Data Engineering, 2003. [11] V. Almeida, A. Bestavros, M. Crovella and A. de Oliveira; “Locality in the WWW” Proc. IEEE Conf. Parallel and Systems (’96), 1996. [12] J. Shim, P. Scheuermann and R. Vingralek (1999). Algorithms: Design, Implementation, and on Knowledge and Data Engineering, v. 11 n. 4, p. 549-562. [13] L. Fan, P. Cao, J. Almeida, and A. Broder, Summary cache: A scalable wide-area web cache sharing protocol, in Proc. ’98, Feb. 1998, 265. [14] A. Chankhunthod et al. , A hierarchical internet object cache, in Proc. 1996. , San Diego, CA, Jan. 1996. [15] in of and Technology in ISSN: 0975-5462 Vol. 3 No. 2 Feb 2011, pp 1069-1076. [16] Y. Z. Guo, K. Ramamohanarao, and A. F.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: Results: in AI After establishing that findings from the clinical notes reflect known disparities in patient population and experience, we evaluated whether predictions made from such notes are fair. There are multiple definitions of algorithmic fairness46-49; here we compare differences in error rates in ICU mortality and 30-day psychiatric readmission for race, gender, and insurance type. Prediction error in the ICU model. Unstructured clinical notes are a powerful source of information in predicting patient mortality—our models achieve an AUC31 of 0. 84 using only the ICU notes. Adding demographic information (age, race, gender, insurance type), improves AUC slightly, to 0. 85. As shown in Figures 1 and 2, error rates for gender and insurance type all have nonoverlapping confidence intervals. For gender, female patients have a higher model error rate than male patients; for insurance type, public insurance patients have a much higher model error rate than private insurance patients. All results are statistically significant at the 95% confidence level. Figure 1. 95% for (Zero-One Loss) in ICU Mortality for 2. 95% for (Zero-One Loss) in ICU Mortality for Prediction in the psychiatric setting. In contrast to ICU mortality, predicting 30-day psychiatric readmission is significantly more challenging, leading to lower model accuracy. 50 One potential cause could be the importance of unmeasured residential, employment, and environmental factors in predicting short-term psychiatric readmission. 51 Another factor could be the level of hospital intervention, such as outpatient appointments. 52 Comparison of prediction errors in ICU and psychiatric models. We compare differences in error rates in 30-day psychiatric readmission and ICU mortality for race, gender, and insurance type. Figure 3 shows differences in error rates in psychiatric readmission between racial groups, which were not statistically significant, with black patients having the highest error rate for psychiatric readmission. Differences in error rates in ICU mortality were also observed between racial groups. 23 Figure 3. 95% for (Zero-One Loss) in for We show consistent gender differences across data sets in Figures 1 and 4, with the highest error rates for female patients, although the difference in error rates between genders was only statistically significant for ICU mortality. Note that because of the smaller size of the psychiatric notes data set, the confidence intervals overlap; however, the heterogeneity in topic enrichment values aligns with the higher error rates for female patients.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Low complexity regions of genomes were masked using the DustMasker program [46]. As expected, the vast majority of reads (93. 6% average across 172 individuals; SD = 2. 6%) mapped to the Ae. aegypti genome. One percent of reads (185,811 reads on average per individual; range: 29,951 – 4,350,613) mapped to bacterial, viral, archaeal, fungal, or protozoan genomes. Quantification of Ae. aegypti microbiome compositionAfter taxonomic classification at the species level for bacterial, viral, archaeal, fungal, or protozoan reads, Bracken [47] was used quantify species abundances per individual. database was built using default parameters and a read length of 100. quantification, species abundance estimates were standardized to account for variation in the overall sequencing depth per individual. Additionally, we controlled for the number of restriction enzyme cut sites per genome using a script that calculated the total number of NlaIII (CATG/GTAC) and MluCI (AATT/TTAA) enzyme recognition sites in each reference genome (code is provided in Additional file 1). Standardizations were conducted separately in each major taxonomical group (e. g. , bacterial, fungi, etc. ). An independent verification of quantitative accuracy was conducted using the original wMel qPCR abundance estimates from Schmidt et al. [20]. For the full set of 172 individuals, abundance estimates were highly reproducible (r = 0. 87; p < 0. 0001). Additionally, we detected wMel, albeit at low average abundance levels, in individuals that were previously deemed to be uninfected by qPCR. After removing these datapoints, the quantitative reproducibility between studies increased (r = 0. 92; p < 0. 0001). For subsequent analyses, rare species were removed from the dataset if they (1) did not constitute > 1% of the total bacterial microbiome in any individual and (2) were present in fewer than 25% of individuals. We note that our analysis was based on DNA isolated from whole mosquitos. It therefore reflects the sum of DNA from microbial populations per individual and it does not provide direct information about tissue-specific microbiome composition.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to Research 10. 7554/eLife. 35453 Open access Copyright information Version of RecordAugust 29, 2018 Open annotations (there are currently 0 annotations on this page). 2,574 views 350 downloads 24 5453. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side 11 figures and 4 tables 1 with 3 supplements Download asset Open asset A single nucleotide variant accumulates following increases in K3L copy number. (A) Following 20 serial infections of the ΔE3L strain (MOI 0. 1 for 48 hr) in HeLa cells (see Materials and methods for further details), replication was measured in triplicate in HeLa cells for every fifth passage, and compared to wild-type (VC-2) or parent (ΔE3L) virus. (B, C) Digested viral DNA from every 5th passage (B) and four plaque-purified clones (C) were probed with a K3L-specific probe by Southern blot analysis. Number of K3L copies (left) and size in kbp (right) are shown. K3LHis47Arg allele frequency for each population (shown below) was estimated by PCR and Sanger sequencing of viral DNA. (D) Replication of plaque purified clones from (C) was measured in HeLa (D) or BHK (Figure 1—figure supplement 1) cells in triplicate. Statistical analysis was performed to compare the means of populations b or c relative to a, or between the means of populations b or c relative to d by one-way ANOVA followed by Dunnett’s multiple comparison test. *p<0. 05, **p<0. 01, ***p<0. 005. K3LHis47Arg and E9LGlu495Gly population-level allele frequencies estimated from Illumina MiSeq reads are shown in Figure 1—figure supplement 2. Replication of clone a compared to ΔE3L is shown in Figure 1—figure supplement 3. All titers were measured multiple times in BHK cells by plaque assay, shown with median and 95% confidence intervals. Figure 1—source data 1 Data used to generate Figure 1A. Download elife-35453-fig1-data1-v1.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: This conversation is part of a series of interviews in which JAMA Editor in Bibbins-and expert guests explore issues surrounding the rapidly evolving intersection of artificial intelligence (AI) and medicine. AI may be on the way to developing an enviable bedside manner. It can aid in early sepsis detection and has been viewed as more empathetic than physicians. But what about some of the fundamental factors that affect health, such as stable housing and access to nutritious food? Efforts are underway to use AI tools to help clinicians learn more about the social risk factors that affect their patients’ health. Andrew Bindman, MD (Video), recently spoke with JAMA Editor in Bibbins-about this and other aspects of AI in health care. As a general internist and executive vice president and chief medical officer (CMO) for Kaiser Permanente, Bindman has a distinct perspective on the challenges and opportunities of AI within a large health care organization. Video. AI and Clinical Practice— in a following is an edited version of their conversation. Dr Bibbins-Domingo:think about with Kaiser is using the data to continue to improve care for individuals as well as for the population. We have the ability to think about using data at scales we’ve not really imagined before, but you all have probably been thinking about this for a while. Dr Bindman:One of the really powerful examples is how we use information collected in our hospital settings—where we integrate information on vital signs, neurologic checks, updates to lab results—in a tool to start to predict which patients are most likely to be headed toward a decline that could result in them having to transfer to an ICU [intensive care unit] setting. The AI tool is able to do this in a rapid fashion and send an alert to the primary physician involved with that patient to say, “Hey, there’s a pattern emerging here that you may not have seen on your own. You might want to go back and look into that. ” We implement these tools in a learning context. And what we have found is that we are in fact better with these tools at predicting who is heading to a decline and might head to the ICU and have a worse outcome.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Statistical analysis of bacterial microbiome compositionMicrobiome complexity was assessed in R [48] using two alpha diversity metrics: observed species richness and Shannon’s diversity index [49]. A linear model was used to assess relationships between alpha diversity measures and total wMel abundance per individual. An analysis of variance (ANOVA) and Tukey’s test was used to compare the density of wMel across sites with historical releases. Co-occurrence and mutually-exclusive interactions between bacterial species were assessed using Pearson’s correlations as implemented by CoNet [50] in Cytoscape [51] on a standardized bacterial species abundance matrix. The matrix was standardized using the Wisconsin function from the R package vegan (version 2. 5–7 [52]). Statistical significance of each pairwise comparison was determined using 1000 bootstrap replications and Benjamini–Hochberg multiple testing correction. The network was visualized using the Cytoscape GUI software [51]. Additional analysis of network composition was based on delineating two subnetworks. The first (hereafter referred to as the “wMel subnetwork”) includes those species with a significant edge interaction directly with wMel and the second (hereafter referred to as the “non-wMel subnetwork”) as those species without a direct edge interaction with wMel. Analysis (PCoA) was then used to further characterize bacterial microbiome variation. A distance matrix was calculated by applying the Bray–Curtis dissimilarity index [53] implemented by the vegdist function from the R package vegan (version 2. 5–7 [52]) to the same standardized matrix used the CoNet analysis (see above). The PCoA was performed using the pcoa function from the R package ape (version 5. 5 [54]). Axes of variation were further investigated using linear models to assess the relationship between (PC) axis loading values and standardized species abundance estimates. Results Ae. aegypti microbiome compositionThe overarching goal of this study was to bioinformatically characterize microbiome variation, including that of wMel, in a wild population of Ae. aegypti.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: xlsx Figure 1—source data 2 Data used to generate Figure 1D. Download elife-35453-fig1-data2-v1. xlsx Figure 1—source data 3 Statistics for Figure 1D, One-way ANOVA followed by Dunnett’s multiple comparison test. Download elife-35453-fig1-data3-v1. xlsx Figure 1—figure supplement 1 Download asset Open asset K3LHis47Arg and K3L CNV are non-adaptive in the permissive BHK cell line. Replication was measured for plaque purified clones (as in Figure 1D) in BHK cells by 48 hr infection (MOI 0. 1) in triplicate. All titers were measured multiple times in BHK cells by plaque assay, shown with median and 95% confidence intervals. Statistical analysis was performed to compare the means of populations b or c relative to a, or between the means of populations b or c relative to d by one-way ANOVA followed by Dunnett’s multiple comparison test. No significant differences were observed between any of the compared populations. Figure 1—figure supplement 1—source data 1 Data used to generate Figure 1—figure supplement 1. Download elife-35453-fig1-figsupp1-data1-v1. xlsx Figure 1—figure supplement 1—source data 2 Statistics for Figure 1—figure supplement 1, One-way ANOVA followed by Dunnett’s multiple comparison test. Download elife-35453-fig1-figsupp1-data2-v1. xlsx Figure 1—figure supplement 2 Download asset Open asset Allele frequencies of the two high-frequency SNVs identified in vaccinia populations. Population-level K3LHis47Arg and E9LGlu495Gly allele frequencies were estimated using freebayes on Illumina MiSeq reads from different passages. Figure 1—figure supplement 2—source data 3 Data used to generate Figure 1—figure supplement 2. Download elife-35453-fig1-figsupp2-data3-v1. xlsx Figure 1—figure supplement 3 Download asset Open asset The E9LGlu495Gly variant does not contribute to virus replication.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: Another example would be our ability to better identify patients coming to an emergency department to figure out, based on written natural language notes, when that patient perhaps had visited an urgent care or saw their primary care physician initially expressing some current concerns about a symptom or some signs. We’ve been able to use AI tools to capture that information and to help the emergency physician better predict who might be at higher risk of having sepsis so that they’re identified earlier. And then finally, one other really interesting example is thinking about social risk factors. We’ve been using AI tools to look at things like missed appointments, missed picking up of medications that were prescribed, and saying, “Huh, I wonder if patients who are showing some of these behaviors are actually signaling to us other challenges that they’re having with, say, transportation or other things going on in their complex lives. ”So the AI tool identifies these kinds of gaps or people who might be slipping through our system and alerts the primary physician to say, “This might be a good patient to reach out to, to identify if they’re having certain challenges related to social risk factors,” and then making an intervention. So these are ways that we’re trying to use and develop these tools and learn about them to improve the health of our patients. Dr Bibbins-Domingo:Let’s talk a little bit more about the nitty gritty. You and I are both general internists, and we know that these tools that are supposed to make our lives easier are the things that we oftentimes like to click and avoid and say, “Okay, stop telling me—I know what I’m doing. ” So how do you know these tools are really working and what guards against thinking they’re supposed to make our lives easier as doctors, but it’s really making it harder and we start to ignore them? Dr Bindman:That’s a great point, and we do have to be sensitive to that kind of alarm fatigue. It’s critical that part of our evaluation of these tools really does take into consideration both what they do to improve our patients’ health but also the experience of our clinicians in terms of using them. It’s still important to pair the use of these predictive tools with a clinician who is still actively involved in providing oversight.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: To this end, DNA-seq reads from 172 Ae. aegypti females were mapped to a database containing the Ae. aegypti genome and the full set of RefSeq genomes from bacteria, fungi, viruses, archaea and protozoans (27,435 genomes in total). Data from each individual resulted in an average of 18. 7 million mapped reads (SD = 220,395), of which the vast majority mapped to the Ae. aegypti reference genome (93. 6%; SD = 2. 6%). On average, 5. 4% (SD = 1. 1%) of reads remained unclassified because they were either low quality reads or derived from species not included in our database. The remaining reads (185,811 reads per individual on average) mapped to bacterial, viral, archaeal, fungal, or protozoan genomes. Of these, the vast majority (90. 6%; SD = 5. 9%) were derived from bacteria (Fig. 1A). Fungi, protozoa, and archaea were consistently identified at low abundances, while viral load was more variable. As expected, given that our analysis was based on DNA sequencing data, 99. 8% of all viral reads identified from bacteriophage (i. e. , DNA viruses) and the observed viral load variation was due to the high abundance of Escherichia bacteriophages in a limited number of individuals. Full information regarding fungal, viral, archaeal, and protozoan identifications can be found in Additional file 2. Fig. 1Population-level Ae. Aegypti microbiome characterization. A Taxonomical distribution of reads mapping to non-Ae. Aegypti genomes. B Taxonomical distribution of the major phyla in the bacterial microbiome. C Bacterial microbiome composition by phyla for all 172 individuals. The eight most abundant phyla are shown and the remaining are grouped as ‘Other’Full size imageBacterial microbiome compositionAfter removing low abundance and rare species, 843 bacterial species remained from 23 phyla and 396 genera (Additional file 3; note that wMel is not included in the following analysis of microbiome composition). These results represent a substantial increase in the size and taxonomical specificity of the characterized Ae. aegypti microbiome. Eight phyla comprised > 99. 0% of total bacterial composition (Fig. 1B).

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: A virus clone containing the E9LGlu495Gly variant as the only genetic change relative to ΔE3L was isolated following four rounds of plaque purification in BHK cells (clone a in Figure 1C). Replication was measured in comparison to ΔE3L virus after 48 hr of infection (MOI 0. 1) of HeLa cells in triplicate. Titers were measured multiple times in BHK cells by plaque assay, shown with median and 95% confidence intervals. ns, not significant by unpaired 2-tailed t test with Welch’s correction. Figure 1—figure supplement 3—source data 4 Data used to generate Figure 1—figure supplement 3. Download elife-35453-fig1-figsupp3-data4-v1. xlsx Figure 1—figure supplement 3—source data 5 Statistics for Figure 1—figure supplement 3, unpaired 2-tailed t test with Welch’s correction. Download elife-35453-fig1-figsupp3-data5-v1. xlsx Figure 2 with 3 supplements Download asset Open asset ONT reads capture SNVs and copy number expansions in individual virus genomes. (A) Representative structure of the K3L locus in the VC-2 reference genome is shown on top, with representative Illumina MiSeq and reads shown to scale below. The K3LHis47Arg variant within reads is indicated by an asterisk. ONT reads that split and re-align to the K3L duplicon are indicative of individual multicopy arrays (shown below). Tandem duplication breakpoints flanking the duplicon are indicated by arrowheads. (B) Population-level K3LHis47Arg allele frequency was estimated using Illumina or ONT reads from different passages. E9LGlu495Gly allele frequencies are shown in Figure 2—figure supplement 1. Error rate calculations for different flow cell chemistries are shown in Figure 2—figure supplement 2. (C) For each sequenced passage, K3L copy number was assessed within each ONT read that aligned at least once to the K3L duplicon (see Materials and methods for further details). Detailed plot of reads containing 6 + K3L copies is shown in Figure 2—figure supplement 3. (D) Representative reads from the specific long read preparation are depicted relative to the VC-2 reference genome.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: We haven’t turned over responsibility of care to machines or AI decision-making. It’s all done partnered with our clinicians. I think these tools can enhance predictive powers that our clinicians recognize are greater than what they can sometimes see on their own. When we’re predicting who’s likely to go into the ICU from our hospital wards, I think our physicians recognize that this is really a great adjunct. But in the primary care practice, I think there’s also going to be this important trade-off where these tools can start to not only add predictive power but also take away work from some of our clinicians. We are experimenting, for example, with the use of these tools to essentially be a quiet listener in the room and to generate notes related to the interaction between a primary care physician and a patient and to create an excellent first draft for our clinicians to then review and to more quickly do the documentation component of their work rather than sitting at a keyboard and glancing occasionally at the patient. This allows for more full attention that the patient deserves. Dr Bibbins-Domingo:I know equity has been an important issue for you, and with new technologies there are several ways in which equity experts have raised concerns. There’s been very nice work showing that when you learn from patterns of care that are not equitable, you end up reproducing that, and potentially even amplifying that, because of the ability of AI to scale. How do you guard against that? Dr Bindman:This is such an important issue. And we all need to really home in on it and learn together how to do it. So step number one, I think, is making sure that we have broad representation in the development of these tools. We are of course very sensitive and want to respect any patients, any members, that say, “Please don’t include my data. ”Many patients are extremely interested in having what has gone on with their care to be part of not only informing their future care but also helping others. There’s tremendous altruism among our members for that. think we have to make sure we have a good communication loop back to our members—anyone who’s giving us this data—to help them see how their information was helpful. We should make them feel like a participant in the process and be really mindful of getting a very broad cross-section of membership.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Consistent with previous studies, all of which relied on 16S rRNA sequencing [43, 55,56,57,58,59], Proteobacteria (49. 0% of bacterial reads) was identified as the predominant phylum. Firmicutes (36. 6%), Tenericutes (5. 9%), Bacteroidetes (4. 7%), Actinobacteria (1. 5%), Cyanobacteria (0. 6%), Spirochaetes (0. 5%), and Fusobacteria (0. 5%) were also identified as substantive contributors to the microbiome (Fig. 1C). At the genus level, our analysis confirmed the presence of several previously reported groups, including (1) Acinetobacter and Stenotrophomonas, which have been identified in reproductive tissues, (2) Bacillus, Chryseobacterium, Enterobacter, Klebsiella and Serratia, which are commonly identified in the midgut, (3) Burkholderia, which has been identified specifically in the salivary glands and (4) a suite of six genera with more complex spatial distributions across multiple tissues [43, 55,56,57,58, 60,61,62,63]. We also identified several novel species within the Mycoplasma, Salmonella, and Mannheimia genera that were either ubiquitous among individuals or highly abundant. Finally, several taxa (e. g. , species in the Asaia, Spiroplasma and Serratia genera) have been demonstrated to negatively influence Wolbachia density or transmission [40, 41, 64]. Our analysis did not identify any species in the genus Asaia, consistent with Audsley et al. [65], but did identify 17 species in the genera Spiroplasma and three in the genera Serratia. Among these species, a negative abundance relationship was observed between wMel and S. marcescens (see below). Towards a core Ae. aegypti bacterial microbiomeOur analysis provided a unique opportunity to leverage intraspecific variation to assess the community composition of the core Ae. aegypti microbiome at a population level. First, we investigated core microbiome composition as a function of species presence across the population.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: The locations of relevant genes are indicated by colored boxes (gene name above or below), and the locations of high frequency variants in K3L and E9L are indicated by arrowheads. Figure 2—source data 1 Single nucleotide variants in virus populations from Illumina or ONT datasets, used to generate Figure 2B. Download elife-35453-fig2-data1-v1. xlsx Figure 2—figure supplement 1 Download asset Open asset E9LGlu495Gly variant dynamics. Population-level E9LGlu495Gly allele frequencies were estimated using freebayes and nanopolish on Illumina or ONT reads, respectively, from different passages as in Figure 2B. Figure 2—figure supplement 1—source data 1 Data used to generate Figure 2—figure supplement 1. Download elife-35453-fig2-figsupp1-data1-v1. xlsx Figure 2—figure supplement 2 Download asset Open asset Error rate profiles in ONT reads. The proportions of non-reference bases aligned to the 5-mers containing the K3LWT (A), K3LHis47Arg (B), E9LWT (C), or E9LGlu495Gly (D) sequences were calculated from alignments of ONT reads from the P15 population generated with three distinct flowcell chemistries (listed above each plot). Kernel density plots represent the distributions of error proportions for each non-reference base across all 5-mers in the vaccinia reference genome. Figure 2—figure supplement 3 Download asset Open asset ONT reads capture high K3L copy number in vaccinia genomes. K3L copy number was assessed in ONT reads from P10, P15, and P20 as described in Figure 2C. Stacked bar plots indicate overall proportions of sequencing reads that contain between 6 and 16 copies of K3L, in ascending order (darker bars represent increasing copy number). The maximum copy number observed in any single ONT read is indicated below each passage. Figure 3 with 4 supplements Download asset Open asset The K3LHis47Arg variant homogenizes within multicopy arrays throughout experimental evolution. Stacked bar plots representing the proportions of mixed and homogeneous K3L arrays were generated from ONT reads for the indicated virus populations (passages are listed above each plot).

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: We’re not just collecting it and hoping to do something with it from a research perspective, but also because we want to take an action step. We have electronically connected and created a network of community-based organizations relevant to each of our geographic areas so that if a clinician identifies a patient who has a need in one of these social areas, they’re able to essentially do the equivalent of writing a prescription to connect that member with a resource in their community that is designed to help them. And that organization is electronically connected with us and communicates with us. And we are particularly homing in on some of our members, for example, those who are going through very complex care like cancer care or who are demonstrating to us that they’re having trouble making appointments or picking up prescriptions. So using certain clues as a way to collect that information and to act responsibly by providing help. Dr Bibbins-Domingo:Whenever we have a new technology like this that we know is going to be disruptive, there will be lots of good coming from it. But there are other issues that we have to protect. What do you think the role is for government—local, state, or federal—to protect the interests of patients, clinicians, or whoever it might be? Dr Bindman:This is a great question and a really important one—that we need to both recognize the incredible promise and possibility of these tools. What we’re seeing with AI and machine learning is both incredibly powerful and exciting as a potential tool. But you and I have enough experience to know that almost any innovation comes with risks of untoward effects. So we need to make sure we’re building a safety mindset and an evaluative mindset into anything that we roll out. What’s tricky to think about is that AI encompasses a wide range of things. As you’re well aware, the FDA [US Food and Drug Administration] has already identified a certain role related to certain kinds of specified software that is very specific in certain areas related to using machine learning. I think where it’s more tricky is in the generative AI area, which has generated so much interest in the last few months since many people are starting to explore that. What’s hard there, as I understand it, is that it doesn’t necessarily perform the same way each time.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: This analysis revealed that 164 species were present in 95% of individuals and 54 species (hereafter referred to as the “core microbiome”) were present consistently across the entire population (Fig. 2A; Additional file 4). Species present in large numbers of individuals also constituted a high proportion of total microbiome composition (Fig. 2B). Specifically, the 517 bacterial species identified in at least 75% of individuals comprised 93. 0% of the total bacterial microbiome. The remaining 7. 0% of the microbiome was a combination of 326 additional species that were of far lower abundance on average. Among the 54 species comprising the core microbiome, five species constituted more than 5% of the core microbiome on average (Fig. 2C). Bacillus subtilis (42. 4%) had the highest average abundance by far, followed by Salmonella enterica (7. 9%), Staphylococcus aureus (6. 2%), Escherichia coli (6. 1%), and Enterococcus faecium (5. 5%). The high abundance of each of these species was likely due to their contribution to the midgut microbiome (Bacillus subtilis, Salmonella sp. , Staphylococcus sp. , Escherichia sp. , and Enterococcus sp. have all been previously identified in the Ae. aegypti midgut microbiome [60, 63, 66,67,68,69]). Although it is noteworthy that several of these species are mammalian pathogens and we cannot rule out possible contamination. Species in the genera Acinetobacter, Clostridium, Cupriavidus, Klebsiella, Serratia were also identified as members of the core microbiome. These genera have all been previously identified in mosquitoes but were not associated with a particular tissue [43, 65, 70, 71]. Unexpectedly, 62. 3% (or 34 species) of the core microbiome was comprised of species previously unidentified in the Ae. aegypti microbiome (Additional file 4). Fig. 2Ae. Aegypti core microbiome. A Number of bacterial species is plotted in relation to the proportion of individuals in which each species was identified. B Percent of microbiome composition is plotted in relation to the proportion of individuals in which each species was identified. Those species present in 75% or more individuals comprised 93% of total microbiome composition.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: The proportions of reads containing homogeneous K3LWT, homogeneous K3LHis47Arg, or any combination of mixed alleles are shown for reads containing 1–5 K3L copies. A simulation of SNV accumulation under a binomial distribution is shown in Figure 3—figure supplement 1, and results from sequencing with different flow cell chemistries is shown in Figure 3—figure supplement 2. Simulations of the effects of ONT sequencing error rates on the identification of mixed and homogeneous arrays are shown in Figure 3—figure supplement 3, and the proportions of each combination of K3L alleles in 3, 4, and 5-copy arrays are shown in Figure 3—figure supplement 4. Figure 3—figure supplement 1 Download asset Open asset Simulated accumulation of the K3LHis47Arg SNV. The K3LHis47Arg allele was uniformly distributed in simulated vaccinia populations with copy number distributions identical to passages P10, P15, and P20 (see Materials and methods for further detail). Stacked bar plots representing the proportions of mixed and homogeneous K3L arrays were generated as in Figure 3. Figure 3—figure supplement 2 Download asset Open asset ONT flowcell chemistries do not affect observed proportions of homogeneous and mixed K3L arrays. The P15 population was sequenced using R7. 3, R9, and R9. 4 ONT flowcell chemistries, and stacked bar plots representing the proportions of mixed and homogeneous K3L arrays were generated as in Figure 3. Figure 3—figure supplement 3 Download asset Open asset ONT sequencing error rates do not affect observed proportions of homogeneous and mixed K3L arrays. (A) Using reads from passage 15 populations sequenced with R7. 3, R9, and R9. 4 flowcell chemistries, all mixed arrays were converted into homogeneous arrays (see Materials and methods for further detail). K3LWT and K3LHis47Arg alleles were then switched as a proxy for sequencing errors, and the proportions of mixed arrays recovered were calculated. Stacked bar plots representing the proportions of mixed and homogeneous arrays were then generated following one simulation. (B) Kernel density plots were also generated, representing the distributions of expected mixed array proportions recovered across 1000 simulations. The observed proportion of mixed arrays using the indicated flowcell chemistry is shown as a dotted red line.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: This creates a bit of a regulatory challenge about what, in fact, is the entity that is being regulated here. This has already come up in our of Conduct work. Is there an entity that already exists that has the right kind of multidisciplinary mindset to be able to do this within government? It seems like this is a broader and more complex task than we’ve historically given FDA alone to do. The FDA may be good for narrow prescribed uses of AI in which it’s analytically bringing together certain data elements to predict a certain outcome, like whether a hospitalized patient is declining and therefore heading toward the ICU. That sort of tool performs the same way over and over again, and the FDA may be in a position to regulate that kind of tool. Generative AI, which functions in a different way in terms of repeated use, may be harder to regulate. But your question raises a key point, which is that we don’t want to introduce a tool that will create risks to people’s sense of privacy and introduces errors that humans are not able to oversee in some way. So we need that in place. The thing I think that has been fascinating from our conversations in this NAM group thus far is that it has pointed out that care as we deliver it today is fraught with all sorts of challenges of taking the best evidence and turning it into the best care, that lots of decisions wander away from what might be the best actual advice to give our patients. So the advantage of these tools in terms of closing the gap between evidence and practice is really powerful. I guess what I would say is what our group has said, “Let’s not make perfection be the goal that has to be reached in terms of how we regulate these things. Could it be that it’s just way better than what we’re currently doing? ”As a result, there should be, some have said, maybe an imperative to actually use these things because of how much better they could be. think that balance has to be found between improvements in care as well as some of the identifiable risks that we’re willing to accept or how we can control those risks in some ways. think this is a real challenge with things, like some of the tools that are being put in place to support cars and how people drive.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Fifty-four species were identified in all 172 individuals. C Contribution of the 54 ubiquitously present microbiome species to core microbiome composition. Species that do not constitute > 5% of the core microbiome have been grouped as ‘Other. ’Full size imagePopulation distribution of wMelHistoric releases of wMel-infected females were conducted in four of the six populations analyzed here, within which stable wMel inheritance has already been documented [20]. Based on recent observations from controlled releases, which revealed very limited geographic dispersal of transinfected individuals [8], we predicted a substantively higher prevalence of wMel in locations with historic releases. This was confirmed by an ANOVA that showed significant variation between the wMel across all sites (p = 0. 0006; Fig. 3A), including significantly higher abundance levels in the populations with historic releases relative to those without historic releases (p = 1. 45 × 10–5; Fig. 3B). Nevertheless, a substantial amount of intraspecific variation in wMel abundance was also observed in populations with historic releases. Overall, average wMel abundance across all populations was 460,458 (coefficient of variance = 1. 05) and ranking individuals by wMel abundance revealed a largely continuous gradient of infection density with the highest density infections exceeding 1. 5 million reads (Fig. 3C). Given that the strength of pathogen blocking is dependent on the density of Wolbachia infection [38], we next sought to assess the relationship between such variation and bacterial microbiome composition. Fig. 3wMel variation across population and individuals. A wMel abundance (normalized read count) per individual for each collection site (status and date of previous wMel release is indicated): Bungalow (BN), West (CNW), East (CNE), North (PPN), South (PPS) and Westcourt (WC) [20]. ANOVA revealed significant variation between the wMel abundance across sites, p = 0. 0006. A post hoc Tukey test revealed that wMel in CNE was significantly less than those from PPN (p = 0. 005) and CNW (p = 0. 001) and in PPS was significantly less than those from PPN (p = 0. 049). B wMel abundance (normalized read count) per individual comparing sites with and without historic releases.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: Figure 3—figure supplement 4 Download asset Open asset Multicopy K3L arrays contain diverse combinations of K3LWT and K3LHis47Arg alleles. (A) The proportions of 3-copy K3L arrays containing each possible combination of K3LWT and K3LHis47Arg alleles at P10, P15, and P20 were counted. Dotted red lines separate mixed and homogeneous arrays, and the total fraction of mixed arrays is indicated above each plot. The proportions of (B) 4-copy and (C) 5-copy arrays with each possible combination of K3L alleles are shown for passage 15, which was re-sequenced to generate a larger number of reads. Figure 4 Download asset Open asset The K3LHis47Arg variant homogenizes in K3L arrays regardless of copy number. (A) ONT reads from every 5th passage were grouped by K3L copy number, and each K3L copy was assessed for the presence or absence of the K3LHis47Arg SNV. Reads containing 1–5 K3L copies are shown. (B) Using reads from the P15 population, homogeneous K3L arrays were removed from the dataset, and K3LHis47Arg SNV frequency was plotted exclusively in mixed arrays. The number of reads of each copy number is indicated to the right of each row. Reads are oriented 5’ to 3’ relative to the VC-2 reference sequence, and the K3LHis47Arg allele frequency in each copy is indicated in blue. Figure 5 Download asset Open asset K3LHis47Arg homogenization within multicopy arrays is independent of intergenomic recombination rate. The P10 population was serially passaged in HeLa cells at different MOIs (listed above each plot), and each of the resulting P15 populations was sequenced with ONT. Stacked bar plots representing the proportions of mixed and homogeneous arrays were generated as in Figure 3. Figure 6 Download asset Open asset K3LHis47Arg variant homogenization is dependent on selection. The P10 population was serially passaged five times in BHK cells (MOI = 0. 1, 48 hr; P15-BHK). P10 and P15 data are included from previous figures for comparison with P15-BHK.

Output: pass


Input: Claim: AI can help identify patterns in medical data that might be missed by human analysis. [SEP] Text: These same tools might make for overall safer driving, but when we see accidents happen with those tools we all get very alarmed because we wonder, did the machine lead to this problem? This is a difficult challenge that society has to figure out, that right balance. We probably can’t eliminate all risks, but these tools can perhaps help us to eliminate some of the risks that are not always visible to us today in how we practice medicine. That’s really some of the discussion we’re having at NAM about how to make those things visible and to figure out what are the right safeguards to put in place. What’s the role of government around that? What’s the role of private actors as well? A model is our Safety Board, where there is federal oversight that brings together the information but also requires private entities, in that case private airlines. In our case, it would be private health systems sharing data in a way that allows everyone to learn from it but isn’t done in a way that introduces new risks—legal risks or things that would get people sued or put at a competitive disadvantage because they’re sharing the information. would love to see thoughts about that kind of a structure put forward because this is such an important area that we all need to learn together. I think there’s an opportunity to consider that kind of a model. Dr Bibbins-Domingo:JAMA recently put out a call for papers on AI in clinical practice and AI in medicine. You have a long history as a researcher bringing data to bear on these important questions. What types of studies would you want to see that would help us as we continue to move forward? Dr Bindman:I’m so glad you’re doing this at JAMA. Thinking creatively about all these applications and how AI might be used is really an exciting thing to create a forum around, for us all to be learning collectively. It really runs the gamut from some of the things that can go on in what I call the back office. Like how do we more effectively do some of the documentation, some of the logistics around effectively helping patients steer to the right level of care based on their problems or symptoms. You and I, as primary care physicians, have personally provided that role for many of our patients over the years.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: Wilcoxon test revealed significantly higher wMel abundance in sites with historic releases (p = 1. 45e-05). C Individuals from all populations ranked by total wMel abundanceFull size imagewMel was a hub of mutually exclusive relationships with other microbiome specieswMel abundance did not correlate with either total bacterial abundance (p = 0. 66) or observed species richness (p = 0. 89). However, there was a significant inverse relationship between wMel abundance and Shannon’s Index (p = 0. 02). Thus, as wMel abundance increased, the relative abundance of other bacterial species in the microbiome became less uniform. A previous investigation of wMel-infected Ae. aegypti revealed a reduction in several low abundance genera across the microbiome, but did not report information about wMel infection variation [43]. To specifically explore the effect of wMel abundance, we constructed an interaction network to assess patterns of co-occurrence and mutual exclusion within the microbiome. This approach revealed a network of 71 species, which was significantly enriched for core microbiome species (obs. = 42; exp. = 4. 6; χ2 = 355. 7; p = 2. 4 × 10–79) and comprised two predominant subnetworks (Fig. 4a; Additional file 5). The first subnetwork (hereafter referred to as the “wMel subnetwork”) comprised 35 mutually-exclusive bacterial relationships specifically with wMel and had a low relative level of connectivity (average of 1. 5 edges per node). The second, larger subnetwork (hereafter referred to as the “non-wMel subnetwork”) comprised 56 species and was notable for a higher average level of connectivity (2. 2 edges per node), and both co-occurrence (n = 66) and mutually exclusive (n = 58) interaction edges. Three additional characteristics further distinguished these subnetworks. First, the wMel subnetwork comprised species identified at significantly lower abundances than those in the non-wMel subnetwork (Fig. 4B) despite the proportion of core microbiome species being indistinguishable between the two (χ2 = 0. 45; p = 0. 50). Second, interaction edges in the wMel subnetwork were significantly weaker in their absolute magnitude than those in the larger network (Fig. 4C; p = 8. 38 × 10–6). Third, the larger network included B.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: (A) K3L copy number was assessed for all sequenced reads that unambiguously aligned to K3L at least once, as in Figure 2C. (B) K3LHis47Arg and E9LGlu495Gly allele frequencies in each population were estimated using ONT reads, as in Figure 2B. Allele frequencies for all sequenced populations are included in Figure 6—source data 1. (C) Stacked bar plots representing the proportions of mixed and homogeneous arrays were generated from sequenced ONT reads, as in Figure 3. (D) ONT reads were assessed for the presence of each breakpoint (shown relative to the genome to the right) by aligning reads to a query sequence containing K3L using BLAST and extracting the starts and ends of individual alignments to the K3L duplicon. Due to sequencing errors, a proportion of reads do not match either breakpoint 1 or breakpoint 2. Figure 6—source data 2 Data used to generate Figure 6D. Download elife-35453-fig6-data2-v1. xlsx Figure 6—source data 1 Single nucleotide variants in all sequenced virus populations from Illumina or ONT datasets. Data used to generate Figure 6B. Download elife-35453-fig6-data1-v1. xlsx Figure 7 Download asset Open asset Model of K3LHis47Arg homogenization within K3L CNV via gene conversion. Author response image 1 Download asset Open asset Variety of mixed vaccinia genomes in P15 sequencing data. We selected a random set of 20 vaccinia genomes of the specified copy number from the P15 sequencing data (a passage with a large proportion of mixed genomes), and plotted the distribution of K3LHis47Arg and K3LWT alleles within each genome. Author response image 2 Download asset Open asset Error rate distribution within K3LHis47Arg sequence context. (A) Kernel density plots representing the distribution of error rates for T>C, T>A, T>G, and T>deletion error across all TATGC 5-mers in data from each flowcell chemistry. (B) Kernel density plots representing the distribution of error rates for C>T, C>A, C>G, and C>deletion errors across all TACGC 5-mers in data from each flowcell chemistry.

Output: pass


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: • Centre for Wildfires, Ecology and Society website is a great resource to learnmore about bushfires and how they affect ecosystems and communities. Its website has sections for new research being published, the latest news in wildfire research and jobs. It also hosts events and talks where you can learn more about the field of bushfire research. • Get in touch with your local university or college to see if it has a department that studies bushfire management and offers any volunteering or research opportunities. Gaining experience of working as a researcher of any kind can provide you with valuable experience and help you explore your career options. What were your interests when you were growing up? As a kid, I always had my pockets full of something. I couldn’t help myself – I was constantly picking things up to take home and investigate later. I collected rocks, plants, sand, sticks and shells… I was always rescuing injured birds, insects, lizards and worms. I just really loved being out in nature. I had a strong environmental focus from an early age. was about 12, I joined a local ‘Stream Watch’ club that assessed water quality and surveyed the local aquatic wildlife once a month. This taught me a lot about how to run scientific experiments, and I was hooked. However, I also really loved acting and singing, so I was often torn between science and the arts. The best part of my current role is that I can combine my love of science with my love of communicating to explain science in fun and exciting ways. Who inspired you to become a scientist? David Attenborough’s documentaries were mind-blowing to me as a kid. I would watch these films and make a ‘bucket list’ of species I wanted to see and places I wanted to go one day. I was also extremely lucky to have family friends who were working zoologists, so I spent many holidays with them wading through lakes, chasing frogs or spotlighting (a way of observing nocturnal animals in the dark). I knew from an early age that I wanted a job that felt meaningful, and that allowed me to be out in nature. What attributes have made you successful as a scientist? I think one of the key things that makes me a successful scientist is that I have a deep curiosity about the world around me and a drive to do research that will have direct and tangible impacts. I like tricky and complicated questions, and I am motivated to find the answers.

Output: pass


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: subtilis, a confirmed Ae. aegypti midgut microbe and the highest abundance species in this study, as well as numerous other high abundance taxa previously identified in the Ae. aegypti midgut (e. g. , B. cereus/thuringiensis, E. faecium, E. coli, S. aureus/simulans/cohnii, S. enterica). Thus, the larger subnetwork was more robustly interconnected and contained a diversity of abundant taxa from the midgut microbiome. In contrast, wMel serves as a hub of negative pairwise relationships with relatively low abundance species, even though many of these are part of the core microbiome. We propose that this structure may derive from more direct interactions between wMel and members of the reproductive microbiome, given the higher abundance of wMel in reproductive tissues relative to somatic tissues [72]. Fig. 4wMel forms a negative interaction hub within the Ae. Aegypti microbiome network. A Microbiome interaction network analysis using Wisconsin normalized read counts resulted in a network of 71 species and 159 edges. Red edges represent mutual exclusion interactions (significant negative Pearson’s correlation coefficient) and green edges represent co-occurrence interactions (significant positive Pearson’s correlation coefficient). Core microbiome species are highlighted in purple. wMel was involved exclusively in negative pairwise interactions with 35 species. B Density plot displaying the strength of interactions edges with wMel (orange) and the remaining edges in the network not involving wMel (grey). C Density plot displaying the mean read count for species included in the network. Species with mutually exclusive interactions with wMel are shown in orange and have a significantly lower average read count (t = 2. 43; p-value = 0. 02) than all other species in the network (grey)Full size imageMicrobiome independent and dependent components of wMel variationTo assess overall patterns of variation within the microbiome, we utilized a Analysis and observed three main axes that captured 76. 34% of the total variation (Fig. 5). The first axis (58. 73% of variation) included wMel as the eigenvector with the largest magnitude (Fig. 5A). The contribution of wMel variation to this axis was supported by the fact that Axis 1 loadings exhibited a significant correlation with wMel abundance across individuals (r = -0.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: Author response image 3 Download asset Open asset sequencing error does not impact observed patterns of K3L copy number or K3LHis47Arg allele heterogeneity. The P15 vaccinia population was sequenced with R7. 3, R9, and R9. 4 chemistry ONT flowcells. (A) Stacked bar plots representing the diversity of allele combinations within single-copy and multicopy reads were generated from sequenced ONT reads from each chemistry, as in Figure 3 and Figure 5 (original manuscript). (B) All mixed genomes observed in the data from each flowcell chemistry were converted to homogeneous genomes (Extended Methods), and sequencing errors were randomly distributed throughout these genomes at a rate equivalent to the median C>T or T>C error rate for the particular chemistry (see 1). Stacked bar plots representing the resulting diversity of allele combinations were then created as in (A). (C) We performed the simulation in (B) a total of 1000 times, and generated kernel density plots representing the distribution of mixed genome proportions recovered in each simulation. The red line indicates the proportion of mixed genomes observed in the experimental data for the sequencing chemistry of interest. . Author response image 4 Download asset Open asset 1 Summary of ONT sequencing datasets Population*Total sequenced readsMean read length (bp)Read length N50 (bp)Total sequenced bases (Gbp)Reads containing K3LP5239,737216859320. 521190P1091,815352376930. 32912P15388,502449369081. 754317P2094,050289377020. 27789 *ONT sequencing datasets for all populations are available in Table 1-source data 1 Table 1-source data 1 Complete summary of ONT sequencing datasets Download elife-35453-fig1-v1. xlsx Table 2 Median sequencing error rates using various ONT flowcell chemistries Mutation and context (amino acid change)R7. 3R9R9. 4TA[T > C]GC (His47Arg)0. 0230. 0230. 005TA[C > T]GC (Arg47His)0. 0150. 0240. 026AT[T > C]CG (Glu495Gly)0. 0140. 0180.

Output: pass


Input: Claim: Wildlife conservation efforts aim to protect endangered species from extinction. [SEP] Text: May 21, 2021 Project: How a successful not-for-profit organisation is working to protect orangutans from extinction Project was founded in 1998 by orangutan expert and conservationist Leif Cocks. Today, it takes a multifaceted approach to conservation, targeting the key threats facing the orangutan population. told us about the vital work of the Project, and about his own ethics which underpin the conservation work. Orangutans are facing an existential threat. As vast tracts of rainforest continue to be hacked down to make way for increasingly unsustainable forms of agriculture, these remarkably intelligent primates are driven ever closer to extinction. This habitat loss threatens global biodiversity more generally, and is contributing to the collapse of indigenous communities, who rely upon the forests for their culture and subsistence. Orangutan expert and conservationist,Leif Cocks. Over the past two decades, Project has achieved huge success in countering these threats, through a diverse approach to conservation. It has targeted deforestation and habitat loss, while funding a multitude of local projects involving education, rehabilitation and habitat regeneration. As of last year, it has helped to legally protect over three million acres of threatened rainforest and is supporting the monitoring of hundreds of orangutans as they are released back into their wild habitat. We interviewed Founder and Cocks, to find out how this work began, and how it might succeed going forward. Could you tell us a little about the experiences which led to you founding Project? I established Project in 1998 so we could collectivise and protect remaining orangutan habitat, which is essentially scraps of remaining forest at this point, to safeguard wild populations and to rescue, rehabilitate and release the orangutans living in captive misery. There’s not one part of that puzzle that can be left undone if we’re genuinely aiming to secure the survival of orangutan species in the wild. Soon after, I led the project to rehabilitate and release the first zoo-born orangutan back into protected Sumatran forest. It was a great moment, because until then it was presumed it couldn’t be done, but at the same time that I was releasing Temera, I was acutely aware that the forests of many other orangutans were being destroyed at a horrifying rate. Saving these remaining ecosystems before it’s too late is the main game. Protecting the forest protects the habitat of elephants and various other species too.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: 90; t = -26. 78, p = 4. 60 × 10–63; Fig. 5B). Thus, Axis 1 largely captures variation in wMel and this variation was largely orthogonal to the other main eigenvectors. However, we note that, of the 10 species with the highest positive loadings on Axis 1 (i. e. , those with loadings opposite to wMel), five participate in direct negative interactions with wMel in the wMel subnetwork (Fig. 4A). Axis 2 also identified wMel as a large negative eigenvector but, in contrast to Axis 1, it included two species with large positive eigenvectors (i. e. , M. haemolytica and B. subtilis; Fig. 5C). The relationship with these species was further supported by a significant positive correlation between Axis 2 loadings and the abundance of wMel (r = -0. 24; t = -3. 21, p = 0. 0016), and a significant negative correlation with M. haemolytica (r = 0. 72; t = 13. 49, p < 2. 2 × 10–16) and B. subtilis (r = 0. 48; t = 7. 08, p = 3. 61 × 10–11; Fig. 5D). Notably, both species were present in our network analysis (Fig. 4A) and their interaction connections to wMel were consistent with the identified PCoA relationships. In summary, Axis 1 captured wMel variation and its weak, mutually-exclusive interaction with a suite of species identified in the wMel network, and Axis 2 captured stronger, but indirect, wMel covariation with two of the most abundant species in the microbiome, including the midgut microbe B. subtilis. Lastly, we explored whether microbiome composition varied by collection site by PCoA and observed limited evidence of clustering above and beyond the variation between sites with and without releases associated with wMel (Additional file 6). Fig. 5Covariance between microbiome composition and wMel abundance. A PCoA analysis of microbiome variation identified a primary axis of variation (PC1: 58. 73%) that corresponded with the eigenvector for wMel which was the largest observed vector. Other large eigenvectors representing highly abundant taxa were largely orthogonal to Axis 1.

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: 009AT[C > T]CG (Gly495Glu)0. 0250. 0090. 009 Table 3 Structural variant breakpoint frequencies during passaging Breakpoint frequency*BreakpointK2L breakK4L breakP5P10P15P20130,284-0. 760. 690. 760. 661-30,8370. 760. 630. 720. 62230,287-0. 140. 060. 100. 082-30,8400. 120. 040. 090. 05 *Due to sequencing errors, a proportion of reads do not match either breakpoint Key resources table Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional informationGene (Vaccinia virus)K3_Gene ID:3707649Strain, strain background (Vaccinia virus)VC-2, Copenhagen(Goebel et al. , 1990) PMID: 2219722NCBI_txid:10249; NCBI_GenBank:M35027. 1Strain, strain background (Vaccinia virus)ΔE3L, Copenhagen(Beattie et al. , 1995) PMID: 7527085Cell line (Homo sapiens)HeLaOtherObtained from Geballelab, University of WashingtonCell line (Mesocricetus auratus)BHKOtherObtained from Geballelab, University of WashingtonCommercial assay or kitCovaris g-.

Output: pass


Input: Claim: Wildlife conservation efforts aim to protect endangered species from extinction. [SEP] Text: Source: FKL. Project undertakes holistic conservation work. What does your holistic conservation entail? First of all, to succeed we need to secure viable ecosystems of the right type, shape and size of forest. We can’t just save rainforest and trees. For us, that means piecing together and supporting the entire ecosystem in which orangutans thrive, and addressing all of the deeply complex issues that have contributed to orangutans becoming in the first place. Our strategy covers legally protecting forests; securing, restoring and patrolling those forests; rescuing, rehabilitating and releasing ex-captive orangutans and educating and empowering local communities. There’s not one part of that puzzle that can be left undone if we’re genuinely aiming to secure the survival of the orangutan species in the wild. What are the biggest threats to the future survival of the orangutan population? They’re facing multiple threats but the main one is habitat loss – over 80% of their essential lowland rainforest has now been destroyed, mainly to establish unsustainable mono-culture plantations. We’ve now only got a few years up our sleeve to secure remaining populations of each subspecies of orangutan, in viable, intact ecosystems. By that I mean the ecosystem itself and each population has to be large enough to be genetically viable. If we continue at the present rate, in ten years’ time we will still have some orangutans left but their populations won’t be viable any more: they won’t have the genetic diversity to survive long term. This isn’t just an orangutan problem – it’s affecting thousands of species and ecosystems across the planet. First zoo-born orangutan to be rehabilitated, Temera, meets Leif in the jungle after her release. You have spoken of the importance of biocentrism. Could you explain what biocentric ethics mean to you? There is a significant shift that we, as humans, need to make if we are to sustain our life on this planet. We need to move from anthropocentrism – which places humans and our interests as the central focus – towards biocentrism, which places us alongside all other living beings.

Output: corroborate


Input: Claim: Everyone's microbiome composition is identical. [SEP] Text: B Axis 1 loadings were significantly correlated with wMel abundance across individuals but not other microbiome species. C PCoA analysis of microbiome variation identified a secondary axis of variation (PC2: 10. 19%) which corresponded with the eigenvectors for B. subtilis, M. haemolytica, and to a lesser extent wMel. D Axis 2 loadings were significantly correlated with B. subtilis and M. haemolytica abundance and anticorrelated with wMel abundance across individualsFull size imageDiscussionThe release of Ae. aegypti mosquitoes transinfected with the wMel strain of Wolbachia has proven to be an effective strategy to limit the spread of DENV [12, 73]. However, less is known about the long-term dynamics of infection within populations and how these dynamics might influence the multivalent utility of wMel more broadly as a biological control agent for arboviruses. Given the mechanisms responsible for pathogen blocking (i. e. , immune priming and resource competition), it is reasonable to assume that wMel infection stability and density may be dependent on interactions with the remainder of the microbiome. Here, we explore this possibility through a population-level Ae. aegypti microbiome survey using a bioinformatic approach that leverages available genomic sequencing data. In addition to achieving a species-level Ae. aegypti microbiome characterization from a wild population, our analyses revealed substantial intraspecific variation in wMel density that covaried with microbiome composition. This variation is of potential relevance to the efficacy of pathogen blocking and the implementation of Wolbachia-based pathogen control strategies against DENV and other arboviruses. Towards an Ae. aegypti population-level core microbiomeInteractions between bacterial components of the microbiome and their mosquito host impact important aspects of fitness, including fertility, longevity, and immunity [14, 40, 59]. As such, the identification of a population-level, core microbiome – those bacterial species present across many or all individuals – has the potential to provide unique insights into taxa that support host development and function [74].

Output: contrast


Input: Claim: Sky surveys generate massive datasets that require sophisticated data management techniques. [SEP] Text: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Related research -aquatic environmental DNA filtration system to maximize recovery potential and promote filtration approach standardization View 10. 7717/peerj. 15360 Published 2023-07-12 Accepted 2023-04-16 Received 2023-01-25 eDNA, Field analysis, Biodiversity, Invasive species Copyright © 2023 DeHart et al. is an open access 2023. An aquatic environmental DNA filtration system to maximize recovery potential and promote filtration approach standardization. PeerJ 11:e15360 Aquatic environmental DNA (eDNA) has emerged as a promising approach to identify organisms in freshwater and marine environments. While the recovery of eDNA from water most commonly involves capture of biological debris on a filter matrix, practitioners are yet to converge on standardized approaches for filtration, particularly in the field. This lack of standardization has resulted in inconsistent handling of samples following collection, limiting interpretation of results across studies and burdening groups with inconvenient storage and transport logistics that may compromise eDNA integrity. simple to assemble and low-cost ($350 USD) water filtration system is demonstrated that can be used in field and laboratory settings to reduce time between sample acquisition and eDNA filtration, maximizing eDNA sample recovery. Quantitative PCR is used to show the utility of the platform for laboratory and marine eDNA analysis. resulting eDNA collection system is easily transported in a rugged water-resistant case, operates for more than eight hours on a 12-volt lead-acid battery, and has an unobstructed filtration rate of 150. 05 ± 7. 01 mL/min and 151. 70 ± 6. 72 mL/min with 0. 22 µm and 0. 45 µm Sterivex filters, respectively. We show that immediate sample filtration increases eDNA recovery in the laboratory, and demonstrate collections in aquaria and marine environments. We anticipate that providing easy to obtain, open hardware designs for eDNA sample collection will increase standardization of aquatic eDNA collection methods and improve cross-study comparisons. DNA (eDNA) has emerged as a powerful tool for the characterization of aquatic biological communities. eDNA metabarcoding technology adoption is being driven by advances in low-cost DNA sequencing and improved analysis methods that capture genetic diversity from both cataloged and previously undescribed species (Chavez et al.

Output: pass